@article {pmid40218647,
year = {2025},
author = {Xu, H and Hassan, SA and Haider, W and Sun, Y and Yu, X},
title = {A Frequency-Shifting Variational Mode Decomposition-Based Approach to MI-EEG Signal Classification for BCIs.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {7},
pages = {},
doi = {10.3390/s25072134},
pmid = {40218647},
issn = {1424-8220},
support = {U2033202, U1333119//National Natural Science Foundation of China and Civil Aviation Administration of China/ ; 52172387//National Natural Science Foundation of China/ ; ILA22032-1A//Fundamental Research Funds for the Central Universities/ ; 2022Z071052001//Aeronautical Science Foundation of China/ ; 2022JGZ14//Northwestern Polytechnical University/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Neural Networks, Computer ; *Brain-Computer Interfaces ; Algorithms ; Brain/physiology ; },
abstract = {Electroencephalogram (EEG) signal analysis is crucial for understanding neural activity and advancing diagnostics in neurology. However, traditional signal decomposition (SD) techniques are hindered by two critical issues, mode mixing and mode aliasing, that compromise the quality of the decomposed signal. These challenges result in poor signal integrity, which significantly affects the accuracy of subsequent EEG interpretations and classifications. As EEG analysis is widely used in diagnosing conditions such as epilepsy, brain injuries, and sleep disorders, the impact of these shortcomings can be far-reaching, leading to misdiagnoses or delayed treatments. Despite extensive research on SD techniques, these issues remain largely unresolved, emphasizing the urgent need for a more reliable and precise approach. This study proposes a novel solution through the frequency-shifting variational mode decomposition (FS-VMD) method, which overcomes the limitations of traditional SD techniques by providing better resolution of intrinsic mode functions (IMFs). The FS-VMD method works by extracting and shifting the fundamental frequency of the EEG signal to a lower frequency range, followed by an iterative decomposition process that enhances signal clarity and reduces mode aliasing. By integrating advanced feature selection techniques and classifiers such as support vector machines (SVM), convolutional neural networks (CNN), and feature-weighted k-nearest neighbors (FWKNN), this approach offers a significant improvement in classification accuracy, with SVM achieving up to 99.99% accuracy in the 18-channel EEG setup with a standard deviation of 0.25. The results demonstrate that FS-VMD can address the critical issues of mode mixing and aliasing, providing a more accurate and efficient solution for EEG signal analysis and diagnostics.},
}

*Electroencephalography/methods
Humans
*Signal Processing, Computer-Assisted
Support Vector Machine
Neural Networks, Computer
*Brain-Computer Interfaces
Algorithms
Brain/physiology

@article {pmid40215960,
year = {2025},
author = {Chen, LN and Zhou, H and Xi, K and Cheng, S and Liu, Y and Fu, Y and Ma, X and Xu, P and Ji, SY and Wang, WW and Shen, DD and Zhang, H and Shen, Q and Chai, R and Zhang, M and Yang, L and Han, F and Mao, C and Cai, X and Zhang, Y},
title = {Proton perception and activation of a proton-sensing GPCR.},
journal = {Molecular cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.molcel.2025.02.030},
pmid = {40215960},
issn = {1097-4164},
abstract = {Maintaining pH at cellular, tissular, and systemic levels is essential for human health. Proton-sensing GPCRs regulate physiological and pathological processes by sensing the extracellular acidity. However, the molecular mechanism of proton sensing and activation of these receptors remains elusive. Here, we present cryoelectron microscopy (cryo-EM) structures of human GPR4, a prototypical proton-sensing GPCR, in its inactive and active states. Our studies reveal that three extracellular histidine residues are crucial for proton sensing of human GPR4. The binding of protons induces substantial conformational changes in GPR4's ECLs, particularly in ECL2, which transforms from a helix-loop to a β-turn-β configuration. This transformation leads to the rearrangements of H-bond network and hydrophobic packing, relayed by non-canonical motifs to accommodate G proteins. Furthermore, the antagonist NE52-QQ57 hinders human GPR4 activation by preventing hydrophobic stacking rearrangement. Our findings provide a molecular framework for understanding the activation mechanism of a human proton-sensing GPCR, aiding future drug discovery.},
}

@article {pmid40215056,
year = {2025},
author = {Fernandez, E},
title = {Invited Session II: Visual Prosthetics: Bidirectional communication with the human visual brain: Towards an advanced cortical visual neuroprosthesis for the blind.},
journal = {Journal of vision},
volume = {25},
number = {5},
pages = {13},
doi = {10.1167/jov.25.5.13},
pmid = {40215056},
issn = {1534-7362},
mesh = {Humans ; *Visual Prosthesis ; *Visual Cortex/physiology/physiopathology ; *Blindness/physiopathology/rehabilitation ; Adult ; Phosphenes/physiology ; Male ; Photic Stimulation/methods ; Electrodes, Implanted ; Female ; Middle Aged ; *Visual Perception/physiology ; Microelectrodes ; Brain-Computer Interfaces ; },
abstract = {A long-held dream by scientists has been to directly transfer information to the visual cortex of blind individuals, to restore a rudimentary form of sight. However, in spite of all the progress in neuroelectronic interfaces, the biological and engineering problems for the success of cortical implants are much more complex than originally believed, and a clinical application has not yet been achieved. We will present our recent results regarding the implantation of intracortical microelectrodes in four blind volunteers (ClinicalTrials.gov identifier NCT02983370). Our findings demonstrate the safety and efficacy of chronic intracortical microstimulation via a large number of electrodes in humans, showing its high potential for restoring functional vision in the blind. The recorded neural activity and the stimulation parameters were stable over the whole experimental period, and multiple electrode stimulation evoked discriminable patterned perceptions that were retained over time. Moreover, there was a learning process that helped the subjects to recognize several simple and complex patterns. Additionally, our results show that we can accurately predict phosphene thresholds, brightness levels, and the number of perceived phosphenes from the recorded neural signals. These results highlight the potential for utilizing the neural activity of neighboring electrodes to accurately infer and control visual perceptions.},
}

Humans
*Visual Prosthesis
*Visual Cortex/physiology/physiopathology
*Blindness/physiopathology/rehabilitation
Adult
Phosphenes/physiology
Male
Photic Stimulation/methods
Electrodes, Implanted
Female
Middle Aged
*Visual Perception/physiology
Microelectrodes
Brain-Computer Interfaces

@article {pmid40213917,
year = {2025},
author = {Wang, N and Wang, Y and Guo, M and Wang, L and Wang, X and Zhu, N and Yang, J and Wang, L and Zheng, C and Ming, D},
title = {Dynamic gamma modulation of hippocampal place cells predominates development of theta sequences.},
journal = {eLife},
volume = {13},
number = {},
pages = {},
pmid = {40213917},
issn = {2050-084X},
support = {2022ZD0205000//National Science and Technology Innovation 2030 Major Project of China/ ; T2322021//National Natural Science Foundation of China/ ; 82271218//National Natural Science Foundation of China/ ; 12271272//National Natural Science Foundation of China/ ; 81925020//National Natural Science Foundation of China/ ; 82371886//National Natural Science Foundation of China/ ; 82202797//National Natural Science Foundation of China/ ; LG-TKN-202204-01//Space Brain Project from Lingang Laboratory/ ; 2022M712365//China Postdoctoral Science Foundation/ ; },
mesh = {Animals ; *Theta Rhythm/physiology ; Rats ; *Gamma Rhythm/physiology ; *Hippocampus/physiology/cytology ; *Place Cells/physiology ; Male ; Rats, Long-Evans ; },
abstract = {The experience-dependent spatial cognitive process requires sequential organization of hippocampal neural activities by theta rhythm, which develops to represent highly compressed information for rapid learning. However, how the theta sequences were developed in a finer timescale within theta cycles remains unclear. In this study, we found in rats that sweep-ahead structure of theta sequences developing with exploration was predominantly dependent on a relatively large proportion of FG-cells, that is a subset of place cells dominantly phase-locked to fast gamma rhythms. These ensembles integrated compressed spatial information by cells consistently firing at precessing slow gamma phases within the theta cycle. Accordingly, the sweep-ahead structure of FG-cell sequences was positively correlated with the intensity of slow gamma phase precession, in particular during early development of theta sequences. These findings highlight the dynamic network modulation by fast and slow gamma in the development of theta sequences which may further facilitate memory encoding and retrieval.},
}

Animals
*Theta Rhythm/physiology
Rats
*Gamma Rhythm/physiology
*Hippocampus/physiology/cytology
*Place Cells/physiology
Male
Rats, Long-Evans

@article {pmid40212471,
year = {2025},
author = {Feng, J and Li, Y and Huang, Z and Chen, Y and Lu, S and Hu, R and Hu, Q and Chen, Y and Wang, X and Fan, Y and He, J},
title = {Heterogeneous transfer learning model for improving the classification performance of fNIRS signals in motor imagery among cross-subject stroke patients.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1555690},
pmid = {40212471},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery functional near-infrared spectroscopy (MI-fNIRS) offers precise monitoring of neural activity in stroke rehabilitation, yet accurate cross-subject classification remains challenging due to limited training samples and significant inter-subject variability. This study proposes a Cross-Subject Heterogeneous Transfer Learning Model (CHTLM) to enhance the generalization of MI-fNIRS signal classification in stroke patients.

METHODS: CHTLM leverages labeled electroencephalogram (EEG) data from healthy individuals as the source domain. An adaptive feature matching network aligns task-relevant feature maps and convolutional layers between source (EEG) and target (fNIRS) domains. Multi-scale fNIRS features are extracted, and a sparse Bayesian extreme learning machine classifies the fused deep learning features.

RESULTS: Experiments utilized two MI-fNIRS datasets from eight stroke patients pre- and post-rehabilitation. CHTLM achieved average accuracies of 0.831 (pre-rehabilitation) and 0.913 (post-rehabilitation), with mean AUCs of 0.887 and 0.930, respectively. Compared to five baselines, CHTLM improved accuracy by 8.6-10.5% pre-rehabilitation and 11.3-15.7% post-rehabilitation.

DISCUSSION: The model demonstrates robust cross-subject generalization by transferring task-specific knowledge from heterogeneous EEG data while addressing domain discrepancies. Its performance gains post-rehabilitation suggest clinical potential for monitoring recovery progress. CHTLM advances MI-fNIRS-based brain-computer interfaces in stroke rehabilitation by mitigating data scarcity and variability challenges.},
}

@article {pmid40210930,
year = {2025},
author = {Qi, W and Zhang, Y and Su, Y and Hui, Z and Li, S and Wang, H and Zhang, J and Shi, K and Wang, M and Zhou, L and Zhu, D},
title = {Exploring cortical excitability in children with cerebral palsy through lower limb robot training based on MI-BCI.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {12285},
pmid = {40210930},
issn = {2045-2322},
mesh = {Humans ; *Cerebral Palsy/physiopathology/rehabilitation ; Child ; Male ; Female ; *Brain-Computer Interfaces ; Child, Preschool ; Electroencephalography ; *Robotics/methods ; *Lower Extremity/physiopathology ; },
abstract = {This study aims to compare brain activity differences under the motor imagery-brain-computer interface (MI-BCI), motor imagery (MI), and resting (REST) paradigms through EEG microstate and functional connectivity (FC) analysis, providing a theoretical basis for applying MI-BCI in the rehabilitation of children with cerebral palsy (CP). This study included 30 subjects aged 4-6 years with GMFCS II-III grade, diagnosed with CP and classified as spastic diplegia. They sequentially completed EEG signal acquisition under REST, MI, and MI-BCI conditions. Clustering analysis was used to analyze EEG microstates and extract EEG microstate temporal parameters. Additionally, the strength of brain FC in different frequency bands was analyzed to compare the differences under various conditions. Four microstate classes (A-D) were identified to best explain the datasets of three groups. Compared to REST, the average duration and coverage rate of microstate D under MI and MI-BCI significantly increased (P < 0.05), while their frequency and the coverage rate and frequency of microstate A decreased. Compared to MI, the average duration of microstate C under MI-BCI significantly decreased (P < 0.05), while the frequency of microstate B significantly increased (P < 0.05). Additionally, the transition probability results showed that other microstates under REST had a higher transition probability to microstate A, while under MI and MI-BCI, other microstates had a higher transition probability to microstate D. The brain network results revealed significant differences in brain network connectivity among REST, MI, and MI-BCI across different frequency bands. No FC differences were found between REST, MI, and MI-BCI in the α2 frequency band. In the δ and γ frequency bands, MI and MI-BCI both had greater inter-electrode connectivity strength than REST. In the θ frequency band, REST had greater inter-electrode connectivity strength than MI-BCI, while MI-BCI had greater inter-electrode connectivity strength than both REST and MI. In the α1 frequency band, MI-BCI had greater inter-electrode connectivity strength than REST, and in the β frequency band, MI-BCI had greater inter-electrode connectivity strength than MI. MI-BCI can significantly alter the brain activity patterns of children with CP, particularly by enhancing the activity intensity of EEG microstates related to attention, motor planning, and execution, as well as the brain FC strength in different frequency bands. It holds high application value in the lower limb motor rehabilitation of children with CP.},
}

Humans
*Cerebral Palsy/physiopathology/rehabilitation
Child
Male
Female
*Brain-Computer Interfaces
Child, Preschool
Electroencephalography
*Robotics/methods
*Lower Extremity/physiopathology

@article {pmid40210429,
year = {2025},
author = {Benioudakis, ES and Kalaitzaki, A and Karlafti, E and Kapageridou, E and Ahanov, O and Kontoninas, Z and Savopoulos, C and Didangelos, T},
title = {Psychometric Properties and Dimensionality of the Greek Version of the Hypoglycemic Confidence Scale.},
journal = {Journal of nursing measurement},
volume = {},
number = {},
pages = {},
doi = {10.1891/JNM-2024-0108},
pmid = {40210429},
issn = {1945-7049},
abstract = {Background and purpose: The prevalence of type 1 diabetes mellitus (T1D) is rising at an alarming rate and is projected to continue increasing in the coming years. The primary approach to preventing diabetes-related complications in individuals with T1D is the exogenous administration of insulin. However, this method can sometimes lead to hypoglycemia, a condition with a wide range of symptoms, including loss of consciousness, seizures, coma, and, in severe cases, death. This study aims to present the psychometric properties of the Greek translation of the Hypoglycemic Confidence Scale (HCS). The HCS measures an individual's sense of personal strength and comfort based on the belief that they possess the necessary resources to manage and prevent hypoglycemia-related complications. Methods: We conducted a forward and backward translation, along with a cultural adaptation, of the HCS into Greek. The psychometric properties of the scale were evaluated through confirmatory factor analysis. To assess the reliability, we calculated the intraclass correlation coefficient, while internal consistency was measured using Cronbach's coefficient α. Construct validity was evaluated through convergent and divergent validity, comparing the HCS-Gr with the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) and hemoglobin A1C levels. Differential validity was assessed using the known-groups method. Results: Ninety-seven adults with T1D, aged between 18 and 57 years (mean age: 38.6 ± 11.7), completed the HCS-Gr. The two structures of the HCS-Gr demonstrated strong internal consistency, with Cronbach's coefficient α values of 0.87 for the eight-item version and 0.86 for the nine-item version. Convergent validity was supported by moderate negative correlations between both HCS-Gr versions and the DQoL-BCI subscales and total score. The HCS-Gr also showed satisfactory test-retest reliability and differential validity, confirming its robustness as a psychometric tool. Conclusion: The HCS-Gr is a valid and reliable tool for assessing confidence (or self-efficacy) in managing hypoglycemic situations among individuals with T1D in Greece.},
}

@article {pmid40209829,
year = {2025},
author = {Ruiz Ibán, MA and García Navlet, M and Marco, SM and Diaz Heredia, J and Hernando, A and Ruiz Díaz, R and Vaquero Comino, C and Alvarez Villar, S and Ávila Lafuente, JL},
title = {AUGMENTATION WITH A BOVINE BIOINDUCTIVE COLLAGEN IMPLANT OF A POSTEROSUPERIOR CUFF REPAIR SHOWS LOWER RETEAR RATES BUT SIMILAR OUTCOMES COMPARED TO NO AUGMENTATION: 2-YEAR RESULTS OF A RANDOMIZED CONTROLLED TRIAL.},
journal = {Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.arthro.2025.03.057},
pmid = {40209829},
issn = {1526-3231},
abstract = {PURPOSE: To assess the clinical and radiological outcomes of the addition of a bioinductive collagen implant (BCI) over the repair of medium-to-large posterosuperior rotator cuff tears at 24-month follow-up.

METHODS: This is an update of a randomized controlled trial that was extended from one to two-year follow-up. 124 subjects with symptomatic full-thickness posterosuperior rotator cuff tears, with fatty infiltration Goutalier grade ≤2 were randomized to two groups in which a transosseous equivalent repair was performed alone (Control group) or with BCI applied over the repair (BCI group). The outcomes reassessed at 2-year follow-up were: Sugaya grade, retear rate and tendon thickness in MRI; and the clinical outcomes (pain levels, EQ-5D-5L, American Shoulder and Elbow Society[ASES] and Constant-Murley scores[CMS]).

RESULTS: There were no relevant differences in preoperative characteristics. There were no additional complications or reinterventions in the second year of follow-up. 114 (59 males-55 males, age=58.1[SD:7.35] years) of 124 randomized patients (91.9%), underwent MRI evaluation 25.4[1.95] months after surgery. There was a lower retear rate (12.3%[7/57]) in the BCI group compared to the Control group (35.1%[20/57]) (p=0.004; relative risk of retear 0.35[CI-95%:0.16 to 0.76]). Sugaya grade was also better in the BCI group (2.58[1.07] vs 3.14[1.19]; p=0.020). Two-year Clinical follow-up at 25.8[2.75] months performed in 114 of 124 patients(91.9%) showed improvements in both groups (p<0.001), with 87% improving more than the MCID for CMS and 90% for ASES, but there were no differences between groups. In subjects with both MRI and clinical assessment (n=112), those with an intact tendon presented better CMS(p=0.035), ASES(p=0.015) and pain(p=0.006) scores than those with a failed repair.

CONCLUSION: Augmentation with a BCI of a TOE repair in posterosuperior rotator cuff tears clearly reduces the retear rate at two-year follow-up without increased complication rates and similar clinical outcomes. Subjects with failed repairs had poorer clinical outcomes.

LEVEL OF EVIDENCE: Level 1, Randomized controlled trial.},
}

@article {pmid40209163,
year = {2025},
author = {Kurmanavičiūtė, D and Kataja, H and Parkkonen, L},
title = {Comparing MEG and EEG measurement set-ups for a brain-computer interface based on selective auditory attention.},
journal = {PloS one},
volume = {20},
number = {4},
pages = {e0319328},
pmid = {40209163},
issn = {1932-6203},
mesh = {Humans ; *Magnetoencephalography/methods ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Attention/physiology ; Young Adult ; Evoked Potentials, Auditory/physiology ; *Auditory Perception/physiology ; Support Vector Machine ; Acoustic Stimulation ; Algorithms ; },
abstract = {Auditory attention modulates auditory evoked responses to target vs. non-target sounds in electro- and magnetoencephalographic (EEG/MEG) recordings. Employing whole-scalp MEG recordings and offline classification algorithms has been shown to enable high accuracy in tracking the target of auditory attention. Here, we investigated the decrease in accuracy when moving from the whole-scalp MEG to lower channel count EEG recordings and when training the classifier only from the initial or middle part of the recording instead of extracting training trials throughout the recording. To this end, we recorded simultaneous MEG (306 channels) and EEG (64 channels) in 18 healthy volunteers while presented with concurrent streams of spoken "Yes"/"No" words and instructed to attend to one of them. We then trained support vector machine classifiers to predict the target of attention from unaveraged trials of MEG/EEG. Classifiers were trained on 204 MEG gradiometers or on EEG with 64, 30, nine or three channels with trials extracted randomly across or only from the beginning of the recording. The highest classification accuracy, 73.2% on average across the participants for one-second trials, was obtained with MEG when the training trials were randomly extracted throughout the recording. With EEG, the accuracy was 69%, 69%, 66%, and 61% when using 64, 30, nine, and three channels, respectively. When training the classifiers with the same amount of data but extracted only from the beginning of the recording, the accuracy dropped by 11%-units on average, causing the result from the three-channel EEG to fall below the chance level. The combination of five consecutive trials partially compensated for this drop such that it was one to 5%-units. Although moving from whole-scalp MEG to EEG reduces classification accuracy, usable auditory-attention-based brain-computer interfaces can be implemented with a small set of optimally placed EEG channels.},
}

Humans
*Magnetoencephalography/methods
*Electroencephalography/methods
*Brain-Computer Interfaces
Male
Female
Adult
*Attention/physiology
Young Adult
Evoked Potentials, Auditory/physiology
*Auditory Perception/physiology
Support Vector Machine
Acoustic Stimulation
Algorithms

@article {pmid40206150,
year = {2025},
author = {Zhang, H and Wang, X and Chen, G and Zhang, Y and Jian, X and He, F and Xu, M and Ming, D},
title = {Noninvasive Intracranial Source Signal Localization and Decoding with High Spatiotemporal Resolution.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0206},
pmid = {40206150},
issn = {2692-7632},
abstract = {High spatiotemporal resolution of noninvasive electroencephalography (EEG) signals is an important prerequisite for fine brain-computer manipulation. However, conventional scalp EEG has a low spatial resolution due to the volume conductor effect, making it difficult to accurately identify the intent of brain-computer manipulation. In recent years, transcranial focused ultrasound modulated EEG technology has increasingly become a research hotspot, which is expected to acquire noninvasive acoustoelectric coupling signals with a high spatial and temporal resolution. In view of this, this study established a transcranial focused ultrasound numerical simulation model and experimental platform based on a real brain model and a 128-array phased array, further constructed a 3-dimensional transcranial multisource dipole localization and decoding numerical simulation model and experimental platform based on the acoustic field platform, and developed a high-precision localization and decoding algorithm. The results show that the simulation-guided phased-array acoustic field experimental platform can achieve accurate focusing in both pure water and transcranial conditions within a safe threshold, with a modulation range of 10 mm, and the focal acoustic pressure can be enhanced by more than 200% compared with that of transducer self-focusing. In terms of dipole localization decoding results, the proposed algorithm in this study has a localization signal-to-noise ratio of 24.18 dB, which is 50.59% higher than that of the traditional algorithm, and the source signal decoding accuracy is greater than 0.85. This study provides a reliable experimental basis and technical support for high-spatiotemporal-resolution noninvasive EEG signal acquisition and precise brain-computer manipulation.},
}

@article {pmid40203098,
year = {2025},
author = {Sîmpetru, RC and Braun, DI and Simon, AU and März, M and Cnejevici, V and de Oliveira, DS and Weber, N and Walter, J and Franke, J and Höglinger, D and Prahm, C and Ponfick, M and Del Vecchio, A},
title = {MyoGestic: EMG interfacing framework for decoding multiple spared motor dimensions in individuals with neural lesions.},
journal = {Science advances},
volume = {11},
number = {15},
pages = {eads9150},
pmid = {40203098},
issn = {2375-2548},
mesh = {Humans ; Male ; Female ; Young Adult ; Adult ; Middle Aged ; *Electromyography/instrumentation ; *Spinal Cord Injuries/rehabilitation ; *Stroke Rehabilitation/instrumentation ; *Amputation, Surgical/rehabilitation ; *Brain-Computer Interfaces ; Machine Learning ; Psychomotor Performance ; Intention ; Software Validation ; },
abstract = {Restoring motor function in individuals with spinal cord injuries (SCIs), strokes, or amputations is a crucial challenge. Recent studies show that spared motor neurons can still be voluntarily controlled using surface electromyography (EMG), even without visible movement. To harness these signals, we developed a wireless, high-density EMG bracelet and a software framework, MyoGestic. Our system enables rapid adaptation of machine learning models to users' needs, allowing real-time decoding of spared motor dimensions. In our study, we successfully decoded motor intent from two participants with traumatic SCI, two with spinal stroke, and three with amputations in real time, achieving multiple controllable motor dimensions within minutes. The decoded neural signals could control a digitally rendered hand, an orthosis, a prosthesis, or a two-dimensional cursor. MyoGestic's participant-centered approach allows a collaborative and iterative development of myocontrol algorithms, bridging the gap between researcher and participant, to advance intuitive EMG interfaces for neural lesions.},
}

Humans
Male
Female
Young Adult
Adult
Middle Aged
*Electromyography/instrumentation
*Spinal Cord Injuries/rehabilitation
*Stroke Rehabilitation/instrumentation
*Amputation, Surgical/rehabilitation
*Brain-Computer Interfaces
Machine Learning
Psychomotor Performance
Intention
Software Validation

@article {pmid40205860,
year = {2025},
author = {Sun, Y and Yu, N and Chen, G and Liu, T and Wen, S and Chen, W},
title = {What Else Is Happening to the Mirror Neurons?-A Bibliometric Analysis of Mirror Neuron Research Trends and Future Directions (1996-2024).},
journal = {Brain and behavior},
volume = {15},
number = {4},
pages = {e70486},
doi = {10.1002/brb3.70486},
pmid = {40205860},
issn = {2162-3279},
support = {21BZX005//National Social Science Fund of China/ ; 21NDQN281YB//Philosophy and Social Sciences Project of Zhejiang Province/ ; 23QNYC19ZD//Special Project for Cultivating Leading Talents in Philosophy and Social Sciences of Zhejiang Province (Cultivation of Young Talents)/ ; },
mesh = {*Bibliometrics ; Humans ; *Mirror Neurons/physiology ; Neurosciences/trends ; Brain/physiology ; Animals ; },
abstract = {BACKGROUND: Since its discovery in the late 20th century, research on mirror neurons has become a pivotal area in neuroscience, linked to various cognitive and social functions. This bibliometric analysis explores the research trajectory, key research topics, and future trends in the field of mirror neuron research.

METHODS: We searched the Web of Science Core Collection (WoSCC) database for publications from 1996 to 2024 on mirror neuron research. Statistical and visualization analyses were performed using CiteSpace and VOSviewer.

RESULTS: Publication output on mirror neurons peaked in 2013 and remained active. High-impact journals such as Science, Brain, Neuron, PNAS, and NeuroImage frequently feature findings on the mirror neuron system, including its distribution, neural coding, and roles in intention understanding, affective empathy, motor learning, autism, and neurological disorders. Keyword clustering reveals major directions in cognitive neuroscience, motor neuroscience, and neurostimulation, whereas burst detection underscores the emerging significance of brain-computer interfaces (BCIs). Research methodologies have been evolving from traditional electrophysiological recordings to advanced techniques such as functional magnetic resonance imaging, transcranial magnetic stimulation, and BCIs, highlighting a dynamic, multidisciplinary progression.

CONCLUSIONS: This study identifies key areas associated with mirror neurons and anticipates that future work will integrate findings with artificial intelligence, clinical interventions, and novel neuroimaging techniques, providing new perspectives on complex socio-cognitive issues and their applications in both basic science and clinical practice.},
}

*Bibliometrics
Humans
*Mirror Neurons/physiology
Neurosciences/trends
Brain/physiology
Animals

@article {pmid40205038,
year = {2025},
author = {Yang, L and Guo, C and Zheng, Z and Dong, Y and Xie, Q and Lv, Z and Li, M and Lu, Y and Guo, X and Deng, R and Liu, Y and Feng, Y and Mu, R and Zhang, X and Ma, H and Chen, Z and Zhang, Z and Dong, Z and Yang, W and Zhang, X and Cui, Y},
title = {Stress dynamically modulates neuronal autophagy to gate depression onset.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {40205038},
issn = {1476-4687},
abstract = {Chronic stress remodels brain homeostasis, in which persistent change leads to depressive disorders[1]. As a key modulator of brain homeostasis[2], it remains elusive whether and how brain autophagy is engaged in stress dynamics. Here we discover that acute stress activates, whereas chronic stress suppresses, autophagy mainly in the lateral habenula (LHb). Systemic administration of distinct antidepressant drugs similarly restores autophagy function in the LHb, suggesting LHb autophagy as a common antidepressant target. Genetic ablation of LHb neuronal autophagy promotes stress susceptibility, whereas enhancing LHb autophagy exerts rapid antidepressant-like effects. LHb autophagy controls neuronal excitability, synaptic transmission and plasticity by means of on-demand degradation of glutamate receptors. Collectively, this study shows a causal role of LHb autophagy in maintaining emotional homeostasis against stress. Disrupted LHb autophagy is implicated in the maladaptation to chronic stress, and its reversal by autophagy enhancers provides a new antidepressant strategy.},
}

@article {pmid40204716,
year = {2025},
author = {Amann, LK and Casasnovas, V and Gail, A},
title = {Visual target and task-critical feedback uncertainty impair different stages of reach planning in motor cortex.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3372},
pmid = {40204716},
issn = {2041-1723},
support = {H2020-FETPROACT-16 732266 WP1//European Commission (EC)/ ; ZN3422//Niedersächsische Ministerium für Wissenschaft und Kultur (Lower Saxony Ministry of Science and Culture)/ ; SFB-889 C4//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; SFB 1690 B09//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; },
mesh = {Animals ; Macaca mulatta ; Male ; *Motor Cortex/physiology ; Uncertainty ; *Feedback, Sensory/physiology ; *Psychomotor Performance/physiology ; Movement/physiology ; Brain-Computer Interfaces ; Hand/physiology ; Visual Perception/physiology ; },
abstract = {Sensory uncertainty jeopardizes accurate movement. During reaching, visual uncertainty can affect the estimation of hand position (feedback) and the desired movement endpoint (target). While impairing motor learning, it is unclear how either form of uncertainty affects cortical reach goal encoding. We show that reach trajectories vary more with higher visual uncertainty of the target, but not the feedback. Accordingly, cortical motor goal activities in male rhesus monkeys are less accurate during planning and movement initiation under target but not feedback uncertainty. Yet, when monkeys critically depend on visual feedback to conduct reaches via a brain-computer interface, then visual feedback uncertainty impairs reach accuracy and neural motor goal encoding around movement initiation. Neural state space analyses reveal a dimension that separates population activity by uncertainty level in all tested conditions. Our findings demonstrate that while both target and feedback uncertainty always reflect in neural activity, uncertain feedback only deteriorates neural reach goal information and behavior when it is task-critical, i.e., when having to rely on the sensory feedback and no other more reliable sensory modalities are available. Further, uncertain target and feedback impair reach goal encoding in a time-dependent manner, suggesting that they are integrated during different stages of reach planning.},
}

Animals
Macaca mulatta
Male
*Motor Cortex/physiology
Uncertainty
*Feedback, Sensory/physiology
*Psychomotor Performance/physiology
Movement/physiology
Brain-Computer Interfaces
Hand/physiology
Visual Perception/physiology

@article {pmid40204228,
year = {2025},
author = {Hasegawa, R and Poulin, R},
title = {Effect of parasite infections on fish body condition: a systematic review and meta-analysis.},
journal = {International journal for parasitology},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.ijpara.2025.03.002},
pmid = {40204228},
issn = {1879-0135},
abstract = {Using host body condition indices (BCIs) based on the relationship between host body mass and length is a general and pervasive approach to assess the negative effects of parasites on host health. Although many researchers, especially fish biologists and fisheries managers, commonly utilize BCIs, the overall general patterns among BCI - infection relationships remain unclear. Here, we first systematically reviewed 985 fish BCI - infection relationships from 216 publications and investigated the factors affecting the strength and directionality of effects in BCI - infection relationships. We specifically predicted that the BCI measure used, parasite taxonomic group, and the infection measure used would influence the observed effect size and directionality of BCI - infection relationships. We found that most studies were heavily biased towards specific BCI measures such as Fulton's BCI and Relative BCI. Furthermore, studies using Fulton's BCI were more likely to report significant results compared with those using other BCI measures, suggesting that index choice could lead to an overestimation of the negative effects of parasites. Our meta-regressions uncovered that the use of parasite intensity as an infection measure and studies based on experimental rather than natural infections were more likely to report significant negative effects, however there were no differences among parasite taxonomic groups. Surprisingly, many studies, especially field studies, did not report significant negative correlations between BCI and infection, contrary to widespread expectations among researchers that parasites would negatively affect fish health. We discuss potential mechanisms underlying these results. Finally, we make several recommendations for the use of BCI - infection relationships in future studies.},
}

@article {pmid40204168,
year = {2025},
author = {Tan, H and Hu, YT and Goudswaard, A and Li, YJ and Balesar, R and Swaab, D and Bao, AM},
title = {Increased oxytocin/vasopressin ratio in bipolar disorder in a cohort of human postmortem adults.},
journal = {Neurobiology of disease},
volume = {},
number = {},
pages = {106904},
doi = {10.1016/j.nbd.2025.106904},
pmid = {40204168},
issn = {1095-953X},
abstract = {Bipolar disorder (BD) and major depressive disorder (MDD) share some common characteristics in stress-related brain circuits, but they also exhibit distinct symptoms. Our previous postmortem research on the immunoreactivity (ir) levels of neuropeptide oxytocin (OT) in the hypothalamic paraventricular nucleus (OT[PVN]) and some clinical research on plasma OT levels suggested that increased levels of OT is a potential trait marker for BD. However, dysregulation of the related neuropeptide arginine vasopressin (AVP), that often shows opposite effects for stress responses compared to OT has not been investigated in BD. Moreover, it remains so far unknown what the contribution may be of OT produced in the hypothalamic supraoptic nucleus (SON), another major source of OT (OT[SON]). Therefore, in the present postmortem study, alterations in levels of OT-ir and for the first time in AVP-ir were determined in the SON and PVN among patients with BD, MDD, and matched controls. We observed a significantly increased OT[PVN]-ir but relatively stable AVP[PVN]-ir in male BD, and a significantly decreased AVP[PVN]-ir but relatively stable OT[PVN]-ir in female BD patients. A significantly increased ratio of OT-ir/AVP-ir was observed only in BD patients in both, the PVN and SON. No significant changes in OT-ir or AVP-ir were found in MDD patients compared with controls. Our data illustrate a clear disease- and sex-specificity of the OT and AVP changes in BD. In addition, since increased AVP-ir was observed in female BD patients with lithium nephropathy, increased AVP may have a direct effect on symptoms of BD.},
}

@article {pmid40203859,
year = {2025},
author = {Pang, Y and Wang, X and Zhao, Z and Han, C and Gao, N},
title = {Multi-view collaborative ensemble classification for EEG signals based on 3D second-order difference plot and CSP.},
journal = {Physics in medicine and biology},
volume = {},
number = {},
pages = {},
doi = {10.1088/1361-6560/adcafa},
pmid = {40203859},
issn = {1361-6560},
abstract = {OBJECTIVE: EEG signal analysis methods based on electrical source imaging (ESI) technique have significantly improved classification accuracy and response time. However, for the refined and informative source signals, the current studies have not fully considered their dynamic variability in feature extraction and lacked an effective integration of their dynamic variability and spatial characteristics. Additionally, the adaptability and complementarity of classifiers have not been considered comprehensively. These two aspects lead to the issue of insufficient decoding of source signals, which still limits the application of brain-computer interface (BCI). To address these challenges, this paper proposes a multi-view collaborative ensemble classification method for EEG signals based on three-dimensional second-order difference plot (3D SODP) and common spatial pattern (CSP).

APPROACH: First, EEG signals are mapped to the source domain using the ESI technique, and then the source signals in the region of interest (ROI) are obtained. Next, features from three viewpoints of the source signals are extracted, including 3D SODP features, spatial features, and the weighted fusion of both. Finally, the extracted multi-view features are integrated with subject-specific sub-classifier combination, and a voting mechanism is used to determine the final classification.

MAIN RESULTS: The results show that the proposed method achieves classification accuracy of 81.3% and 82.6% respectively in two sessions of the OpenBMI dataset, which is nearly 5% higher than the state-of-the-art method, and maintains the analysis response time required for online BCI.

SIGNIFICANCE: This paper employs multi-view feature extraction to fully capture the characteristics of the source signals and enhances feature utilization through collaborative ensemble classification. The results demonstrate high accuracy and robust performance, providing a novel approach for online BCI.},
}

@article {pmid40203855,
year = {2025},
author = {Yin, S and Yue, Z and Qu, H and Wang, J and Shi, B and Zhang, J},
title = {Enhancing lower-limb motor imagery using a paradigm with visual and spatiotemporal tactile synchronized stimulation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adcaec},
pmid = {40203855},
issn = {1741-2552},
abstract = {UNLABELLED: Vibrotactile stimulation (VS) has been widely used as an appropriate motor imagery (MI) guidance strategy to improve MI performance. However, most vibrotactile stimulation induced by a single vibrator cannot provide spatiotemporal information of tactile sensation associated with the visual guidance of the imagined motion process, not vividly providing MI guidance for subjects.

METHODS: This paper proposed a paradigm with visual and spatiotemporal tactile synchronized stimulation (VSTSS) to provide vivid MI guidance to help subjects perform lower-limb MI tasks and improve MI-based brain-computer interface (MI-BCI) performance, with a focus on poorly performing subjects. The proposed paradigm provided subjects with the natural spatiotemporal tactile sensation associated with the visual guidance of the foot movement process during MI.

EXPERIMENTS: Fourteen healthy subjects were recruited to participate in the MI and Rest tasks and divided into good and poor performers. Furthermore, electrophysiological features and classification performance were analyzed to assess motor cortical activation and MI-BCI performance under no VS (NVS), VS, and VSTSS.

RESULTS: The phenomenon of event-related desynchronization (ERD) in the sensorimotor cortex during MI under the VSTSS was more pronounced compared to the NVS and VS. Specifically, the VSTSS could improve the average ERD values in the motor cortex during the task segment by 34.70% and 14.28% than the NVS and VS in the alpha rhythm for poor performers, respectively. Additionally, the VSTSS could significantly enhance the classification accuracy between the MI and Rest tasks by 12.52% and 4.05% compared to NVS and VS for poor performers, respectively.

CONCLUSION: The proposed paradigm could enhance motor cortical activation during MI and classification performance by providing vivid MI guidance for subjects, offering a crucial promise for practical applications of lower-limb MI-BCI. .},
}

@article {pmid40203854,
year = {2025},
author = {Collinger, J and Vansteensel, MJ and Mrachacz-Kersting, N and Mattia, D and Valeriani, D and Vaughan, TM},
title = {Special Issue on Brain-Computer Interfaces: Highlighting Research from the 10th International Brain-Computer Interface Meeting.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adcaed},
pmid = {40203854},
issn = {1741-2552},
abstract = {N/A.},
}

@article {pmid40199879,
year = {2025},
author = {Zhao, Y and Wu, JT and Feng, JB and Cai, XY and Wang, XT and Wang, L and Xie, W and Gu, Y and Liu, J and Chen, W and Zhou, L and Shen, Y},
title = {Dual and plasticity-dependent regulation of cerebello-zona incerta circuits on anxiety-like behaviors.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3339},
pmid = {40199879},
issn = {2041-1723},
support = {2021ZD0204000//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 2023YFE0206800//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 2021ZD0204000//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 81625006//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31820103005//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200620//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32225021//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32170976//National Natural Science Foundation of China (National Science Foundation of China)/ ; LY21C090003//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *Anxiety/physiopathology/metabolism ; Male ; *Neuronal Plasticity/physiology ; Mice ; *Zona Incerta/physiology/physiopathology ; *Cerebellum/physiology ; Mice, Inbred C57BL ; *Cerebellar Nuclei/physiology ; Neurons/physiology/metabolism ; Behavior, Animal/physiology ; Glutamic Acid/metabolism ; Neural Pathways ; Synaptic Transmission/physiology ; },
abstract = {Clinical observation has identified cerebellar cognitive affective syndrome, which is characterized by various non-motor dysfunctions such as social disorders and anxiety. Increasing evidence has revealed reciprocal mono-/poly-synaptic connections of cerebello-cerebral circuits, forming the concept of the cerebellar connectome. In this study, we demonstrate that neurons in the cerebellar nuclei (CN) of male mice project to a subset of zona incerta (ZI) neurons through long-range glutamatergic and GABAergic transmissions, both capable of encoding acute stress. Furthermore, activating or inhibiting glutamatergic and GABAergic transmissions in the CN → ZI pathway can positively or negatively regulate anxiety and place preference through presynaptic plasticity-dependent mechanisms, as well as mediate motor-induced alleviation of anxiety. Our data support the close relationship between the cerebellum and emotional processes and suggest that targeting cerebellar outputs may be an effective approach for treating anxiety.},
}

Animals
*Anxiety/physiopathology/metabolism
Male
*Neuronal Plasticity/physiology
Mice
*Zona Incerta/physiology/physiopathology
*Cerebellum/physiology
Mice, Inbred C57BL
*Cerebellar Nuclei/physiology
Neurons/physiology/metabolism
Behavior, Animal/physiology
Glutamic Acid/metabolism
Neural Pathways
Synaptic Transmission/physiology

@article {pmid40199863,
year = {2025},
author = {Guttmann-Flury, E and Sheng, X and Zhu, X},
title = {Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {587},
pmid = {40199863},
issn = {2052-4463},
support = {91948302//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91948302//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91948302//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; *Eye-Tracking Technology ; *Eye Movements ; Blinking ; Video Recording ; },
abstract = {In Brain-Computer Interface (BCI) research, the detailed study of blinks is crucial. They can be considered as noise, affecting the efficiency and accuracy of decoding users' cognitive states and intentions, or as potential features, providing valuable insights into users' behavior and interaction patterns. We introduce a large dataset capturing electroencephalogram (EEG) signals, eye-tracking, high-speed camera recordings, as well as subjects' mental states and characteristics, to provide a multifactor analysis of eye-related movements. Four paradigms - motor imagery, motor execution, steady-state visually evoked potentials, and P300 spellers - are selected due to their capacity to evoke various sensory-motor responses and potential influence on ocular activity. This online-available dataset contains over 46 hours of data from 31 subjects across 63 sessions, totaling 2520 trials for each of the first three paradigms, and 5670 for P300. This multimodal and multi-paradigms dataset is expected to allow the development of algorithms capable of efficiently handling eye-induced artifacts and enhancing task-specific classification. Furthermore, it offers the opportunity to evaluate the cross-paradigm robustness involving the same participants.},
}

Humans
*Electroencephalography
*Brain-Computer Interfaces
*Eye-Tracking Technology
*Eye Movements
Blinking
Video Recording

@article {pmid40198632,
year = {2025},
author = {Ueda, M and Ueno, K and Inoue, T and Sakiyama, M and Shiroma, C and Ishii, R and Naito, Y},
title = {Detection of motor-related mu rhythm desynchronization by ear EEG.},
journal = {PloS one},
volume = {20},
number = {4},
pages = {e0321107},
doi = {10.1371/journal.pone.0321107},
pmid = {40198632},
issn = {1932-6203},
mesh = {Humans ; Male ; *Electroencephalography/methods/instrumentation ; Female ; Adult ; Young Adult ; Hand/physiology ; Movement/physiology ; *Ear/physiology ; Brain-Computer Interfaces ; },
abstract = {Event-related desynchronization (ERD) of the mu rhythm (8-13 Hz) is an important indicator of motor execution, neurofeedback, and brain-computer interface in EEG. This study investigated the feasibility of an ear electroencephalography (EEG) device monitoring mu-ERD during hand grasp and release movements. The EEG data of the right hand movement and the eye opened resting condition were measured with an ear EEG device. We calculated and compared mu rhythm power and time-frequency data from 20 healthy participants during right hand movement and eye opened resting. Our results showed a significant difference of mean mu rhythm power between the eye opened rest condition and the right hand movement condition and significant suppression in the 9-12.5 Hz frequency band in the time-frequency data. These results support the utility of ear EEG in detecting motor activity-related mu-ERD. Ear EEG could be instrumental in refining rehabilitation strategies by providing in-situ assessment of motor function and tailored feedback.},
}

Humans
Male
*Electroencephalography/methods/instrumentation
Female
Adult
Young Adult
Hand/physiology
Movement/physiology
*Ear/physiology
Brain-Computer Interfaces

@article {pmid40198304,
year = {2025},
author = {Wang, Z and Li, A and Wang, Z and Zhou, T and Xu, T and Hu, H},
title = {BSAN: A Self-Adapted Motor Imagery Decoding Framework Based on Contextual Information.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3557499},
pmid = {40198304},
issn = {2168-2208},
abstract = {In motor imagery (MI) decoding, it still remains challenging to excavate enough contextual information of MI in different brain regions and to bridge the cross-session variance in feature distributions. In light of these issues, our study presents an innovative Bi-Stream Adaptation Network (BSAN) to bolster network efficacy, aiming to improve MI-based brain-computer interface (BCI) robustness across sessions. Our framework consists of the Bi-attention module, feature extractor, classifier, and Bi-discriminator. Precisely, we devise the Bi-attention module to reveal granular context information of MI with performing multi-scale convolutions asymptotically. Then, after features extraction, Bi-discriminator is involved to align the features from different MI sessions such that a uniform and accurate representation of neural patterns is achieved. By such a workflow, the proposed BSAN allows for the effective fusion of context coherence and session-invariance within the network architecture, therefore diminishing the reliance of redundant MI trials for MI-BCI re-calibration. To empirically substantiate BSAN, comprehensive experiments are conducted based on two public MI datasets. With average accuracies of 78.97% and 83.79% on two public datasets, and an inference time of 2.99 ms on CPU-only devices, it is believed that our approach has the potential to accelerate the practical deployment of MI-BCI.},
}

@article {pmid40197656,
year = {2025},
author = {Won, C and Cho, S and Jang, KI and Park, JU and Cho, JH and Lee, T},
title = {Emerging fiber-based neural interfaces with conductive composites.},
journal = {Materials horizons},
volume = {},
number = {},
pages = {},
doi = {10.1039/d4mh01854k},
pmid = {40197656},
issn = {2051-6355},
abstract = {Neural interfaces that enable bidirectional communication between neural systems and external devices are crucial for treating neurological disorders and advancing brain-machine interfaces. Key requirements for these neural interfaces are the ability to modulate electrophysiological activity without causing tissue damage in the nerve system and long-term usability. Recent advances in biomedical neural electrodes aim to reduce mechanical mismatch between devices and surrounding tissues/organs while maintaining their electrical conductivity. Among these, fiber electrodes stand out as essential candidates for future neural interfaces owing to their remarkable flexibility, controllable scalability, and facile integration with systems. Herein, we introduce fiber-based devices with conductive composites, along with their fabrication technologies, and integration strategies for future neural interfaces. Compared to conventional neural electrodes, fiber electrodes readily combine with conductive materials such as metal nanoparticles, carbon-based nanomaterials, and conductive polymers. Their fabrication technologies enable high electrical performance without sacrificing mechanical properties. In addition, the neural modulation techniques of fiber electrodes; electrical, optical, and chemical, and their applications in central and peripheral nervous systems are carefully discussed. Finally, current limitations and potential advancements in fiber-based neural interfaces are highlighted for future innovations.},
}

@article {pmid40196469,
year = {2025},
author = {Tor, A and Clarke, SE and Bray, IE and Nuyujukian, P and , },
title = {Material Damage to Multielectrode Arrays after Electrolytic Lesioning is in the Noise.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.03.26.645429},
pmid = {40196469},
issn = {2692-8205},
abstract = {1The quality of stable long-term recordings from chronically implanted electrode arrays is essential for experimental neuroscience and brain-computer interfaces. This work uses scanning electron microscopy (SEM) to image and analyze eight 96-channel Utah arrays previously implanted in motor cortical regions of four subjects (subject H = 2242 days implanted, F = 1875, U = 2680, C = 594), providing important contributions to a growing body of long-term implant research leveraging this imaging technology. Four of these arrays have been used in electrolytic lesioning experiments (H = 10 lesions, F = 1, U = 4, C = 1), a novel electrolytic perturbation technique using small direct currents. In addition to surveying physical damage, such as biological debris and material deterioration, this work also analyzes whether electrolytic lesioning created damage beyond what is typical for these arrays. Each electrode was scored in six damage categories, identified from the literature: abnormal debris, metal coating cracks, silicon tip breakage, parylene C delamination, parylene C cracks, and shank fracture. This analysis confirms previous results that observed damage on explanted arrays is more severe on the outer-edge electrodes versus inner electrodes. These findings also indicate that are no statistically significant differences between the damage observed on normal electrodes versus electrodes used for electrolytic lesioning. This work provides evidence that electrolytic lesioning does not significantly affect the quality of chronically implanted electrode arrays and can be a useful tool in understanding perturbations to neural systems. Finally, this work also includes the largest collection of single-electrode SEM images for previously implanted multielectrode Utah arrays, spanning eleven different intact arrays and one broken array. As the clinical relevance of chronically implanted electrodes with single-neuron resolution continues to grow, these images may be used to provide the foundation for a larger public database and inform further electrode design and analyses.},
}

@article {pmid40196347,
year = {2025},
author = {Yu, H and Mu, Q and Wang, Z and Guo, Y and Zhao, J and Wang, G and Wang, Q and Meng, X and Dong, X and Wang, S and Sun, J},
title = {A study on early diagnosis for fracture non-union prediction using deep learning and bone morphometric parameters.},
journal = {Frontiers in medicine},
volume = {12},
number = {},
pages = {1547588},
pmid = {40196347},
issn = {2296-858X},
abstract = {BACKGROUND: Early diagnosis of non-union fractures is vital for treatment planning, yet studies using bone morphometric parameters for this purpose are scarce. This study aims to create a fracture micro-CT image dataset, design a deep learning algorithm for fracture segmentation, and develop an early diagnosis model for fracture non-union.

METHODS: Using fracture animal models, micro-CT images from 12 rats at various healing stages (days 1, 7, 14, 21, 28, and 35) were analyzed. Fracture lesion frames were annotated to create a high-resolution dataset. We proposed the Vision Mamba Triplet Attention and Edge Feature Decoupling Module UNet (VM-TE-UNet) for fracture area segmentation. And we extracted bone morphometric parameters to establish an early diagnostic evaluation system for the non-union of fractures.

RESULTS: A dataset comprising 2,448 micro-CT images of the rat fracture lesions with fracture Region of Interest (ROI), bone callus and healing characteristics was established and used to train and test the proposed VM-TE-UNet which achieved a Dice Similarity Coefficient of 0.809, an improvement over the baseline's 0.765, and reduced the 95th Hausdorff Distance to 13.1. Through ablation studies, comparative experiments, and result analysis, the algorithm's effectiveness and superiority were validated. Significant differences (p < 0.05) were observed between the fracture and fracture non-union groups during the inflammatory and repair phases. Key indices, such as the average CT values of hematoma and cartilage tissues, BS/TS and BS/TV of mineralized cartilage, BS/TV of osteogenic tissue, and BV/TV of osteogenic tissue, align with clinical methods for diagnosing fracture non-union by assessing callus presence and local soft tissue swelling. On day 14, the early diagnosis model achieved an AUC of 0.995, demonstrating its ability to diagnose fracture non-union during the soft-callus phase.

CONCLUSION: This study proposed the VM-TE-UNet for fracture areas segmentation, extracted micro-CT indices, and established an early diagnostic model for fracture non-union. We believe that the prediction model can effectively screen out samples of poor fracture rehabilitation caused by blood supply limitations in rats 14 days after fracture, rather than the widely accepted 35 or 40 days. This provides important reference for the clinical prediction of fracture non-union and early intervention treatment.},
}

@article {pmid40196232,
year = {2025},
author = {Yang, Y and Zhao, H and Hao, Z and Shi, C and Zhou, L and Yao, X},
title = {Recognition of brain activities via graph-based long short-term memory-convolutional neural network.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1546559},
pmid = {40196232},
issn = {1662-4548},
abstract = {INTRODUCTION: Human brain activities are always difficult to recognize due to its diversity and susceptibility to disturbance. With its unique capability of measuring brain activities, magnetoencephalography (MEG), as a high temporal and spatial resolution neuroimaging technique, has been used to identify multi-task brain activities. Accurately and robustly classifying motor imagery (MI) and cognitive imagery (CI) from MEG signals is a significant challenge in the field of brain-computer interface (BCI).

METHODS: In this study, a graph-based long short-term memory-convolutional neural network (GLCNet) is proposed to classify the brain activities in MI and CI tasks. It was characterized by implementing three modules of graph convolutional network (GCN), spatial convolution and long short-term memory (LSTM) to effectively extract time-frequency-spatial features simultaneously. For performance evaluation, our method was compared with six benchmark algorithms of FBCSP, FBCNet, EEGNet, DeepConvNets, Shallow ConvNet and MEGNet on two public datasets of MEG-BCI and BCI competition IV dataset 3.

RESULTS: The results demonstrated that the proposed GLCNet outperformed other models with the average accuracies of 78.65% and 65.8% for two classification and four classification on the MEG-BCI dataset, respectively.

DISCUSSION: It was concluded that the GLCNet enhanced the model's adaptability in handling individual variability with robust performance. This would contribute to the exploration of brain activates in neuroscience.},
}

@article {pmid40195935,
year = {2025},
author = {Hasegawa, R and Poulin, R},
title = {Cause or consequence? Exploring authors' interpretations of correlations between fish body condition and parasite infection.},
journal = {Journal of fish biology},
volume = {},
number = {},
pages = {},
doi = {10.1111/jfb.70048},
pmid = {40195935},
issn = {1095-8649},
support = {202460294//Japan Society for the Promotion of Science/ ; JP22KJ0086//Japan Society for the Promotion of Science/ ; },
abstract = {We reviewed 194 publications that reported relationships between fish body condition indices (BCIs) and parasite infections, and examined the authors' intention behind this cross-sectional analysis, that is, whether authors interpreted the negative correlations as the negative effects of parasites or as fish with poor BCIs being more susceptible to infections. While 89% of studies only considered parasite infections as causes of poor BCI, studies acknowledging the opposite or bidirectional causal links were rare. We recommend considering both possibilities in any given fish host and parasite association.},
}

@article {pmid40195900,
year = {2025},
author = {Shin, H and Kim, K and Lee, J and Nam, J and Baeg, E and You, C and Choi, H and Kim, M and Chung, CK and Kim, JG and Ahn, JH and Han, M and Kim, J and Yang, S and Lee, SQ and Yang, S},
title = {A Wireless Cortical Surface Implant for Diagnosing and Alleviating Parkinson's Disease Symptoms in Freely Moving Animals.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e2405179},
doi = {10.1002/adhm.202405179},
pmid = {40195900},
issn = {2192-2659},
support = {//High Risk, High Return Research Program/ ; //ETRI grant (23YB1210, Collective Behavioral Modelling in Socially Interacting Group)/ ; },
abstract = {Parkinson's disease (PD), one of the most common neurodegenerative diseases, is involved in motor abnormality, primarily arising from the degeneration of dopaminergic neurons. Previous studies have examined the electrotherapeutic effects of PD using various methodological contexts, including live conditions, wireless control, diagnostic/therapeutic aspects, removable interfaces, or biocompatible materials, each of which is separately utilized for testing the diagnosis or alleviation of various brain diseases. Here, a cortical surface implant designed to improve motor function in freely moving PD animals is presented. This implant, a minimally invasive system equipped with a graphene electrode array, is the first integrated system to exhibit biocompatibility, wearability, removability, target specificity, and wireless control. The implant positioned at the motor cortical surface activates the motor cortex to maximize therapeutic effects and minimize off-target effects while monitoring motor activities. In PD animals, cortical motor surface stimulation restores motor function and brain waves, which corresponds to potentiated synaptic responses. Furthermore, these changes are associated with the upregulation of metabotropic glutamate receptor 5 (mGluR5, Grm5) and D5 dopamine receptor (D5R, Drd5) genes in the glutamatergic synapse. The newly designed wireless neural implant demonstrates capabilities in both real-time diagnostics and targeted therapeutics, suggesting its potential as a wireless system for biomedical devices for patients with PD and other neurodegenerative diseases.},
}

@article {pmid40195429,
year = {2025},
author = {Ming, Z and Yu, W and Fan, J and Ling, G and Fengming, C and Wei, T},
title = {Efficacy of kinesthetic motor imagery based brain computer interface combined with tDCS on upper limb function in subacute stroke.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {11829},
pmid = {40195429},
issn = {2045-2322},
support = {XWRCHT20220045//the Xuzhou Key Medical Talents Project/ ; No.52375224//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; Male ; Female ; *Upper Extremity/physiopathology ; *Brain-Computer Interfaces ; Middle Aged ; *Stroke Rehabilitation/methods ; *Stroke/physiopathology/therapy ; Aged ; Electroencephalography ; *Kinesthesis/physiology ; Treatment Outcome ; Adult ; },
abstract = {This study investigates whether the combined effect of kinesthetic motor imagery-based brain computer interface (KI-BCI) and transcranial direct current stimulation (tDCS) on upper limb function in subacute stroke patients is more effective than using KI-BCI or tDCS alone. Forty-eight subacute stroke survivors were randomized to the KI-BCI, tDCS, or BCI-tDCS group. The KI-BCI group performed 30 min of KI-BCI training. Patients in tDCS group received 30 min of tDCS. Patients in BCI-tDCS group received 15 min of tDCS and 15 min of KI-BCI. The treatment cycle was five times a week, for four weeks. After all intervention, the Fugl-Meyer Assessment-Upper Extremity, Motor Status Scale, and the Modified Barthel Index scores of the KI-BCI group were superior to those of the tDCS group. The BCI-tDCS group was superior to the tDCS group in terms of the Motor Status Scale. Although quantitative EEG showed no significant group differences, the quantitative EEG indices in the tDCS group were significantly lower than before treatment. In conclusion, after treatment, although all intervention strategies improved upper limb motor function and daily living abilities in subacute stroke patients, KI-BCI demonstrated significantly better efficacy than tDCS. Under the same total treatment duration, the combined use of tDCS and KI-BCI did not achieve the hypothesized optimal outcome. Notably, tDCS reduced QEEG indices, possibly indicating favorable future outcomes in future.Trial registry number: ChiCTR2000034730.},
}

Humans
*Transcranial Direct Current Stimulation/methods
Male
Female
*Upper Extremity/physiopathology
*Brain-Computer Interfaces
Middle Aged
*Stroke Rehabilitation/methods
*Stroke/physiopathology/therapy
Aged
Electroencephalography
*Kinesthesis/physiology
Treatment Outcome
Adult

@article {pmid40194524,
year = {2025},
author = {Johnson, TR and Haddix, CA and Ajiboye, AB and Taylor, DM},
title = {Simplified control of neuromuscular stimulation systems for restoration of reach with limb stiffness as a modifiable degree of freedom.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc9e3},
pmid = {40194524},
issn = {1741-2552},
abstract = {Brain-controlled functional electrical stimulation (FES) of the upper limb has been used to restore arm function to paralyzed individuals in the lab. Able-bodied individuals naturally modulate limb stiffness throughout movements and in anticipation of perturbations. Our goal is to develop, via simulation, a framework for incorporating stiffness modulation into the currently-used 'lookup-table-based' FES control systems while addressing several practical issues: 1) optimizing stimulation across muscles with overlap in function, 2) coordinating stimulation across joints, and 3) minimizing errors due to fatigue. Our calibration process also needs to account for when current spread causes additional muscles to become activated. Approach: We developed an analytical framework for building a lookup-table-based FES controller and simulated the clinical process of calibrating and using the arm. A computational biomechanical model of a human paralyzed arm responding to stimulation was used for simulations with six muscles controlling the shoulder and elbow in the horizontal plane. Both joints had multiple muscles with overlapping functional effects, as well as biarticular muscles to reflect complex interactions between joints. Performance metrics were collected in silico, and real-time use was demonstrated with a Rhesus macaque using its cortical signals to control the computational arm model in real time. Main Results: By explicitly including stiffness as a definable degree of freedom in the lookup table, our analytical approach was able to achieve all our performance criteria. While using more empirical data during controller parameterization produced more accurate lookup tables, interpolation between sparsely sampled points (e.g., 20 degree angular intervals) still produced good results with median endpoint position errors of less than 1 cm-a range that should be easy to correct for with real-time visual feedback. Significance: Our simplified process for generating an effective FES controller now makes translating upper limb FES systems into mainstream clinical practice closer to reality. .},
}

@article {pmid40193612,
year = {2025},
author = {Kim, H and Kim, JH and Lee, YJ and Lee, J and Han, H and Yi, H and Kim, H and Kim, H and Kang, TW and Chung, S and Ban, S and Lee, B and Lee, H and Im, CH and Cho, SJ and Sohn, JW and Yu, KJ and Kang, TJ and Yeo, WH},
title = {Motion artifact-controlled micro-brain sensors between hair follicles for persistent augmented reality brain-computer interfaces.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {15},
pages = {e2419304122},
doi = {10.1073/pnas.2419304122},
pmid = {40193612},
issn = {1091-6490},
support = {ECCS-2025462//NSF (NSF)/ ; P0017303//Korea Institute for Advancement of Technology (KIAT)/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Hair Follicle/physiology ; Electroencephalography/methods/instrumentation ; *Augmented Reality ; Artifacts ; *Brain/physiology ; Motion ; Algorithms ; Electrodes ; Evoked Potentials, Visual/physiology ; },
abstract = {Modern brain-computer interfaces (BCI), utilizing electroencephalograms for bidirectional human-machine communication, face significant limitations from movement-vulnerable rigid sensors, inconsistent skin-electrode impedance, and bulky electronics, diminishing the system's continuous use and portability. Here, we introduce motion artifact-controlled micro-brain sensors between hair strands, enabling ultralow impedance density on skin contact for long-term usable, persistent BCI with augmented reality (AR). An array of low-profile microstructured electrodes with a highly conductive polymer is seamlessly inserted into the space between hair follicles, offering high-fidelity neural signal capture for up to 12 h while maintaining the lowest contact impedance density (0.03 kΩ·cm[-2]) among reported articles. Implemented wireless BCI, detecting steady-state visually evoked potentials, offers 96.4% accuracy in signal classification with a train-free algorithm even during the subject's excessive motions, including standing, walking, and running. A demonstration captures this system's capability, showing AR-based video calling with hands-free controls using brain signals, transforming digital communication. Collectively, this research highlights the pivotal role of integrated sensors and flexible electronics technology in advancing BCI's applications for interactive digital environments.},
}

*Brain-Computer Interfaces
Humans
*Hair Follicle/physiology
Electroencephalography/methods/instrumentation
*Augmented Reality
Artifacts
*Brain/physiology
Motion
Algorithms
Electrodes
Evoked Potentials, Visual/physiology

@article {pmid40193313,
year = {2025},
author = {Estivalet, KM and Pettenuzzo, TSA and Mazzilli, NL and Ferreira, LF and Cechetti, F},
title = {The use of brain-machine interface, motor imagery, and action observation in the rehabilitation of individuals with Parkinson's disease: A protocol study for a randomized clinical trial.},
journal = {PloS one},
volume = {20},
number = {4},
pages = {e0315148},
pmid = {40193313},
issn = {1932-6203},
mesh = {Humans ; *Parkinson Disease/rehabilitation/physiopathology ; *Brain-Computer Interfaces ; Male ; Female ; *Imagery, Psychotherapy/methods ; Single-Blind Method ; Middle Aged ; Aged ; Upper Extremity/physiopathology ; Cognition ; },
abstract = {BACKGROUND: Parkinson's disease (PD) is a neurodegenerative condition that impacts motor planning and control of the upper limbs (UL) and leads to cognitive impairments. Rehabilitation approaches, including motor imagery (MI) and action observation (AO), along with the use of brain-machine interfaces (BMI), are essential in the PD population to enhance neuroplasticity and mitigate symptoms.

OBJECTIVE: To provide a description of a rehabilitation protocol for evaluating the effects of isolated and combined applications of MI and action observation (AO), along with BMI, on upper limb (UL) motor changes and cognitive function in PD.

METHODS: This study provides a detailed protocol for a single-blinded, randomized clinical trial. After selection, participants will be randomly assigned to one of five experimental groups. Each participant will be assessed at three points: pre-intervention, post-intervention, and at a follow-up four weeks after the intervention ends. The intervention consists of 10 sessions, each lasting approximately 60 minutes.

EXPECTED RESULTS: The primary outcome expected is an improvement in the Test d'Évaluation des Membres Supérieurs de Personnes Âgées score, accompanied by a reduction in task execution time. Secondary outcomes include motor symptoms in the upper limbs, assessed via the Unified Parkinson's Disease Rating Scale - Part III and the 9-Hole Peg Test; cognitive function, assessed with the PD Cognitive Rating Scale; and occupational performance, assessed with the Canadian Occupational Performance Measure.

DISCUSSION: This study protocol is notable for its intensive daily sessions. Both MI and AO are low-cost, enabling personalized interventions that physiotherapists and occupational therapists can readily replicate in practice. While BMI use does require professionals to acquire an exoskeleton, the protocol ensures the distinctiveness of the interventions and, to our knowledge, is the first to involve individuals with PD.

TRIAL REGISTRATION: ClinicalTrials.gov NCT05696925.},
}

Humans
*Parkinson Disease/rehabilitation/physiopathology
*Brain-Computer Interfaces
Male
Female
*Imagery, Psychotherapy/methods
Single-Blind Method
Middle Aged
Aged
Upper Extremity/physiopathology
Cognition

@article {pmid40191683,
year = {2025},
author = {Miri, M and Abootalebi, V and Saeedi-Sourck, H and Van De Ville, D and Behjat, H},
title = {Graphs Constructed from Instantaneous Amplitude and Phase of Electroencephalogram Successfully Differentiate Motor Imagery Tasks.},
journal = {Journal of medical signals and sensors},
volume = {15},
number = {},
pages = {7},
pmid = {40191683},
issn = {2228-7477},
abstract = {BACKGROUND: Accurate classification of electroencephalogram (EEG) signals is challenging given the nonlinear and nonstationary nature of the data as well as subject-dependent variations. Graph signal processing (GSP) has shown promising results in the analysis of brain imaging data.

METHODS: In this article, a GSP-based approach is presented that exploits instantaneous amplitude and phase coupling between EEG time series to decode motor imagery (MI) tasks. A graph spectral representation of the Hilbert-transformed EEG signals is obtained, in which simultaneous diagonalization of covariance matrices provides the basis of a subspace that differentiates two classes of right hand and right foot MI tasks. To determine the most discriminative subspace, an exploratory analysis was conducted in the spectral domain of the graphs by ranking the graph frequency components using a feature selection method. The selected features are fed into a binary support vector machine that predicts the label of the test trials.

RESULTS: The performance of the proposed approach was evaluated on brain-computer interface competition III (IVa) dataset.

CONCLUSIONS: Experimental results reflect that brain functional connectivity graphs derived using the instantaneous amplitude and phase of the EEG signals show comparable performance with the best results reported on these data in the literature, indicating the efficiency of the proposed method compared to the state-of-the-art methods.},
}

@article {pmid40164740,
year = {2025},
author = {Littlejohn, KT and Cho, CJ and Liu, JR and Silva, AB and Yu, B and Anderson, VR and Kurtz-Miott, CM and Brosler, S and Kashyap, AP and Hallinan, IP and Shah, A and Tu-Chan, A and Ganguly, K and Moses, DA and Chang, EF and Anumanchipalli, GK},
title = {A streaming brain-to-voice neuroprosthesis to restore naturalistic communication.},
journal = {Nature neuroscience},
volume = {28},
number = {4},
pages = {902-912},
pmid = {40164740},
issn = {1546-1726},
support = {5U01DC018671//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; F30DC021872//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Speech/physiology ; *Voice/physiology ; Adult ; Female ; Communication Devices for People with Disabilities ; *Sensorimotor Cortex/physiology ; Communication ; Paralysis/physiopathology/rehabilitation ; },
abstract = {Natural spoken communication happens instantaneously. Speech delays longer than a few seconds can disrupt the natural flow of conversation. This makes it difficult for individuals with paralysis to participate in meaningful dialogue, potentially leading to feelings of isolation and frustration. Here we used high-density surface recordings of the speech sensorimotor cortex in a clinical trial participant with severe paralysis and anarthria to drive a continuously streaming naturalistic speech synthesizer. We designed and used deep learning recurrent neural network transducer models to achieve online large-vocabulary intelligible fluent speech synthesis personalized to the participant's preinjury voice with neural decoding in 80-ms increments. Offline, the models demonstrated implicit speech detection capabilities and could continuously decode speech indefinitely, enabling uninterrupted use of the decoder and further increasing speed. Our framework also successfully generalized to other silent-speech interfaces, including single-unit recordings and electromyography. Our findings introduce a speech-neuroprosthetic paradigm to restore naturalistic spoken communication to people with paralysis.},
}

Humans
*Brain-Computer Interfaces
Male
*Speech/physiology
*Voice/physiology
Adult
Female
Communication Devices for People with Disabilities
*Sensorimotor Cortex/physiology
Communication
Paralysis/physiopathology/rehabilitation

@article {pmid39935067,
year = {2025},
author = {Yao, Y and Ahnood, A and Chambers, A and Tong, W and Prawer, S},
title = {Nitrogen-Doped Ultrananocrystalline Diamond - Optoelectronic Biointerface for Wireless Neuronal Stimulation.},
journal = {Advanced healthcare materials},
volume = {14},
number = {9},
pages = {e2403901},
pmid = {39935067},
issn = {2192-2659},
support = {2029454//National Health and Medical Research Council/ ; DE220100302//Australian Research Council/ ; DE210102750//Australian Research Council/ ; },
mesh = {Animals ; *Diamond/chemistry ; *Nitrogen/chemistry ; Rats ; *Neurons/physiology ; Retina ; *Wireless Technology ; Semiconductors ; Light ; },
abstract = {This study presents a semiconducting optoelectronic system for light-controlled non-genetic neuronal stimulation using visible light. The system architecture is entirely wireless, comprising a thin film of nitrogen-doped ultrananocrystalline diamond directly grown on a semiconducting silicon substrate. When immersed in a physiological medium and subjected to pulsed illumination in the visible (595 nm) or near-infrared wavelength (808 nm) range, charge accumulation at the device-medium interface induces a transient ionic displacement current capable of electrically stimulating neurons with high temporal resolution. With a measured photoresponsivity of 7.5 mA W[-1], the efficacy of this biointerface is demonstrated through optoelectronic stimulation of degenerate rat retinas using 595 nm irradiation, pulse durations of 50-500 ms, and irradiance levels of 1.1-4.3 mW mm[-2], all below the safe ocular threshold. This work presents the pioneering utilization of a diamond-based optoelectronic platform, capable of generating sufficiently large photocurrents for neuronal stimulation in the retina.},
}

Animals
*Diamond/chemistry
*Nitrogen/chemistry
Rats
*Neurons/physiology
Retina
*Wireless Technology
Semiconductors
Light

@article {pmid40189874,
year = {2025},
author = {Li, X and Zhang, J and Shi, B and Li, Y and Wang, Y and Shuai, K and Li, Y and Ming, G and Song, T and Pei, W and Sun, B},
title = {Freestanding Transparent Organic-Inorganic Mesh E-Tattoo for Breathable Bioelectrical Membranes with Enhanced Capillary-Driven Adhesion.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c00565},
pmid = {40189874},
issn = {1944-8252},
abstract = {The electronic tattoo (e-tattoo), a cutting-edge wearable sensor technology adhered to human skin, has garnered significant attention for its potential in brain-computer interfaces (BCIs) and routine health monitoring. Conventionally, flexible substrates with adhesion force on dewy surfaces pursue seamless contact with skin, employing compact airtight substrates, hindering air circulation between skin and the surrounding environment, and compromising long-term wearing comfort. To address these challenges, we have developed a freestanding transparent e-tattoo featuring flexible serpentine mesh bridges with a unique full-breathable multilayer structure. The mesh e-tattoo demonstrates remarkable ductility and air permeability while maintaining robust electronic properties, even after significant mechanical deformation. Furthermore, it exhibits an impressive visible-light transmittance of up to 95%, coupled with a low sheet resistance of 0.268 Ω sq[-1], ensuring both optical clarity and electrical efficiency. By increasing the number of menisci between the mesh e-tattoo and the skin, the total adhesion force increases due to the cumulative capillary-driven effect. We also successfully demonstrated high-quality bioelectric signal collections. In particular, the controlling virtual reality (VR) objects using electrooculogram (EOG) signals collected by mesh e-tattoos were achieved to demonstrate their potential for human-computer interactions (HCIs). This freestanding transparent e-tattoo with a fully breathable mesh structure represents a significant advancement in flexible electrodes for bioelectrical signal monitoring applications.},
}

@article {pmid40189123,
year = {2025},
author = {Wang, F and Ren, J and Cai, Q and Liang, R and Wang, L and Yang, Q and Tian, Y and Zheng, C and Yang, J and Ming, D},
title = {Theta-gamma phase-amplitude coupling as a promising neurophysiological biomarker for evaluating the efficacy of low-intensity focused ultrasound stimulation on vascular dementia treatment.},
journal = {Experimental neurology},
volume = {},
number = {},
pages = {115237},
doi = {10.1016/j.expneurol.2025.115237},
pmid = {40189123},
issn = {1090-2430},
abstract = {Low-intensity focused ultrasound stimulation (LIFUS) has garnered attention for its potential in vascular dementia (VD) treatment. However, the lack of sufficient data supporting its efficacy and elucidating its mechanisms of action limits its further clinical translation and application. Considerable researches support the idea that LIFUS can improve the disturbance of neural oscillation modes caused by a variety of neurological diseases. However, the effect of LIFUS on neural oscillation modes in VD remains unclear. Therefore, this study aims to investigate the therapeutic effects of LIFUS on neural oscillation modes in VD. To achieve this purpose, the VD model was established via the bilateral common carotid artery occlusion, followed by two weeks of LIFUS treatment targeting the bilateral hippocampus. The therapeutic effects of LIFUS were evaluated by behavioral tests and cerebral blood flow measurement. Electrophysiological signals were recorded from the hippocampal CA1 and CA3 and medial prefrontal cortex (mPFC). The results indicated LIFUS could effectively improve cognitive dysfunction in VD rats. The underlying electrophysiological mechanisms involved the restoration of phase-amplitude coupling (PAC) of theta-gamma oscillations within both the CA3-CA1 local circuit and the hippocampus-mPFC cross-brain circuit. Classification results based on PAC characteristics suggested that PAC metrics are effective for evaluating the efficacy of LIFUS in treating VD, with optimal recognition performance observed in the hippocampus-mPFC cross-brain circuit. Our findings provide neuroelectrophysiological insights into the mechanisms of LIFUS in VD treatment and propose a promising diagnostic biomarker for evaluating LIFUS efficacy in future applications.},
}

@article {pmid40187178,
year = {2025},
author = {S, P and M, S},
title = {Design of asynchronous low-complexity SSVEP-based brain control interface speller.},
journal = {Computers in biology and medicine},
volume = {190},
number = {},
pages = {110062},
doi = {10.1016/j.compbiomed.2025.110062},
pmid = {40187178},
issn = {1879-0534},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) provide a transformative solution, addressing communication challenges for individuals with speech impairments or neuromuscular disorders. The real-time wireless asynchronous BCI speller system utilizes electroencephalography (EEG) signals, tapping the brain's electrical activity for effective communication.

METHODS: Users interact with a screen featuring flickering stimuli, each representing cursor movement and character selection. The system includes cursor movements, displays selected characters, and produces an audio output of the complete word. Users generate real-time SSVEP responses captured wirelessly through an EEG acquisition system by directing attention to the stimulus. The single-channel EEG signal is wirelessly transmitted to a Raspberry Pi processing module through Wi-Fi. The EEG signals are decoded using modified power spectral density (PSD) analysis to identify the user's focus, maneuvering the cursor for character selection.

RESULTS: In experiments with ten subjects, the single-channel asynchronous low-complexity BCI speller system achieved 95.2% SSVEP identification accuracy with a detection time of 1.05 s for selecting each character/target and an information transfer rate (ITR) of 119.82 bits/min.

CONCLUSION: This underscores its efficacy in enabling individuals to spell words and communicate efficiently. The proposed real-time wireless BCI speller system is an effective tool for communication-challenged individuals, enhancing communication efficiency through brain signals.},
}

@article {pmid40183071,
year = {2025},
author = {Muthukrishnan, SP and Atyabi, A},
title = {Editorial: Neural mechanisms of motor planning in assisted voluntary movement.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1582214},
pmid = {40183071},
issn = {1662-5161},
}

@article {pmid40182217,
year = {2025},
author = {Wirawan, IMA and Paramarta, K},
title = {Acquisition Of Balinese Imagined Spelling using Electroencephalogram (BISE) Dataset.},
journal = {Data in brief},
volume = {60},
number = {},
pages = {111454},
pmid = {40182217},
issn = {2352-3409},
abstract = {One of the main goals of today's technology is to create a connected environment between humans and technological devices to perform daily physical activities. However, users with speech disorders cannot use this application. Loss of verbal communication can be caused by injuries and neurodegenerative diseases that affect motor production, speech articulation, and language comprehension. To overcome this problem, Brain-Computer Interfaces (BCI) use EEG signals as assistive technology to provide a new communication channel for individuals who cannot communicate due to loss of motor control. Of the several BCI studies that use EEG signals, no studies have studied Balinese characters. As a first step, this study examines the acquisition of EEG signal data for Balinese character recognition. There are several stages in obtaining EEG signal data for Balinese character spelling imagination in this study: preparation of research documents, preparation of stimulus media, submission of ethical permits, determination of participants, recording process, data presentation, and publication of datasets. The result datasets from this study are in the form of raw data, and data was analyzed for 18 Balinese and 6 vowel characters, both spelling and imagined.},
}

@article {pmid40095842,
year = {2025},
author = {Tian, B and Zhang, S and Xue, D and Chen, S and Zhang, Y and Peng, K and Wang, D},
title = {Decoding Intrinsic Fluctuations of Engagement From EEG Signals During Fingertip Motor Tasks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1271-1283},
doi = {10.1109/TNSRE.2025.3551819},
pmid = {40095842},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; Male ; *Fingers/physiology ; Adult ; Female ; Young Adult ; Machine Learning ; Algorithms ; Virtual Reality ; Brain-Computer Interfaces ; Psychomotor Performance/physiology ; Reproducibility of Results ; Motor Skills/physiology ; Signal Processing, Computer-Assisted ; Movement/physiology ; },
abstract = {Maintaining a high mental engagement is critical for motor rehabilitation interventions. Achieving a flow experience, often conceptualized as a highly engaged mental state, is an ideal goal for motor rehabilitation tasks. This paper proposes a virtual reality-based fine fingertip motor task in which the difficulty is maintained to match individual abilities. The aim of this study is to decode the intrinsic fluctuations of flow experience from electroencephalogram (EEG) signals during the execution of a motor task, addressing a gap in flow research that overlooks these fluctuations. To resolve the conflict between sparse self-reported flow sampling and the high dimensionality of neural signals, we use motor behavioral measures to represent flow and label the EEG data, thereby increasing the number of samples. A machine learning-based neural decoder is then established to classify each trial into high-flow or low-flow using spectral power and coherence features extracted from the EEG signals. Cross-validation reveals that the classification accuracy of the neural decoder can exceed 80%. Notably, we highlight the contributions of high-frequency bands in EEG activities to flow decoding. Additionally, EEG feature analyses reveal significant increases in the power of parietal-occipital electrodes and global coherence values, specifically in the alpha and beta bands, during high-flow durations. This study validates the feasibility of decoding the intrinsic flow fluctuations during fine motor task execution with a high accuracy. The methodology and findings in this work lay a foundation for future applications in manipulating flow experience and enhancing engagement levels in motor rehabilitation practice.},
}

Humans
*Electroencephalography/methods
Male
*Fingers/physiology
Adult
Female
Young Adult
Machine Learning
Algorithms
Virtual Reality
Brain-Computer Interfaces
Psychomotor Performance/physiology
Reproducibility of Results
Motor Skills/physiology
Signal Processing, Computer-Assisted
Movement/physiology

@article {pmid40182177,
year = {2025},
author = {Paillard, J and Hipp, JF and Engemann, DA},
title = {GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals.},
journal = {Patterns (New York, N.Y.)},
volume = {6},
number = {3},
pages = {101182},
pmid = {40182177},
issn = {2666-3899},
abstract = {Spectral analysis using wavelets is widely used for identifying biomarkers in EEG signals. Recently, Riemannian geometry has provided an effective mathematical framework for predicting biomedical outcomes from multichannel electroencephalography (EEG) recordings while showing concord with neuroscientific domain knowledge. However, these methods rely on handcrafted rules and sequential optimization. In contrast, deep learning (DL) offers end-to-end trainable models achieving state-of-the-art performance on various prediction tasks but lacks interpretability and interoperability with established neuroscience concepts. We introduce Gabor Riemann EEGNet (GREEN), a lightweight neural network that integrates wavelet transforms and Riemannian geometry for processing raw EEG data. Benchmarking on six prediction tasks across four datasets with over 5,000 participants, GREEN outperformed non-deep state-of-the-art models and performed favorably against large DL models while using orders-of-magnitude fewer parameters. Computational experiments showed that GREEN facilitates learning sparse representations without compromising performance. By integrating domain knowledge, GREEN combines a desirable complexity-performance trade-off with interpretable representations.},
}

@article {pmid40181137,
year = {2025},
author = {Wang, G and Wang, W and Wang, Z and Huang, S and Liu, Y and Ming, D},
title = {The sixth finger illusion induced by palm outside stroking shows stable ownership and independence.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {11447},
pmid = {40181137},
issn = {2045-2322},
support = {2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; },
mesh = {Humans ; *Illusions/physiology ; Male ; Female ; *Fingers/physiology ; Adult ; Young Adult ; *Hand/physiology ; *Touch Perception/physiology ; },
abstract = {Recently, the sixth finger illusion has been widely studied for body representation. It remains unclear how the stroking area, visual effects and the number of trials affect the illusion. We recruited 80 participants to conduct five trials by stroking the palm outside or little finger outside in conditions with and without wearing supernumerary rubber finger. The results show the stroking area has a greater impact on the intensity and independence of the illusion. And the palm outside can induce a stronger and more independent illusion. In addition, the sixth finger illusion induced by these four conditions was significantly influenced by the number of trials, and there is a significant enhancement in the intensity of the illusion induced by the palm outside as the number of trials increases. These indicate that stroking the outer lateral side of the palm can induce a relatively stronger and more independent sixth finger illusion, and the intensity of it reaches a steady state after three trials when wearing a supernumerary rubber finger and five trials when not wearing a supernumerary rubber finger. This study adds evidence to the research on multisensory integration and sensory feedback of the supernumerary robotic fingers.},
}

Humans
*Illusions/physiology
Male
Female
*Fingers/physiology
Adult
Young Adult
*Hand/physiology
*Touch Perception/physiology

@article {pmid40181122,
year = {2025},
author = {Rawat, K and Sharma, T},
title = {An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {11379},
pmid = {40181122},
issn = {2045-2322},
mesh = {Humans ; Electroencephalography/methods ; Deep Learning ; *Nervous System Diseases/diagnosis/physiopathology ; Electrocardiography ; *Neural Networks, Computer ; *Mental Disorders/diagnosis/physiopathology ; Adult ; Male ; Female ; },
abstract = {Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observations, which can make it difficult to examine mental health illnesses due to their subjective and complicated nature. In this work, we offer an innovative methodology for predicting mental illnesses such as epilepsy, sleep disorders, bipolar disorder, eating disorders, and depression using a multimodal deep learning framework that integrates neurocardiac data fusion. The proposed framework combines MEG, EEG, and ECG signals to create a more comprehensive understanding of brain and cardiac function in individuals with mental disorders. The multimodal deep learning approach uses an integrated CNN-Bi-Transformer, i.e., CardioNeuroFusionNet, which can process multiple types of inputs simultaneously, allowing for the fusion of various modalities and improving the performance of the predictive representation. The proposed framework has undergone testing on data from the Deep BCI Scalp Database and was further validated on the Kymata Atlas dataset to assess its generalizability. The model achieved promising results with high accuracy (98.54%) and sensitivity (97.77%) in predicting mental problems, including neurological and psychiatric conditions. The neurocardiac data fusion has been found to provide additional insights into the relationship between brain and cardiac function in neurological conditions, which could potentially lead to more accurate diagnosis and personalized treatment options. The suggested method overcomes the shortcomings of earlier studies, which tended to concentrate on single-modality data, lacked thorough neurocardiac data fusion, and made use of less advanced machine learning algorithms. The comprehensive experimental findings, which provide an average improvement in accuracy of 2.72%, demonstrate that the suggested work performs better than other cutting-edge AI techniques and generalizes effectively across diverse datasets.},
}

Humans
Electroencephalography/methods
Deep Learning
*Nervous System Diseases/diagnosis/physiopathology
Electrocardiography
*Neural Networks, Computer
*Mental Disorders/diagnosis/physiopathology
Adult
Male
Female

@article {pmid40180157,
year = {2025},
author = {Liang, W and Xu, R and Wang, X and Cichocki, A and Jin, J},
title = {Enhancing Robustness of Spatial Filters in Motor Imagery based Brain-Computer Interface via Temporal Learning.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110441},
doi = {10.1016/j.jneumeth.2025.110441},
pmid = {40180157},
issn = {1872-678X},
abstract = {BACKGROUND: In motor imagery-based brain-computer interface (MI-BCI) EEG decoding, spatial filtering play a crucial role in feature extraction. Recent studies have emphasized the importance of temporal filtering for extracting discriminative features in MI tasks. While many efforts have been made to optimize feature extraction externally, stabilizing features from spatial filtering remains underexplored.

NEW METHOD: To address this problem, we propose an approach to improve the robustness of temporal features by minimizing instability in the temporal domain. Specifically, we utilize Jensen-Shannon divergence to quantify temporal instability and integrate decision variables to construct an objective function that minimizes this instability. Our method enhances the stability of variance and mean values in the extracted features, improving the identification of discriminative features and reducing the effects of instability.

RESULTS: The proposed method was applied to spatial filtering models, and tested on two publicly datasets as well as a self-collected dataset. Results demonstrate that the proposed method significantly boosts classification accuracy, confirming its effectiveness in enhancing temporal feature stability.

We compared our method with spatial filtering methods, and the-state-of-the-art models. The proposed approach achieves the highest accuracy, with 92.43% on BCI competition III IVa dataset, 84.45% on BCI competition IV 2a dataset, and 73.18% on self-collected dataset.

CONCLUSIONS: Enhancing the instability of temporal features contributes to improved MI-BCI performance. This not only improves classification performance but also provides a stable foundation for future advancements. The proposed method shows great potential for EEG decoding.},
}

@article {pmid39466869,
year = {2025},
author = {Ke, Y and Du, J and Liu, S and Ming, D},
title = {Corrections to "Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework".},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1169},
doi = {10.1109/TNSRE.2024.3487206},
pmid = {39466869},
issn = {1558-0210},
mesh = {Humans ; Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Reproducibility of Results ; },
abstract = {In the above article [1], we found the formula (1) is presented incorrectly because of an error in the formula editing process. The correction is as follows.},
}

Humans
Algorithms
*Brain-Computer Interfaces
*Electroencephalography/methods
*Evoked Potentials, Visual/physiology
Reproducibility of Results

@article {pmid40179638,
year = {2025},
author = {Thielen, J and Tangermann, M and Aarnoutse, EJ and Ramsey, NF and Vansteensel, MJ},
title = {Towards an sEEG-based BCI using code-modulated VEP: A case study showing the influence of electrode location on decoding efficiency.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {173},
number = {},
pages = {213-215},
doi = {10.1016/j.clinph.2025.03.034},
pmid = {40179638},
issn = {1872-8952},
}

@article {pmid40177878,
year = {2025},
author = {Bhamidipaty, V and Botchu, B and Bhamidipaty, DL and Guntoory, I and Iyengar, KP},
title = {ChatGPT for speech-impaired assistance.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-3},
doi = {10.1080/17483107.2025.2483300},
pmid = {40177878},
issn = {1748-3115},
abstract = {Background: Speech and language impairments, though often used interchangeably, are two very distinct types of challenges. A speech impairment may lead to impaired ability to produce speech sounds whilst communication may be affected due to lack of fluency or articulation of words. Consequently this may affect a person's ability to articulate may affect academic achievement, social development and progress in life. ChatGPT (Generative Pretrained Transformer) is an open access AI (Artificial Intelligence) tool developed by Open AI® based on Large language models (LLMs) with the ability to respond to human prompts to generate texts using Supervised and Unsupervised Machine Learning (ML) Algorithms. This article explores the current role and future perspectives of ChatGPT AI Tool for Speech-Impaired Assistance. Methods: A cumulative search strategy using databases of PubMed, Google Scholar, Scopus and grey literature was conducted to generate this narrative review. Results: A spectrum of Enabling Technologies for Speech & Language Impairment have been explored. Augmentative and Alternative Communication technology (AAC), Integration with Neuroprosthesis technology and Speech therapy applications offer considerable potential to aid speech and language impaired individuals. Conclusion: Current applications of AI, ChatGPT and other LLM's offer promising solutions in enhancing communication in people affected by Speech and Language impairment. However, further research and development is required to ensure affordability, accessibility and authenticity of these AI Tools in clinical Practice.},
}

@article {pmid40175961,
year = {2025},
author = {Guo, X and Deng, R and Lai, J and Hu, S},
title = {Is muscarinic receptor agonist effective and tolerant for schizophrenia?.},
journal = {BMC psychiatry},
volume = {25},
number = {1},
pages = {323},
pmid = {40175961},
issn = {1471-244X},
mesh = {Humans ; *Schizophrenia/drug therapy ; *Muscarinic Agonists/adverse effects/therapeutic use ; Randomized Controlled Trials as Topic ; *Antipsychotic Agents/therapeutic use/adverse effects ; },
abstract = {BACKGROUND: Several randomized clinical trials (RCTs) have recently examined the efficacy and tolerability of muscarinic receptor agonists in schizophrenia. However, whether therapeutics targeting muscarinic receptors improve symptom management and reduce side effects remains systemically unexplored.

METHODS: Embase, PubMed, and Web of Science were searched from inception until Jan 9, 2025. Altogether, the efficacy and safety outcomes of four RCTs (397 individuals in the muscarinic receptor agonists group, and 374 in the placebo control group) were meta-analyzed. To compare scores of positive and negative syndrome scale (PANSS), response rate, discontinuation rate, and adverse events with muscarinic receptor agonists vs. placebo in patients with schizophrenia, scale changes were pooled as mean difference (MD) for continuous outcomes and risk ratio (RR) for categorical outcomes.

RESULTS: It revealed that muscarinic receptor agonists were superior to placebo in terms of decrease in the total PANSS score (MD, - 9.92; 95% CI, -12.46 to -7.37; I[2] = 0%), PANSS positive symptom subscore (MD, - 3.21; 95% CI, -4.02 to -2.40; I[2] = 0%), and PANSS negative symptom subscore (MD, -1.79; 95% CI, -2.47 to -1.11; I[2] = 48%). According to the study-defined response rate, the pooled muscarinic receptor agonists vs. placebo RR was 2.08 (95% CI, 1.59 to 2.72; I[2] = 0%). No significance was found in the discontinuation rate. Muscarinic receptor agonists were associated with a higher risk of nausea (RR = 4.61, 95% CI, 2.65 to 8.02; I[2] = 3%), and in particular, xanomeline-trospium was associated with risks of dyspepsia, vomiting, and constipation.

CONCLUSIONS: The findings highlighted an efficacy advantage with tolerated adverse event profiles for muscarinic receptor agonists in schizophrenia.},
}

Humans
*Schizophrenia/drug therapy
*Muscarinic Agonists/adverse effects/therapeutic use
Randomized Controlled Trials as Topic
*Antipsychotic Agents/therapeutic use/adverse effects

@article {pmid40175631,
year = {2025},
author = {Qi, Y and Zhu, X and Xiong, X and Yang, X and Ding, N and Wu, H and Xu, K and Zhu, J and Zhang, J and Wang, Y},
title = {Human motor cortex encodes complex handwriting through a sequence of stable neural states.},
journal = {Nature human behaviour},
volume = {},
number = {},
pages = {},
pmid = {40175631},
issn = {2397-3374},
support = {62276228//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62336007//National Natural Science Foundation of China (National Science Foundation of China)/ ; LR24F020002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {How the human motor cortex (MC) orchestrates sophisticated sequences of fine movements such as handwriting remains a puzzle. Here we investigate this question through Utah array recordings from human MC during attempted handwriting of Chinese characters (n = 306, each consisting of 6.3 ± 2.0 strokes). We find that MC activity evolves through a sequence of states corresponding to the writing of stroke fragments during complicated handwriting. The directional tuning curve of MC neurons remains stable within states, but its gain or preferred direction strongly varies across states. By building models that can automatically infer the neural states and implement state-dependent directional tuning, we can significantly better explain the firing pattern of individual neurons and reconstruct recognizable handwriting trajectories with 69% improvement compared with baseline models. Our findings unveil that skilled and sophisticated movements are encoded through state-specific neural configurations.},
}

@article {pmid40175376,
year = {2025},
author = {Dong, L and Ke, Y and Zhu, X and Liu, S and Ming, D},
title = {Long-term cognitive and neurophysiological effects of mental rotation training.},
journal = {NPJ science of learning},
volume = {10},
number = {1},
pages = {16},
pmid = {40175376},
issn = {2056-7936},
support = {81741139//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Mental rotation, a crucial aspect of spatial cognition, can be improved through repeated practice. However, the long-term effects of combining training with non-invasive brain stimulation and its neurophysiological correlates are not well understood. This study examined the lasting effects of a 10-day mental rotation training with high-definition transcranial direct current stimulation (HD-tDCS) on behavioral and neural outcomes in 34 healthy participants. Participants were randomly assigned to the Active and Shan groups, with equal group sizes. Mental rotation tests and EEG recordings were conducted at baseline, 1 day, 20 days, and 90 days post-training. Although HD-tDCS showed no significant effect, training led to improved accuracy, faster response times, and enhanced task-evoked EEG responses, with benefits lasting up to 90 days. Notably, task-evoked EEG responses remained elevated 20 days post-training. Individual differences, such as gender and baseline performance, influenced the outcomes. These results emphasize the potential of mental rotation training for cognitive enhancement and suggest a need for further investigation into cognition-related neuroplasticity.},
}

@article {pmid40174604,
year = {2025},
author = {Kojima, S and Kortenbach, BE and Aalberts, C and Miloševska, S and de Wit, K and Zheng, R and Kanoh, S and Musso, M and Tangermann, M},
title = {Influence of pitch modulation on event-related potentials elicited by Dutch word stimuli in a brain-computer interface language rehabilitation task.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc83d},
pmid = {40174604},
issn = {1741-2552},
abstract = {OBJECTIVE: Recently, a novel language training using an auditory brain-computer interface (BCI) based on electroencephalogram recordings has been proposed for chronic stroke patients with aphasia. Tested with native German patients, it has shown significant and medium to large effect sizes in improving multiple aspects of language. During the training, the auditory BCI system delivers word stimuli using six spatially arranged loudspeakers. As delivering the word stimuli via headphones reduces spatial cues and makes the attention to target words more difficult, we investigate the influence of added pitch information. While pitch modulations have shown benefits for tone stimuli, they have not yet been investigated in the context of language stimuli.

APPROACH: The study translated the German experimental setup into Dutch. Seventeen native Dutch speakers participated in a single session of an exploratory study. An incomplete Dutch sentence cued them to listen to a target word embedded into a sequence of comparable non-target words while an electroencephalogram was recorded. Four conditions were compared within-subject to investigate the influence of pitch modulation: presenting the words spatially from six loudspeakers without (6D) and with pitch modulation (6D-Pitch), via stereo headphones with simulated spatial cues and pitch modulation (Stereo-Pitch), and via headphones without spatial cues or pitch modulation (Mono).

MAIN RESULTS: Comparing the 6D conditions of both language setups, the Dutch setup could be validated. For the Dutch setup, the binary AUC classification score in the 6D and the 6D-Pitch condition were 0.75 and 0.76, respectively, and adding pitch information did not significantly alter the binary classification accuracy of the event-related potential responses. The classification scores in the 6D condition and the Stereo-Pitch condition were on the same level.

SIGNIFICANCE: The competitive performance of pitch-modulated word stimuli suggests that the complex hardware setup of the 6D condition could be replaced by a headphone condition. If future studies with aphasia patients confirm the effectiveness and higher usability of a headphone-based language rehabilitation training, a simplified setup could be implemented more easily outside of clinics to deliver frequent training sessions to patients in need.},
}

@article {pmid40174326,
year = {2025},
author = {Wan, P and Xue, H and Zhang, S and Kong, W and Shao, W and Wen, B and Zhang, D},
title = {Image by co-reasoning: A collaborative reasoning-based implicit data augmentation method for dual-view CEUS classification.},
journal = {Medical image analysis},
volume = {102},
number = {},
pages = {103557},
doi = {10.1016/j.media.2025.103557},
pmid = {40174326},
issn = {1361-8423},
abstract = {Dual-view contrast-enhanced ultrasound (CEUS) data are often insufficient to train reliable machine learning models in typical clinical scenarios. A key issue is that limited clinical CEUS data fail to cover the underlying texture variations for specific diseases. Implicit data augmentation offers a flexible way to enrich sample diversity, however, inter-view semantic consistency has not been considered in previous studies. To address this issue, we propose a novel implicit data augmentation method for dual-view CEUS classification, which performs a sample-adaptive data augmentation with collaborative semantic reasoning across views. Specifically, the method constructs a feature augmentation distribution for each ultrasound view of an individual sample, accounting for intra-class variance. To maintain semantic consistency between the augmented views, plausible semantic changes in one view are transferred from similar instances in the other view. In this retrospective study, we validate the proposed method on the dual-view CEUS datasets of breast cancer and liver cancer, obtaining the superior mean diagnostic accuracy of 89.25% and 95.57%, respectively. Experimental results demonstrate its effectiveness in improving model performance with limited clinical CEUS data. Code: https://github.com/wanpeng16/CRIDA.},
}

@article {pmid40173067,
year = {2025},
author = {Tian, M and Li, S and Xu, R and Cichocki, A and Jin, J},
title = {An Interpretable Regression Method for Upper Limb Motion Trajectories Detection with EEG Signals.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3557255},
pmid = {40173067},
issn = {1558-2531},
abstract = {OBJECTIVE: The motion trajectory prediction (MTP) based brain-computer interface (BCI) leverages electroencephalography (EEG) signals to reconstruct the three-dimensional trajectory of upper limb motion, which is pivotal for the advancement of prosthetic devices that can assist motor-disabled individuals. Most research focused on improving the performance of regression models while neglecting the correlation between the implicit information extracted from EEG features across various frequency bands with limb kinematics. Current work aims to identify key channels that capture information related to various motion execution movements from different frequency bands and reconstruct three-dimensional motion trajectories based on EEG features.

METHODS: We propose an interpretable motion trajectory regression framework that extracts bandpower features from different frequency bands and concatenates them into multi-band fusion features. The extreme gradient boosting regression model with Bayesian optimization and Shapley additive explanation methods are introduced to provide further explanation.

RESULTS: The experimental results demonstrate that the proposed method achieves a mean Pearson correlation coefficient (PCC) value of 0.452, outperforming traditional regression models.

CONCLUSION: Our findings reveal that the contralateral side contributes the most to motion trajectory regression than the ipsilateral side which improves the clarity and interpretability of the motion trajectory regression model. Specifically, the feature from channel C5 in the Mu band is crucial for the movement of the right hand, while the feature from channel C3 in the Beta band plays a vital role.

SIGNIFICANCE: This work provides a novel perspective on the comprehensive study of movement disorders.},
}

@article {pmid40172963,
year = {2025},
author = {Yang, Z and Zheng, Y and Ma, D and Wang, L and Zhang, J and Song, T and Wang, Y and Zhang, Y and Nan, F and Su, N and Gao, Z and Guo, J},
title = {Phosphatidylinositol 4,5-bisphosphate activation mechanism of human KCNQ5.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {14},
pages = {e2416738122},
doi = {10.1073/pnas.2416738122},
pmid = {40172963},
issn = {1091-6490},
support = {2020YFA0908501//MOST | National Key Research and Development Program of China (NKPs)/ ; 32371204//MOST | National Natural Science Foundation of China (NSFC)/ ; },
mesh = {Humans ; *Phosphatidylinositol 4,5-Diphosphate/metabolism ; *KCNQ Potassium Channels/metabolism/chemistry/genetics ; *Cryoelectron Microscopy ; Calmodulin/metabolism/chemistry ; Protein Binding ; Ion Channel Gating ; Models, Molecular ; Protein Conformation ; },
abstract = {The human voltage-gated potassium channels KCNQ2, KCNQ3, and KCNQ5 can form homo- and heterotetrameric channels that are responsible for generating the neuronal M current and maintaining the membrane potential stable. Activation of KCNQ channels requires both the depolarization of membrane potential and phosphatidylinositol 4,5-bisphosphate (PIP2). Here, we report cryoelectron microscopy structures of the human KCNQ5-calmodulin (CaM) complex in the apo, PIP2-bound, and both PIP2- and the activator HN37-bound states in either a closed or an open conformation. In the closed conformation, a PIP2 molecule binds in the middle of the groove between two adjacent voltage-sensing domains (VSDs), whereas in the open conformation, one additional PIP2 binds to the interface of VSD and the pore domain, accompanying structural rearrangement of the cytosolic domain of KCNQ and CaM. The structures, along with electrophysiology analyses, reveal the two different binding modes of PIP2 and elucidate the PIP2 activation mechanism of KCNQ5.},
}

Humans
*Phosphatidylinositol 4,5-Diphosphate/metabolism
*KCNQ Potassium Channels/metabolism/chemistry/genetics
*Cryoelectron Microscopy
Calmodulin/metabolism/chemistry
Protein Binding
Ion Channel Gating
Models, Molecular
Protein Conformation

@article {pmid40172828,
year = {2025},
author = {Li, M and Zhao, Q and Zhang, T and Ge, J and Wang, J and Xu, G},
title = {A Personalized Predictor of Motor Imagery Ability Based on Multi-frequency EEG Features.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40172828},
issn = {1995-8218},
abstract = {A brain-computer interface (BCI) based on motor imagery (MI) provides additional control pathways by decoding the intentions of the brain. MI ability has great intra-individual variability, and the majority of MI-BCI systems are unable to adapt to this variability, leading to poor training effects. Therefore, prediction of MI ability is needed. In this study, we propose an MI ability predictor based on multi-frequency EEG features. To validate the performance of the predictor, a video-guided paradigm and a traditional MI paradigm are designed, and the predictor is applied to both paradigms. The results demonstrate that all subjects achieved > 85% prediction precision in both applications, with a maximum of 96%. This study indicates that the predictor can accurately predict the individuals' MI ability in different states, provide the scientific basis for personalized training, and enhance the effect of MI-BCI training.},
}

@article {pmid40172544,
year = {2025},
author = {Li, H and Li, C and Zhao, H and Li, Q and Zhao, Y and Gong, J and Li, G and Yu, H and Tian, Q and Liu, Z and Han, F},
title = {Flexible fibrous electrodes for implantable biosensing.},
journal = {Nanoscale},
volume = {},
number = {},
pages = {},
doi = {10.1039/d4nr04542d},
pmid = {40172544},
issn = {2040-3372},
abstract = {Flexible fibrous electrodes have emerged as a promising technology for implantable biosensing applications, offering significant advancements in the monitoring and manipulation of biological signals. This review systematically explores the key aspects of flexible fibrous electrodes, including the materials, structural designs, and fabrication methods. A detailed discussion of electrode performance metrics is provided, covering factors such as conductivity, stretchability, axial channel count, and implantation duration. The diverse applications of these electrodes in electrophysiological signal monitoring, electrochemical sensing, tissue strain monitoring, and in vivo electrical stimulation are reviewed, highlighting their potential in biomedical settings. Finally, the review discusses the eight major challenges currently faced by implantable fibrous electrodes and explores future development directions, providing critical technical analysis and potential solutions for the advancement of next-generation flexible implantable fiber-based biosensors.},
}

@article {pmid40172075,
year = {2025},
author = {Wu, X and Liang, C and Bustillo, J and Kochunov, P and Wen, X and Sui, J and Jiang, R and Yang, X and Fu, Z and Zhang, D and Calhoun, VD and Qi, S},
title = {The Impact of Atlas Parcellation on Functional Connectivity Analysis Across Six Psychiatric Disorders.},
journal = {Human brain mapping},
volume = {46},
number = {5},
pages = {e70206},
doi = {10.1002/hbm.70206},
pmid = {40172075},
issn = {1097-0193},
support = {BE2023668//Jiangsu Provincial Key Research and Development Program/ ; BK20220889//Natural Science Foundation of Jiangsu Province/ ; 62376124//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Adult ; *Atlases as Topic ; Male ; Female ; *Connectome/methods ; *Bipolar Disorder/diagnostic imaging/physiopathology/pathology ; *Schizophrenia/diagnostic imaging/physiopathology/pathology ; *Depressive Disorder, Major/diagnostic imaging/physiopathology ; *Mental Disorders/diagnostic imaging/physiopathology ; Autism Spectrum Disorder/diagnostic imaging/physiopathology ; Young Adult ; Nerve Net/diagnostic imaging/physiopathology ; Attention Deficit Disorder with Hyperactivity/diagnostic imaging/physiopathology ; Psychotic Disorders/diagnostic imaging/physiopathology/pathology ; Middle Aged ; Adolescent ; },
abstract = {Neuropsychiatric disorders are associated with altered functional connectivity (FC); however, the reported regional patterns of functional alterations suffered from low replicability and high variability. This is partly because of differences in the atlas and delineation techniques used to measure FC-related deficits within/across disorders. We systematically investigated the impact of the brain parcellation approach on the FC-based brain network analysis. We focused on identifying the replicable FCs using three structural brain atlases, including Automated Anatomical Labeling (AAL), Brainnetome atlas (BNA) and HCP_MMP_1.0, and four functional brain parcellation approaches: Yeo-Networks (Yeo), Gordon parcel (Gordon) and two Schaefer parcelletions, among correlation, group difference, and classification tasks in six neuropsychiatric disorders: attention deficit and hyperactivity disorder (ADHD, n = 340), autism spectrum disorder (ASD, n = 513), schizophrenia (SZ, n = 200), schizoaffective disorder (SAD, n = 142), bipolar disorder (BP, n = 172), and major depression disorder (MDD, n = 282). Our cross-atlas/disorder analyses demonstrated that frontal-related FC deficits were reproducible in all disorders, independent of the atlasing approach; however, replicable FC extraction in other areas and the classification accuracy were affected by the parcellation schema. Overall, functional atlases with finer granularity performed better in classification tasks. Specifically, the Schaefer atlases generated the most repeatable FC deficit patterns across six illnesses. These results indicate that frontal-related FCs may serve as potential common and robust neuro-abnormalities across 6 psychiatric disorders. Furthermore, in order to improve the replicability of rsfMRI-based FC analyses, this study suggests the use of functional templates at larger granularity.},
}

Humans
*Magnetic Resonance Imaging/methods
Adult
*Atlases as Topic
Male
Female
*Connectome/methods
*Bipolar Disorder/diagnostic imaging/physiopathology/pathology
*Schizophrenia/diagnostic imaging/physiopathology/pathology
*Depressive Disorder, Major/diagnostic imaging/physiopathology
*Mental Disorders/diagnostic imaging/physiopathology
Autism Spectrum Disorder/diagnostic imaging/physiopathology
Young Adult
Nerve Net/diagnostic imaging/physiopathology
Attention Deficit Disorder with Hyperactivity/diagnostic imaging/physiopathology
Psychotic Disorders/diagnostic imaging/physiopathology/pathology
Middle Aged
Adolescent

@article {pmid40171943,
year = {2025},
author = {Lin, C and Zhou, X and Li, M and Zhang, C and Zhai, H and Li, H and Wang, H and Wang, X},
title = {S-ketamine Alleviates Neuroinflammation and Attenuates Lipopolysaccharide-Induced Depression Via Targeting SIRT2.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2416481},
doi = {10.1002/advs.202416481},
pmid = {40171943},
issn = {2198-3844},
support = {2021ZD0203000//STI2030-Major Projects/ ; 2021ZD0203003//STI2030-Major Projects/ ; T2341003//National Natural Science Foundation of China/ ; 22207105//National Natural Science Foundation of China/ ; 2023C038-3//Jilin Provincial Development and Reform Commission/ ; BMI2400014//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; },
abstract = {Depression, a pervasive mental health condition, has increasingly been linked to neuroinflammation, as evidenced by elevated levels of pro-inflammatory markers such as TNF-α and IL-1β observed in patients, which underscores the role of inflammation in its pathophysiology. This study investigates the differential effects of S-ketamine (S-KET) and R-ketamine (R-KET) on inflammation-induced depression using a lipopolysaccharide (LPS)-induced mouse model. Results showed that S-KET, but not R-KET, significantly alleviated depressive-like behaviors and reduced levels of pro-inflammatory factors in the medial prefrontal cortex (mPFC). Activity-based protein profiling identified SIRT2 as a key intracellular target of S-KET, with direct binding observed at the Q167 residue, whereas R-KET showed no such binding. S-KET enhanced SIRT2 interaction with NF-κB subunit p65, reducing its acetylation and suppressing pro-inflammatory gene expression, effects not seen with R-KET. In vitro studies with RNA interference and the SIRT2 inhibitor AK-7, along with in vivo pharmacological blockade, confirmed that SIRT2 is crucial for the anti-inflammatory and antidepressant actions of S-KET. These findings suggest that SIRT2 mediates the therapeutic effects of S-KET, highlighting its potential as a target for treating inflammation-associated depression. This study provides novel insights into the stereospecific actions of ketamine enantiomers and the promise of targeting SIRT2 for neuroinflammatory depression.},
}

@article {pmid40170996,
year = {2025},
author = {Ping, A and Wang, J and Ángel García-Cabezas, M and Li, L and Zhang, J and Gothard, KM and Zhu, J and Roe, AW},
title = {Brainwide mesoscale functional networks revealed by focal infrared neural stimulation of the amygdala.},
journal = {National science review},
volume = {12},
number = {4},
pages = {nwae473},
pmid = {40170996},
issn = {2053-714X},
abstract = {The primate amygdala serves to evaluate the emotional content of sensory inputs and modulate emotional and social behaviors; it modulates cognitive, multisensory and autonomic circuits predominantly via the basal, lateral and central nuclei, respectively. Recent evidence has suggested the mesoscale (millimeter-scale) nature of intra-amygdala functional organization. However, the connectivity patterns by which these mesoscale regions interact with brainwide networks remain unclear. Using infrared neural stimulation of single mesoscale sites coupled with mapping in ultrahigh field 7-T functional magnetic resonance imaging, we have discovered that these mesoscale sites exert influence over a surprisingly extensive scope of the brain. Our findings strongly indicate that mesoscale sites within the amygdala modulate brainwide networks through a 'one-to-many' (integral) way. Meanwhile, these connections exhibit a point-to-point (focal) topography. Our work provides new insights into the functional architecture underlying emotional and social behavioral networks, thereby opening up possibilities for individualized modulation of psychological disorders.},
}

@article {pmid40169544,
year = {2025},
author = {Zhu, M and Peng, J and Wang, M and Lin, S and Zhang, H and Zhou, Y and Dai, X and Zhao, H and Yu, YQ and Shen, L and Li, XM and Chen, J},
title = {Transcriptomic and spatial GABAergic neuron subtypes in zona incerta mediate distinct innate behaviors.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3107},
pmid = {40169544},
issn = {2041-1723},
support = {81870898//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *GABAergic Neurons/metabolism ; *Zona Incerta/metabolism ; Mice ; *Transcriptome ; Male ; Behavior, Animal/physiology ; Grooming ; Optogenetics ; Mice, Inbred C57BL ; Sleep/physiology/genetics ; Wakefulness/physiology/genetics ; Periaqueductal Gray/metabolism/cytology/physiology ; Female ; Mice, Transgenic ; },
abstract = {Understanding the anatomical connection and behaviors of transcriptomic neuron subtypes is critical to delineating cell type-specific functions in the brain. Here we integrated single-nucleus transcriptomic sequencing, in vivo circuit mapping, optogenetic and chemogenetic approaches to dissect the molecular identity and function of heterogeneous GABAergic neuron populations in the zona incerta (ZI) in mice, a region involved in modulating various behaviors. By microdissecting ZI for transcriptomic and spatial gene expression analyses, our results revealed two non-overlapping Ecel1- and Pde11a-expressing GABAergic neurons with dominant expression in the rostral and medial zona incerta (ZIr[Ecel1] and ZIm[Pde11a]), respectively. The GABAergic projection from ZIr[Ecel1] to periaqueductal gray mediates self-grooming, while the GABAergic projection from ZIm[Pde11a] to the oral part of pontine reticular formation promotes transition from sleep to wakefulness. Together, our results revealed the molecular markers, spatial organization and specific neuronal circuits of two discrete GABAergic projection neuron populations in segregated subregions of the ZI that mediate distinct innate behaviors, advancing our understanding of the functional organization of the brain.},
}

Animals
*GABAergic Neurons/metabolism
*Zona Incerta/metabolism
Mice
*Transcriptome
Male
Behavior, Animal/physiology
Grooming
Optogenetics
Mice, Inbred C57BL
Sleep/physiology/genetics
Wakefulness/physiology/genetics
Periaqueductal Gray/metabolism/cytology/physiology
Female
Mice, Transgenic

@article {pmid40168986,
year = {2025},
author = {Wang, P and Han, L and Wang, L and Tao, Q and Guo, Z and Luo, T and He, Y and Xu, Z and Yu, J and Liu, Y and Wu, Z and Xu, B and Jin, B and Wei, Y and Yang, Y and Cheng, M and Jiang, Y and Tian, C and Zheng, H and Fan, Z and Jiang, P and Gao, Y and Wu, J and Wang, S and Sun, B and Fang, Z and Lei, J and Luo, B and Wen, H and Peng, G and Tang, Y and Yang, T and Chen, J and Zhuang, Z and Su, X and Pan, C and Zhu, K and Shen, Y and Liu, S and Bao, A and Yao, J and Wang, J and Xu, X and Li, XM and Liu, L and Duan, S and Zhang, J},
title = {Molecular pathways and diagnosis in spatially resolved Alzheimer's hippocampal atlas.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.03.002},
pmid = {40168986},
issn = {1097-4199},
abstract = {We employed Stereo-seq combined with single-nucleus RNA sequencing (snRNA-seq) to investigate the gene expression and cell composition changes in human hippocampus with or without Alzheimer's disease (AD). The transcriptomic map, with single-cell precision, unveiled AD-associated alterations with spatial specificity, which include the following: (1) elevated synapse pruning gene expression in the fimbria of AD, with disrupted microglia-astrocyte communication likely leading to disorganized synaptic structure; (2) a globally increased energy generation in the cornu ammonis (CA) region, with varying degrees across its subregions; (3) a significant reduction in the number of CA1 neurons in AD, while CA4 neurons remained largely unaffected, potentially due to gene alterations in CA4 conferring resilience to AD; and (4) aggravated amyloid-beta (Aβ) plaques in CA1 and stratum lucidum, radiatum, and moleculare (SLRM), and integration of Stereo-seq map with Aβ staining revealed a sequential enrichment of microglia and astrocytes around Aβ plaques. Finally, reduced brain-derived extracellular vesicles carrying cholecystokinin (CCK) and peripheral myelin protein 2 (PMP2) in AD plasma highlighted their diagnostic potential for clinical applications.},
}

@article {pmid40168808,
year = {2025},
author = {Denis-Robichaud, J and Barbeau-Grégoire, N and Gauthier, ML and Dufour, S and Roy, JP and Buczinski, S and Dubuc, J},
title = {Validity of purulent vaginal discharge, esterase, luminometry, and three bacteriological tests for diagnosing uterine infection in dairy cows using Bayesian latent class analysis.},
journal = {Preventive veterinary medicine},
volume = {239},
number = {},
pages = {106521},
doi = {10.1016/j.prevetmed.2025.106521},
pmid = {40168808},
issn = {1873-1716},
abstract = {This prospective cross-sectional study aimed to evaluate the ability of laboratory bacterial culture, Petrifilm, Tri-Plate, luminometry, purulent vaginal discharge (PVD), and esterase to correctly identify uterine infection in dairy cows, and to assess these tests' usefulness in different situations. We sampled dairy cows between 29 and 43 days in milk in seven farms. We considered all six tests imperfect to identify uterine infection and used Bayesian latent class analyses to estimate their sensitivity and specificity. We created ten scenarios, including tests alone, in series, or in parallel, and we calculated predictive values and misclassification cost terms (MCTs). All estimates are presented with 95 % Bayesian credibility intervals (BCI). A total of 326 uterine samples were collected. The laboratory culture had the best validity (sensitivity = 0.87, 95 % BCI = 0.77-0.97; specificity = 0.71, 95 % BCI = 0.58-0.86). The other tests had similar specificity but lower sensitivity, with PVD having the lowest sensitivity (0.05, 95 % BCI = 0.01-0.10). If treating a healthy cow was considered worse than leaving a cow with a uterine infection untreated, luminometry yielded an MCT similar to the laboratory culture. These findings highlight that the on-farm tools currently used to identify cows that could benefit from intrauterine antimicrobial treatment do not identify uterine infection accurately. While the laboratory culture was the most accurate test, it cannot easily be implemented on farms. Luminometry's validity was good, but additional research is necessary to understand how it can be implemented to improve judicious intrauterine antimicrobial use.},
}

@article {pmid40166115,
year = {2025},
author = {Yu, Z and Yang, B and Wei, P and Xu, H and Shan, Y and Fan, X and Zhang, H and Wang, C and Wang, J and Yu, S and Zhao, G},
title = {Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field.},
journal = {Fundamental research},
volume = {5},
number = {1},
pages = {103-114},
pmid = {40166115},
issn = {2667-3258},
abstract = {To derive critical signal features from intracranial electroencephalograms of epileptic patients in order to design instructions for feedback-type electrical stimulation systems. The Detrended Fluctuation Analysis (DFA) exponent is chosen as the classification exponent, and the disparities between indicators representing distinct seizure states and the classification efficacy of rudimentary machine learning models are computed. The DFA exponent exhibited a statistically significant variation among the pre-ictal, ictal period, and post-ictal stages. The Linear Discriminant Analysis model demonstrates the highest accuracy among the three basic machine learning models, whereas the Naive Bayesian model necessitates the least amount of computational and storage space. The set of DFA exponents is employed as an intermediary variable in the machine learning process. The resultant model possesses the capability to function as a feedback trigger program for electrical stimulation systems of the feedback variety, specifically within the domain of neural modulation in epilepsy.},
}

@article {pmid40166113,
year = {2025},
author = {Sun, Y and Chen, X and Liu, B and Liang, L and Wang, Y and Gao, S and Gao, X},
title = {Signal acquisition of brain-computer interfaces: A medical-engineering crossover perspective review.},
journal = {Fundamental research},
volume = {5},
number = {1},
pages = {3-16},
pmid = {40166113},
issn = {2667-3258},
abstract = {Brain-computer interface (BCI) technology represents a burgeoning interdisciplinary domain that facilitates direct communication between individuals and external devices. The efficacy of BCI systems is largely contingent upon the progress in signal acquisition methodologies. This paper endeavors to provide an exhaustive synopsis of signal acquisition technologies within the realm of BCI by scrutinizing research publications from the last ten years. Our review synthesizes insights from both clinical and engineering viewpoints, delineating a comprehensive two-dimensional framework for understanding signal acquisition in BCIs. We delineate nine discrete categories of technologies, furnishing exemplars for each and delineating the salient challenges pertinent to these modalities. This review furnishes researchers and practitioners with a broad-spectrum comprehension of the signal acquisition landscape in BCI, and deliberates on the paramount issues presently confronting the field. Prospective enhancements in BCI signal acquisition should focus on harmonizing a multitude of disciplinary perspectives. Achieving equilibrium between signal fidelity, invasiveness, biocompatibility, and other pivotal considerations is imperative. By doing so, we can propel BCI technology forward, bolstering its effectiveness, safety, and dependability, thereby contributing to an auspicious future for human-technology integration.},
}

@article {pmid40164913,
year = {2025},
author = {Cheng, YA and Sanayei, M and Chen, X and Jia, K and Li, S and Fang, F and Watanabe, T and Thiele, A and Zhang, RY},
title = {A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning.},
journal = {Nature human behaviour},
volume = {},
number = {},
pages = {},
pmid = {40164913},
issn = {2397-3374},
support = {3230085//National Natural Science Foundation of China (National Science Foundation of China)/ ; T2421004//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31930053//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32441102//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100901//National Natural Science Foundation of China (National Science Foundation of China)/ ; R01EY019466//U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI)/ ; BCS-2241417//NSF | Directorate for Social, Behavioral & Economic Sciences | Division of Behavioral and Cognitive Sciences (Behavioral & Cognitive Sciences)/ ; G0700976//RCUK | Medical Research Council (MRC)/ ; },
abstract = {Visual perceptual learning (VPL), defined as long-term improvement in a visual task, is considered a crucial tool for elucidating underlying visual and brain plasticity. Previous studies have proposed several neural models of VPL, including changes in neural tuning or in noise correlations. Here, to adjudicate different models, we propose that all neural changes at single units can be conceptualized as geometric transformations of population response manifolds in a high-dimensional neural space. Following this neural geometry approach, we identified neural manifold shrinkage due to reduced trial-by-trial population response variability, rather than tuning or correlation changes, as the primary mechanism of VPL. Furthermore, manifold shrinkage successfully explains VPL effects across artificial neural responses in deep neural networks, multivariate blood-oxygenation-level-dependent signals in humans and multiunit activities in monkeys. These converging results suggest that our neural geometry approach comprehensively explains a wide range of empirical results and reconciles previously conflicting models of VPL.},
}

@article {pmid40163319,
year = {2025},
author = {van Balen, B},
title = {Somatosensory Feedback in BCIs: Why Aiming for Naturalness Raises Ethical Concerns.},
journal = {AJOB neuroscience},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/21507740.2025.2478427},
pmid = {40163319},
issn = {2150-7759},
abstract = {Recent developments in the domain of bi-directional Brain-Computer Interface (BCI) technology are directed at generating naturalistic sensory perceptual experiences for disabled people. I argue that conceptualizing and operationalizing "naturalness" in this context has profound impact on disabled people and their experiences. I ask (1) what does it mean to have a "natural" perceptual experience and (2) should the bi-directional BCI-community strive for naturalness in this context? Inspired by phenomenological and 4E-cognition approaches to perception, I argue that the terms "natural" and "naturalness" should not be used in this context because of (1) polysemicity and (2) an implicit bias favoring able-bodied perception over disabled perception. I offer the phenomenological concept of transparency as a positive alternative to denote the underlying goal of embodiment and effortless use. I cash out methodological ramifications of my argument for research in bi-directional BCIs and plea for a transdisciplinary dialogue between end-users, phenomenologists and neuroscientists.},
}

@article {pmid40162212,
year = {2025},
author = {Wang, D and Ramesh, R and Azgomi, HF and Louie, K and Balakid, J and Marks, J},
title = {At-Home Movement State Classification Using Totally Implantable Bidirectional Cortical-Basal Ganglia Neural Interface.},
journal = {Research square},
volume = {},
number = {},
pages = {},
doi = {10.21203/rs.3.rs-6058394/v1},
pmid = {40162212},
issn = {2693-5015},
abstract = {Movement decoding from invasive human recordings typically relies on a distributed system employing advanced machine learning algorithms programmed into an external computer for state classification. These brain-computer interfaces are limited to short-term studies in laboratory settings that may not reflect behavior and neural states in the real world. The development of implantable devices with sensing capabilities is revolutionizing the study and treatment of brain circuits. However, it is unknown whether these devices can decode natural movement state from recorded neural activity or accurately classify states in real-time using on-board algorithms. Here, using a totally implanted sensing-enabled neurostimulator to perform long-term, at-home recordings from the motor cortex and pallidum of four subjects with Parkinson's disease, we successfully identified highly sensitive and specific personalized signatures of gait state, as determined by wearable sensors. Additionally, we demonstrated the feasibility of using at-home data to generate biomarkers compatible with the classifier embedded on-board the neurostimulator. These findings offer a pipeline for ecologically valid movement biomarker identification that can advance therapy across a variety of diseases.},
}

@article {pmid40162168,
year = {2025},
author = {Yang, H and Jiang, L},
title = {Regulating neural data processing in the age of BCIs: Ethical concerns and legal approaches.},
journal = {Digital health},
volume = {11},
number = {},
pages = {20552076251326123},
doi = {10.1177/20552076251326123},
pmid = {40162168},
issn = {2055-2076},
abstract = {Brain-computer interfaces (BCIs) have seen increasingly fast growth under the help from AI, algorithms, and cloud computing. While providing great benefits for both medical and educational purposes, BCIs involve processing of neural data which are uniquely sensitive due to their most intimate nature, posing unique risks and ethical concerns especially related to privacy and safe control of our neural data. In furtherance of human right protection such as mental privacy, data laws provide more detailed and enforceable rules for processing neural data which may balance the tension between privacy protection and need of the public for wellness promotion and scientific progress through data sharing. This article notes that most of the current data laws like GDPR have not covered neural data clearly, incapable of providing full protection in response to its specialty. The new legislative reforms in the U.S. states of Colorado and California made pioneering advances to incorporate neural data into data privacy laws. Yet regulatory gaps remain as such reforms have not provided special additional rules for neural data processing. Potential problems such as static consent, vague research exceptions, and loopholes in regulating non-personal neural data need to be further addressed. We recommend relevant improved measures taken through amending data laws or making special data acts.},
}

@article {pmid40161643,
year = {2025},
author = {Haro, S and Beauchene, C and Quatieri, TF and Smalt, CJ},
title = {A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.03.13.641661},
pmid = {40161643},
issn = {2692-8205},
abstract = {OBJECTIVE: There is significant research in accurately determining the focus of a listener's attention in a multi-talker environment using auditory attention decoding (AAD) algorithms. These algorithms rely on neural signals to identify the intended speaker, assuming that these signals consistently reflect the listener's focus. However, some listeners struggle with this competing talkers task, leading to suboptimal tracking of the desired speaker due to potential interference from distractors. The goal of this study was to enhance a listener's attention to the target speaker in real time and investigate the underlying neural bases of this improvement.

APPROACH: This paper describes a closed-loop neurofeedback system that decodes the auditory attention of the listener in real time, utilizing data from a non-invasive, wet electroencephalography (EEG) brain-computer interface (BCI). Fluctuations in the listener's real-time attention decoding accuracy was used to provide acoustic feedback. As accuracy improved, the ignored talker in the two-talker listening scenario was attenuated; making the desired talker easier to attend to due to the improved attended talker signal-to-noise ratio (SNR). A one-hour session was divided into a 10-minute decoder training phase, with the rest of the session allocated to observing changes in neural decoding.

RESULTS: In this study, we found evidence of suppression of (i.e., reduction in) neural tracking of the unattended talker when comparing the first and second half of the neurofeedback session (p = 0.012). We did not find a statistically significant increase in the neural tracking of the attended talker.

SIGNIFICANCE: These results establish a single session performance benchmark for a time-invariant, non-adaptive attended talker linear decoder utilized to extract attention from a listener integrated within a closed-loop neurofeedback system. This research lays the engineering and scientific foundation for prospective multi-session clinical trials of an auditory attention training paradigm.},
}

@article {pmid40161457,
year = {2025},
author = {Ren, X and Wang, Y and Li, X and Wang, X and Liu, Z and Yang, J and Wang, L and Zheng, C},
title = {Attenuated heterogeneity of hippocampal neuron subsets in response to novelty induced by amyloid-β.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {56},
doi = {10.1007/s11571-025-10237-x},
pmid = {40161457},
issn = {1871-4080},
abstract = {Alzheimer's disease (AD) patients exhibited episodic memory impairments including location-object recognition in a spatial environment, which was also presented in animal models with amyloid-β (Aβ) accumulation. A potential cellular mechanism was the unstable representation of spatial information and lack of discrimination ability of novel stimulus in the hippocampal place cells. However, how the firing characteristics of different hippocampal subsets responding to diverse spatial information were interrupted by Aβ accumulation remains unclear. In this study, we observed impaired novel object-location recognition in Aβ-treated Long-Evans rats, with larger receptive fields of place cells in hippocampal CA1, compared with those in the saline-treated group. We identified two subsets of place cells coding object information (ObjCell) and global environment (EnvCell) during the task, with firing heterogeneity in response to introduced novel information. ObjCells displayed a dynamic representation responding to the introduction of novel information, while EnvCells exhibited a stable representation to support the recognition of the familiar environment. However, the dynamic firing patterns of these two subsets of cells were disrupted to present attenuated heterogeneity under Aβ accumulation. The impaired spatial representation novelty information could be due to the disturbed gamma modulation of neural activities. Taken together, these findings provide new evidence for novelty recognition impairments of AD rats with spatial representation dysfunctions of hippocampal subsets.},
}

@article {pmid40159374,
year = {2025},
author = {Phang, CR and Hirata, A},
title = {Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.},
journal = {Annals of the New York Academy of Sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/nyas.15322},
pmid = {40159374},
issn = {1749-6632},
support = {KAKENHI 21H04956//Japan Society for the Promotion of Science/ ; },
abstract = {Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment. We proposed a novel integration technique between deep RL and BCI to improve beneficial human interventions in autonomous systems and the performance in decoding brain activities by considering environmental factors. Shared autonomy was allowed between the action command decoded from the electroencephalography (EEG) of the human agent and the action generated from the twin delayed DDPG (TD3) agent for a given complex environment. Our proposed copilot control scheme with a full blocker (Co-FB) significantly outperformed the individual EEG (EEG-NB) or TD3 control. The Co-FB model achieved a higher target-approaching score, lower failure rate, and lower human workload than the EEG-NB model. We also proposed a disparity d $d$ -index to evaluate the effect of contradicting agent decisions on the control accuracy and authority of the copilot model. We observed that shifting control authority to the TD3 agent improved performance when BCI decoding was not optimal. These findings indicate that the copilot system can effectively handle complex environments and that BCI performance can be improved by considering environmental factors.},
}

@article {pmid40153908,
year = {2025},
author = {Liu, J and Yang, X and Musmar, B and Hasan, DM},
title = {Trans-arterial approach for neural recording and stimulation: Present and future.},
journal = {Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia},
volume = {135},
number = {},
pages = {111180},
doi = {10.1016/j.jocn.2025.111180},
pmid = {40153908},
issn = {1532-2653},
abstract = {Neural recording and stimulation are fundamental techniques used for brain computer interfaces (BCIs). BCIs have significant potential for use in a range of brain disorders. However, for most BCIs, electrode implantation requires invasive craniotomy procedures, which have a risk of infection, hematoma, and immune responses. Such drawbacks may limit the extensive application of BCIs. There has been a rapid increase in the development of endovascular technologies and devices. Indeed, in a clinical trial, stent electrodes have been endovascularly implanted via a venous approach and provided an effective endovascular BCI to help disabled patients. Several authors have reviewed the use of endovascular recordings or endovascular BCIs. However, there is limited information on the use of trans-arterial BCIs. Herein, we reviewed the literature on the use of trans-arterial neural recording and stimulation for BCIs, and discuss their potential in terms of anatomical features, device innovations, and clinical applications. Although the use of trans-arterial recording and stimulation in the brain remains challenging, we believe it has high potential for both scientists and physicians.},
}

@article {pmid40152578,
year = {2025},
author = {Li, K and Cui, Y},
title = {The Emerging Role of Astrocytes in Learning and Memory Recall.},
journal = {Journal of integrative neuroscience},
volume = {24},
number = {3},
pages = {38721},
doi = {10.31083/JIN38721},
pmid = {40152578},
issn = {0219-6352},
support = {2022ZD0211700//STI2030-Major projects/ ; 32371057//National Natural Science Foundation of China/ ; },
}

@article {pmid40151645,
year = {2025},
author = {Iwama, S and Ueno, T and Fujimaki, T and Ushiba, J},
title = {Enhanced human sensorimotor integration via self-modulation of the somatosensory activity.},
journal = {iScience},
volume = {28},
number = {4},
pages = {112145},
pmid = {40151645},
issn = {2589-0042},
abstract = {Motor performance improvement through self-modulation of brain activity has been demonstrated through neurofeedback. However, the sensorimotor plasticity induced through the training remains unclear. Here, we combined individually tailored closed-loop neurofeedback, neurophysiology, and behavioral assessment to characterize how the training can modulate the somatosensory system and improve performance. The real-time neurofeedback of human electroencephalogram (EEG) signals enhanced participants' self-modulation ability of intrinsic neural oscillations in the primary somatosensory cortex (S1) within 30 min. Further, the short-term reorganization in S1 was corroborated by the post-training changes in somatosensory evoked potential (SEP) amplitude of the early component from S1. Meanwhile those derived from peripheral and spinal sensory fibers were maintained (N9 and N13 components), suggesting that the training manipulated S1 activities. Behavioral evaluation demonstrated improved performance during keyboard touch-typing indexed by resolved speed-accuracy trade-off. Collectively, our results provide evidence that neurofeedback training induces functional reorganization of S1 and sensorimotor function.},
}

@article {pmid40149743,
year = {2025},
author = {Zheng, J and Li, Y and Chen, L and Wang, F and Gu, B and Sun, Q and Gao, X and Zhou, F},
title = {Effects of Packet Loss on Neural Decoding Effectiveness in Wireless Transmission.},
journal = {Brain sciences},
volume = {15},
number = {3},
pages = {},
doi = {10.3390/brainsci15030221},
pmid = {40149743},
issn = {2076-3425},
support = {2021ZD0200405//National Key R&D Program of China/ ; 2024M752811//China Postdoctoral Science Foundation under Grant Number/ ; 202204A09//Key Agricultural and Social Development Projects of Hangzhou/ ; },
abstract = {BACKGROUND: In brain-computer interfaces, neural decoding plays a central role in translating neural signals into meaningful physical actions. These signals are transmitted to processors for decoding via wired or wireless channels; however, they are often subject to data loss, commonly referred to as "packet loss". Despite their importance, the effects of different types and degrees of packet loss on neural decoding have not yet been comprehensively studied. Understanding these effects is critical for advancing neural signal processing.

METHODS: This study addresses this gap by constructing four distinct packet loss models that simulate the congestion, distribution, and burst loss scenarios. Using macaque superior arm movement decoding experiments, we analyzed the effects of the aforementioned packet loss types on decoding performance across six parameters (position, velocity, and acceleration in the x and y dimensions). The performance was assessed using the R2 metric and statistical comparisons across different loss scenarios.

RESULTS: Our results indicate that sudden, consecutive packet loss significantly degraded decoding performance. For the same packet loss probability, burst loss led to the largest decrease in the R2 value. Notably, when the packet loss rate reached 10%, the decoding performance for acceleration dropped to 73% of the original R2 value. On the other hand, when the packet loss rate was within 2%, the neural signal decoding results across all packet loss models remained largely unaffected. However, as the packet loss rate increased, the impact became more pronounced. These findings highlight the varying degrees to which different packet loss models affect decoding outcomes.

CONCLUSIONS: This study quantitatively evaluated the relationship between packet loss and neural decoding outcomes, highlighting the differential effects of loss patterns on decoding parameters, and it proposed some methods and devices to solve the problem of packet loss. These findings offer valuable insights for the development of resilient neural signal acquisition and processing systems capable of mitigating the impact of packet loss.},
}

@article {pmid40149575,
year = {2025},
author = {Calderone, A and Manuli, A and Arcadi, FA and Militi, A and Cammaroto, S and Maggio, MG and Pizzocaro, S and Quartarone, A and De Nunzio, AM and Calabrò, RS},
title = {The Impact of Visualization on Stroke Rehabilitation in Adults: A Systematic Review of Randomized Controlled Trials on Guided and Motor Imagery.},
journal = {Biomedicines},
volume = {13},
number = {3},
pages = {},
doi = {10.3390/biomedicines13030599},
pmid = {40149575},
issn = {2227-9059},
abstract = {Background/Objectives: Guided imagery techniques, which include mentally picturing motions or activities to help motor recovery, are an important part of neuroplasticity-based motor therapy in stroke patients. Motor imagery (MI) is a kind of guided imagery in neurorehabilitation that focuses on mentally rehearsing certain motor actions in order to improve performance. This systematic review aims to evaluate the current evidence on guided imagery techniques and identify their therapeutic potential in stroke motor rehabilitation. Methods: Randomized controlled trials (RCTs) published in the English language were identified from an online search of PubMed, Web of Science, Embase, EBSCOhost, and Scopus databases without a specific search time frame. The inclusion criteria take into account guided imagery interventions and evaluate their impact on motor recovery through validated clinical, neurophysiological, or functional assessments. This review has been registered on Open OSF with the following number: DOI 10.17605/OSF.IO/3D7MF. Results: This review synthesized 41 RCTs on MI in stroke rehabilitation, with 996 participants in the intervention group and 757 in the control group (average age 50-70, 35% female). MI showed advantages for gait, balance, and upper limb function; however, the RoB 2 evaluation revealed 'some concerns' related to allocation concealment, blinding, and selective reporting issues. Integrating MI with gait training or action observation (AO) seems to improve motor recovery, especially in balance and walking. Technological methods like brain-computer interfaces (BCIs) and hybrid models that combine MI with circuit training hold potential for enhancing functional mobility and motor results. Conclusions: Guided imagery shows promise as a beneficial adjunct in stroke rehabilitation, with the potential to improve motor recovery across several domains such as gait, upper limb function, and balance.},
}

@article {pmid40148520,
year = {2025},
author = {Li, J and Shi, J and Yu, P and Yan, X and Lin, Y},
title = {Feature-aware domain invariant representation learning for EEG motor imagery decoding.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {10664},
pmid = {40148520},
issn = {2045-2322},
support = {2023QN06002//Inner Mongolia Natural Science Foundation project of China/ ; JJ230408//Inner Mongolia University high-level talent research project/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Signal-To-Noise Ratio ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Imagination/physiology ; Machine Learning ; Movement/physiology ; },
abstract = {Electroencephalography (EEG)-based motor imagery (MI) is extensively utilized in clinical rehabilitation and virtual reality-based movement control. Decoding EEG-based MI signals is challenging because of the inherent spatio-temporal variability of the original signal representation, coupled with a low signal-to-noise ratio (SNR), which impedes the extraction of clean and robust features. To address this issue, we propose a multi-scale spatio-temporal domain-invariant representation learning method, termed MSDI. By decomposing the original signal into spatial and temporal components, the proposed method extracts invariant features at multiple scales from both components. To further constrain the representation to invariant domains, we introduce a feature-aware shift operation that resamples the representation based on its feature statistics and feature measure, thereby projecting the features into a domain-invariant space. We evaluate our proposed method via two publicly available datasets, BNCI2014-001 and BNCI2014-004, demonstrating state-of-the-art performance on both datasets. Furthermore, our method exhibits superior time efficiency and noise resistance.},
}

Humans
*Electroencephalography/methods
Signal-To-Noise Ratio
Brain-Computer Interfaces
Signal Processing, Computer-Assisted
Algorithms
Imagination/physiology
Machine Learning
Movement/physiology

@article {pmid40147158,
year = {2025},
author = {Tang, J and Xi, X and Wang, T and Li, L and Yang, J},
title = {Evaluation of the impacts of neuromuscular electrical stimulation based on cortico-muscular-cortical functional network.},
journal = {Computer methods and programs in biomedicine},
volume = {265},
number = {},
pages = {108735},
doi = {10.1016/j.cmpb.2025.108735},
pmid = {40147158},
issn = {1872-7565},
abstract = {BACKGROUND AND OBJECTIVE: Neuromuscular electrical stimulation (NMES) has been extensively applied for recovery of motor functions. However, its impact on the cortical network changes related to muscle activity remains unclear, which is crucial for understanding the changes in the collaborative working patterns within the sensory-motor control system post-stroke.

METHODS: In this research, we have integrated cortico-muscular interactions, intercortical interactions, and intramuscular interactions to propose a novel closed-loop network structure, namely the cortico-muscular-cortical functional network (CMCFN). The framework is endowed with the capability to distinguish the directionality of causal interactions and local frequency band characteristics through transfer spectral entropy (TSE). Subsequently, the CMCFN is applied to stroke patients to elucidate the potential influence of NMES on cortical physiological function changes during motor induction.

RESULTS: The results indicate that short-term modulation by NMES significantly enhanced the cortico-muscular interactions of the contralateral cerebral hemisphere and the affected upper limb (p < 0.001), while coexistence of facilitatory and inhibitory effects is observed in the intermuscular coupling across different electromyography (EMG) signals. Furthermore, following NMES treatment, the connectivity of the brain functional network is significantly strengthened, particularly in the γ frequency band (30-45 Hz), with marked improvements in the clustering coefficient and shortest path length (p < 0.001).

CONCLUSIONS: As a new framework, CMCFN offers a novel perspective for studying motor cortical networks related to muscle activity.},
}

@article {pmid40145943,
year = {2025},
author = {Jilderda, MF and Zhang, Y and Rebattu, V and Salunga, R and Mesker, W and Wong, J and de Munck, L and Fornander, T and Nordenskjöld, B and Stål, O and Anderson, AKL and Bastiaannet, E and Treuner, K and Liefers, GJ},
title = {Identification of Early-Stage Breast Cancer with Minimal Risk of Recurrence by Breast Cancer Index.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {},
number = {},
pages = {},
doi = {10.1158/1078-0432.CCR-24-3836},
pmid = {40145943},
issn = {1557-3265},
abstract = {PURPOSE: This study assessed the prognostic ability of Breast Cancer Index (BCI) to identify patients at minimal risk (<5%) of 10-year distant recurrence (DR) who are unlikely to benefit from adjuvant endocrine therapy.

EXPERIMENTAL DESIGN: This prospective translational study included postmenopausal patients with early-stage, HR+ N0 breast cancer from the Stockholm (STO-3) trial who underwent surgery alone ("untreated") or surgery plus adjuvant tamoxifen ("treated") and the Netherlands Cancer Registry (NCR; surgery alone). The primary endpoint was time to DR. An adjusted BCI model with an additional cut-point was developed that stratified patients into 4 prognostic risk groups.

RESULTS: Across cohorts, 16%-22% of patients were classified as minimal risk of 10-year DR. In the Stockholm untreated cohort (n = 283), risks in the minimal, low, intermediate, and high risk groups were 2.3%, 15.5% (hazard ratio, 4.71 [95% CI, 1.09-20.29] versus minimal risk), 19.8% (6.97 [1.61-30.18]), and 35.9% (13.21 [3.07-56.76]), respectively (P < .001). In the Stockholm treated cohort (n = 317), risks were 4.3%, 5.0% (1.16 [0.35-3.85]), 11.7% (2.45 [0.74-8.14]), and 21.1% (5.27 [1.72-16.16]; P < .001). In the NCR cohort (n = 1245), risks were 4.5%, 7.5% (sub-distribution hazard ratio, 1.67 [95% CI, 0.81-3.45]), 10.3% (2.40 [1.14-5.03]), and 13.1% (3.13 [1.50-6.55]; P = .005). BCI risk scores provided additional independent information over standard prognostic factors (likelihood ratio, c2 = 7.98; P = .004).

CONCLUSIONS: The adjusted BCI model identified women with early-stage, HR+ N0 breast cancer at minimal risk of DR who may consider de-escalating adjuvant endocrine therapy.},
}

@article {pmid40144587,
year = {2025},
author = {Marzulli, M and Bleuzé, A and Saad, J and Martel, F and Ciuciu, P and Aksenova, T and Struber, L},
title = {Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1521491},
pmid = {40144587},
issn = {1662-5161},
abstract = {INTRODUCTION: Phase-amplitude coupling (PAC), the modulation of high-frequency neural oscillations by the phase of slower oscillations, is increasingly recognized as a marker of goal-directed motor behavior. Despite this interest, its specific role and potential value in decoding attempted motor movements remain unclear.

METHODS: This study investigates whether PAC-derived features can be leveraged to classify different motor behaviors from ECoG signals within Brain-Computer Interface (BCI) systems. ECoG data were collected using the WIMAGINE implant during BCI experiments with a tetraplegic patient performing mental motor tasks. The data underwent preprocessing to extract complex neural oscillation features (amplitude, phase) through spectral decomposition techniques. These features were then used to quantify PAC by calculating different coupling indices. PAC metrics served as input features in a machine learning pipeline to evaluate their effectiveness in predicting mental tasks (idle state, right-hand movement, left-hand movement) in both offline and pseudo-online modes.

RESULTS: The PAC features demonstrated high accuracy in distinguishing among motor tasks, with key classification features highlighting the coupling of theta/low-gamma and beta/high-gamma frequency bands.

DISCUSSION: These preliminary findings hold significant potential for advancing our understanding of motor behavior and for developing optimized BCI systems.},
}

@article {pmid40144585,
year = {2025},
author = {Saad, J and Evans, A and Jaoui, I and Roux-Sibillon, V and Hardy, E and Anghel, L},
title = {Comparison metrics and power trade-offs for BCI motor decoding circuit design.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1547074},
pmid = {40144585},
issn = {1662-5161},
abstract = {Brain signal decoders are increasingly being used in early clinical trials for rehabilitation and assistive applications such as motor control and speech decoding. As many Brain-Computer Interfaces (BCIs) need to be deployed in battery-powered or implantable devices, signal decoding must be performed using low-power circuits. This paper reviews existing hardware systems for BCIs, with a focus on motor decoding, to better understand the factors influencing the power and algorithmic performance of such systems. We propose metrics to compare the energy efficiency of a broad range of on-chip decoding systems covering Electroencephalography (EEG), Electrocorticography (ECoG), and Microelectrode Array (MEA) signals. Our analysis shows that achieving a given classification rate requires an Input Data Rate (IDR) that can be empirically estimated, a finding that is helpful for sizing new BCI systems. Counter-intuitively, our findings show a negative correlation between the power consumption per channel (PpC) and the Information Transfer Rate (ITR). This suggests that increasing the number of channels can simultaneously reduce the PpC through hardware sharing and increase the ITR by providing new input data. In fact, for EEG and ECoG decoding circuits, the power consumption is dominated by the complexity of signal processing. To better understand how to minimize this power consumption, we review the optimizations used in state-of-the-art decoding circuits.},
}

@article {pmid40143846,
year = {2025},
author = {Andronache, C and Curǎvale, D and Nicolae, IE and Neacşu, AA and Nicolae, G and Ivanovici, M},
title = {Tackling the possibility of extracting a brain digital fingerprint based on personal hobbies predilection.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1487175},
pmid = {40143846},
issn = {1662-4548},
abstract = {In an attempt to create a more familiar brain-machine interaction for biometric authentication applications, we investigated the efficiency of using the users' personal hobbies, interests, and memory collections. This approach creates a unique and pleasant experience that can be later utilized within an authentication protocol. This paper presents a new EEG dataset recorded while subjects watch images of popular hobbies, pictures with no point of interest and images with great personal significance. In addition, we propose several applications that can be tackled with our newly collected dataset. Namely, our study showcases 4 types of applications and we obtain state-of-the-art level results for all of them. The tackled tasks are: emotion classification, category classification, authorization process, and person identification. Our experiments show great potential for using EEG response to hobby visualization for people authentication. In our study, we show preliminary results for using predilection for personal hobbies, as measured by EEG, for identifying people. Also, we propose a novel authorization process paradigm using electroencephalograms. Code and dataset are available here.},
}

@article {pmid40141951,
year = {2025},
author = {Aydin, S and Melek, M and Gökrem, L},
title = {A Safe and Efficient Brain-Computer Interface Using Moving Object Trajectories and LED-Controlled Activation.},
journal = {Micromachines},
volume = {16},
number = {3},
pages = {},
doi = {10.3390/mi16030340},
pmid = {40141951},
issn = {2072-666X},
support = {2023/86//Tokat Gaziosmanpaşa Üniversitesi/ ; },
abstract = {Nowadays, brain-computer interface (BCI) systems are frequently used to connect individuals who have lost their mobility with the outside world. These BCI systems enable individuals to control external devices using brain signals. However, these systems have certain disadvantages for users. This paper proposes a novel approach to minimize the disadvantages of visual stimuli on the eye health of system users in BCI systems employing visual evoked potential (VEP) and P300 methods. The approach employs moving objects with different trajectories instead of visual stimuli. It uses a light-emitting diode (LED) with a frequency of 7 Hz as a condition for the BCI system to be active. The LED is assigned to the system to prevent it from being triggered by any involuntary or independent eye movements of the user. Thus, the system user will be able to use a safe BCI system with a single visual stimulus that blinks on the side without needing to focus on any visual stimulus through moving balls. Data were recorded in two phases: when the LED was on and when the LED was off. The recorded data were processed using a Butterworth filter and the power spectral density (PSD) method. In the first classification phase, which was performed for the system to detect the LED in the background, the highest accuracy rate of 99.57% was achieved with the random forest (RF) classification algorithm. In the second classification phase, which involves classifying moving objects within the proposed approach, the highest accuracy rate of 97.89% and an information transfer rate (ITR) value of 36.75 (bits/min) were achieved using the RF classifier.},
}

@article {pmid40140571,
year = {2025},
author = {Gazit Shimoni, N and Tose, AJ and Seng, C and Jin, Y and Lukacsovich, T and Yang, H and Verharen, JPH and Liu, C and Tanios, M and Hu, E and Read, J and Tang, LW and Lim, BK and Tian, L and Földy, C and Lammel, S},
title = {Changes in neurotensin signalling drive hedonic devaluation in obesity.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {40140571},
issn = {1476-4687},
abstract = {Calorie-rich foods, particularly those that are high in fat and sugar, evoke pleasure in both humans and animals[1]. However, prolonged consumption of such foods may reduce their hedonic value, potentially contributing to obesity[2-4]. Here we investigated this phenomenon in mice on a chronic high-fat diet (HFD). Although these mice preferred high-fat food over regular chow in their home cages, they showed reduced interest in calorie-rich foods in a no-effort setting. This paradoxical decrease in hedonic feeding has been reported previously[3-7], but its neurobiological basis remains unclear. We found that in mice on regular diet, neurons in the lateral nucleus accumbens (NAcLat) projecting to the ventral tegmental area (VTA) encoded hedonic feeding behaviours. In HFD mice, this behaviour was reduced and uncoupled from neural activity. Optogenetic stimulation of the NAcLat→VTA pathway increased hedonic feeding in mice on regular diet but not in HFD mice, though this behaviour was restored when HFD mice returned to a regular diet. HFD mice exhibited reduced neurotensin expression and release in the NAcLat→VTA pathway. Furthermore, neurotensin knockout in the NAcLat and neurotensin receptor blockade in the VTA each abolished optogenetically induced hedonic feeding behaviour. Enhancing neurotensin signalling via overexpression normalized aspects of diet-induced obesity, including weight gain and hedonic feeding. Together, our findings identify a neural circuit mechanism that links the devaluation of hedonic foods with obesity.},
}

@article {pmid40139011,
year = {2025},
author = {Luo, C and Zhu, X and Zhang, Y and Wen, Y and Wan, L and Qian, Z},
title = {Competitive electrochemical immunosensor for trace phosphorylated Tau181 analysis in plasma: Toward point-of-care technologies of Alzheimer's disease.},
journal = {Talanta},
volume = {292},
number = {},
pages = {128009},
doi = {10.1016/j.talanta.2025.128009},
pmid = {40139011},
issn = {1873-3573},
abstract = {Accurate detection of core Alzheimer's disease (AD) biomarkers in biofluids is crucial for identifying preclinical AD and predicting disease progression. Phosphorylated tau 181 (p-tau181), a key biomarker, holds promise for early diagnosis. This work presents a sensitive and rapid electrochemical immunosensor (EC-iSensor) based on screen-printed electrodes (SPEs) for p-tau181 quantification. Employing a competitive immunoassay format, the EC-iSensor utilizes biotinylated p-tau181 as a competitor against the target analyte for binding to immobilized capture antibodies. Signal transduction is achieved via horseradish peroxidase (HRP) and tetramethylbenzidine (TMB) substrate. The EC-iSensor exhibits a low detection limit of 1.91 fg/mL and a wide dynamic range spanning 6.97 fg/mL to 100 ng/mL in PBS. Furthermore, successful detection of p-tau181 in blood samples from AD patients demonstrated its practical applicability. This cost-effective SPE-based EC-iSensor offers a simple and highly sensitive platform for p-tau181 detection, presenting potential for point-of-care technologies (POCT) of AD.},
}

@article {pmid40138736,
year = {2025},
author = {Yang, W and Wang, X and Qi, W and Wang, W},
title = {LGFormer: Integrating local and global representations for EEG decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc5a3},
pmid = {40138736},
issn = {1741-2552},
abstract = {OBJECTIVE: Electroencephalography (EEG) decoding is challenging because of its temporal variability and low signal-to-noise ratio, which complicate the extraction of meaningful information from signals. Although convolutional neural networks (CNNs) effectively extract local features from EEG signals, they are constrained by restricted receptive fields. In contrast, transformers excel at capturing global dependencies through self-attention mechanisms but often require extensive training data and computational resources, which limits their efficiency on EEG datasets with limited samples.

APPROACH: In this paper, we propose LGFormer, a hybrid network designed to efficiently learn both local and global representations for EEG decoding. LGFormer employs a deep attention module to extract global information from EEG signals, dynamically adjusting the focus of CNNs. Subsequently, LGFormer incorporates a local-enhanced transformer, combining the strengths of CNNs and transformers to achieve multiscale perception from local to global. Despite integrating multiple advanced techniques, LGFormer maintains a lightweight design and training efficiency.

MAIN RESULTS: LGFormer achieves state-of-the-art performance within 200 training epochs across four public datasets, including motor imagery, cognitive workload, and error-related negativity decoding tasks. Additionally, we propose a novel spatial and temporal attention visualization method, revealing that LGFormer captures discriminative spatial and temporal features, enhancing model interpretability and providing insights into its decision-making process.

SIGNIFICANCE: In summary, LGFormer demonstrates superior performance while maintaining high training efficiency across different tasks, highlighting its potential as a versatile and practical model for EEG decoding.},
}

@article {pmid40136841,
year = {2025},
author = {Mohamed, AK and Aharonson, V},
title = {Single-Trial Electroencephalography Discrimination of Real, Regulated, Isometric Wrist Extension and Wrist Flexion.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {3},
pages = {},
pmid = {40136841},
issn = {2313-7673},
support = {99207 and 117965//National Research Foundation of South Africa/ ; },
abstract = {Improved interpretation of electroencephalography (EEG) associated with the neural control of essential hand movements, including wrist extension (WE) and wrist flexion (WF), could improve the performance of brain-computer interfaces (BCIs). These BCIs could control a prosthetic or orthotic hand to enable motor-impaired individuals to regain the performance of activities of daily living. This study investigated the interpretation of neural signal patterns associated with kinematic differences between real, regulated, isometric WE and WF movements from recorded EEG data. We used 128-channel EEG data recorded from 14 participants performing repetitions of the wrist movements, where the force, speed, and range of motion were regulated. The data were filtered into four frequency bands: delta and theta, mu and beta, low gamma, and high gamma. Within each frequency band, independent component analysis was used to isolate signals originating from seven cortical regions of interest. Features were extracted from these signals using a time-frequency algorithm and classified using Mahalanobis distance clustering. We successfully classified bilateral and unilateral WE and WF movements, with respective accuracies of 90.68% and 69.80%. The results also demonstrated that all frequency bands and regions of interest contained motor-related discriminatory information. Bilateral discrimination relied more on the mu and beta bands, while unilateral discrimination favoured the gamma bands. These results suggest that EEG-based BCIs could benefit from the extraction of features from multiple frequencies and cortical regions.},
}

@article {pmid40136836,
year = {2025},
author = {Rusev, G and Yordanov, S and Nedelcheva, S and Banderov, A and Sauter-Starace, F and Koprinkova-Hristova, P and Kasabov, N},
title = {Decoding Brain Signals in a Neuromorphic Framework for a Personalized Adaptive Control of Human Prosthetics.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {3},
pages = {},
pmid = {40136836},
issn = {2313-7673},
support = {101070891/01.10.2022//European Commission under the HORIZON-EIC action/ ; },
abstract = {Current technological solutions for Brain-machine Interfaces (BMI) achieve reasonable accuracy, but most systems are large in size, power consuming and not auto-adaptive. This work addresses the question whether current neuromorphic technologies could resolve these problems? The paper proposes a novel neuromorphic framework of a BMI system for prosthetics control via decoding Electro Cortico-Graphic (ECoG) brain signals. It includes a three-dimensional spike timing neural network (3D-SNN) for brain signals features extraction and an on-line trainable recurrent reservoir structure (Echo state network (ESN)) for Motor Control Decoding (MCD). A software system, written in Python using NEST Simulator SNN library is described. It is able to adapt continuously in real time in supervised or unsupervised mode. The proposed approach was tested on several experimental data sets acquired from a tetraplegic person. First simulation results are encouraging, showing also the need for a further improvement via multiple hyper-parameters tuning. Its future implementation on a neuromorphic hardware platform that is smaller in size and significantly less power consuming is discussed too.},
}

@article {pmid40136825,
year = {2025},
author = {Li, H and Wang, Y and Fu, P},
title = {A Novel Multi-Dynamic Coupled Neural Mass Model of SSVEP.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {3},
pages = {},
pmid = {40136825},
issn = {2313-7673},
support = {2024JC-YBON-0659//Natural Science Basic Research Program of Shaanxi Province/ ; TC2023JC16//Basic Research Programs of Taicang/ ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) leverage high-speed neural synchronization to visual flicker stimuli for efficient device control. While SSVEP-BCIs minimize user training requirements, their dependence on physical EEG recordings introduces challenges, such as inter-subject variability, signal instability, and experimental complexity. To overcome these limitations, this study proposes a novel neural mass model for SSVEP simulation by integrating frequency response characteristics with dual-region coupling mechanisms. Specific parallel linear transformation functions were designed based on SSVEP frequency responses, and weight coefficient matrices were determined according to the frequency band energy distribution under different visual stimulation frequencies in the pre-recorded SSVEP signals. A coupled neural mass model was constructed by establishing connections between occipital and parietal regions, with parameters optimized through particle swarm optimization to accommodate individual differences and neuronal density variations. Experimental results demonstrate that the model achieved a high-precision simulation of real SSVEP signals across multiple stimulation frequencies (10 Hz, 11 Hz, and 12 Hz), with maximum errors decreasing from 2.2861 to 0.8430 as frequency increased. The effectiveness of the model was further validated through the real-time control of an Arduino car, where simulated SSVEP signals were successfully classified by the advanced FPF-net model and mapped to control commands. This research not only advances our understanding of SSVEP neural mechanisms but also releases the user from the brain-controlled coupling system, thus providing a practical framework for developing more efficient and reliable BCI-based systems.},
}

@article {pmid40134759,
year = {2025},
author = {Sakel, M and Ozolins, CA and Saunders, K and Biswas, R},
title = {A home-based EEG neurofeedback treatment for chronic neuropathic pain-a pilot study.},
journal = {Frontiers in pain research (Lausanne, Switzerland)},
volume = {6},
number = {},
pages = {1479914},
pmid = {40134759},
issn = {2673-561X},
abstract = {OBJECTIVE: This study assessed the effect of an 8-week home-based neurofeedback intervention in chronic neuropathic pain patients.

SUBJECTS/PATIENTS: A cohort of eleven individuals with chronic neuropathic pain receiving treatment within the NHS framework.

METHODS: Participants were trained to operate a home-based neurofeedback system. Each received a portable Axon system for one week of electroencephalogram (EEG) baselines, followed by an 8-week neurofeedback intervention, and subsequent 12 weeks of follow-up EEG baselines. Primary outcome measures included changes in the Brief Pain Inventory and Visual Analogue Pain Scale at post-intervention, and follow-ups compared with the baseline. Secondary outcomes included changes in depression, anxiety, stress, pain catastrophizing, central sensitization, sleep quality, and quality of life. EEG activities were monitored throughout the trial.

RESULTS: Significant improvements were noted in pain scores, with all participants experiencing overall pain reduction. Clinically significant pain improvement (≥30%) was reported by 5 participants (56%). Mood scores showed a significant decrease in depression (p < 0.05), and pain catastrophizing (p < 0.05) scores improved significantly at post-intervention, with continued improvement at the first-month follow-up.

CONCLUSION: The findings indicate that an 8-week home-based neurofeedback intervention improved pain and psychological well-being in this sample of chronic neuropathic pain patients. A randomized controlled trial is required to replicate these results in a larger cohort. Clinical Trial Registration: https://clinicaltrials.gov/study/NCT05464199, identifier: (NCT05464199).},
}

@article {pmid40133571,
year = {2025},
author = {Xu, X and Sha, L and Basang, S and Peng, A and Zhou, X and Liu, Y and Li, Y and Chen, L},
title = {Mortality in patients with epilepsy: a systematic review.},
journal = {Journal of neurology},
volume = {272},
number = {4},
pages = {291},
pmid = {40133571},
issn = {1432-1459},
support = {Grant Recipient//Science and Technology Major Project of Sichuan Province/ ; },
mesh = {Humans ; *Epilepsy/mortality/epidemiology ; Sudden Unexpected Death in Epilepsy/epidemiology ; },
abstract = {BACKGROUND: Epilepsy is linked to a significantly higher risk of death, yet public awareness remains low. This study aims to investigate mortality characteristics, to reduce epilepsy-related deaths and improve prevention strategies.

METHODS: This study systematically reviews mortality data in relevant literature from PubMed and Embase up until June 2024. Data quality is assessed using the Newcastle-Ottawa Scale, and analysis includes trends, regional differences, and the economic impact of premature death. Global Burden of Disease (GBD) data are used to validate trends. In addition, a review of guidelines and expert statements on sudden unexpected death in epilepsy (SUDEP) is included to explore intervention strategies and recommendations.

RESULTS: Annual mortality rates of epilepsy have gradually declined, mainly due to improvements in low-income countries, while high-income regions have experienced an upward trend. Male patients exhibit higher mortality rates than females. Age-based analysis shows that the elderly contributes most to this increase due to chronic conditions such as cardiovascular disease and pneumonia related to epilepsy. This may be a key factor contributing to the increased mortality among epilepsy patients in aging high-income regions. Accidents and suicides are more prevalent in low-income regions. The highest mortality risks occur in the early years post-diagnosis and during prolonged, uncontrolled epilepsy. SUDEP remains a leading cause of death.

CONCLUSION: This study highlights the impact of gender, region, and disease duration on epilepsy mortality. Future research should focus on elderly epilepsy patients mortality characteristics and personalized interventions for SUDEP.},
}

Humans
*Epilepsy/mortality/epidemiology
Sudden Unexpected Death in Epilepsy/epidemiology

@article {pmid40135785,
year = {2025},
author = {Chen, J and Liu, B and Peng, G and Zhou, L and Tan, C and Qin, J and Li, J and Hong, Z and Wu, Y and Lu, M and Cai, F and Huang, Y},
title = {Achieving High-Performance Transcranial Ultrasound Transmission Through Mie and Fano Resonance in Flexible Metamaterials.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2500170},
doi = {10.1002/advs.202500170},
pmid = {40135785},
issn = {2198-3844},
support = {2021YFB3801802//National Key R&D Program of China/ ; 2022YFB3204300//National Key R&D Program of China/ ; 226-2024-00120//Fundamental Research Funds for the Central Universities/ ; 2023R5231//Special Support Plan for High Level Talents in Zhejiang Province/ ; },
abstract = {Transcranial ultrasound holds great potential in medical applications. However, the effective transmission of ultrasound through the skull remains challenging due to the acoustic impedance mismatch, as well as the non-uniform thickness, and the curved surface. To overcome these challenges, this work introduces an innovative Mie-resonance flexible metamaterial (MRFM), which consists of periodically arranged low-speed micropillars embedded within a high-speed flexible substrate. The MRFM generates Mie-resonance, which couples with the skull to form Fano resonance, thereby enhancing ultrasound transmittance through the skull. Simulation results demonstrate that the proposed resonance solution significantly increases transcranial ultrasound transmittance from 33.7% to 75.2% at 0.309 MHz. For the fabrication of the MRFM, porous nickel foam is used as the Mie micropillars, and agarose hydrogel serves as the flexible substrate. Experimental results demonstrate enhanced ultrasound transmittance from 20.6% to 73.3% at 0.33 MHz with the MRFM, which shows good agreement with the simulation results, further validating the effectiveness of the design. The simplicity, tunability, and flexibility of the MRFM represent a significant breakthrough, addressing the limitations of conventional rigid metamaterials. This work lays a solid theoretical and experimental foundation for advancing the clinical application of transcranial ultrasound stimulation and neuromodulation.},
}

@article {pmid40131365,
year = {2025},
author = {Almanna, MA and Elkaim, LM and Alvi, MA and Levett, JJ and Li, B and Mamdani, M and Al-Omran, M and Alotaibi, NM},
title = {Public Perception of the Brain-Computer Interface: Insights from a Decade of Data on X.},
journal = {JMIR formative research},
volume = {},
number = {},
pages = {},
doi = {10.2196/60859},
pmid = {40131365},
issn = {2561-326X},
abstract = {BACKGROUND: Given the recent evolution and achievements in Brain-Computer interface (BCI) technologies, understanding public perception and sentiments towards such novel technologies is important for guiding their communication strategies in marketing and education.

OBJECTIVE: This study aims to explore the public perception of BCI technology by examining posts on X (Twitter), utilizing Natural Language Processing (NLP) methods.

METHODS: A mixed-methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,926 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We utilized the Sentiment.ai tool to infer users' demographics by matching pre-defined attributes in users' profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI.

RESULTS: The analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk's announcement of Neuralink. Sentiment analysis revealed that 59.38% of posts were neutral, 32.75% were positive, and 7.85% were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic = 0.266, tau = 0.266, P<.001). Most posts were objective (77.81%), with a smaller proportion being subjective (22.02%). Biographic analysis showed that the 'Broadcasting' group contributed the most to BCI discussions (30.67%), but the 'Scientific' group, which contributed 27.58% of the discussions, had the highest overall engagement metrics. Emotional analysis identified anticipation (20.56%), trust (17.59%), and fear (13.98%) as the most prominent emotions in BCI discussions. Key topics included Neuralink and Elon Musk, practical applications of BCIs, and the potential for gamification.

CONCLUSIONS: This NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy-making, and communication strategies.},
}

@article {pmid40129720,
year = {2025},
author = {Daly, I and Matran-Fernandez, A and Lebedev, MA and Kübler, A and Valeriani, D},
title = {Editorial: Datasets for brain-computer interface applications, volume II.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1569216},
pmid = {40129720},
issn = {1662-4548},
}

@article {pmid40129499,
year = {2025},
author = {Maltezou-Papastylianou, C and Scherer, R and Paulmann, S},
title = {How do voice acoustics affect the perceived trustworthiness of a speaker? A systematic review.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1495456},
pmid = {40129499},
issn = {1664-1078},
abstract = {Trust is a multidimensional and dynamic social and cognitive construct, considered the glue of society. Gauging someone's perceived trustworthiness is essential for forming and maintaining healthy relationships across various domains. Humans have become adept at inferring such traits from speech for survival and sustainability. This skill has extended to the technological space, giving rise to humanlike voice technologies. The inclination to assign personality traits to these technologies suggests that machines may be processed along similar social and vocal dimensions as human voices. Given the increasing prevalence of voice technology in everyday tasks, this systematic review examines the factors in the psychology of voice acoustics that influence listeners' trustworthiness perception of speakers, be they human or machine. Overall, this systematic review has revealed that voice acoustics impact perceptions of trustworthiness in both humans and machines. Specifically, combining multiple acoustic features through multivariate methods enhances interpretability and yields more balanced findings compared to univariate approaches. Focusing solely on isolated features like pitch often yields inconclusive results when viewed collectively across studies without considering other factors. Crucially, situational, or contextual factors should be utilised for enhanced interpretation as they tend to offer more balanced findings across studies. Moreover, this review has highlighted the significance of cross-examining speaker-listener demographic diversity, such as ethnicity and age groups; yet, the scarcity of such efforts accentuates the need for increased attention in this area. Lastly, future work should involve listeners' own trust predispositions and personality traits with ratings of trustworthiness perceptions.},
}

@article {pmid40128831,
year = {2025},
author = {Liu, XY and Wang, WL and Liu, M and Chen, MY and Pereira, T and Doda, DY and Ke, YF and Wang, SY and Wen, D and Tong, XG and Li, WG and Yang, Y and Han, XD and Sun, YL and Song, X and Hao, CY and Zhang, ZH and Liu, XY and Li, CY and Peng, R and Song, XX and Yasi, A and Pang, MJ and Zhang, K and He, RN and Wu, L and Chen, SG and Chen, WJ and Chao, YG and Hu, CG and Zhang, H and Zhou, M and Wang, K and Liu, PF and Chen, C and Geng, XY and Qin, Y and Gao, DR and Song, EM and Cheng, LL and Chen, X and Ming, D},
title = {Recent applications of EEG-based brain-computer-interface in the medical field.},
journal = {Military Medical Research},
volume = {12},
number = {1},
pages = {14},
pmid = {40128831},
issn = {2054-9369},
support = {2021YFF1200602//The National Key R&D Program of China/ ; 0401260011//The National Science Fund for Excellent Overseas Scholars/ ; c02022088//The National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences/ ; 82472098//National Natural Science Foundation of China (General Program)/ ; 82202798//The National Natural Science Foundation of China/ ; 22YF1404200//The Shanghai Sailing Program/ ; },
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Electroencephalography/methods/trends/instrumentation ; Epilepsy/physiopathology ; },
abstract = {Brain-computer interfaces (BCIs) represent an emerging technology that facilitates direct communication between the brain and external devices. In recent years, numerous review articles have explored various aspects of BCIs, including their fundamental principles, technical advancements, and applications in specific domains. However, these reviews often focus on signal processing, hardware development, or limited applications such as motor rehabilitation or communication. This paper aims to offer a comprehensive review of recent electroencephalogram (EEG)-based BCI applications in the medical field across 8 critical areas, encompassing rehabilitation, daily communication, epilepsy, cerebral resuscitation, sleep, neurodegenerative diseases, anesthesiology, and emotion recognition. Moreover, the current challenges and future trends of BCIs were also discussed, including personal privacy and ethical concerns, network security vulnerabilities, safety issues, and biocompatibility.},
}

Humans
*Brain-Computer Interfaces/trends
*Electroencephalography/methods/trends/instrumentation
Epilepsy/physiopathology

@article {pmid40127544,
year = {2025},
author = {Davis, KC and Wyse-Sookoo, K and Raza, F and Meschede-Krasa, B and Prins, NW and Fisher, L and Brown, EN and Cajigas, I and Ivan, ME and Jagid, JR and Prasad, A},
title = {5-year follow-up of a fully implanted brain-computer interface in a spinal cord injury patient.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc48c},
pmid = {40127544},
issn = {1741-2552},
abstract = {INTRODUCTION: Spinal cord injury (SCI) affects over 250,000 individuals in the US. Brain-computer interfaces (BCIs) may improve quality of life by controlling external devices. Invasive intracortical BCIs have shown promise in clinical trials but degrade in the chronic period and tether patients to acquisition hardware. Alternatively, electrocorticography (ECoG) records data from electrodes on the cortex, and studies evaluating fully implanted BCI-ECoG systems are scarce. Thus, we seek to address this need using a fully implanted ECoG-based BCI that allows for home use in SCI.

METHOD: The patient used a long-term BCI system, initially controlling an FES orthosis in the lab and later using an external mechanical orthosis at home. To evaluate its long-term viability, electrode contact impedance, signal quality, and decoder performance were measured. Signal quality was assessed using signal-to-noise ratio and maximum bandwidth of the signal. Decoder performance was monitored using the area under the receiver operator characteristic curve (AUROC).

RESULTS: The study analyzed data from the patient's home environment over 54 months, revealing that the device was used at home for 38 ± 24 minutes on average daily. After six months, we observed stable event-related desynchronization that aided in determining the onset of motor intention. The decoder's average AUROC across months was 0.959. Importantly, 40 months of the data collected was gather from the subject's home or community environment. The results indicate long-term ECoG recordings were stable for motor-imagery classification and motor control in the environment in a case of an individual with SCI.

CONCLUSION: This study presents the long-term feasibility and viability of an ECoG-based BCI system that persists in the home environment in a case of SCI. Future research should explore larger electrode counts with more participants to confirm this stability. Understanding these trends is crucial for clinical utility and chronic viability in broader patient populations.},
}

@article {pmid40127541,
year = {2025},
author = {Marissens Cueva, V and Bougrain, L and Lotte, F and Rimbert, S},
title = {Reliable predictor of BCI motor imagery performance using median nerve stimulation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc48d},
pmid = {40127541},
issn = {1741-2552},
abstract = {OBJECTIVE: Predicting performance in Brain-Computer Interfaces (BCI) is crucial for enhancing user experience, optimizing training and identifying the most efficient BCI approach for each individual.

APPROACH: This study explores the use of Median Nerve Stimulation (MNS) as a predictor of Motor Imagery (MI)-BCI performance. MNS induces Event Related (De)Synchronization (ERD/ERS) patterns in the brain that are similar to those generated during MI tasks, providing a non-invasive, user-independent, and easy-to-setup method for performance prediction.

MAIN RESULTS: Our proposed predictor, based on the minimum value of the ERD induced by the MNS, not only exhibits a robust correlation with the MI-BCI performance accuracy (rho = -0.71, p < 0.001), but also effectively predicts this performance with a significant correlation (rho = 0.61, mean absolute error = 9.0, p < 0.01). These results demonstrate its validity as a reliable predictor of MI-BCI performance.

SIGNIFICANCE: By systematically analyzing patterns induced by MNS and correlating them with subsequent MI-BCI task performance, we aim to establish a robust predictive method of motor activity to each individual only based on MNS, making it possible, among other things, to passively predict BCI deficiency or proficiency, and to potentially adapt BCI parameters for an efficient BCI experience or BCI-based recovery.},
}