@article {pmid39900901,
year = {2025},
author = {Derosiere, G and Shokur, S and Vassiliadis, P},
title = {Reward signals in the motor cortex: from biology to neurotechnology.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {1307},
pmid = {39900901},
issn = {2041-1723},
mesh = {*Motor Cortex/physiology ; *Reward ; Humans ; *Brain-Computer Interfaces ; Movement/physiology ; Animals ; Learning/physiology ; },
abstract = {Over the past decade, research has shown that the primary motor cortex (M1), the brain's main output for movement, also responds to rewards. These reward signals may shape motor output in its final stages, influencing movement invigoration and motor learning. In this Perspective, we highlight the functional roles of M1 reward signals and propose how they could guide advances in neurotechnologies for movement restoration, specifically brain-computer interfaces and non-invasive brain stimulation. Understanding M1 reward signals may open new avenues for enhancing motor control and rehabilitation.},
}

*Motor Cortex/physiology
*Reward
Humans
*Brain-Computer Interfaces
Movement/physiology
Animals
Learning/physiology

@article {pmid39899980,
year = {2025},
author = {Gusman, JT and Hosman, T and Crawford, R and Singer-Clark, T and Kapitonava, A and Kelemen, J and Hahn, N and Henderson, JM and Hochberg, L and Simeral, J and Vargas-Irwin, C},
title = {Multi-gesture drag-and-drop decoding in a 2D iBCI control task.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adb180},
pmid = {39899980},
issn = {1741-2552},
abstract = {OBJECTIVE: Intracortical brain-computer interfaces (iBCIs) have demonstrated the ability to enable point and click as well as reach and grasp control for people with tetraplegia. However, few studies have investigated iBCIs during long-duration discrete movements that would enable common computer interactions such as "click-and-hold" or "drag-and-drop".

APPROACH: Here, we examined the performance of multi-class and binary (attempt/no-attempt) classification of neural activity in the left precentral gyrus of two BrainGate2 clinical trial participants performing hand gestures for 1, 2, and 4 seconds in duration. We then designed a novel "latch decoder" that utilizes parallel multi-class and binary decoding processes and evaluated its performance on data from isolated sustained gesture attempts and a multi-gesture drag-and-drop task.

MAIN RESULTS: Neural activity during sustained gestures revealed a marked decrease in the discriminability of hand gestures sustained beyond 1 second. Compared to standard direct decoding methods, the latch decoder demonstrated substantial improvement in decoding accuracy for gestures performed independently or in conjunction with simultaneous 2D cursor control.

SIGNIFICANCE: This work highlights the unique neurophysiologic response patterns of sustained gesture attempts in human motor cortex and demonstrates a promising decoding approach that could enable individuals with tetraplegia to intuitively control a wider range of consumer electronics using an iBCI.},
}

@article {pmid39899514,
year = {2025},
author = {Chandravadia, N and Pendekanti, S and Roberts, D and Tran, R and Panchavati, S and Arnold, C and Pouratian, N and Speier, W},
title = {Comparing P300 flashing paradigms in online typing with language models.},
journal = {PloS one},
volume = {20},
number = {2},
pages = {e0303390},
doi = {10.1371/journal.pone.0303390},
pmid = {39899514},
issn = {1932-6203},
mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Brain-Computer Interfaces ; *Language ; Male ; Female ; Adult ; Young Adult ; },
abstract = {The P300 Speller is a brain-computer interface system that allows victims of motor neuron diseases to regain the ability to communicate by typing characters into a computer by thought. Since the system has a relatively slow typing speed, different stimulus presentation paradigms have been proposed designed to allow users to input information faster by reducing the number of required stimuli or increase signal fidelity. This study compares the typing speeds of the Row-Column, Checkerboard, and Combinatorial Paradigms to examine how their performance compares in online and offline settings. When the different flashing patterns were tested in conjunction with other established optimization techniques such as language models and dynamic stopping, they did not make a significant impact on P300 speller performance. This result could indicate that further performance improvements on the system lie beyond optimizing flashing patterns.},
}

Humans
*Event-Related Potentials, P300/physiology
*Brain-Computer Interfaces
*Language
Male
Female
Adult
Young Adult

@article {pmid39898793,
year = {2025},
author = {Perkins, SM and Amematsro, EA and Cunningham, J and Wang, Q and Churchland, MM},
title = {An emerging view of neural geometry in motor cortex supports high-performance decoding.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
doi = {10.7554/eLife.89421},
pmid = {39898793},
issn = {2050-084X},
support = {1R01NS135240/NH/NIH HHS/United States ; SCGB//Simons Foundation/ ; Graduate Fellowship//National Science Foundation/ ; },
mesh = {*Brain-Computer Interfaces ; *Motor Cortex/physiology ; Humans ; Machine Learning ; Neurons/physiology ; Animals ; },
abstract = {Decoders for brain-computer interfaces (BCIs) assume constraints on neural activity, chosen to reflect scientific beliefs while yielding tractable computations. Recent scientific advances suggest that the true constraints on neural activity, especially its geometry, may be quite different from those assumed by most decoders. We designed a decoder, MINT, to embrace statistical constraints that are potentially more appropriate. If those constraints are accurate, MINT should outperform standard methods that explicitly make different assumptions. Additionally, MINT should be competitive with expressive machine learning methods that can implicitly learn constraints from data. MINT performed well across tasks, suggesting its assumptions are well-matched to the data. MINT outperformed other interpretable methods in every comparison we made. MINT outperformed expressive machine learning methods in 37 of 42 comparisons. MINT's computations are simple, scale favorably with increasing neuron counts, and yield interpretable quantities such as data likelihoods. MINT's performance and simplicity suggest it may be a strong candidate for many BCI applications.},
}

*Brain-Computer Interfaces
*Motor Cortex/physiology
Humans
Machine Learning
Neurons/physiology
Animals

@article {pmid39896750,
year = {2025},
author = {Angulo, IN and Iáñez, E and Ubeda, A},
title = {Editorial: Recent applications of noninvasive physiological signals and artificial intelligence.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1543103},
pmid = {39896750},
issn = {1662-5196},
}

@article {pmid39896041,
year = {2025},
author = {Sarikaya, MA and Ince, G},
title = {Improved BCI calibration in multimodal emotion recognition using heterogeneous adversarial transfer learning.},
journal = {PeerJ. Computer science},
volume = {11},
number = {},
pages = {e2649},
pmid = {39896041},
issn = {2376-5992},
abstract = {The use of brain-computer interface (BCI) technology to identify emotional states has gained significant interest, especially with the rise of virtual reality (VR) applications. However, the extensive calibration required for precise emotion recognition models presents a significant challenge, particularly for sensitive groups such as children, elderly, and patients. This study presents a novel approach that utilizes heterogeneous adversarial transfer learning (HATL) to synthesize electroencephalography (EEG) data from various other signal modalities, reducing the need for lengthy calibration phases. We benchmark the efficacy of three generative adversarial network (GAN) architectures, such as conditional GAN (CGAN), conditional Wasserstein GAN (CWGAN), and CWGAN with gradient penalty (CWGAN-GP) within this framework. The proposed framework is rigorously tested on two conventional open sourced datasets, SEED-V and DEAP. Additionally, the framework was applied to an immersive three-dimensional (3D) dataset named GraffitiVR, which we collected to capture the emotional and behavioral reactions of individuals experiencing urban graffiti in a VR environment. This expanded application provides insights into emotion recognition frameworks in VR settings, providing a wider range of contexts for assessing our methodology. When the accuracy of emotion recognition classifiers trained with CWGAN-GP-generated EEG data combined with non-EEG sensory data was compared against those trained using a combination of real EEG and non-EEG sensory data, the accuracy ratios were 93% on the SEED-V dataset, 99% on the DEAP dataset, and 97% on the GraffitiVR dataset. Moreover, in the GraffitiVR dataset, using CWGAN-GP-generated EEG data with non-EEG sensory data for emotion recognition models resulted in up to a 30% reduction in calibration time compared to classifiers trained on real EEG data with non-EEG sensory data. These results underscore the robustness and versatility of the proposed approach, significantly enhancing emotion recognition processes across a variety of environmental settings.},
}

@article {pmid39890467,
year = {2025},
author = {Ouchi, T and Scholl, LR and Rajeswaran, P and Canfield, RA and Smith, LI and Orsborn, AL},
title = {Mapping eye, arm, and reward information in frontal motor cortices using electrocorticography in non-human primates.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.1536-24.2025},
pmid = {39890467},
issn = {1529-2401},
abstract = {Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms, and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (µECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over primary motor cortex, premotor cortex, frontal eye field, and dorsolateral pre-frontal cortex. Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. Although eye and arm movement temporally overlapped, phase clustering analyses enabled us to resolve differences in eye and arm information across brain regions. This analysis revealed that eye and arm information spatially overlapped in motor cortex, which we further confirmed by demonstrating that arm movement decoding performance from motor cortex activity was impacted by task-irrelevant eye movements. Phase clustering analyses also identified reward-related activity in the pre-frontal and premotor cortex. Our results demonstrate µECoG's strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces.Significance statement Picking up your coffee mug requires coordinating movements of your eyes and hand and processing the outcomes of those movements. Mapping how neural activity relates to different functions helps us understand how the brain performs these computations. Many mapping techniques have limited spatial or temporal resolution, restricting our ability to dissect computations that overlap closely in space and time. We used micro-electrocorticography recordings to map neural activity across multiple cortical areas while monkeys made goal-directed reaches. These measurements revealed high spatial and temporal resolution maps of neural activity related to eye, arm, and reward information processing. These maps reveal overlapping neural computations underlying movement and open opportunities to use eye and reward information to improve therapies to restore motor function.},
}

@article {pmid39889306,
year = {2025},
author = {Li, D and Huang, Y and Luo, R and Zhao, L and Xiao, X and Wang, K and Yi, W and Xu, M and Ming, D},
title = {Enhancing detection of SSVEPs using discriminant compacted network.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adb0f2},
pmid = {39889306},
issn = {1741-2552},
abstract = {UNLABELLED: Abstract-Objective. Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have gained significant attention due to their simplicity, high signal to noise ratio (SNR) and high information transfer rates (ITRs). Currently, accurate detection is a critical issue for enhancing the performance of SSVEP-BCI systems.

APPROACH: This study proposed a novel decoding method called Discriminant Compacted Network (Dis-ComNet), which exploited the advantages of both spatial filtering and deep learning. Specifically, this study enhanced SSVEP features using Global template alignment (GTA) and Discriminant Spatial Pattern (DSP), and then designed a Compacted Temporal-Spatio module (CTSM) to extract finer features. The proposed method was evaluated on a self-collected high-frequency dataset, a public Benchmark dataset and a public wearable dataset.

MAIN RESULTS: The results showed that Dis-ComNet significantly outperformed state-of-the-art spatial filtering methods, deep learning methods, and other fusion methods. Remarkably, Dis-ComNet improved the classification accuracy by 3.9%, 3.5%, 3.2%, 13.3%, 17.4%, 37.5%, and 2.5% when comparing with eTRCA, eTRCA-R, TDCA, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively in the high-frequency dataset. The achieved results were 4.7%, 4.6%, 23.6%, 52.5%, 31.7%, and 7.0% higher than those of eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net, respectively, and were comparable to those of TDCA in Benchmark dataset.The accuracy of Dis-ComNet in the wearable dataset was 9.5%, 7.1%, 36.1%, 26.3%, 15.7% and 4.7% higher than eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively, and comparable to TDCA. Besides, our model achieved the ITRs up to 126.0 bits/min, 236.4 bits/min and 103.6 bits/min in the high-frequency, Benchmark and the wearable datasets respectively.

SIGNIFICANCE: This study develops an effective model for the detection of SSVEPs, facilitating the development of high accuracy SSVEP-BCI systems.},
}

@article {pmid39887443,
year = {2025},
author = {Li, Z and Liang, C and He, Q and Feiweier, T and Hsu, YC and Li, J and Bai, R},
title = {Comparison of water exchange measurements between filter-exchange imaging and diffusion time-dependent kurtosis imaging in the human brain.},
journal = {Magnetic resonance in medicine},
volume = {},
number = {},
pages = {},
doi = {10.1002/mrm.30454},
pmid = {39887443},
issn = {1522-2594},
support = {82111530201//National Natural Science Foundation of China (NSFC)/ ; 82222032//National Natural Science Foundation of China (NSFC)/ ; 82172050//National Natural Science Foundation of China (NSFC)/ ; 2022ZD0206000//the STI2030-Major Projects of China/ ; },
abstract = {PURPOSE: Filter-exchange imaging (FEXI) and diffusion time (t)-dependent kurtosis imaging (DKI(t)) are two diffusion-based methods that have been proposed for in vivo measurements of water exchange rates. Few studies have directly compared these methods. We aimed to investigate whether FEXI and DKI(t) yield comparable water exchange measurements in the human brain in vivo.

METHODS: Eight healthy volunteers underwent multiple-direction FEXI and DKI(t) acquisitions on a 3T scanner. We performed region of interest (ROI) analysis to determine correlations between FEXI-derived apparent exchange rate (AXR) and DKI(t)-derived reciprocal of exchange time (1 / τ ex $$ 1/{\tau}_
{ex}
$$).

RESULTS: In both white matter (WM) and gray matter (GM), DKI(t) revealed substantial diffusion-time dependence of diffusivity and kurtosis. However, at t ≥ 100 ms, the diffusivity showed weak time dependence. In WM, this time dependence may be due to water exchange between myelin water and "free" water with different T1 values, although other factors, such as remaining restrictive effects from microstructural barriers, cannot be excluded. We found a significant correlation between DKI(t)-derived 1 / τ ex $$ 1/{\tau}_
{ex}
$$ and FEXI-derived AXR in the axial direction within WM. No such correlation was present in GM, although both values showed similar ranges.

CONCLUSION: These results suggest that FEXI and DKI(t) could be sensitive to the same water exchange process only when the diffusion time in DKI(t) is sufficiently long, and only in WM. In both GM and WM, the restrictive effect of microstructure is non-negligible, especially at short diffusion times (<100 ms).},
}

@article {pmid39886803,
year = {2025},
author = {Ali, HS and Ismail, AI and El-Rabaie, EM and Abd El-Samie, FE},
title = {Diagonal loading common spatial patterns with Pearson correlation coefficient based feature selection for efficient motor imagery classification.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/10255842.2025.2457122},
pmid = {39886803},
issn = {1476-8259},
abstract = {The conversion of a person's intentions into device commands through the use of brain-computer interface (BCI) is a feasible communication method for individuals with nervous system disorders. While common spatial pattern (CSP) is commonly used for feature extraction in BCIs, it has limitations. It is known for its susceptibility to noise and tendency to overfit. Moreover, high-dimensional, and irrelevant features can make it harder for a classifier to learn effectively. To address these challenges, exploring potential solutions is crucial. This paper introduces Regularized CSP with diagonal loading (DL-CSP) and Pearson correlation coefficient (PCC) based feature selection to extract the most discriminative motor imagery EEG (MI-EEG) features. Three classifiers in an ensemble are considered; bidirectional long short-term memory (Bi-LSTM), K-nearest neighbors (KNN) and naïve Bayes (NB). Decision level fusion through majority voting is exploited to leverage diverse perspectives and increase the overall system robustness. Experiments have been implemented using three publicly available datasets for MI classification; BCI competition IV-IIA (data-1), BCI Competition III-IVa (data-2), and a stroke patients' dataset (data-3). The accuracy achieved, according to the results, is 86.96% for data-1, 91.70% for data-2, and 85.75% for data-3. These percentages outperform the accuracy achieved by any state-of-the-art techniques.},
}

@article {pmid39886225,
year = {2025},
author = {Huang, J and Zhang, L and Shao, N and Zhang, Y and Xu, Y and Zhou, Y and Zhang, D and Zhang, J and Lee, HJ},
title = {Lipid Metabolic Heterogeneity during Early Embryogenesis Revealed by Hyper-3D Stimulated Raman Imaging.},
journal = {Chemical & biomedical imaging},
volume = {3},
number = {1},
pages = {15-24},
pmid = {39886225},
issn = {2832-3637},
abstract = {Studying embryogenesis is fundamental to understanding developmental biology and reproductive medicine. Its process requires precise spatiotemporal regulations in which lipid metabolism plays a crucial role. However, the spatial dynamics of lipid species at the subcellular level remains obscure due to technical limitations. To address this challenge, we developed a hyperspectral 3D imaging and analysis method based on stimulated Raman scattering microscopy (hyper-3D SRS) to quantitatively assess lipid profiles in individual embryos through submicrometer resolution (x-y), 3D optical sectioning (z), and chemical bond-selective (Ω) imaging. Using hyper-3D SRS, individual lipid droplets (LDs) in single cells were identified and quantified. Our findings revealed that the LD profiles within a single embryo are not uniform, even as early as the 2-cell stage. Notably, we also discovered a dynamic relationship between the LD size and unsaturation degree as embryos develop, indicating diverse lipid metabolism during early development. Furthermore, abnormal LDs were observed in oocytes of a progeria mouse model, suggesting that LDs could serve as a potential biomarker for assessing oocyte/embryo quality. Overall, our results highlight the potential of hyper-3D SRS as a noninvasive method for studying lipid content, composition, and subcellular distribution in embryos. This technique provides valuable insights into lipid metabolism during embryonic development and has the potential for clinical applications in evaluating oocyte/embryo quality.},
}

@article {pmid39886177,
year = {2024},
author = {Murthy, V and Kashid, SR and Pal, M and Vadassery, A and Maitre, P and Arora, A and Singh, P and Joshi, A and Bakshi, G and Prakash, G},
title = {Prospective comparative study of quality of life in patients with bladder cancer undergoing cystectomy with ileal conduit or bladder preservation.},
journal = {BMJ oncology},
volume = {3},
number = {1},
pages = {e000435},
pmid = {39886177},
issn = {2752-7948},
abstract = {OBJECTIVE: To compare health-related quality of life (HRQOL) in patients undergoing radical cystectomy with ileal conduit (RC) or bladder preservation (BP) with (chemo)radiotherapy for bladder cancer.

METHODS AND ANALYSIS: Patients with bladder cancer, stage cT1-T4, cN0-N1, M0 with a minimum follow-up of 6 months from curative treatment (RC or BP) and without disease were eligible for inclusion. Two HRQOL instruments were administered: Bladder Cancer Index (BCI) for bladder cancer-specific HRQOL and European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). The mean QOL scores across various domains and specific questions were compared between the two treatment groups using an independent t-test.

RESULTS: Out of the 104 enrolled patients, 56 underwent RC and 48 opted for BP, with 95 (91.3%) being male. The median time from treatment completion to QOL assessment was 22 months (IQR 10-56). The median age for the entire cohort was 62 years (IQR 55-68), 65.5 years (IQR 55-71) in BP and 59.5 years (IQR 55-66) in RC. There was no significant difference in mean BCI urinary and bowel scores in function or bother subdomains between the two groups. Overall, BCI sexual scores were low in both groups but significantly better after BP (BPmean 56.9, RCmean 41.5, p=0.01). Mean scores for sexual function subdomain were BPmean 38.4 and RCmean 25 (p=0.07) and for sexual bother were BPmean 81 RCmean 62 (p=0.02). The EORTC QLQ-C30 outcomes did not show a significant difference in either group.

CONCLUSION: The BP group showed significantly better results in the sexual domain compared with the RC group. Both groups had good QOL in terms of urinary and bowel functions.},
}

@article {pmid39883960,
year = {2025},
author = {Tortolani, AF and Kunigk, NG and Sobinov, AR and Boninger, ML and Bensmaia, SJ and Collinger, J and Hatsopoulos, N and Downey, J},
title = {How different immersive environments affect intracortical brain computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adb078},
pmid = {39883960},
issn = {1741-2552},
abstract = {OBJECTIVE: As brain-computer interface (BCI) research advances, many new applications are being developed. Tasks can be performed in different virtual environments, and whether a BCI user can switch environments seamlessly will influence the ultimate utility of a clinical device. Approach: Here we investigate the importance of the immersiveness of the virtual environment used to train BCI decoders on the resulting decoder and its generalizability between environments. Two participants who had intracortical electrodes implanted in their precentral gyrus used a BCI to control a virtual arm, both viewed immersively through virtual reality goggles and at a distance on a flat television monitor. Main Results: Each participant performed better with a decoder trained and tested in the environment they had used the most prior to the study, one for each environment type. The neural tuning to the desired movement was minimally influenced by the immersiveness of the environment. Finally, in further testing with one of the participants, we found that decoders trained in one environment generalized well to the other environment, but the order in which the environments were experienced within a session mattered.

SIGNIFICANCE: Overall, experience with an environment was more influential on performance than the immersiveness of the environment, but BCI performance generalized well after accounting for experience. .},
}

@article {pmid39886316,
year = {2022},
author = {Ju, J and Feleke, AG and Luo, L and Fan, X},
title = {Recognition of Drivers' Hard and Soft Braking Intentions Based on Hybrid Brain-Computer Interfaces.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {2022},
number = {},
pages = {9847652},
pmid = {39886316},
issn = {2692-7632},
abstract = {In this paper, we propose simultaneous and sequential hybrid brain-computer interfaces (hBCIs) that incorporate electroencephalography (EEG) and electromyography (EMG) signals to classify drivers' hard braking, soft braking, and normal driving intentions to better assist driving for the first time. The simultaneous hBCIs adopt a feature-level fusion strategy (hBCI-FL) and classifier-level fusion strategies (hBCIs-CL). The sequential hBCIs include the hBCI-SE1, where EEG signals are prioritized to detect hard braking, and hBCI-SE2, where EMG signals are prioritized to detect hard braking. Experimental results show that the proposed hBCI-SE1 with spectral features and the one-vs-rest classification strategy performs best with an average system accuracy of 96.37% among hBCIs. This work is valuable for developing human-centric intelligent assistant driving systems to improve driving safety and driving comfort and promote the application of BCIs.},
}

@article {pmid39883957,
year = {2025},
author = {Lutes, N and Nadendla, VSS and Krishnamurthy, K},
title = {Few-shot transfer learning for individualized braking intent detection on neuromorphic hardware.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adb079},
pmid = {39883957},
issn = {1741-2552},
abstract = {This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. The efficacy of the method is studied on an advanced driver assist system related task of predicting braking intention. \emph{Approach}:
Data are collected from participants operating an NVIDIA JetBot on a testbed simulating urban streets for three different scenarios. Participants receive a braking indicator in the form of: 1) an audio countdown in a nominal baseline, stress-free environment; 2) an audio countdown in an environment with added elements of physical fatigue and active cognitive distraction; 3) a visual cue given through stoplights in a stress-free environment. These datasets are then used to develop individual-level models from group-level models using a few-shot transfer learning method, which involves: 1) creating a group-level model by training a CNN on group-level data followed by quantization and recouping any performance loss using quantization-aware retraining; 2) converting the CNN to be compatible with Akida AKD1000 processor; and 3) training the final decision layer on individual-level data subsets to create individual-customized models using an online Akida edge-learning algorithm. Main Results: Efficacy of the above methodology to develop individual-specific braking intention predictive models by rapidly adapting the group-level model in as few as three training epochs while achieving at least 90\% accuracy, true positive rate and true negative rate is presented. Further, results show the energy-efficiency of the neuromorphic hardware through a power reduction of over 97\% with only a $1.3\times$ increase in latency when using the Akida AKD1000 processor for network inference compared to an Intel Xeon central processing unit. Similar results were obtained in a subsequent ablation study using a subset of five out of 19 channels. Significance: Especially relevant to real-time applications, this work presents an energy-efficient, few-shot transfer learning method that is implemented on a neuromorphic processor capable of training a CSNN as new data becomes available, operating conditions change, or to customize group-level models to yield personalized models unique to each individual.},
}

@article {pmid39883372,
year = {2025},
author = {Xiong, H and Yan, Y and Chen, Y and Liu, J},
title = {Graph convolution network-based eeg signal analysis: a review.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {39883372},
issn = {1741-0444},
abstract = {With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The applications and major achievements of Graph Convolution Network (GCN) techniques in EEG signal analysis are reviewed in this paper. Through an exhaustive search of the published literature, a module-by-module discussion is carried out for the first time to address the current research status of GCN. An exhaustive classification of methods and a systematic analysis of key modules, such as brain map construction, node feature extraction, and GCN architecture design, are presented. In addition, we pay special attention to several key research issues related to GCN. This review enhances the understanding of the future potential of GCN in the field of EEG signal analysis. At the same time, several valuable development directions are sorted out for researchers in related fields, such as analysing the applicability of different GCN layers, building task-oriented GCN models, and improving adaptation to limited data.},
}

@article {pmid39882377,
year = {2024},
author = {Ma, Y and Huang, J and Liu, C and Shi, M},
title = {A portable EEG signal acquisition system and a limited-electrode channel classification network for SSVEP.},
journal = {Frontiers in neurorobotics},
volume = {18},
number = {},
pages = {1502560},
pmid = {39882377},
issn = {1662-5218},
abstract = {Brain-computer interfaces (BCIs) have garnered significant research attention, yet their complexity has hindered widespread adoption in daily life. Most current electroencephalography (EEG) systems rely on wet electrodes and numerous electrodes to enhance signal quality, making them impractical for everyday use. Portable and wearable devices offer a promising solution, but the limited number of electrodes in specific regions can lead to missing channels and reduced BCI performance. To overcome these challenges and enable better integration of BCI systems with external devices, this study developed an EEG signal acquisition platform (Gaitech BCI) based on the Robot Operating System (ROS) using a 10-channel dry electrode EEG device. Additionally, a multi-scale channel attention selection network based on the Squeeze-and-Excitation (SE) module (SEMSCS) is proposed to improve the classification performance of portable BCI devices with limited channels. Steady-state visual evoked potential (SSVEP) data were collected using the developed BCI system to evaluate both the system and network performance. Offline data from ten subjects were analyzed using within-subject and cross-subject experiments, along with ablation studies. The results demonstrated that the SEMSCS model achieved better classification performance than the comparative reference model, even with a limited number of channels. Additionally, the implementation of online experiments offers a rational solution for controlling external devices via BCI.},
}

@article {pmid39880895,
year = {2025},
author = {Haghi, B and Aflalo, T and Kellis, S and Guan, C and Gamez de Leon, JA and Huang, AY and Pouratian, N and Andersen, RA and Emami, A},
title = {Author Correction: Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41551-025-01355-2},
pmid = {39880895},
issn = {2157-846X},
}

@article {pmid39881836,
year = {2024},
author = {Butorova, AS and Koryukin, EA and Khomenko, NM and Sergeev, AP},
title = {Assessment of Accuracy of Spatial Object Localization by Means of Mono and Stereo Modes of Visual-to-Auditory Sensory Substitution in People with Visual Impairments (a Pilot Study).},
journal = {Sovremennye tekhnologii v meditsine},
volume = {16},
number = {4},
pages = {29-36},
pmid = {39881836},
issn = {2309-995X},
mesh = {Humans ; Pilot Projects ; Adult ; Male ; Female ; *Vision Disorders/physiopathology ; Persons with Visual Disabilities ; },
abstract = {UNLABELLED: The aim of the study is to assess the accuracy of spatial object localization in mono and stereo modes of visual-to-auditory sensory substitution by means of the developed system tested on persons with normal or corrected-to-normal vision.

MATERIALS AND METHODS: A prototype of a visual-to-auditory sensory substitution device based on a video camera with two lenses was prepared. Software to convert the signal from a video camera into an audio signal in mono and stereo modes was developed.To assess the developed system, an experimental study with 30 blindfolded sighted participants was conducted. 15 persons were tested in mono mode, 15 - in stereo mode. All persons were trained to use the visual-to-auditory sensory substitution system. During the experiment, participants were to locate a white plastic cube with dimensions of 4×4×4 cm[3] on a working surface. The researcher placed the cube in one of 20 positions on the working surface in a pseudo-random order.

RESULTS: To assess the accuracy of the cube localization, deviations along the X- and Y-axes and absolute deviations were calculated. The general dynamics of localization accuracy was positive both in mono and stereo modes. Absolute deviation and X-axis deviation were significantly higher in stereo mode; there was no significant difference in Y-axis deviation between modes. On average, participants tended to underestimate the distance to the cube when it was on the left, right, or far side of the working surface, and overestimate the distance to the cube when it was on the near side of the working surface.

CONCLUSION: Tests demonstrated that the accuracy of object localization in stereo mode can be improved by increasing the time for training the participants and by showing them more presentations. The results of the study can be used to develop assistive techniques for people with visual impairments, to manufacture medical equipment, and create brain-computer interfaces.},
}

Humans
Pilot Projects
Adult
Male
Female
*Vision Disorders/physiopathology
Persons with Visual Disabilities

@article {pmid39880008,
year = {2025},
author = {Loss, J and Betsch, C and Ellermann, C and Ewert, B and Grill, E and Jenny, MA and Jordan, S and Kubiak, T and Mata, J and Rebitschek, FG and Rehfuess, EA and Sniehotta, F},
title = {[Not Available].},
journal = {Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany))},
volume = {},
number = {},
pages = {},
doi = {10.1055/a-2528-8276},
pmid = {39880008},
issn = {1439-4421},
}

@article {pmid39878111,
year = {2025},
author = {Badr, Y and AlSawaftah, N and Husseini, G},
title = {User-Centered Design of Neuroprosthetics: Advancements and Limitations.},
journal = {CNS & neurological disorders drug targets},
volume = {},
number = {},
pages = {},
doi = {10.2174/0118715273335487250102093150},
pmid = {39878111},
issn = {1996-3181},
abstract = {Neurological conditions resulting from severe spinal cord injuries, brain injuries, and other traumatic incidents often lead to the loss of essential bodily functions, including sensory and motor capabilities. Traditional prosthetic devices, though standard, have limitations in delivering the required dexterity and functionality. The advent of neuroprosthetics marks a paradigm shift, aiming to bridge the gap between prosthetic devices and the human nervous system. This review paper explores the evolution of neuroprosthetics, categorizing devices into sensory and motor neuroprosthetics and emphasizing their significance in addressing specific challenges. The discussion section delves into long-term challenges in clinical practice, encompassing device durability, ethical considerations, and issues of accessibility and affordability. Furthermore, the paper proposes potential solutions with a specific focus on enhancing sensory experiences and the importance of user-friendly interfaces. In conclusion, this paper offers a comprehensive overview of the current state of neuroprosthetics, outlining future research and development directions to guide advancements in the field.},
}

@article {pmid39875198,
year = {2025},
author = {Chen, S and Jiang, D and Li, M and Xuan, X and Li, H},
title = {Brain-Computer Interface and Electrochemical Sensor Based on Boron-Nitrogen Co-Doped Graphene-Diamond Microelectrode for EEG and Dopamine Detection.},
journal = {ACS sensors},
volume = {},
number = {},
pages = {},
doi = {10.1021/acssensors.4c02461},
pmid = {39875198},
issn = {2379-3694},
abstract = {The simultaneous detection of electroencephalography (EEG) signals and neurotransmitter levels plays an important role as biomarkers for the assessment and monitoring of emotions and cognition. This paper describes the development of boron and nitrogen codoped graphene-diamond (BNGrD) microelectrodes with a diameter of only 200 μm for sensing EEG signals and dopamine (DA) levels, which have been developed for the first time. The optimized BNGrD microelectrode responded sensitively to both EEG and DA signals, with a signal-to-noise ratio of 9 dB for spontaneous EEG signals and a limit of detection as low as 124 nM for DA. Furthermore, the BNGrD microelectrodes demonstrate excellent repeatability, reproducibility, and stability for the detection of EEG and dopamine. These results indicate that the BNGrD microelectrode creates suitable conditions for establishing a correlation between the EEG signals and neurotransmitters. A flexible printed circuit board with BNGrD microelectrodes for an eight-channel EEG headband, portable EEG collector, and light stimulation glasses are designed. The self-designed EEG collector adopts a split design strategy of digital and analog signal modules and uses miniaturized impedance-matched BNGrD microelectrodes, which effectively reduce the noise of the electrophysiological signals. The BNGrD microelectrode-based portable EEG/electrochemical analysis system detects EEG signals and DA levels in a noninvasive and minimally invasive manner and has application prospects in remote online diagnosis and treatment of patients with emotional and cognition-related diseases.},
}

@article {pmid39875073,
year = {2025},
author = {Géraudie, A and De Rossi, P and Canney, M and Carpentier, A and Delatour, B},
title = {Effects of blood-brain barrier opening using ultrasound on tauopathies: A systematic review.},
journal = {Journal of controlled release : official journal of the Controlled Release Society},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jconrel.2025.01.056},
pmid = {39875073},
issn = {1873-4995},
abstract = {UNLABELLED: Blood-brain barrier opening with ultrasound can potentiate drug efficacy in the treatment of brain pathologies and also provides therapeutic effects on its own. It is an innovative tool to transiently, repeatedly and safely open the barrier, with studies showing beneficial effects in both preclinical models for Alzheimer's disease and recent clinical studies. The first preclinical and clinical work has mainly shown a decrease in amyloid burden in mice models and in patients. However, Alzheimer's disease pathology also encompasses tauopathy, which is closely related to cognitive decline, making it a crucial therapeutic target. The effects of blood-brain barrier opening with ultrasound have been rarely assessed on tau and are still unclear.

METHODS: This systematic review, conducted through searches using Pubmed, Embase, Web of Science and Cochrane Central databases, extracted results of 15 studies reporting effects of blood-brain barrier opening using ultrasound on tau proteins.

RESULTS: This review of the literature indicates that blood-brain barrier opening using ultrasound can decrease the extent of the tau pathology or potentialize the effect of a therapeutic drug. However, selected studies report paradoxically that blood-brain barrier opening can increase tau pathology burden and induce brain damage.

DISCUSSION: Apparent discrepancies between reports could originate from the variability in protocols or analytical methods that may impact the effects of blood-brain barrier opening with ultrasound on tauopathies, glial populations, tissue integrity and functional outcomes.

CONCLUSION: This calls for a better standardization effort combined with improved methodologies allowing between-studies comparisons, and for further understanding of the effects of blood-brain barrier opening on tau pathology as an essential prerequisite before translation to clinic.},
}

@article {pmid39874948,
year = {2025},
author = {Tian, F and Liu, Y and Chen, M and Schriver, KE and Roe, AW},
title = {Selective activation of mesoscale functional circuits via multichannel infrared stimulation of cortical columns in ultra-high-field 7T MRI.},
journal = {Cell reports methods},
volume = {5},
number = {1},
pages = {100961},
doi = {10.1016/j.crmeth.2024.100961},
pmid = {39874948},
issn = {2667-2375},
mesh = {*Magnetic Resonance Imaging/methods ; *Visual Cortex/physiology/diagnostic imaging ; Humans ; Animals ; Photic Stimulation/methods ; Cats ; Infrared Rays ; Brain Mapping/methods ; },
abstract = {To restore vision in the blind, advances in visual cortical prosthetics (VCPs) have offered high-channel-count electrical interfaces. Here, we design a 100-fiber optical bundle interface apposed to known feature-specific (color, shape, motion, and depth) functional columns that populate the visual cortex in humans, primates, and cats. Based on a non-viral optical stimulation method (INS, infrared neural stimulation; 1,875 nm), it can deliver dynamic patterns of stimulation, is non-penetrating and non-damaging to tissue, and is movable and removable. In addition, its magnetic resonance (MR) compatibility (INS-fMRI) permits systematic mapping of brain-wide circuits. In the MRI, we illustrate (1) the single-point activation of functionally specific networks, (2) shifting cortical networks activated via shifting points of stimulation, and (3) "moving dot" stimulation-evoked activation of higher-order motion-selective areas. We suggest that, by mimicking patterns of columnar activation normally activated by visual stimuli, a columnar VCP opens doors for the planned activation of feature-specific circuits and their associated visual percepts.},
}

*Magnetic Resonance Imaging/methods
*Visual Cortex/physiology/diagnostic imaging
Humans
Animals
Photic Stimulation/methods
Cats
Infrared Rays
Brain Mapping/methods

@article {pmid39874664,
year = {2025},
author = {Qin, Y and Li, B and Wang, W and Shi, X and Peng, C and Wang, X and Wang, H},
title = {ECA-FusionNet: A hybrid EEG-fNIRS signals network for MI classification.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adaf58},
pmid = {39874664},
issn = {1741-2552},
abstract = {OBJECTIVE: Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows users to control external devices by imagining movements rather than actually performing actions. This property holds important promise for clinical applications, especially in areas such as stroke rehabilitation. Electroencephalogram (EEG) signals and functional near-infrared spectroscopy (fNIRS) signals are two of the more popular neuroimaging techniques for obtaining MI signals from the brain. However, the performance of MI-based unimodal classification methods is low due to the limitations of EEG or fNIRS.

APPROACH: In this paper, we propose a new multimodal fusion classification method capable of combining the potential complementary advantages of EEG and fNIRS. First, we propose a feature extraction network capable of extracting spatio-temporal features from EEG-based and fNIRS-based MI signals. Then, we successively fused the EEG and fNIRS at the feature-level and the decision-level to improve the adaptability and robustness of the model.

MAIN RESULTS: We validate the performance of ECA-FusionNet on a publicly available EEG-fNIRS dataset. The results show that ECA-FusionNet outperforms unimodal classification methods, as well as existing fusion classification methods, in terms of classification accuracy for MI.

SIGNIFICANCE: ECA-FusionNet may provide a useful reference for the field of multimodal fusion classification.},
}

@article {pmid39874652,
year = {2025},
author = {Ziebell, P and Modde, A and Roland, E and Eidel, M and Vansteensel, MJ and Mrachacz-Kersting, N and Vaughan, TM and Kubler, A},
title = {Designing an online BCI forum: insights from researchers and end-users.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adaf57},
pmid = {39874652},
issn = {1741-2552},
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) can support non-muscular communication and device control for severely paralyzed people. However, efforts that directly involve potential or actual end-users and address their individual needs are scarce, demonstrating a translational gap. An online BCI forum supported by the BCI Society could initiate and sustainably strengthen interactions between BCI researchers and end-users to bridge this gap.

APPROACH: We interviewed six severely paralyzed individuals and surveyed 121 BCI researchers to capture their opinions and wishes concerning an online BCI forum. Data were analyzed with a mixed-method quantitative and qualitative content analysis.

MAIN RESULTS: All end-users and most researchers (83%) reported an interest in participating in an online BCI forum. Rating questions and open comments to identify design aspects included what should be featured most prominently, how people would get engaged in the online BCI forum, and which pitfalls should be considered.

SIGNIFICANCE: Responses support establishing an online BCI forum to serve as a meaningful resource for the entire BCI community.},
}

@article {pmid39874276,
year = {2025},
author = {Longo, L and Reilly, RB},
title = {onEEGwaveLAD: A fully automated online EEG wavelet-based learning adaptive denoiser for artefacts identification and mitigation.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0313076},
doi = {10.1371/journal.pone.0313076},
pmid = {39874276},
issn = {1932-6203},
mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; Wavelet Analysis ; Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Signal-To-Noise Ratio ; Male ; Brain/physiology ; Adult ; Female ; },
abstract = {Electroencephalographic signals are obtained by amplifying and recording the brain's spontaneous biological potential using electrodes positioned on the scalp. While proven to help find changes in brain activity with a high temporal resolution, such signals are contaminated by non-stationary and frequent artefacts. A plethora of noise reduction techniques have been developed, achieving remarkable performance. However, they often require multi-channel information and additional reference signals, are not fully automated, require human intervention and are mostly offline. With the popularity of Brain-Computer Interfaces and the application of Electroencephalography in daily activities and other ecological settings, there is an increasing need for robust, online, near real-time denoising techniques, without additional reference signals, that is fully automated and does not require human supervision nor multi-channel information. This research contributes to the body of knowledge by introducing onEEGwaveLAD, a novel, fully automated, ONline, EEG wavelet-based Learning Adaptive Denoiser pipeline for artefact identification and reduction. It is a specific framework that can be instantiated for various types of artefacts paving the path towards real-time denoising. As the first of its kind, it is described and instantiated for the particular problem of blink detection and reduction, and evaluated across a general and a specific analysis of the signal to noise ratio across 30 participants.},
}

*Electroencephalography/methods
Humans
*Artifacts
Wavelet Analysis
Signal Processing, Computer-Assisted
Algorithms
Brain-Computer Interfaces
Signal-To-Noise Ratio
Male
Brain/physiology
Adult
Female

@article {pmid39873797,
year = {2025},
author = {Mohan, A and Anand, RS},
title = {Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.},
journal = {Brain topography},
volume = {38},
number = {2},
pages = {25},
pmid = {39873797},
issn = {1573-6792},
mesh = {Humans ; *Machine Learning ; *Speech/physiology ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain/physiology ; Brain-Computer Interfaces ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, accurately classifying imagined speech signals remains challenging due to their complex and non-stationary nature. Existing approaches often struggle with low signal-to-noise ratios and high inter-subject variability. A proposed method named imagined speech functional connectivity graph (ISFCG) is implemented to deal with these issues. The functional connectivity graphs capture the complex relationships between brain regions during imagined speech tasks. These graphs are then used to extract features that serve as inputs to various machine-learning models. The ISFCG provides an alternative representation of imagined speech signals, focusing on brain connectivity features to enhance the analysis and classification process. Also, a convolutional neural network (CNN) is proposed to learn features from these complex graphs, leading to improved classification accuracy. Experimental results on a benchmark dataset demonstrate the effectiveness of our method.},
}

Humans
*Machine Learning
*Speech/physiology
*Electroencephalography/methods
*Imagination/physiology
*Brain/physiology
Brain-Computer Interfaces
Neural Networks, Computer
Signal Processing, Computer-Assisted

@article {pmid39872211,
year = {2025},
author = {Wang, L and Zhang, C and Hao, Z and Yao, S and Bai, L and Oliveira, JM and Wang, P and Zhang, K and Zhang, C and He, J and Reis, RL and Li, D},
title = {Bioaugmented design and functional evaluation of low damage implantable array electrodes.},
journal = {Bioactive materials},
volume = {47},
number = {},
pages = {18-31},
pmid = {39872211},
issn = {2452-199X},
abstract = {Implantable neural electrodes are key components of brain-computer interfaces (BCI), but the mismatch in mechanical and biological properties between electrode materials and brain tissue can lead to foreign body reactions and glial scarring, and subsequently compromise the long-term stability of electrical signal transmission. In this study, we proposed a new concept for the design and bioaugmentation of implantable electrodes (bio-array electrodes) featuring a heterogeneous gradient structure. Different composite polyaniline-gelatin-alginate based conductive hydrogel formulations were developed for electrode surface coating. In addition, the design, materials, and performance of the developed electrode was optimized through a combination of numerical simulations and physio-chemical characterizations. The long-term biological performance of the bio-array electrodes were investigated in vivo using a C57 mouse model. It was found that compared to metal array electrodes, the surface charge of the bio-array electrodes increased by 1.74 times, and the impedance at 1 kHz decreased by 63.17 %, with a doubling of the average capacitance. Long-term animal experiments showed that the bio-array electrodes could consistently record 2.5 times more signals than those of the metal array electrodes, and the signal-to-noise ratio based on action potentials was 2.1 times higher. The study investigated the mechanisms of suppressing the scarring effect by the bioaugmented design, revealing reduces brain damage as a result of the interface biocompatibility between the bio-array electrodes and brain tissue, and confirmed the long-term in vivo stability of the bio-array electrodes.},
}

@article {pmid39870740,
year = {2025},
author = {Hu, D and Sato, T and Rockland, KS and Tanifuji, M and Tanigawa, H},
title = {Relationship between functional structures and horizontal connections in macaque inferior temporal cortex.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {3436},
pmid = {39870740},
issn = {2045-2322},
support = {31872776//National Natural Science Foundation of China/ ; 2018YFA0701402//National Key Research and Development Program of China/ ; },
mesh = {Animals ; *Temporal Lobe/physiology ; Visual Cortex/physiology ; Male ; Macaca mulatta ; Axons/physiology ; Photic Stimulation ; Brain Mapping/methods ; Macaca ; Visual Pathways/physiology ; },
abstract = {Horizontal connections in anterior inferior temporal cortex (ITC) are thought to play an important role in object recognition by integrating information across spatially separated functional columns, but their functional organization remains unclear. Using a combination of optical imaging, electrophysiological recording, and anatomical tracing, we investigated the relationship between stimulus-response maps and patterns of horizontal axon terminals in the macaque ITC. In contrast to the "like-to-like" connectivity observed in the early visual cortex, we found that horizontal axons in ITC do not preferentially connect sites with similar object selectivity. While some axon terminal patches shared responsiveness to specific visual features with the injection site, many connected to regions with different selectivity. Our results suggest that horizontal connections in anterior ITC exhibit diverse functional connectivity, potentially supporting flexible integration of visual information for advanced object recognition processes.},
}

Animals
*Temporal Lobe/physiology
Visual Cortex/physiology
Male
Macaca mulatta
Axons/physiology
Photic Stimulation
Brain Mapping/methods
Macaca
Visual Pathways/physiology

@article {pmid39819752,
year = {2025},
author = {Chen, J and Chen, X and Wang, R and Le, C and Khalilian-Gourtani, A and Jensen, E and Dugan, P and Doyle, W and Devinsky, O and Friedman, D and Flinker, A and Wang, Y},
title = {Transformer-based neural speech decoding from surface and depth electrode signals.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adab21},
pmid = {39819752},
issn = {1741-2552},
mesh = {Humans ; *Electrocorticography/methods/instrumentation ; *Electrodes, Implanted ; Male ; Electroencephalography/methods ; Speech/physiology ; Female ; Brain-Computer Interfaces ; Adult ; Deep Learning ; },
abstract = {Objective.This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e. Electrocorticographic (ECoG) or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface ECoG and depth (stereotactic EEG or sEEG) electrodes. The architecture should allow training on data from multiple participants with large variability in electrode placements. The model should not have subject-specific layers and the trained model should perform well on participants unseen during training.Approach.We propose a novel transformer-based model architecture named SwinTW that can work with arbitrarily positioned electrodes by leveraging their 3D locations on the cortex rather than their positions on a 2D grid. We train subject-specific models using data from a single participant and multi-subject models exploiting data from multiple participants.Main results.The subject-specific models using only low-density 8 × 8 ECoG data achieved high decoding Pearson Correlation Coefficient with ground truth spectrogram (PCC = 0.817), overN= 43 participants, significantly outperforming our prior convolutional ResNet model and the 3D Swin transformer model. Incorporating additional strip, depth, and grid electrodes available in each participant (N= 39) led to further improvement (PCC = 0.838). For participants with only sEEG electrodes (N= 9), subject-specific models still enjoy comparable performance with an average PCC = 0.798. A single multi-subject model trained on ECoG data from 15 participants yielded comparable results (PCC = 0.837) as 15 models trained individually for these participants (PCC = 0.831). Furthermore, the multi-subject models achieved high performance on unseen participants, with an average PCC = 0.765 in leave-one-out cross-validation.Significance.The proposed SwinTW decoder enables future speech decoding approaches to utilize any electrode placement that is clinically optimal or feasible for a particular participant, including using only depth electrodes, which are more routinely implanted in chronic neurosurgical procedures. The success of the single multi-subject model when tested on participants within the training cohort demonstrates that the model architecture is capable of exploiting data from multiple participants with diverse electrode placements. The architecture's flexibility in training with both single-subject and multi-subject data, as well as grid and non-grid electrodes, ensures its broad applicability. Importantly, the generalizability of the multi-subject models in our study population suggests that a model trained using paired acoustic and neural data from multiple patients can potentially be applied to new patients with speech disability where acoustic-neural training data is not feasible.},
}

Humans
*Electrocorticography/methods/instrumentation
*Electrodes, Implanted
Male
Electroencephalography/methods
Speech/physiology
Female
Brain-Computer Interfaces
Adult
Deep Learning

@article {pmid39872550,
year = {2023},
author = {Huo, C and Cui, Q and Bai, G},
title = {Uncovering the mystery of genetic heterogeneity in inherited peripheral neuropathies.},
journal = {Life medicine},
volume = {2},
number = {4},
pages = {lnad026},
pmid = {39872550},
issn = {2755-1733},
}

@article {pmid39870041,
year = {2025},
author = {Konrad, PE and Gelman, KR and Lawrence, J and Bhatia, S and Dister, J and Sharma, R and Ho, E and Byun, YW and Mermel, CH and Rapoport, BI},
title = {First-in-human experience performing high-resolution cortical mapping using a novel microelectrode array containing 1,024 electrodes.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adaeed},
pmid = {39870041},
issn = {1741-2552},
abstract = {Localization of function within the brain and central nervous system is an essential aspect of clinical neuroscience. Classical descriptions of functional neuroanatomy provide a foundation for understanding the functional significance of identifiable anatomic structures. However, individuals exhibit substantial variation, particularly in the presence of disorders that alter tissue structure or impact function. Furthermore, functional regions do not always correspond to identifiable structural features. Understanding function at the level of individual patients-and diagnosing and treating such patients-often requires techniques capable of correlating neural activity with cognition, behavior, and experience in anatomically precise ways. Approach: Recent advances in brain-computer interface technology have given rise to a new generation of electrophysiologic tools for scalable, nondestructive functional mapping with spatial precision in the range of tens to hundreds of micrometers, and temporal resolutions in the range of tens to hundreds of microseconds. Here we describe our initial intraoperative experience with novel, thin-film arrays containing 1024 surface microelectrodes for electrocorticographic mapping in a first-in-human study. Main results: Six patients undergoing standard electrophysiologic cortical mapping during resection of eloquent-region brain tumors consented to brief sessions of concurrent mapping (micro-electrocorticography) using the novel arrays. Three patients underwent motor mapping using somatosensory evoked potentials while under general anesthesia, and three underwent awake language mapping, using both standard paradigms and the novel microelectrode array. Somatosensory evoked potential phase reversal was identified in the region predicted by conventional mapping, but at higher resolution (0.4 mm) and as a contour rather than as a point. In Broca's area (confirmed by direct cortical stimulation), speech planning was apparent in the micro-electrocorticogram as high-amplitude beta-band activity immediately prior to the articulatory event. Significance: These findings support the feasibility and potential clinical utility of incorporating micro-electrocorticography into the intraoperative workflow for systematic cortical mapping of functional brain regions.},
}

@article {pmid39870272,
year = {2025},
author = {Abedi, M and Arbabi, M and Gholampour, R and Amini, J and Barandeh, Z and Hosseini, S and Abedi, A and Gholibegloo, E and Zomorrodian, H and Raoufi, M},
title = {Zinc oxide nanoparticle-embedded tannic acid/chitosan-based sponge: A highly absorbent hemostatic agent with enhanced antimicrobial activity.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {140337},
doi = {10.1016/j.ijbiomac.2025.140337},
pmid = {39870272},
issn = {1879-0003},
abstract = {This study reports the development of a highly absorbent Chitosan (CS)/Tannic Acid (TA) sponge, synthesized via chemical cross-linking with Epichlorohydrin (ECH) and integrated with zinc oxide nanoparticles (ZnO NPs) as a novel hemostatic anti-infection agent. The chemical properties of the sponges were characterized using Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and zeta potential measurements. Morphological and elemental analyses conducted through scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDAX) revealed a uniform distribution of ZnO NPs, with particle sizes below 20 nm. Compression tests indicated that the incorporation of ECH enhanced the compressive strength of the TA/CS sample, increasing from 0.614 MPa to 1.03 MPa for TA/CS-ECH and 1.16 MPa for ZnO@TA/CS-ECH, while preserving its flexibility. ZnO@TA/CS-ECH sponges exhibited high swelling ratios, consistent with their mesoporous structure revealed by porosity analysis. MTT assays confirmed that the addition of ECH did not adversely affect the biocompatibility of the final ZnO@TA/CS-ECH sample. Hemostatic performance was assessed through prothrombin time (PT), activated partial thromboplastin time (aPTT), blood clotting index (BCI), blood clotting time (BCT) assays, and platelet adhesion imaging. ZnO@TA/CS-ECH significantly reduced the BCT of untreated blood from 349 to 49 s, outperforming Celox™ (182 s). This performance was further confirmed using a rat liver hemostatic model. Moreover, ZnO@TA/CS-ECH demonstrated substantial antimicrobial activity against E. coli, S. aureus, and C. albicans, comparable to standard antibiotics and antifungals. These findings suggest that the three-dimensional ZnO@TA/CS-ECH sponge holds promise in managing infected bleeding and inspiring the next-generation of hemostatic agents.},
}

@article {pmid39870046,
year = {2025},
author = {Zhang, L and Zhang, H and Yan, S and Li, R and Yao, D and Hu, Y and Zhang, R},
title = {Improving pre-movement patterns detection with multi-dimensional EEG features for readiness potential decrease.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adaef2},
pmid = {39870046},
issn = {1741-2552},
abstract = {The Readiness Potential (RP) is an important neural characteristic in motor preparation-based brain-computer interface (MP-BCI). In our previous research, we observed a significant decrease of the RP amplitude in some cases, which severely affects the pre-movement patterns detection. In this paper, we aimed to improve the accuracy of pre-movement patterns detection in the condition of RP decrease. Approach : We analyzed multi-dimensional EEG features in terms of time-frequency, brain networks, and cross-frequency coupling. And, a multi-dimensional Electroencephalogram feature combination (MEFC) algorithm was proposed. The features used include: 1) waveforms of the RP; 2) energy in alpha and beta bands; 3) brain network in alpha and beta bands; and 4) cross-frequency coupling value between 2 and 10 Hz. Main results: By employing support vector machines, the MEFC method achieved an average recognition rate of 88.9% and 85.5% under normal and RP decrease conditions, respectively. Compared to classical algorithm, the average accuracy for both tasks improved by 7.8% and 8.8% respectively. Significance: This method can effectively improve the accuracy of pre-movement patterns decoding in the condition of RP decrease. .},
}

@article {pmid39870043,
year = {2025},
author = {Candelori, B and Bardella, G and Spinelli, I and Ramawat, S and Pani, P and Ferraina, S and Scardapane, S},
title = {Spatio-temporal transformers for decoding neural movement control.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adaef0},
pmid = {39870043},
issn = {1741-2552},
abstract = {Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to analyze neural activity in vivo remains a challenge, requiring a delicate balance between efficiency in low-data regimes and the interpretability of the results. Approach: To address this challenge, we introduce a novel specialized transformer architecture to analyze single-neuron spiking activity. The model is tested on multi-electrode recordings from the dorsal premotor cortex (PMd) of non-human primates performing a motor inhibition task. Main Results The proposed architecture provides an early prediction of the correct movement direction, achieving accurate results no later than 230 ms after the Go signal presentation across animals. Additionally, the model can forecast whether the movement will be generated or withheld before a Stop signal, unattended, is actually presented. To further understand the internal dynamics of the model, we compute the predicted correlations between time steps and between neurons at successive layers of the architecture, with the evolution of these correlations mirrors findings from previous theoretical analyses. Significance Overall, our framework provides a comprehensive use case for the practical implementation of deep learning tools in motor control research, highlighting both the predictive capabilities and interpretability of the proposed architecture.},
}

@article {pmid39869167,
year = {2025},
author = {Sun, L and Duan, S},
title = {The Paraventricular Hypothalamus: A Sorting Center for Visceral and Somatic Pain.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {39869167},
issn = {1995-8218},
}

@article {pmid39866662,
year = {2025},
author = {Chen, L and Hu, Y and Wang, Z and Zhang, L and Jian, C and Cheng, S and Ming, D},
title = {Effects of transcutaneous auricular vagus nerve stimulation (taVNS) on motor planning: a multimodal signal study.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {35},
pmid = {39866662},
issn = {1871-4080},
abstract = {Motor planning plays a pivotal role in daily life. Transcutaneous auricular vagus nerve stimulation (taVNS) has been demonstrated to enhance decision-making efficiency, illustrating its potential use in cognitive modulation. However, current research primarily focuses on behavioral and single-modal electrophysiological signal, such as electroencephalography (EEG) and electrocardiography (ECG). To investigate the effect of taVNS on motor planning, a total of 21 subjects were recruited for this study and were divided into two groups: active group (n = 10) and sham group (n = 11). Each subject was required to be involved in a single-blind, sham-controlled, between-subject end-state comfort (ESC) experiment. The study compared behavioral indicators and electrophysiological features before and following taVNS. The results indicated a notable reduction in reaction time and an appreciable increase in the proportion of end-state comfort among the participants following taVNS, accompanied by notable alterations in motor-related cortical potential (MRCP) amplitude, low-frequency power of HRV (LF), and cortico-cardiac coherence, particularly in the parietal and occipital regions. These findings show that taVNS may impact the brain and heart, potentially enhancing their interaction, and improve participants' ability of motor planning.},
}

@article {pmid39866661,
year = {2025},
author = {Mathumitha, R and Maryposonia, A},
title = {Emotion analysis of EEG signals using proximity-conserving auto-encoder (PCAE) and ensemble techniques.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {32},
pmid = {39866661},
issn = {1871-4080},
abstract = {Emotion recognition plays a crucial role in brain-computer interfaces (BCI) which helps to identify and classify human emotions as positive, negative, and neutral. Emotion analysis in BCI maintains a substantial perspective in distinct fields such as healthcare, education, gaming, and human-computer interaction. In healthcare, emotion analysis based on electroencephalography (EEG) signals is deployed to provide personalized support for patients with autism or mood disorders. Recently, several deep learning (DL) based approaches have been developed for accurate emotion recognition tasks. Yet, previous works often struggle with poor recognition accuracy, high dimensionality, and high computational time. This research work designed an innovative framework named Proximity-conserving Auto-encoder (PCAE) for accurate emotion recognition based on EEG signals and resolves challenges faced by traditional emotion analysis techniques. For preserving local structures among the EEG data and reducing dimensionality, the proposed PCAE approach is introduced and it captures the essential features related to emotional states. The EEG data are collected from the EEG Brainwave dataset using a Muse EEG headband and applying preprocessing steps to enhance signal quality. The proposed PCAE model incorporates multiple convolution and deconvolution layers for encoding and decoding and deploys a Local Proximity Preservation Layer for preserving local correlations in the latent space. In addition, it develops a Proximity-conserving Squeeze-and-Excitation Auto-encoder (PC-SEAE) model to further improve the feature extraction ability of the PCAE technique. The proposed PCAE technique utilizes Maximum Mean Discrepancy (MMD) regularization to decrease the distribution discrepancy between input data and the extracted features. Moreover, the proposed model designs an ensemble model for emotion categorization that incorporates a one-versus-support vector machine (SVM), random forest (RF), and Long Short-Term Memory (LSTM) networks by utilizing each classifier's strength to enhance classification accuracy. Further, the performance of the proposed PCAE model is evaluated using diverse performance measures and the model attains outstanding results including accuracy, precision, and Kappa coefficient of 98.87%, 98.69%, and 0.983 respectively. This experimental validation proves that the proposed PCAE framework provides a significant contribution to accurate emotion recognition and classification systems.},
}

@article {pmid39866660,
year = {2025},
author = {Liu, H and Jin, X and Liu, D and Kong, W and Tang, J and Peng, Y},
title = {Joint disentangled representation and domain adversarial training for EEG-based cross-session biometric recognition in single-task protocols.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {31},
pmid = {39866660},
issn = {1871-4080},
abstract = {The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance. To address these issues, we propose the Joint Disentangled Representation with Domain Adversarial Training (JDR-DAT) framework for EEG-based cross-session biometric recognition within single-task protocols. The JDR-DAT framework disentangles identity-specific features through mutual information estimation and incorporates domain adversarial training to enhance longitudinal robustness. Extensive experiments on longitudinal EEG data from two publicly available single-task protocol datasets-RSVP-based (Rapid Serial Visual Presentation) and MI-based (Motor Imagery)-demonstrate the efficacy of the JDR-DAT framework, with the proposed method achieving average accuracies of 85.83% and 96.72%, respectively.},
}

@article {pmid39866658,
year = {2025},
author = {Zhou, Y and Song, Y and Song, X and He, F and Xu, M and Ming, D},
title = {Review of directional leads, stimulation patterns and programming strategies for deep brain stimulation.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {33},
pmid = {39866658},
issn = {1871-4080},
abstract = {Deep brain stimulation (DBS) is a well-established treatment for both neurological and psychiatric disorders. Directional DBS has the potential to minimize stimulation-induced side effects and maximize clinical benefits. Many new directional leads, stimulation patterns and programming strategies have been developed in recent years. Therefore, it is necessary to review new progress in directional DBS. This paper summarizes progress for directional DBS from the perspective of directional DBS leads, stimulation patterns, and programming strategies which are three key elements of DBS systems. Directional DBS leads are reviewed in electrode design and volume of tissue activated visualization strategies. Stimulation patterns are reviewed in stimulation parameters and advances in stimulation patterns. Programming strategies are reviewed in computational modeling, monopolar review, direction indicators and adaptive DBS. This review will provide a comprehensive overview of primary directional DBS leads, stimulation patterns and programming strategies, making it helpful for those who are developing DBS systems.},
}

@article {pmid39862529,
year = {2025},
author = {Lan, Z and Li, Z and Yan, C and Xiang, X and Tang, D and Wu, M and Chen, Z},
title = {RMKD: Relaxed matching knowledge distillation for short-length SSVEP-based brain-computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {185},
number = {},
pages = {107133},
doi = {10.1016/j.neunet.2025.107133},
pmid = {39862529},
issn = {1879-2782},
abstract = {Accurate decoding of electroencephalogram (EEG) signals in the shortest possible time is essential for the realization of a high-performance brain-computer interface (BCI) system based on the steady-state visual evoked potential (SSVEP). However, the degradation of decoding performance of short-length EEG signals is often unavoidable due to the reduced information, which hinders the development of BCI systems in real-world applications. In this paper, we propose a relaxed matching knowledge distillation (RMKD) method to transfer both feature-level and logit-level knowledge in a relaxed manner to improve the decoding performance of short-length EEG signals. Specifically, the long-length EEG signals and short-length EEG signals are decoded into the frequency representation by the teacher and student models, respectively. At the feature-level, the frequency-masked generation distillation is designed to improve the representation ability of student features by forcing the randomly masked student features to generate full teacher features. At the logit-level, the non-target class knowledge distillation and the inter-class relation distillation are combined to mitigate loss conflicts by imitating the distribution of non-target classes and preserve the inter-class relation in the prediction vectors of the teacher and student models. We conduct comprehensive experiments on two public SSVEP datasets in the subject-independent scenario with six different signal lengths. The extensive experimental results demonstrate that the proposed RMKD method has significantly improved the decoding performance of short-length EEG signals in SSVEP-based BCI systems.},
}

@article {pmid39860813,
year = {2025},
author = {Mallat, S and Hkiri, E and Albarrak, AM and Louhichi, B},
title = {A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain-Computer Interfaces to Enhance Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {2},
pages = {},
doi = {10.3390/s25020443},
pmid = {39860813},
issn = {1424-8220},
support = {IMSIU-RG23077//Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University, Saudi Arabia/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; Imagination/physiology ; Brain/physiology ; Algorithms ; },
abstract = {Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the use of brain-computer interfaces (BCIs), which establish a direct communication pathway between users and machines. This technology holds the potential to revolutionize human-machine interaction, especially for individuals diagnosed with motor disabilities. Despite this promise, extracting reliable control signals from noisy brain data remains a critical challenge. In this paper, we introduce a novel approach leveraging the collaborative synergy of five convolutional neural network (CNN) models to improve the classification accuracy of motor imagery tasks, which are essential components of BCI systems. Our method demonstrates exceptional performance, achieving an accuracy of 79.44% on the BCI Competition IV 2a dataset, surpassing existing state-of-the-art techniques in using multiple CNN models. This advancement offers significant promise for enhancing the efficacy and versatility of BCIs in a wide range of real-world applications, from assistive technologies to neurorehabilitation, thereby providing robust solutions for individuals with motor disabilities.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
*Neural Networks, Computer
Imagination/physiology
Brain/physiology
Algorithms

@article {pmid39860606,
year = {2025},
author = {Baruch, Y and Barba, M and Cola, A and Frigerio, M},
title = {The Role of Anterior Vaginal Prolapse in Co-Existent Underactive Overactive Bladder Syndrome-A Retrospective Cohort Study.},
journal = {Journal of clinical medicine},
volume = {14},
number = {2},
pages = {},
doi = {10.3390/jcm14020600},
pmid = {39860606},
issn = {2077-0383},
abstract = {Background: CUOB (co-existent underactive overactive bladder) syndrome is a clinical entity that embraces storage and emptying symptoms, not strictly correlated with urodynamic findings. We assessed the differences between patients diagnosed with CUOB with/without cystocele. Methods: The study group was allocated from 2000 women who underwent urodynamic studies between 2008 and 2016. The demographic and clinical data of 369 patients with complaints consistent with CUOB were retrieved. The study group was subdivided using the Pelvic Organ Prolapse Quantification System. The International Consultation on Incontinence Questionnaire Short Form (ICIQ-UI SF) was used to quantify LUTS severity. Results: A total of 185 women had no or grade I cystocele (group 1), and 185 had grade II or III cystocele (group 2). No difference in mean age was computed. Patients from group 1 had a higher BMI (27 vs. 25, p = 0.02). Risk factors for prolapse, such as parity (1.7 vs. 2.1, p = 0.001) and maximal birthweight (3460 g vs. 3612 g, p = 0.049), were higher in group 2. Pelvic Organ Prolapse symptoms were 4.5 times more frequent in group 2 [n = 36/185 (19.5%) vs. n = 162/184 (88%) p < 0.001]. The rate of stress (70.8% vs. 55.4%, p = 0.002) and urge (64.9% vs. 50%, p = 0.04), urinary incontinence, and ICIQ-UI-SF scores (8 vs. 5, p < 0.001) were higher in group 1. Qmax measured lower in group 2 (17 vs. 15 mL/s, p = 0.008). Detrusor pressure at maximum flow was identical (24 cm H2O). The Bladder Contractility Index (BCI) was higher in group 1 (108 vs. 96.5, p = 0.017), and weak contraction (BCI < 100) was more common in group 2 (73/185; 39.5% vs. 95/184; 52.7%, p = 0.011). Conclusions: Based on our results, we assume that CUOB could be further subdivided based on its association with cystocele. The effect of prolapse repair in women with CUOB and cystocele remains to be evaluated in order to afford better counseling in the future.},
}

@article {pmid39860555,
year = {2025},
author = {Onciul, R and Tataru, CI and Dumitru, AV and Crivoi, C and Serban, M and Covache-Busuioc, RA and Radoi, MP and Toader, C},
title = {Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.},
journal = {Journal of clinical medicine},
volume = {14},
number = {2},
pages = {},
doi = {10.3390/jcm14020550},
pmid = {39860555},
issn = {2077-0383},
abstract = {The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments. Beyond applications, neuroscience itself has inspired AI innovations, with neural architectures and brain-like processes shaping advances in learning algorithms and explainable models. This bidirectional exchange has fueled breakthroughs such as dynamic connectivity mapping, real-time neural decoding, and closed-loop brain-computer systems that adaptively respond to neural states. However, challenges persist, including issues of data integration, ethical considerations, and the "black-box" nature of many AI systems, underscoring the need for transparent, equitable, and interdisciplinary approaches. By synthesizing the latest breakthroughs and identifying future opportunities, this review charts a path forward for the integration of AI and neuroscience. From harnessing multimodal data to enabling cognitive augmentation, the fusion of these fields is not just transforming brain science, it is reimagining human potential. This partnership promises a future where the mysteries of the brain are unlocked, offering unprecedented advancements in healthcare, technology, and beyond.},
}

@article {pmid39858677,
year = {2024},
author = {Wen, B and Shen, L and Kang, X},
title = {Laser Welding of Micro-Wire Stent Electrode as a Minimally Invasive Endovascular Neural Interface.},
journal = {Micromachines},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/mi16010021},
pmid = {39858677},
issn = {2072-666X},
support = {61904038//National Natural Science Foundation of China/ ; 2021YFC0122700//National Key Research and Development Program of China/ ; 2021SHZDZX0103//Shanghai Municipal Science and Technology Major Project/ ; },
abstract = {Minimally invasive endovascular stent electrodes are an emerging technology in neural engineering, designed to minimize the damage to neural tissue. However, conventional stent electrodes often rely on resistive welding and are relatively bulky, restricting their use primarily to large animals or thick blood vessels. In this study, the feasibility is explored of fabricating a laser welding stent electrode as small as 300 μm. A high-precision laser welding technique was developed to join micro-wire electrodes without compromising structural integrity or performance. To ensure consistent results, a novel micro-wire welding with platinum pad method was introduced during the welding process. The fabricated electrodes were integrated with stent structures and subjected to detailed electrochemical performance testing to evaluate their potential as neural interface components. The laser-welded endovascular stent electrodes exhibited excellent electrochemical properties, including low impedance and stable charge transfer capabilities. At the same time, in this study, a simulation is conducted of the electrode distribution and arrangement on the stent structure, optimizing the utilization of the available surface area for enhanced functionality. These results demonstrate the potential of the fabricated electrodes for high-performance neural interfacing in endovascular applications. The approach provided a promising solution for advancing endovascular neural engineering technologies, particularly in applications requiring compact electrode designs.},
}

@article {pmid39856396,
year = {2025},
author = {Qin, Y and Zhang, L and Yu, B},
title = {A cross-domain-based channel selection method for motor imagery.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {39856396},
issn = {1741-0444},
support = {51977020//National Natural Science Foundation of China/ ; },
abstract = {Selecting channels for motor imagery (MI)-based brain-computer interface (BCI) systems can not only enhance the portability of the systems, but also improve the decoding performance. Hence, we propose a cross-domain-based channel selection (CDCS) approach, which effectively minimizes the number of EEG channels used while maintaining high accuracy in MI recognition. The EEG source imaging (ESI) technique is employed to map scalp EEG into the cortical source domain. We divide the equivalent dipoles in the source domain into different regions by k-means clustering. Then, we calculate the band energy (5-40 Hz) of time series of dipoles in these regions by power spectral density (PSD), and the regions with the highest and lowest band energy are selected as the region of interests (ROIs) in the source domain. Subsequently, Pearson correlation coefficients between the dipole time series in ROIs and scalp EEG are used as the criterion for channel selection and a multi-trial-sorting-based channel selection strategy is proposed. Finally, we propose the CDCS-based MI classification framework, where common spatial pattern is applied to extract features and linear discriminant analysis is used to identify MI tasks. The CDCS method demonstrated significant improvement in decoding accuracy on two public datasets, achieving increases of 18.51% and 13.37% compared to all-channel method, and 10.74% and 3.43% compared to the three-channel method. The experimental results validated that CDCS is effective in selecting important channels.},
}

@article {pmid39856240,
year = {2025},
author = {Zhou, Y and Tang, X and Zhang, D and Lee, HJ},
title = {Machine learning empowered coherent Raman imaging and analysis for biomedical applications.},
journal = {Communications engineering},
volume = {4},
number = {1},
pages = {8},
pmid = {39856240},
issn = {2731-3395},
support = {82372011//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12074339//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {In situ and in vivo visualization and analysis of functional, endogenous biomolecules in living systems have generated a pivotal impact in our comprehension of biology and medicine. An increasingly adopted approach involves the utilization of molecular vibrational spectroscopy, which delivers notable advantages such as label-free imaging, high spectral density, high sensitivity, and molecule specificity. Nonetheless, analyzing and processing the intricate, multi-dimensional imaging data to extract interpretable and actionable information poses a fundamental obstacle. In contrast to conventional multivariate methods, machine learning has recently gained considerable attention for its capability of discerning essential features from massive datasets. Here, we present a comprehensive review of the latest advancements in the application of machine learning in the molecular spectroscopic imaging fields. We also discuss notable attributes of spectroscopic imaging modalities and explore their broader impact on other imaging techniques.},
}

@article {pmid39856041,
year = {2025},
author = {Lai, QL and Cai, MT and Li, EC and Fang, GL and Shen, CH and Xu, YF and Qiao, S and Wang, JJ and Weng, QJ and Zhang, YX},
title = {Neurofilament light chain levels in neuronal surface antibody-associated autoimmune encephalitis: a systematic review and meta-analysis.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {25},
pmid = {39856041},
issn = {2158-3188},
support = {LQ23H090004//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 82222069//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82073857//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Neurofilament Proteins/blood/cerebrospinal fluid/immunology ; *Autoantibodies/blood/cerebrospinal fluid ; *Hashimoto Disease/immunology/blood/cerebrospinal fluid ; *Encephalitis/immunology/blood/cerebrospinal fluid ; Biomarkers/blood/cerebrospinal fluid ; },
abstract = {BACKGROUND: Neuronal surface antibody-associated autoimmune encephalitis (NSAE) is a group of neuro-inflammatory disorders that is mediated by autoantibodies against the cell-surface and synaptic antigens. Studies have explored the role of neurofilament light chain (NfL) in NSAE and provided inconsistent data. We performed a systematic review and meta-analysis to evaluate the NfL levels in the serum and cerebrospinal fluid (CSF) of patients with NSAE.

METHODS: The National Center for Biotechnology Information (NCBI, PubMed), Web of Knowledge, and the Cochrane Library databases were searched for studies reporting NfL levels in patients with NSAE. Random-effects meta-analysis was used to pool results across studies.

RESULTS: Thirteen studies were included in the final systematic review and meta-analysis. The serum NfL levels were significantly higher in patients with NSAE compared to unaffected controls (standardized mean difference [SMD] = 0.909, 95% confidence interval [CI]: 0.536-1.282). Similarly, the CSF NfL levels were elevated in patients with NSAE (SMD = 0.897, 95% CI: 0.508-1.286). The serum and CSF NfL levels were not significantly correlated with disease severity, prognosis, and abnormalities in magnetic resonance imaging, electroencephalography, and CSF.

CONCLUSIONS: NfL levels in the serum and CSF were higher in patients with NSAE compared to unaffected controls. However, the NfL levels were not shown to be significantly associated with clinical or paraclinical features.},
}

Humans
*Neurofilament Proteins/blood/cerebrospinal fluid/immunology
*Autoantibodies/blood/cerebrospinal fluid
*Hashimoto Disease/immunology/blood/cerebrospinal fluid
*Encephalitis/immunology/blood/cerebrospinal fluid
Biomarkers/blood/cerebrospinal fluid

@article {pmid39855567,
year = {2025},
author = {Wei, Y and Zhao, L and Wei, J and Yu, X and Wei, L and Ni, R and Li, T},
title = {Hippocampal transcriptome analysis in ClockΔ19 mice identifies pathways associated with glial cell differentiation and myelination.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jad.2025.01.039},
pmid = {39855567},
issn = {1573-2517},
abstract = {BACKGROUND: ClockΔ19 mice demonstrate behavioral characteristics and neurobiological changes that closely resemble those observed in bipolar disorder (BD). Notably, abnormalities in the hippocampus have been observed in patients with BD, yet direct molecular investigation of human hippocampal tissue remains challenging due to its limited accessibility.

METHODS: To model BD, ClockΔ19 mice were employed. Weighted gene co-expression network analysis (WGCNA) was utilized to identify mutation-related modules, and changes in cell populations were determined using the computational deconvolution CIBERSORTx. Furthermore, GeneMANIA and protein-protein interactions (PPIs) were leveraged to construct a comprehensive interaction network.

RESULTS: 174 differentially expressed genes (DEGs) were identified, revealing abnormalities in rhythmic processes, mitochondrial metabolism, and various cell functions including morphology, differentiation, and receptor activity. Analysis identified 5 modules correlated with the mutation, with functional enrichment highlighting disturbances in rhythmic processes and neural cell differentiation due to the mutation. Furthermore, a decrease in neural stem cells (NSC), and an increase in astrocyte-restricted precursors (ARP), ependymocytes (EPC), and hemoglobin-expressing vascular cells (Hb-VC) in the mutant mice were observed. A network comprising 12 genes that link rhythmic processes to neural cell differentiation in the hippocampus was also identified.

LIMITATIONS: This study focused on the hippocampus of mice, hence the applicability of these findings to human patients warrants further exploration.

CONCLUSION: The ClockΔ19 mutation may disrupt circadian rhythm, myelination, and the differentiation of neural stem cells (NSCs) into glial cells. These abnormalities are linked to altered expression of key genes, including DPB, CIART, NR1D1, GFAP, SLC20A2, and KL. Furthermore, interactions between SLC20A2 and KL might provide a connection between circadian rhythm regulation and cell type transitions.},
}

@article {pmid39854845,
year = {2025},
author = {Bellicha, A and Struber, L and Pasteau, F and Juillard, V and Devigne, L and Karakas, S and Chabardès, S and Babel, M and Charvet, G},
title = {Depth-sensor-based shared control assistance for mobility and object manipulation: toward long-term home-use of BCI-controlled assistive robotic devices.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adae36},
pmid = {39854845},
issn = {1741-2552},
abstract = {Objective. Assistive robots can be developed to restore or provide more autonomy for individuals with motor impairments. In particular, power wheelchairs can compensate lower-limb impairments, while robotic manipulators can compensate upper-limbs impairments. Recent studies have shown that Brain-Computer Interfaces (BCI) can be used to operate this type of devices. However, activities of daily living and long-term use in real-life contexts such as home require robustness and adaptability to complex, changing and cluttered environments which can be problematic given the neural signals that do not always allow a safe and efficient use. Approach. This article describes assist-as-needed sensor-based shared control methods relying on the blending of BCI and depth-sensor-based control. The proposed assistance targets the BCI-teleoperation of effectors for tasks that answer mobility and manipulation needs in a at-home context. Main Results. The assistance provided by the proposed methods was evaluated through a wheelchair mobility and reach-and-grasp laboratory-based experiments in a controlled environment, as part of a clinical trial with a quadriplegic patient implanted with a wireless 64-channel ElectroCorticoGram (ECoG) recording implant named WIMAGINE. Results showed that the proposed methods can assist BCI users in both tasks. Indeed, the time to perform the tasks and the number of changes of mental tasks were reduced. Moreover, unwanted actions, such as wheelchair collisions with the environment, and gripper opening that could result in the fall of the object were avoided. Significance. The proposed methods are steps toward at-home use of BCI-teleoperated assistive robots. Indeed, the proposed shared control methods improved the performance of the two assistive devices. Clinical trial, registration number: NCT02550522.},
}

@article {pmid39854835,
year = {2025},
author = {Choi, BJ and Liu, J},
title = {A low-cost transhumeral prosthesis operated via an ML-assisted EEG-head gesture control system.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adae35},
pmid = {39854835},
issn = {1741-2552},
abstract = {Objective Key challenges in upper limb prosthetics include a lack of effective control systems, the often invasive surgical requirements of brain-controlled limbs, and prohibitive costs. As a result, disuse rates remain high despite potential for increased quality of life. To address these concerns, this project developed a low cost, noninvasive transhumeral neuroprosthesis-operated via a combination of electroencephalography (EEG) signals and head gestures. Approach To address the shortcomings of current noninvasive neural monitoring techniques-namely, single-channel EEG-we leveraged machine learning (ML), creating a neural network-based EEG interpretation algorithm. ML generation was guided by two underlying goals: (1) to improve overall system performance by combining discrete models using a prediction voting scheme, and (2) to favor model diversity within these new neural network ensembles, as opposed to individual model performance. EEG data from eight frequency bands was collected from human subjects to train a machine learning algorithm employing a hierarchical mixture-of-experts (MoE) structure. We also implemented head gesture-based control to assist in the generation of additional stable classes for the control system. Main Results The final model performs competitively with existing EEG interpretation systems. IMU-based head gestures supplement the neural control system, with 270° actuation of synovial elbow and radial wrist joints driven by intuitive corresponding head gestures. The brain-controlled prosthesis presented in this study costs US$300 to manufacture and achieved competitive performance on a Box and Block Test (BBT). Significance These results suggest proof-of-concept for potential application as an alternative to current prosthetics, but it is important to note that the demonstration in this study remains exploratory. Future work includes broader clinical testing and exploring further uses for the developed ML system.},
}

@article {pmid39854573,
year = {2025},
author = {Eckert, AL and Fuehrer, E and Schmitter, C and Straube, B and Fiehler, K and Endres, D},
title = {Modelling sensory attenuation as Bayesian causal inference across two datasets.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0317924},
doi = {10.1371/journal.pone.0317924},
pmid = {39854573},
issn = {1932-6203},
mesh = {Humans ; *Bayes Theorem ; Male ; Female ; Adult ; Movement/physiology ; Markov Chains ; Touch/physiology ; Young Adult ; Touch Perception/physiology ; },
abstract = {INTRODUCTION: To interact with the environment, it is crucial to distinguish between sensory information that is externally generated and inputs that are self-generated. The sensory consequences of one's own movements tend to induce attenuated behavioral- and neural responses compared to externally generated inputs. We propose a computational model of sensory attenuation (SA) based on Bayesian Causal Inference, where SA occurs when an internal cause for sensory information is inferred.

METHODS: Experiment 1investigates sensory attenuation during a stroking movement. Tactile stimuli on the stroking finger were suppressed, especially when they were predictable. Experiment 2 showed impaired delay detection between an arm movement and a video of the movement when participants were moving vs. when their arm was moved passively. We reconsider these results from the perspective of Bayesian Causal Inference (BCI). Using a hierarchical Markov Model (HMM) and variational message passing, we first qualitatively capture patterns of task behavior and sensory attenuation in simulations. Next, we identify participant-specific model parameters for both experiments using optimization.

RESULTS: A sequential BCI model is well equipped to capture empirical patterns of SA across both datasets. Using participant-specific optimized model parameters, we find a good agreement between data and model predictions, with the model capturing both tactile detections in Experiment 1 and delay detections in Experiment 2.

DISCUSSION: BCI is an appropriate framework to model sensory attenuation in humans. Computational models of sensory attenuation may help to bridge the gap across different sensory modalities and experimental paradigms and may contribute towards an improved description and understanding of deficits in specific patient groups (e.g. schizophrenia).},
}

Humans
*Bayes Theorem
Male
Female
Adult
Movement/physiology
Markov Chains
Touch/physiology
Young Adult
Touch Perception/physiology

@article {pmid39851820,
year = {2024},
author = {Yu, W and Wu, C},
title = {Development and Validation of the Interpreting Learning Engagement Scale (ILES).},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {15},
number = {1},
pages = {},
doi = {10.3390/bs15010016},
pmid = {39851820},
issn = {2076-328X},
support = {20CG40//Shanghai Chenguang Talent Program/ ; 2022KFKT009//Open project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; B202205//open project of Key Laboratory of Multilingual Education with AI/ ; C2022411//Shanghai Education and Science Research Project/ ; 2019114002//Shanghai International Studies University Planned Project/ ; },
abstract = {This study developed and validated the Interpreting Learning Engagement Scale (ILES), which was designed to measure the engagement of students in the interpreting learning context. Recognizing the crucial role of learning engagement in academic success and the acquisition of interpreting skills, which demands considerable cognitive effort and active involvement, this research addresses the gap in empirical studies on engagement within the field of interpreting. The ILES, comprising 18 items across four dimensions (behavioral, emotional, cognitive, and agentic engagement), was validated with data collected from a cohort of 306 students from five universities in China. The study employed exploratory and confirmatory factor analyses to establish the scale's theoretical underpinnings and provided further reliability and validity evidence, demonstrating its adequate psychometric properties. Additionally, the scale's scores showed a significant correlation with grit, securing the external validity of the ILES. This study not only contributes a validated instrument for assessing student engagement in interpreting learning but also provides implications for promoting engagement through potential interventions, with the ultimate aim of achieving high levels of interpreting competence.},
}

@article {pmid39851633,
year = {2024},
author = {Ding, X and Zhang, Z and Wang, K and Xiao, X and Xu, M},
title = {A Lightweight Network with Domain Adaptation for Motor Imagery Recognition.},
journal = {Entropy (Basel, Switzerland)},
volume = {27},
number = {1},
pages = {},
doi = {10.3390/e27010014},
pmid = {39851633},
issn = {1099-4300},
abstract = {Brain-computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. However, traditional intention-recognition methods face several challenges, such as prolonged training times and limited cross-subject adaptability, which restrict their practical application. This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation. A lightweight feature extraction module is designed to extract key features from both the source and target domains, effectively reducing the model's parameters and improving the real-time performance and computational efficiency. To address differences in sample distributions, a domain adaptation strategy is introduced to optimize the feature alignment. Furthermore, domain adversarial training is employed to promote the learning of domain-invariant features, significantly enhancing the model's cross-subject generalization ability. The proposed method was evaluated on an fNIRS motor imagery dataset, achieving an average accuracy of 87.76% in a three-class classification task. Additionally, lightweight experiments were conducted from two perspectives: model structure optimization and data feature selection. The results demonstrated the potential advantages of this method for practical applications in motor imagery recognition systems.},
}

@article {pmid39851418,
year = {2025},
author = {Zhu, X and Meng, M and Yan, Z and Luo, Z},
title = {Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble.},
journal = {Brain sciences},
volume = {15},
number = {1},
pages = {},
doi = {10.3390/brainsci15010050},
pmid = {39851418},
issn = {2076-3425},
support = {62271181 and 62171171//National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Decoding motor intentions from electroencephalogram (EEG) signals is a critical component of motor imagery-based brain-computer interface (MI-BCIs). In traditional EEG signal classification, effectively utilizing the valuable information contained within the electroencephalogram is crucial.

OBJECTIVES: To further optimize the use of information from various domains, we propose a novel framework based on multi-domain feature rotation transformation and stacking ensemble for classifying MI tasks.

METHODS: Initially, we extract the features of Time Domain, Frequency domain, Time-Frequency domain, and Spatial Domain from the EEG signals, and perform feature selection for each domain to identify significant features that possess strong discriminative capacity. Subsequently, local rotation transformations are applied to the significant feature set to generate a rotated feature set, enhancing the representational capacity of the features. Next, the rotated features were fused with the original significant features from each domain to obtain composite features for each domain. Finally, we employ a stacking ensemble approach, where the prediction results of base classifiers corresponding to different domain features and the set of significant features undergo linear discriminant analysis for dimensionality reduction, yielding discriminative feature integration as input for the meta-classifier for classification.

RESULTS: The proposed method achieves average classification accuracies of 92.92%, 89.13%, and 86.26% on the BCI Competition III Dataset IVa, BCI Competition IV Dataset I, and BCI Competition IV Dataset 2a, respectively.

CONCLUSIONS: Experimental results show that the method proposed in this paper outperforms several existing MI classification methods, such as the Common Time-Frequency-Spatial Patterns and the Selective Extract of the Multi-View Time-Frequency Decomposed Spatial, in terms of classification accuracy and robustness.},
}

@article {pmid39851397,
year = {2024},
author = {Suresh, RE and Zobaer, MS and Triano, MJ and Saway, BF and Grewal, P and Rowland, NC},
title = {Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study.},
journal = {Brain sciences},
volume = {15},
number = {1},
pages = {},
doi = {10.3390/brainsci15010028},
pmid = {39851397},
issn = {2076-3425},
support = {5 P20 GM109040//Center of Biomedical Research Excellence (COBRE) in Stroke Recovery-Junior Investigator Research Project. Source: National Institutes of Health/ ; },
abstract = {BACKGROUND/OBJECTIVES: Noninvasive brain stimulation (NIBS) can boost motor recovery after a stroke. Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying movement phases in hemiparetic individuals undergoing simultaneous NIBS and EEG recordings. We hypothesized that transcranial direct current stimulation (tDCS), a form of NIBS, would enhance EEG signals related to movement phases and improve classification accuracy compared to sham stimulation.

METHODS: EEG data from 10 chronic stroke patients and 11 healthy controls were recorded before, during, and after tDCS. Eight machine learning algorithms and five ensemble methods were used to classify two movement phases (hold posture and reaching) during each of these periods. Data preprocessing included z-score normalization and frequency band power binning.

RESULTS: In chronic stroke participants who received active tDCS, the classification accuracy for hold vs. reach phases increased from pre-stimulation to the late intra-stimulation period (72.2% to 75.2%, p < 0.0001). Late active tDCS surpassed late sham tDCS classification (75.2% vs. 71.5%, p < 0.0001). Linear discriminant analysis was the most accurate (74.6%) algorithm with the shortest training time (0.9 s). Among ensemble methods, low gamma frequency (30-50 Hz) achieved the highest accuracy (74.5%), although this result did not achieve statistical significance for actively stimulated chronic stroke participants.

CONCLUSIONS: Machine learning algorithms showed enhanced movement phase classification during active tDCS in chronic stroke participants. These results suggest their feasibility for real-time movement detection in neurorehabilitation, including brain-computer interfaces for stroke recovery.},
}

@article {pmid39851395,
year = {2024},
author = {Pan, S and Shen, T and Lian, Y and Shi, L},
title = {A Task-Related EEG Microstate Clustering Algorithm Based on Spatial Patterns, Riemannian Distance, and a Deep Autoencoder.},
journal = {Brain sciences},
volume = {15},
number = {1},
pages = {},
doi = {10.3390/brainsci15010027},
pmid = {39851395},
issn = {2076-3425},
support = {23CJL006//National Social Science Fund of China/ ; },
abstract = {BACKGROUND: The segmentation of electroencephalography (EEG) signals into a limited number of microstates is of significant importance in the field of cognitive neuroscience. Currently, the microstate analysis algorithm based on global field power has demonstrated its efficacy in clustering resting-state EEG. The task-related EEG was extensively analyzed in the field of brain-computer interfaces (BCIs); however, its primary objective is classification rather than segmentation.

METHODS: We propose an innovative algorithm for analyzing task-related EEG microstates based on spatial patterns, Riemannian distance, and a modified deep autoencoder. The objective of this algorithm is to achieve unsupervised segmentation and clustering of task-related EEG signals.

RESULTS: The proposed algorithm was validated through experiments conducted on simulated EEG data and two publicly available cognitive task datasets. The evaluation results and statistical tests demonstrate its robustness and efficiency in clustering task-related EEG microstates.

CONCLUSIONS: The proposed unsupervised algorithm can autonomously discretize EEG signals into a finite number of microstates, thereby facilitating investigations into the temporal structures underlying cognitive processes.},
}

@article {pmid39850117,
year = {2025},
author = {Mahheidari, N and Alizadeh, M and Kamalabadi Farahani, M and Arabpour, Z and Rezaei Kolarijani, N and Djalilian, AR and Salehi, M},
title = {Regeneration of the skin wound by two different crosslinkers: In vitro and in vivo studies.},
journal = {Iranian journal of basic medical sciences},
volume = {28},
number = {2},
pages = {194-208},
pmid = {39850117},
issn = {2008-3866},
abstract = {OBJECTIVES: For designing a suitable hydrogel, two crosslinked Alginate/ Carboxymethyl cellulose (Alg/CMC) hydrogel, using calcium chloride (Ca[2+]) and glutaraldehyde (GA) as crosslinking agents were synthesized and compared.

MATERIALS AND METHODS: All samples were characterized by Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Blood compatibility (BC), Blood clotting index (BCI), weight loss (WL), water absorption (WA), pH, and Electrochemical Impedance Spectroscopy (EIS). Cell viability and cell migration were investigated using the MTT assay and the wound scratch test, respectively. Besides, the wound healing potential of prepared hydrogels was evaluated on the rat models with full-thickness skin excision. To further investigation, TGF β1, IGF-I, COL1, ACT-A (alfa-SMA), and GAPDH expression levels were also reported by RT-PCR.

RESULTS: Water absorption and weight loss properties were compared between different crosslinker agents, and the most nontoxic crosslinker concentration was determined. We have shown that GA (20 µl/ml) and Ca[2+] (50 or 75 mM) enhanced the physical stability of Alg-CMC hydrogel, and they are nontoxic and suitable crosslinkers for wound dressing applications. Although in vivo assessments indicated that the GA (20 µl/ml) had a cytotoxic effect on tissue repair, Ca[2+] (75 mM) boosted the wound healing process. Further, RT-PCR results revealed that TGF β1, IGF-I, COL1, ACT-A (alfa-SMA), and GAPDH expression levels were increased in GA (20 µl/ml). Moreover, this trend is the opposite in the Ca[2+] (75 mM) treatment groups.

CONCLUSION: This research shows that Ca[2+] (75 mM) boosts tissue regeneration and wound healing process.},
}

@article {pmid39850073,
year = {2024},
author = {He, J and Huang, Z and Li, Y and Shi, J and Chen, Y and Jiang, C and Feng, J},
title = {Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1481493},
pmid = {39850073},
issn = {1662-5161},
abstract = {INTRODUCTION: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges.

METHODS: The proposed method integrates Discrete Wavelet Transformation (DWT) and Independent Component Analysis (ICA) for noise removal, followed by a robust Kalman filter enhanced with convex optimization to preserve critical EEG components. The norm-constrained ELM employs L1/L2 regularization to improve generalization and classification performance. Experimental data were collected using a Schulte Grid paradigm and TGAM sensors, along with publicly available datasets for validation.

RESULTS: The robust Kalman filter demonstrated superior denoising performance, achieving an average AUC of 0.8167 and a maximum AUC of 0.8678 on self-collected datasets, and an average AUC of 0.8344 with a maximum of 0.8950 on public datasets. The method outperformed traditional Kalman filtering, LMS adaptive filtering, and TGAM's eSense algorithm in both noise reduction and attention classification accuracy.

DISCUSSION: The study highlights the effectiveness of combining advanced signal processing and machine learning techniques to improve the robustness and generalization of EEG-based attention classification. Limitations include the small sample size and limited demographic diversity, suggesting future research should expand participant groups and explore broader applications, such as mental health monitoring and neurofeedback.},
}

@article {pmid39846559,
year = {2025},
author = {Pantaleo, A and Curatoli, L and Cavallaro, G and Auricchio, D and Murri, A and Quaranta, N},
title = {Unilateral Versus Bilateral Cochlear Implants in Adults: A Cross-Sectional Questionnaire Study Across Multiple Hearing Domains.},
journal = {Audiology research},
volume = {15},
number = {1},
pages = {},
doi = {10.3390/audiolres15010006},
pmid = {39846559},
issn = {2039-4330},
abstract = {AIM: The aim of this study was to assess the subjective experiences of adults with different cochlear implant (CI) configurations-unilateral cochlear implant (UCI), bilateral cochlear implant (BCI), and bimodal stimulation (BM)-focusing on their perception of speech in quiet and noisy environments, music, environmental sounds, people's voices and tinnitus.

METHODS: A cross-sectional survey of 130 adults who had undergone UCI, BCI, or BM was conducted. Participants completed a six-item online questionnaire, assessing difficulty levels and psychological impact across auditory domains, with responses measured on a 10-point scale. Statistical analyses were performed to compare the subjective experiences of the three groups.

RESULTS: Patients reported that understanding speech in noise and tinnitus perception were their main concerns. BCI users experienced fewer difficulties with understanding speech in both quiet (p < 0.001) and noisy (p = 0.008) environments and with perceiving non-vocal sounds (p = 0.038) compared to UCI and BM users; no significant differences were found for music perception (p = 0.099), tinnitus perception (p = 0.397), or voice naturalness (p = 0.157). BCI users also reported less annoyance in quiet (p = 0.004) and noisy (p = 0.047) environments, and in the perception of voices (p = 0.009) and non-vocal sounds (p = 0.019). Tinnitus-related psychological impact showed no significant differences between groups (p = 0.090).

CONCLUSIONS: Although speech perception in noise and tinnitus remain major problems for CI users, the results of our study suggest that bilateral cochlear implantation offers significant subjective advantages over unilateral implantation and bimodal stimulation in adults, particularly in difficult listening environments.},
}

@article {pmid39844431,
year = {2025},
author = {Guo, M and Han, X and Liu, H and Zhu, J and Zhang, J and Bai, Y and Ni, G},
title = {MI-Mamba: A hybrid motor imagery electroencephalograph classification model with Mamba's global scanning.},
journal = {Annals of the New York Academy of Sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/nyas.15288},
pmid = {39844431},
issn = {1749-6632},
support = {2023YFF1203500//National Key Research and Development Program of China/ ; },
abstract = {Deep learning has revolutionized electroencephalograph (EEG) decoding, with convolutional neural networks (CNNs) being a predominant tool. However, CNNs struggle with long-term dependencies in sequential EEG data. Models like long short-term memory and transformers improve performance but still face challenges of computational efficiency and long sequences. Mamba, a state space model-based method, excels in modeling long sequences. To overcome the limitations of existing EEG decoding models and exploit Mamba's potential in EEG analysis, we propose MI-Mamba, a model integrating CNN with Mamba for motor imagery (MI) data decoding. MI-Mamba processes multi-channel EEG signals through a single convolutional layer to capture spatial features in the local temporal domain, followed by a Mamba module that processes global temporal features. A fully connected, layer-based classifier is used to derive classification results. Evaluated on two public MI datasets, MI-Mamba achieves 80.59% accuracy in the four-class MI task of the BCI Competition IV 2a dataset and 84.42% in the two-class task of the BCI Competition IV 2b dataset, while reducing parameter count by nearly six times compared to the most advanced previous models. These results highlight MI-Mamba's effectiveness in MI decoding and its potential as a new backbone for general EEG decoding.},
}

@article {pmid39843750,
year = {2025},
author = {O'Connell, KS and Koromina, M and van der Veen, T and Boltz, T and David, FS and Yang, JMK and Lin, KH and Wang, X and Coleman, JRI and Mitchell, BL and McGrouther, CC and Rangan, AV and Lind, PA and Koch, E and Harder, A and Parker, N and Bendl, J and Adorjan, K and Agerbo, E and Albani, D and Alemany, S and Alliey-Rodriguez, N and Als, TD and Andlauer, TFM and Antoniou, A and Ask, H and Bass, N and Bauer, M and Beins, EC and Bigdeli, TB and Pedersen, CB and Boks, MP and Børte, S and Bosch, R and Brum, M and Brumpton, BM and Brunkhorst-Kanaan, N and Budde, M and Bybjerg-Grauholm, J and Byerley, W and Cabana-Domínguez, J and Cairns, MJ and Carpiniello, B and Casas, M and Cervantes, P and Chatzinakos, C and Chen, HC and Clarence, T and Clarke, TK and Claus, I and Coombes, B and Corfield, EC and Cruceanu, C and Cuellar-Barboza, A and Czerski, PM and Dafnas, K and Dale, AM and Dalkner, N and Degenhardt, F and DePaulo, JR and Djurovic, S and Drange, OK and Escott-Price, V and Fanous, AH and Fellendorf, FT and Ferrier, IN and Forty, L and Frank, J and Frei, O and Freimer, NB and Fullard, JF and Garnham, J and Gizer, IR and Gordon, SD and Gordon-Smith, K and Greenwood, TA and Grove, J and Guzman-Parra, J and Ha, TH and Hahn, T and Haraldsson, M and Hautzinger, M and Havdahl, A and Heilbronner, U and Hellgren, D and Herms, S and Hickie, IB and Hoffmann, P and Holmans, PA and Huang, MC and Ikeda, M and Jamain, S and Johnson, JS and Jonsson, L and Kalman, JL and Kamatani, Y and Kennedy, JL and Kim, E and Kim, J and Kittel-Schneider, S and Knowles, JA and Kogevinas, M and Kranz, TM and Krebs, K and Kushner, SA and Lavebratt, C and Lawrence, J and Leber, M and Lee, HJ and Liao, C and Lucae, S and Lundberg, M and MacIntyre, DJ and Maier, W and Maihofer, AX and Malaspina, D and Manchia, M and Maratou, E and Martinsson, L and Mattheisen, M and McGregor, NW and McInnis, MG and McKay, JD and Medeiros, H and Meyer-Lindenberg, A and Millischer, V and Morris, DW and Moutsatsou, P and Mühleisen, TW and O'Donovan, C and Olsen, CM and Panagiotaropoulou, G and Papiol, S and Pardiñas, AF and Park, HY and Perry, A and Pfennig, A and Pisanu, C and Potash, JB and Quested, D and Rapaport, MH and Regeer, EJ and Rice, JP and Rivera, M and Schulte, EC and Senner, F and Shadrin, A and Shilling, PD and Sigurdsson, E and Sindermann, L and Sirignano, L and Siskind, D and Slaney, C and Sloofman, LG and Smeland, OB and Smith, DJ and Sobell, JL and Soler Artigas, M and Stein, DJ and Stein, F and Su, MH and Sung, H and Świątkowska, B and Terao, C and Tesfaye, M and Tesli, M and Thorgeirsson, TE and Thorp, JG and Toma, C and Tondo, L and Tooney, PA and Tsai, SJ and Tsermpini, EE and Vawter, MP and Vedder, H and Vreeker, A and Walters, JTR and Winsvold, BS and Witt, SH and Won, HH and Ye, R and Young, AH and Zandi, PP and Zillich, L and , and Adolfsson, R and Alda, M and Alfredsson, L and Backlund, L and Baune, BT and Bellivier, F and Bengesser, S and Berrettini, WH and Biernacka, JM and Boehnke, M and Børglum, AD and Breen, G and Carr, VJ and Catts, S and Cichon, S and Corvin, A and Craddock, N and Dannlowski, U and Dikeos, D and Etain, B and Ferentinos, P and Frye, M and Fullerton, JM and Gawlik, M and Gershon, ES and Goes, FS and Green, MJ and Grigoroiu-Serbanescu, M and Hauser, J and Henskens, FA and Hjerling-Leffler, J and Hougaard, DM and Hveem, K and Iwata, N and Jones, I and Jones, LA and Kahn, RS and Kelsoe, JR and Kircher, T and Kirov, G and Kuo, PH and Landén, M and Leboyer, M and Li, QS and Lissowska, J and Lochner, C and Loughland, C and Luykx, JJ and Martin, NG and Mathews, CA and Mayoral, F and McElroy, SL and McIntosh, AM and McMahon, FJ and Medland, SE and Melle, I and Milani, L and Mitchell, PB and Morken, G and Mors, O and Mortensen, PB and Müller-Myhsok, B and Myers, RM and Myung, W and Neale, BM and Nievergelt, CM and Nordentoft, M and Nöthen, MM and Nurnberger, JI and O'Donovan, MC and Oedegaard, KJ and Olsson, T and Owen, MJ and Paciga, SA and Pantelis, C and Pato, CN and Pato, MT and Patrinos, GP and Pawlak, JM and Ramos-Quiroga, JA and Reif, A and Reininghaus, EZ and Ribasés, M and Rietschel, M and Ripke, S and Rouleau, GA and Roussos, P and Saito, T and Schall, U and Schalling, M and Schofield, PR and Schulze, TG and Scott, LJ and Scott, RJ and Serretti, A and Smoller, JW and Squassina, A and Stahl, EA and Stefansson, H and Stefansson, K and Stordal, E and Streit, F and Sullivan, PF and Turecki, G and Vaaler, AE and Vieta, E and Vincent, JB and Waldman, ID and Weickert, CS and Weickert, TW and Werge, T and Whiteman, DC and Zwart, JA and Edenberg, HJ and McQuillin, A and Forstner, AJ and Mullins, N and Di Florio, A and Ophoff, RA and Andreassen, OA and , },
title = {Genomics yields biological and phenotypic insights into bipolar disorder.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {39843750},
issn = {1476-4687},
abstract = {Bipolar disorder is a leading contributor to the global burden of disease[1]. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown[2]. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings[3], and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder[4], highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.},
}

@article {pmid39842626,
year = {2025},
author = {Xu, X and Gong, Q and Wang, XD},
title = {MK-801 attenuates one-trial tolerance in the elevated plus maze via the thalamic nucleus reuniens.},
journal = {Neuropharmacology},
volume = {},
number = {},
pages = {110318},
doi = {10.1016/j.neuropharm.2025.110318},
pmid = {39842626},
issn = {1873-7064},
abstract = {Anxiety, a future-oriented negative emotional state, is characterized by heightened arousal and vigilance. The elevated plus maze (EPM) test is a widely used assay of anxiety-related behaviors in rodents and shows a phenomenon where animals with prior test experience tend to avoid open arms in retest sessions. While this one-trial tolerance (OTT) phenomenon limits the reuse of the EPM test, the potential mechanisms remain unsolved. Here, we found that neither anxiogenic factors like acute restraint stress nor anxiolytic factors like diazepam (2 mg/kg) influenced the emergence of the OTT phenomenon in mice in the EPM test. In contrast, OTT was markedly attenuated by MK-801 (0.1 mg/kg), a non-competitive N-methyl-D-aspartate receptor antagonist. Through the use of c-fos mapping, MK-801 was found to increase neuronal activation in the thalamic nucleus reuniens (Re). Moreover, chemogenetic inactivation of Re neurons could prevent the effects of MK-801. Our findings suggest the Re as a crucial brain region in emotional adaptation in the EPM and shed light on the experimental design optimization and mechanistic investigation of anxiety-related behaviors.},
}

@article {pmid39845389,
year = {2023},
author = {Tan, J and Joe, N and Kong, V and Clarke, D and Ko, J and Amey, J and Denize, B and Marsden, G and Yen, DA and Christey, G},
title = {Contemporary management of blunt colonic injuries - Experience from a level one trauma centre in New Zealand.},
journal = {Surgery in practice and science},
volume = {13},
number = {},
pages = {100179},
pmid = {39845389},
issn = {2666-2620},
abstract = {INTRODUCTION: Blunt colonic injury (BCI) is relatively rare, and literature on the topic is sparse. This study reviews our contemporary experience in its management at a level-one trauma centre in New Zealand.

MATERIALS AND METHODS: This was a retrospective study (January 2012 to December 2020) that included all patients who sustained a BCI managed at Waikato Hospital, New Zealand.

RESULTS: Of the total of 1181 patients with blunt abdominal trauma, 69 (6%) of them sustained a BCI (49% male, mean age: 36 years). 78 separate colonic injuries were identified in the 69 cases. The most commonly injured segment was the ascending colon 49% (38/78). Eighty percent (55/69) underwent a CT scan, with only 16 showing definite evidence of a colonic injury. AAST Grade 1 was the most common (81%). Fifteen patients underwent damage control surgery. All 11 grade 1 injuries were repaired primarily, whilst the other four grade 4 and 5 colonic injuries were resected, with 3 having a subsequent stoma formation and one delayed anastomosis. There were four mortalities. Patients who had negative or equivocal admission CT findings for colonic injury had delays to the operating theatre and had poorer outcomes.

CONCLUSION: BCI is rare but is associated with a prolonged hospital stay. The treatment of BCI is similar to that of penetrating colonic injury. CT appeared inaccurate in many cases.},
}

@article {pmid39841068,
year = {2025},
author = {Zhang, J and Yang, X and Liang, Z and Lou, H and Cui, T and Shen, C and Gao, Z},
title = {A brain-computer interface system for lower-limb exoskeletons based on motor imagery and stacked ensemble approach.},
journal = {The Review of scientific instruments},
volume = {96},
number = {1},
pages = {},
doi = {10.1063/5.0232481},
pmid = {39841068},
issn = {1089-7623},
mesh = {*Brain-Computer Interfaces ; Humans ; *Exoskeleton Device ; *Lower Extremity/physiology ; Support Vector Machine ; Algorithms ; Imagination/physiology ; Electroencephalography/instrumentation ; Male ; },
abstract = {Existing lower limb exoskeletons (LLEs) have demonstrated a lack of sufficient patient involvement during rehabilitation training. To address this issue and better incorporate the patient's motion intentions, this paper proposes an online brain-computer interface (BCI) system for LLE based motor imagery and stacked ensemble. The establishment of this online BCI system enables a comprehensive closed-loop control process, which includes the collection and decoding of brain signals, robotic control, and real-time feedback mechanisms. Additionally, an online experimental protocol that integrates visual and proprioceptive feedback is developed. To enhance decoding precision, we proposed a novel classification algorithm based on the stacking technique, termed weighted random forests-support vector machines (WRF-SVM). In this algorithm, WRFs function as the base learning models, while SVMs act as the meta-learning layer. To assess the efficacy of the BCI system and the classification algorithm, eight subjects were recruited for testing. The outcomes of both online and offline experiments exhibit high classification accuracy, confirming the viability and utility of the BCI system. We are confident that our approach holds significant promise for practical applications in the field of LLE technology.},
}

*Brain-Computer Interfaces
Humans
*Exoskeleton Device
*Lower Extremity/physiology
Support Vector Machine
Algorithms
Imagination/physiology
Electroencephalography/instrumentation
Male

@article {pmid39840009,
year = {2024},
author = {Yang, XY and Huang, S and Fu, QJ and Galvin, J and Chen, B and Liu, JS and Tao, DD},
title = {Preliminary evaluation of the FastCAP for users of the Nurotron cochlear implant.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1523212},
pmid = {39840009},
issn = {1662-4548},
abstract = {BACKGROUND: Electrically evoked compound action potential (ECAP) can be used to measure the auditory nerve's response to electrical stimulation in cochlear implant (CI) users. In the Nurotron CI system, extracting the ECAP waveform from the stimulus artifact is time-consuming.

METHOD: We developed a new paradigm ("FastCAP") for use with Nurotron CI devices. In electrically evoked compound action potential in fast mode (FastCAP), N recordings are averaged directly on the CI hardware before data transmission, significantly reducing data transmission time. FastCAPs and ECAPs were measured across five electrodes and four stimulation levels per electrode. The FastCAP stimulation rate (33.3 Hz) is also faster than the ECAP rate (2.5 Hz).

RESULTS: Results showed strong correlations between ECAPs and FastCAPs for N1 latency (r = 0.84, p < 0.001) and N1 amplitude (r = 0.97, p < 0.001). Test-retest reliability for FastCAPs was also high, with intraclass correlation coefficients of r = 0.87 for N1 latency (p < 0.001) and r = 0.96 for N1 amplitude (p < 0.001). The mean test time was 46.9 ± 1.4 s for the FastCAP and 340.3 ± 6.3 s for the ECAP. The FastCAP measurement time was significantly shorter than the ECAP measurement time (W = -210.0, p < 0.001). FastCAP thresholds were significantly correlated with behavioral thresholds in 7/20 participants and with comfortable loudness levels in 11/20 participants. The time required to measure FastCAPs was significantly lower than that for ECAPs. The FastCAP paradigm maintained the accuracy and reliability the ECAP measurements while offering a significant reduction in time requirements.

CONCLUSION: This preliminary evaluation suggests that the FastCAP could be an effective clinical tool to optimize CI processor settings (e.g., threshold stimulation levels) in users of the Nurotron CI device.},
}

@article {pmid39839676,
year = {2024},
author = {Kusano, K and Hayashi, M and Iwama, S and Ushiba, J},
title = {Improved motor imagery skills after repetitive passive somatosensory stimulation: a parallel-group, pre-registered study.},
journal = {Frontiers in neural circuits},
volume = {18},
number = {},
pages = {1510324},
pmid = {39839676},
issn = {1662-5110},
mesh = {Humans ; Male ; Female ; Adult ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Imagination/physiology ; *Electric Stimulation/methods ; Young Adult ; Motor Skills/physiology ; Somatosensory Cortex/physiology ; Sensorimotor Cortex/physiology ; },
abstract = {INTRODUCTION: Motor-imagery-based Brain-Machine Interface (MI-BMI) has been established as an effective treatment for post-stroke hemiplegia. However, the need for long-term intervention can represent a significant burden on patients. Here, we demonstrate that motor imagery (MI) instructions for BMI training, when supplemented with somatosensory stimulation in addition to conventional verbal instructions, can help enhance MI capabilities of healthy participants.

METHODS: Sixteen participants performed MI during scalp EEG signal acquisition before and after somatosensory stimulation to assess MI-induced cortical excitability, as measured using the event-related desynchronization (ERD) of the sensorimotor rhythm (SMR). The non-dominant left hand was subjected to neuromuscular electrical stimulation above the sensory threshold but below the motor threshold (St-NMES), along with passive movement stimulation using an exoskeleton. Participants were randomly divided into an intervention group, which received somatosensory stimulation, and a control group, which remained at rest without stimulation.

RESULTS: The intervention group exhibited a significant increase in SMR-ERD compared to the control group, indicating that somatosensory stimulation contributed to improving MI ability.

DISCUSSION: This study demonstrates that somatosensory stimulation, combining electrical and mechanical stimuli, can improve MI capability and enhance the excitability of the sensorimotor cortex in healthy individuals.},
}

Humans
Male
Female
Adult
*Brain-Computer Interfaces
*Electroencephalography/methods
*Imagination/physiology
*Electric Stimulation/methods
Young Adult
Motor Skills/physiology
Somatosensory Cortex/physiology
Sensorimotor Cortex/physiology

@article {pmid39837322,
year = {2025},
author = {Coulter, ME and Gillespie, AK and Chu, J and Denovellis, EL and Nguyen, TTK and Liu, DF and Wadhwani, K and Sharma, B and Wang, K and Deng, X and Eden, UT and Kemere, C and Frank, LM},
title = {Closed-loop modulation of remote hippocampal representations with neurofeedback.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2024.12.023},
pmid = {39837322},
issn = {1097-4199},
abstract = {Humans can remember specific remote events without acting on them and influence which memories are retrieved based on internal goals. However, animal models typically present sensory cues to trigger memory retrieval and then assess retrieval based on action. Thus, it is difficult to determine whether measured neural activity patterns relate to the cue(s), the memory, or the behavior. We therefore asked whether retrieval-related neural activity could be generated in animals without cues or a behavioral report. We focused on hippocampal "place cells," which primarily represent the animal's current location (local representations) but can also represent locations away from the animal (remote representations). We developed a neurofeedback system to reward expression of remote representations and found that rats could learn to generate specific spatial representations that often jumped directly to the experimenter-defined target location. Thus, animals can deliberately engage remote representations, enabling direct study of retrieval-related activity in the brain.},
}

@article {pmid39834791,
year = {2025},
author = {Alotaibi, S and Alotaibi, MM and Alghamdi, FS and Alshehri, MA and Bamusa, KM and Almalki, ZF and Alamri, S and Alghamdi, AJ and Alhazmi, M and Osman, H and Khandaker, MU},
title = {The role of fMRI in the mind decoding process in adults: a systematic review.},
journal = {PeerJ},
volume = {13},
number = {},
pages = {e18795},
doi = {10.7717/peerj.18795},
pmid = {39834791},
issn = {2167-8359},
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Adult ; *Brain/physiology/diagnostic imaging ; *Brain Mapping/methods ; Mentalization/physiology ; Theory of Mind/physiology ; Temporal Lobe/physiology/diagnostic imaging ; },
abstract = {BACKGROUND: Functional magnetic resonance imaging (fMRI) has revolutionized our understanding of brain activity by non-invasively detecting changes in blood oxygen levels. This review explores how fMRI is used to study mind-reading processes in adults.

METHODOLOGY: A systematic search was conducted across Web of Science, PubMed, and Google Scholar. Studies were selected based on strict inclusion and exclusion criteria: peer-reviewed; published between 2000 and 2024 (in English); focused on adults; investigated mind-reading (mental state decoding, brain-computer interfaces) or related processes; and employed various mind-reading techniques (pattern classification, multivariate analysis, decoding algorithms).

RESULTS: This review highlights the critical role of fMRI in uncovering the neural mechanisms of mind-reading. Key brain regions involved include the superior temporal sulcus (STS), medial prefrontal cortex (mPFC), and temporoparietal junction (TPJ), all crucial for mentalizing (understanding others' mental states).

CONCLUSIONS: This review emphasizes the importance of fMRI in advancing our knowledge of how the brain interprets and processes mental states. It offers valuable insights into the current state of mind-reading research in adults and paves the way for future exploration in this field.},
}

Humans
*Magnetic Resonance Imaging/methods
Adult
*Brain/physiology/diagnostic imaging
*Brain Mapping/methods
Mentalization/physiology
Theory of Mind/physiology
Temporal Lobe/physiology/diagnostic imaging

@article {pmid39834638,
year = {2024},
author = {Liu, J and Yang, Z and Wang, Y and Wang, L and Li, Z},
title = {Editorial: Micro/nano devices and technologies for neural science and medical applications.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {12},
number = {},
pages = {1545853},
doi = {10.3389/fbioe.2024.1545853},
pmid = {39834638},
issn = {2296-4185},
}

@article {pmid39833722,
year = {2025},
author = {Kocher, HM and , and , and , and Sasieni, P and Corrie, P and McNamara, MG and Sarker, D and Froeling, FEM and Christie, A and Gillmore, R and Khan, K and Propper, D},
title = {Study protocol: multi-centre, randomised controlled clinical trial exploring stromal targeting in locally advanced pancreatic cancer; STARPAC2.},
journal = {BMC cancer},
volume = {25},
number = {1},
pages = {106},
pmid = {39833722},
issn = {1471-2407},
support = {MR/S036601/1/MRC_/Medical Research Council/United Kingdom ; MR/S036601/1/MRC_/Medical Research Council/United Kingdom ; PCRFTB//Pancreatic Cancer Research Fund/ ; PCRFTB//Pancreatic Cancer Research Fund/ ; },
mesh = {Humans ; *Pancreatic Neoplasms/drug therapy/pathology/mortality/surgery ; *Deoxycytidine/analogs & derivatives/therapeutic use/administration & dosage ; *Gemcitabine ; *Paclitaxel/therapeutic use/administration & dosage ; *Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Albumins/administration & dosage/therapeutic use ; Carcinoma, Pancreatic Ductal/drug therapy/pathology/surgery/mortality ; Tretinoin/therapeutic use/administration & dosage ; Male ; Female ; Progression-Free Survival ; Multicenter Studies as Topic ; Adult ; Clinical Trials, Phase II as Topic ; Randomized Controlled Trials as Topic ; Middle Aged ; Aged ; Stromal Cells/pathology ; },
abstract = {BACKGROUND: Pancreatic cancer (PDAC: pancreatic ductal adenocarcinoma, the commonest form), a lethal disease, is best treated with surgical excision but is feasible in less than a fifth of patients. Around a third of patients presentlocally advanced, inoperable, non-metastatic (laPDAC), whose stadrd of care is palliative chemotherapy; a small minority are down-sized sufficiently to enable surgical excision. We propose a phase II clinical trial to test whether a combination of standard chemotherapy (gemcitabine & nab-Paclitaxel: GEM-NABP) and repurposing All Trans Retinoic Acid (ATRA) to target the stroma may extend progression-free survival and enable successful surgical resection for patients with laPDAC, since data from phase IB clinical trial demonstrate safety of GEM-NABP-ATRA combination to patients with advanced PDAC with potential therapeutic benefit.

METHODS: Patients with laPDAC will receive at least six cycles of GEM-NABP with 1:1 randomisation to receive this with or without ATRA to assess response, until progression or intolerance. Those with stable/responding disease may undergo surgical resection. Primary endpoint is progression free survival (PFS) defined as the time from the date of randomisation to the date of first documented tumour progression (response evaluation criteria in solid tumours [RECIST] v1.1) or death from any cause, whichever occurs first. Secondary endpoints include objective response rate (ORR), overall survival (OS), safety and tolerability, surgical resection rate, R0 surgical resection rate and patient reported outcome measures (PROMS) as measured by questionnaire EQ-5D-5L. Exploratory endpoints include a decrease or increase in CA19-9 and serum Vitamin A over time correlated with ORR, PFS, and OS.

DISCUSSION: STARPAC2 aims to assess the role of stromal targeting in laPDAC.

TRIAL REGISTRATION: EudraCT: 2019-004231-23; NCT04241276; ISRCTN11503604.},
}

Humans
*Pancreatic Neoplasms/drug therapy/pathology/mortality/surgery
*Deoxycytidine/analogs & derivatives/therapeutic use/administration & dosage
*Gemcitabine
*Paclitaxel/therapeutic use/administration & dosage
*Antineoplastic Combined Chemotherapy Protocols/therapeutic use
Albumins/administration & dosage/therapeutic use
Carcinoma, Pancreatic Ductal/drug therapy/pathology/surgery/mortality
Tretinoin/therapeutic use/administration & dosage
Male
Female
Progression-Free Survival
Multicenter Studies as Topic
Adult
Clinical Trials, Phase II as Topic
Randomized Controlled Trials as Topic
Middle Aged
Aged
Stromal Cells/pathology

@article {pmid39833405,
year = {2025},
author = {Willsey, MS and Shah, NP and Avansino, DT and Hahn, NV and Jamiolkowski, RM and Kamdar, FB and Hochberg, LR and Willett, FR and Henderson, JM},
title = {A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis.},
journal = {Nature medicine},
volume = {},
number = {},
pages = {},
pmid = {39833405},
issn = {1546-170X},
support = {U01-DC017844//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; R01-DC014034//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; },
abstract = {People with paralysis express unmet needs for peer support, leisure activities and sporting activities. Many within the general population rely on social media and massively multiplayer video games to address these needs. We developed a high-performance, finger-based brain-computer-interface system allowing continuous control of three independent finger groups, of which the thumb can be controlled in two dimensions, yielding a total of four degrees of freedom. The system was tested in a human research participant with tetraplegia due to spinal cord injury over sequential trials requiring fingers to reach and hold on targets, with an average acquisition rate of 76 targets per minute and completion time of 1.58 ± 0.06 seconds-comparing favorably to prior animal studies despite a twofold increase in the decoded degrees of freedom. More importantly, finger positions were then used to control a virtual quadcopter-the number-one restorative priority for the participant-using a brain-to-finger-to-computer interface to allow dexterous navigation around fixed- and random-ringed obstacle courses. The participant expressed or demonstrated a sense of enablement, recreation and social connectedness that addresses many of the unmet needs of people with paralysis.},
}

@article {pmid39833404,
year = {2025},
author = {Ramsey, NF and Vansteensel, MJ},
title = {The expanding repertoire of brain-computer interfaces.},
journal = {Nature medicine},
volume = {},
number = {},
pages = {},
pmid = {39833404},
issn = {1546-170X},
}

@article {pmid39833218,
year = {2025},
author = {Sun, Z and Huang, J and Ma, X and Liang, J and Sun, C and Hu, L and He, H and Yu, G},
title = {A Low-Field MRI Dataset For Spatiotemporal Analysis of Developing Brain.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {109},
pmid = {39833218},
issn = {2052-4463},
support = {62076218//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {*Magnetic Resonance Imaging ; Humans ; *Brain/diagnostic imaging/growth & development ; Infant ; Spatio-Temporal Analysis ; Infant, Newborn ; },
abstract = {Recently, imaging investigation of brain development has increasingly captured the attention of researchers and clinicians in an attempt to understand the link between the brain and behavioral changes. Although high-field MR imaging of infants is feasible, the necessary customizations have limited its accessibility, affordability, and reproducibility. Low-field MR, as an emerging solution for scrutinizing developing brain, has exhibited its unique advantages in safety, portability, and cost-effectiveness. The presented low-field infant structural MR data aims to manifest the feasibility of using low-field MR image to exam brain structural changes during early life in infants. The dataset comprises 100 T2 weighed MR images from infants with in-plane resolution of ~0.85 mm and ~6 mm slice thickness. To demonstrate the potential utility, we conducted atlas-based whole brain segmentations and volumetric quantifications to analyze brain development features in first 10 week in postnatal life. This dataset addresses the scarcity of a large, extended-span infant brain dataset that restricts the further tracking of infant brain development trajectories and the development of routine low-field MR imaging pipelines.},
}

*Magnetic Resonance Imaging
Humans
*Brain/diagnostic imaging/growth & development
Infant
Spatio-Temporal Analysis
Infant, Newborn

@article {pmid39833018,
year = {2025},
author = {Pickles, J and Griffiths, L and McCloskey, AP and Vasey, N and Lim, E and Rathbone, AP},
title = {Developing a behaviour change intervention using information about greenhouse gas emissions to reduce liquid antibiotic prescribing.},
journal = {Research in social & administrative pharmacy : RSAP},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.sapharm.2025.01.006},
pmid = {39833018},
issn = {1934-8150},
abstract = {INTRODUCTION: The determinants of antimicrobial prescribing often involve social influence, which can be harnessed through behaviour change techniques (BCTs). While previous studies have used BCTs to address antimicrobial resistance, there is a lack of evidence regarding their application to address climate change-related issues in antibiotic prescribing. This study aimed to develop a behaviour change intervention (BCI) using information about greenhouse gas emissions to reduce liquid antibiotic prescribing.

METHODS: A convenience sample of participants from a primary care practice in North East England participated in semi-structured interviews. The intervention design was guided by the Theoretical Domains Framework (TDF) and the Capability, Opportunity, Motivation - Behaviour (COM-B) model. Data were analysed thematically, mapped to the TDF, and used to refine the BCI.

FINDINGS: Participants identified motivating factors related to high rates of liquid prescribing, climate change, and solid oral dosage form (pill) aversion. The broader context of practice, such as initiatives reduce cost and improve sustainability, provided opportunities for intervention. Participants demonstrated the capability to change prescribing behaviours and expressed willingness to share resources within their teams.

CONCLUSION: This study underscores the potential of BCIs using greenhouse gas emissions data to reduce liquid antibiotic prescribing. Further research should focus on implementing and evaluating these interventions in practice settings.},
}

@article {pmid39832646,
year = {2025},
author = {Chen, Q and Pan, C and Shen, Y and Pan, Q and Zhang, Q and Wang, J and Hu, Y and Xu, H and Gong, M and Jia, K},
title = {Atypical subcortical involvement in emotional face processing in major depressive disorder with and without comorbid social anxiety.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jad.2025.01.081},
pmid = {39832646},
issn = {1573-2517},
abstract = {Previous research on major depressive disorder (MDD) has largely focused on cognitive biases and abnormalities in cortico-limbic circuitry during emotional face processing. However, it remains unclear whether these abnormalities start at early perceptual stages via subcortical pathways and how comorbid social anxiety influences this process. Here, we investigated subcortical mechanisms in emotional face processing using a psychophysical method that measures monocular advantage (i.e., superior discrimination performance when two stimuli are presented to the same eye than to different eyes). Participants included clinical patients diagnosed with MDD (n = 32), patients with MDD comorbid with social anxiety (comorbid MDD-SAD, n = 32), and a control group of healthy participants (HC, n = 32). We assessed monocular advantage across different emotions (neutral, sad, angry) and among groups. Results indicated that individuals with MDD showed a stronger monocular advantage for sad expressions compared to neutral and angry expressions. In contrast, HC and comorbid MDD-SAD groups showed a greater monocular advantage for neural over negative expressions. Cross-group comparisons revealed that MDD group had a stronger monocular advantage for sad expressions than both HC and comorbid MDD-SAD groups. Additionally, self-reported depressive symptoms were positively correlated with monocular advantage for sad expressions, while social anxiety symptoms were negatively correlated with monocular advantage for negative expressions. These findings suggest atypical early perceptual processing of sadness in individuals with MDD via subcortical mechanisms, with comorbid social anxiety potentially counteracting this effect. This study may inform novel interventions targeting sensory processing and expand beyond cognitive bias modification.},
}

@article {pmid39832388,
year = {2025},
author = {Haddix, C and Bates, M and Garcia Pava, S and Salmon Powell, E and Sawaki, L and Sunderam, S},
title = {Electroencephalogram Features Reflect Effort Corresponding to Graded Finger Extension: Implications for Hemiparetic Stroke.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/adabeb},
pmid = {39832388},
issn = {2057-1976},
abstract = {Brain-computer interfaces (BCIs) offer disabled individuals the means to interact with devices by decoding the electroencephalogram (EEG). However, decoding intent in fine motor tasks can be challenging, especially in stroke survivors with cortical lesions. Here, we attempt to decode graded finger extension from the EEG in stroke patients with left-hand paresis and healthy controls. Participants extended their fingers to one of four levels: low, medium, high, or "no-go" (none), while hand, muscle (electromyography: EMG), and brain (EEG) activity were monitored. Event-related desynchronization (ERD) was measured as the change in 8-30 Hz EEG power during movement. Classifiers were trained on the ERD, EMG power, or both (EEG+EMG) to decode finger extension, and accuracy assessed via four-fold cross-validation for each hand of each participant. Mean accuracy exceeded chance (25%) for controls (n=11) at 62% for EMG, 60% for EEG, and 71% for EEG+EMG on the left hand; and 67%, 60%, and 74%, respectively, on the right hand. Accuracies were similar on the unimpaired right hand for the stroke group (n=3): 61%, 68%, and 78%, respectively. But on the paretic left hand, EMG only discriminated no-go from movement above chance (41%); in contrast, EEG gave 65% accuracy (68% for EEG+EMG), comparable to the non-paretic hand. The median ERD was significant (p < 0.01) over the cortical hand area in both groups and increased with each level of finger extension. But while the ERD favored the hemisphere contralateral to the active hand as expected, it was ipsilateral for the left hand of stroke due to the lesion in the right hemisphere, which may explain its discriminative ability. Hence, the ERD captures effort in finger extension regardless of success or failure at the task; and harnessing residual EMG improves the correlation. This marker could be leveraged in rehabilitative protocols that focus on fine motor control.},
}

@article {pmid39832179,
year = {2025},
author = {Jiang, M and Tan, Y and Wang, X and Hao, Y},
title = {The Phonograms' Genuine-Character Status: What the Embedded Semantic Radicals' Semantic Activation Live by.},
journal = {Brain and behavior},
volume = {15},
number = {1},
pages = {e70277},
doi = {10.1002/brb3.70277},
pmid = {39832179},
issn = {2162-3279},
mesh = {Humans ; *Semantics ; Male ; Young Adult ; Female ; Adult ; Reading ; },
abstract = {BACKGROUND: In Chinese phonogram processing studies, it is widely accepted that both character and non-character semantic radicals could be semantically activated. However, little attention was paid to the underlying workings that enabled the semantic radicals' semantic activation.

PURPOSE: The present study aimed to address the above issue by conducting two experiments.

METHODS: Experiment 1 was committed to confirming whether both character and non-character semantic radicals could be semantically activated when embedded in genuine Chinese phonograms. Experiment 2 was devoted to exploring whether the same semantic radicals could also be semantically activated when incorporated in Chinese pseudo-characters.

RESULTS: Results demonstrated that both character and non-character semantic radicals embedded in the genuine phonograms were semantically activated, but those placed in the pseudo-characters underwent no semantic activation, suggesting that the semantic activation of semantic radicals was genuine-character status-dependent, irrespective of the semantic radicals' characterhood.

CONCLUSION: It seems that the genuine-character status and the meaning of the host phonogram have strong sway on the semantic activation of semantic radicals.},
}

Humans
*Semantics
Male
Young Adult
Female
Adult
Reading

@article {pmid39830198,
year = {2024},
author = {Liu, D and Wang, N and Song, M and Chai, X and He, Q and Cao, T and Kong, D and Song, Z and Zhang, G and Liu, L and Wang, X and Chen, G and Yin, S and Yang, Y and Zhao, J},
title = {Global glucose metabolism rate as diagnostic marker for disorder of consciousness of patients: quantitative FDG-PET study.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1425271},
doi = {10.3389/fneur.2024.1425271},
pmid = {39830198},
issn = {1664-2295},
abstract = {OBJECTIVE: This study was to employ 18F-flurodeoxyglucose (FDG-PET) to evaluate the resting-state brain glucose metabolism in a sample of 46 patients diagnosed with disorders of consciousness (DoC). The aim was to identify objective quantitative metabolic indicators and predictors that could potentially indicate the level of awareness in these patients.

METHODS: A cohort of 46 patients underwent Coma Recovery Scale-Revised (CRS-R) assessments in order to distinguish between the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS). Additionally, resting-state FDG-PET data were acquired from both the patient group and a control group consisting of 10 healthy individuals. The FDG-PET data underwent reorientation, spatial normalization to a stereotaxic space, and smoothing. The normalization procedure utilized a customized template following the methodology outlined by Phillips et al. Mean cortical metabolism of the overall sample was utilized for distinguishing between UWS and MCS, as well as for predicting the outcome at a 1-year follow-up through the application of receiver operating characteristic (ROC) analysis.

RESULTS: We used Global Glucose Metabolism as the Diagnostic Marker. A one-way ANOVA revealed that there was a statistically significant difference in cortical metabolic index between two groups (F(2, 53) = 7.26, p < 0.001). Multiple comparisons found that the mean of cortical metabolic index was significantly different between MCS (M = 4.19, SD = 0.64) and UWS group (M = 2.74, SD = 0.94,p < 0.001). Also, the mean of cortical metabolic index was significantly different between MCS and healthy group (M = 7.88, SD = 0.80,p < 0.001). Using the above diagnostic criterion, the diagnostic accuracy yielded an area under the curve (AUC) of 0.89 across the pooled cohort (95%CI 0.79-0.99). There was an 85% correct classification between MCS and UWS, with 88% sensitivity and 81% specificity for MCS. The best classification rate in the derivation cohort was achieved at a metabolic index of 3.32 (41% of the mean cortical metabolic index in healthy controls).

CONCLUSION: Our findings demonstrate that conscious awareness requires a minimum of 41% of normal cortical activity, as indicated by metabolic rates.},
}

@article {pmid39825141,
year = {2025},
author = {Oby, ER and Degenhart, AD and Grigsby, EM and Motiwala, A and McClain, NT and Marino, PJ and Yu, BM and Batista, AP},
title = {Dynamical constraints on neural population activity.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {39825141},
issn = {1546-1726},
support = {NCS BCS1533672//National Science Foundation (NSF)/ ; NS105318//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; },
abstract = {The manner in which neural activity unfolds over time is thought to be central to sensory, motor and cognitive functions in the brain. Network models have long posited that the brain's computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate. We leveraged a brain-computer interface to challenge monkeys to violate the naturally occurring time courses of neural population activity that we observed in the motor cortex. This included challenging animals to traverse the natural time course of neural activity in a time-reversed manner. Animals were unable to violate the natural time courses of neural activity when directly challenged to do so. These results provide empirical support for the view that activity time courses observed in the brain indeed reflect the underlying network-level computational mechanisms that they are believed to implement.},
}

@article {pmid39824976,
year = {2025},
author = {Zhao, J and Shen, Q and Yong, X and Li, X and Tian, X and Sun, S and Xu, Z and Zhang, X and Zhang, L and Yang, H and Shao, Z and Xu, H and Jiang, Y and Zhang, Y and Yan, W},
title = {Cryo-EM reveals cholesterol binding in the lysosomal GPCR-like protein LYCHOS.},
journal = {Nature structural & molecular biology},
volume = {},
number = {},
pages = {},
pmid = {39824976},
issn = {1545-9985},
abstract = {Cholesterol plays a pivotal role in modulating the activity of mechanistic target of rapamycin complex 1 (mTOR1), thereby regulating cell growth and metabolic homeostasis. LYCHOS, a lysosome-localized G-protein-coupled receptor-like protein, emerges as a cholesterol sensor and is capable of transducing the cholesterol signal to affect the mTORC1 function. However, the precise mechanism by which LYCHOS recognizes cholesterol remains unknown. Here, using cryo-electron microscopy, we determined the three-dimensional structural architecture of LYCHOS in complex with cholesterol molecules, revealing a unique arrangement of two sequential structural domains. Through a comprehensive analysis of this structure, we elucidated the specific structural features of these two domains and their collaborative role in the process of cholesterol recognition by LYCHOS.},
}

@article {pmid39824751,
year = {2025},
author = {Cong, L and Wang, X and Wang, J and Liu, W and Xu, W and Zhang, S and Xu, S},
title = {Three-Dimensional SERS-Active Hydrogel Microbeads Enable Highly Sensitive Homogeneous Phase Detection of Alkaline Phosphatase in Biosystems.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.4c18139},
pmid = {39824751},
issn = {1944-8252},
abstract = {Alkaline phosphatase (ALP) is a biomarker for many diseases, and monitoring its activity level is important for disease diagnosis and treatment. In this study, we used the microdroplet technology combined with an in situ laser-induced polymerization method to prepare the Ag nanoparticle (AgNP) doped hydrogel microbeads (HMBs) with adjustable pore sizes that allow small molecules to enter while blocking large molecules. The AgNPs embedded in the hydrogel microspheres can provide SERS activity, improving the SERS signal of small molecules that diffuse to the AgNPs. A specific hydrolysis reaction of ALP on 5-bromo-4-chloro-3-indolylphosphate (BCIP) was introduced and itsproduct 5,5'-dibromo-4,4'-dichloro-1H,1H-[2,2']bisindolyl-3,3'-dione (BCI) was employed to assess ALP activity due to its highly resonance Raman activity. The sensing platform was applied to model ALP activity in serum and evaluate ALP inhibitors. The SERS assay showed higher sensitivity than UV-vis absorption spectroscopy, with the lowest detectable ALP concentration of 1.0 × 10[-20] M. In addition, the ALP activity in HepG2 cells was evaluated using this sensing platform, showing lower ALP-expressing activity than that of controls in response to hypoxia and iron metastasis. This SERS-activated HMB shows great potential in detecting ALP and is expected to help analyze complex clinical samples.},
}

@article {pmid39823647,
year = {2025},
author = {Ma, X and Rizzoglio, F and Bodkin, KL and Miller, LE},
title = {Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adab93},
pmid = {39823647},
issn = {1741-2552},
abstract = {OBJECTIVE: Creating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.

APPROACH: We devised an unsupervised approach that relies on the structure of a low-dimensional neural manifold to implement a piecewise linear decoder. We created a distinctive dataset in which monkeys performed a diverse set of tasks, some trained, others innate, while we recorded neural signals from the motor cortex (M1) and electromyographs (EMGs) from upper limb muscles. We used both linear and nonlinear dimensionality reduction techniques to discover neural manifolds and applied unsupervised algorithms to identify clusters within those spaces. Finally, we fit a linear decoder of EMG for each cluster. A specific decoder was activated corresponding to the cluster each new neural data point belonged to.

MAIN RESULTS: We found clusters in the neural manifolds corresponding with the different tasks or task sub-phases. The performance of piecewise decoding improved as the number of clusters increased and plateaued gradually. With only two clusters it already outperformed a global linear decoder, and unexpectedly, it outperformed even a global recurrent neural network (RNN) decoder with 10-12 clusters.

SIGNIFICANCE: This study introduced a computationally lightweight solution for creating iBCI decoders that can function effectively across a broad range of tasks. EMG decoding is particularly challenging, as muscle activity is used, under varying contexts, to control interaction forces and limb stiffness, as well as motion. The results suggest that a piecewise linear decoder can provide a good approximation to the nonlinearity between neural activity and motor outputs, a result of our increased understanding of the structure of neural manifolds in motor cortex.},
}

@article {pmid39821163,
year = {2025},
author = {Zou, Y and Xu, J and Chen, Y},
title = {Volatility, correlation and risk spillover effect between freight rates in BCI and BPI markets: Evidence from static and dynamic GARCH-Copula and dynamic CoVaR models.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0315167},
doi = {10.1371/journal.pone.0315167},
pmid = {39821163},
issn = {1932-6203},
mesh = {Humans ; *Commerce ; Models, Economic ; Ships ; Risk ; },
abstract = {The dry bulk shipping market plays a crucial role in global trade. To examine the volatility, correlation, and risk spillover between freight rates in the BCI and BPI markets, this paper employs the GARCH-Copula-CoVaR model. We analyze the dynamic behavior of the secondary market freight index for dry bulk cargo, highlighting its performance in a complex financial environment and offering empirical support for the shipping industry and financial markets. The findings reveal that: (1) There are significant differences in correlation across various routes, with the correlation between BCI and BPI routes fluctuating over time. Among all route combinations, C5 and P3A_03 exhibit the highest positive correlation. (2) A one-way risk spillover exists between P1A_03 an C5, while two-way positive risk spillover is observed between other routes. This suggests that when a risk materializes on a specific route, other routes are also exposed to potential risks, with varying intensities of spillover. (3) The distance and geographical location of routes may be key factors influencing the differing intensities of risk spillover. This highlights the need to consider the geographical characteristics of routes in understanding risk transmission. This paper aims to provide risk management strategies based on these empirical findings, assisting shipping companies and investors in developing more effective responses to market volatility.},
}

Humans
*Commerce
Models, Economic
Ships
Risk

@article {pmid39820611,
year = {2025},
author = {D, J and K C, S},
title = {Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0311942},
doi = {10.1371/journal.pone.0311942},
pmid = {39820611},
issn = {1932-6203},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Signal-To-Noise Ratio ; },
abstract = {In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation. In this work, we propose a novel method that addresses these challenges by employing empirical mode decomposition (EMD) for feature extraction and a parallel convolutional neural network (PCNN) for feature classification. This approach aims to mitigate non-stationary issues, improve performance speed, and enhance classification accuracy. We validate the effectiveness of our proposed method using datasets from the BCI competition IV, specifically datasets 2a and 2b, which contain motor imagery EEG signals. Our method focuses on identifying two- and four-class motor imagery EEG signal classifications. Additionally, we introduce a transfer learning technique to fine-tune the model for individual subjects, leveraging important features extracted from a group dataset. Our results demonstrate that the proposed EMD-PCNN method outperforms existing approaches in terms of classification accuracy. We conduct both qualitative and quantitative analyses to evaluate our method. Qualitatively, we employ confusion matrices and various performance metrics such as specificity, sensitivity, precision, accuracy, recall, and f1-score. Quantitatively, we compare the classification accuracies of our method with those of existing approaches. Our findings highlight the superiority of the proposed EMD-PCNN method in accurately classifying motor imagery EEG signals. The enhanced performance and robustness of our method underscore its potential for broader applicability in real-world scenarios.},
}

*Electroencephalography/methods
Humans
*Neural Networks, Computer
*Brain-Computer Interfaces
Algorithms
Signal Processing, Computer-Assisted
Imagination/physiology
Signal-To-Noise Ratio

@article {pmid39819697,
year = {2025},
author = {Xue, YY and Zhang, ZS and Lin, RR and Huang, HF and Zhu, KQ and Chen, DF and Wu, ZY and Tao, QQ},
title = {Correction: CD2AP deficiency aggravates Alzheimer's disease phenotypes and pathology through p38 MAPK activation.},
journal = {Translational neurodegeneration},
volume = {14},
number = {1},
pages = {3},
pmid = {39819697},
issn = {2047-9158},
}

@article {pmid39819671,
year = {2025},
author = {Liu, DH and Kumar, S and Alawieh, H and Racz, FS and Millan, JDR},
title = {Personalized μ-transcranial alternating current stimulation improves online brain-computer interface control.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ada980},
pmid = {39819671},
issn = {1741-2552},
abstract = {OBJECTIVE: A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS).

APPROACH: Previous studies have identified that the peak power spectral density (PSD) value in sensorimotor idling rhythms is a neural correlate of participants' upper limb MI-BCI performances. In this active-controlled, single-blind study, we applied 20 minutes of tACS at the participant-specific, peak µ frequency in resting-state sensorimotor rhythms (SMRs), with the goal of enhancing resting-state µ SMRs.

MAIN RESULTS: After tACS, we observed significant improvements in event-related desynchronizations (ERDs) of µ sensorimotor rhythms (SMRs), and in the performance of an online MI-BCI that decodes left versus right hand commands in healthy participants (N=10) -but not in an active control-stimulation control group (N=10). Lastly, we showed a significant correlation between the resting-state µ SMRs and µ ERD, offering a mechanistic interpretation behind the observed changes in online BCI performances.

SIGNIFICANCE: Our research lays the groundwork for future non-invasive interventions designed to enhance BCI performances, thereby improving the independence and interactions of individuals who rely on these systems.},
}

@article {pmid39819299,
year = {2025},
author = {Tsai, PC and Akpan, A and Tang, KT and Lakany, H},
title = {Brain computer interfaces for cognitive enhancement in older people - challenges and applications: a systematic review.},
journal = {BMC geriatrics},
volume = {25},
number = {1},
pages = {36},
pmid = {39819299},
issn = {1471-2318},
mesh = {Humans ; *Brain-Computer Interfaces ; Aged ; *Cognitive Dysfunction/psychology/therapy ; *Neurofeedback/methods/physiology ; *Electroencephalography/methods ; Cognition/physiology ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) offers promising solutions to cognitive enhancement in older people. Despite the clear progress received, there is limited evidence of BCI implementation for rehabilitation. This systematic review addresses BCI applications and challenges in the standard practice of EEG-based neurofeedback (NF) training in healthy older people or older people with mild cognitive impairment (MCI).

METHODS: Articles were searched via MEDLINE, PubMed, SCOPUS, SpringerLink, and Web of Science. 16 studies between 1st January 2010 to 1st November 2024 are included after screening using PRISMA. The risk of bias, system design, and neurofeedback protocols are reviewed.

RESULTS: The successful BCI applications in NF trials in older people were biased by the randomisation process and outcome measurement. Although the studies demonstrate promising results in effectiveness of research-grade BCI for cognitive enhancement in older people, it is premature to make definitive claims about widespread BCI usability and applicability.

SIGNIFICANCE: This review highlights the common issues in the field of EEG-based BCI for older people. Future BCI research could focus on trial design and BCI performance gaps between the old and the young to develop a robust BCI system that compensates for age-related declines in cognitive and motor functions.},
}

Humans
*Brain-Computer Interfaces
Aged
*Cognitive Dysfunction/psychology/therapy
*Neurofeedback/methods/physiology
*Electroencephalography/methods
Cognition/physiology

@article {pmid39818889,
year = {2025},
author = {Marasco, PD},
title = {Navigating the complexity of touch.},
journal = {Science (New York, N.Y.)},
volume = {387},
number = {6731},
pages = {248-249},
doi = {10.1126/science.adu7929},
pmid = {39818889},
issn = {1095-9203},
mesh = {*Brain-Computer Interfaces ; *Touch/physiology ; Humans ; Somatosensory Cortex/physiology ; Animals ; Touch Perception/physiology ; Electric Stimulation ; },
abstract = {Precise cortical microstimulation improves tactile experience in brain-machine interfaces.},
}

*Brain-Computer Interfaces
*Touch/physiology
Humans
Somatosensory Cortex/physiology
Animals
Touch Perception/physiology
Electric Stimulation

@article {pmid39818280,
year = {2025},
author = {Li, C and Ma, K and Li, S and Meng, X and Wang, R and Zhang, D and Zhu, Q},
title = {Multi-channel spatio-temporal graph attention contrastive network for brain disease diagnosis.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121013},
doi = {10.1016/j.neuroimage.2025.121013},
pmid = {39818280},
issn = {1095-9572},
abstract = {Dynamic brain networks (DBNs) can capture the intricate connections and temporal evolution among brain regions, becoming increasingly crucial in the diagnosis of neurological disorders. However, most existing researches tend to focus on isolated brain network sequence segmented by sliding windows, and they are difficult to effectively uncover the higher-order spatio-temporal topological pattern in DBNs. Meantime, it remains a challenge to utilize the structure connectivity prior in the DBNs analysis. To address these problems, we propose a multi-channel spatio-temporal graph attention contrastive network for DBNs analysis. Specifically, we first construct dynamic brain functional networks from fMRI data with sliding windows, and embed the structural connectivity derived from diffusion tensor imaging (DTI) to the dynamic functional connectivity graph representation to construct multi-modal brain network. Second, we develop a multi-channel spatial attention contrastive network to extract topological features from the brain network within each time window. This network incorporates an intra-window graph contrastive constraint to enhance the discriminative ability of the extracted features. Moreover, temporal dependencies across windows are captured by integrating feature embeddings through a self-attention mechanism, and the inter-window recurrent contrastive constraint is devised to extract higher-order spatio-temporal topological features. Finally, a multi-layer perceptron (MLP) is used to classify the brain networks. Experiments on epilepsy and ADNI datasets show that our method outperforms several state-of-the-art approaches in diagnosing performance, and it provides discriminative graph features for related brain diseases.},
}

@article {pmid39815608,
year = {2025},
author = {Dai, C and Fu, Y and Li, X and Lin, M and Li, Y and Li, X and Huang, K and Zhou, C and Xie, J and Zhao, Q and Hu, S},
title = {Clinical efficacy and safety of vortioxetine as an adjuvant drug for patients with bipolar depression.},
journal = {Journal of Zhejiang University. Science. B},
volume = {26},
number = {1},
pages = {26-38},
pmid = {39815608},
issn = {1862-1783},
support = {OO2020491//the Construction Fund of Key Medical Disciplines of Hangzhou/ ; 2023YFC2506200//the National Key Research and Development Program of China/ ; 2021C03107//the Zhejiang Provincial Key Research and Development Program/ ; JNL-2023001B//the Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021R52016//the Leading Talent of Scientific and Technological Innovation‒"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//the Innovation Team for Precision Diagnosis and Treatment of Major Brain Diseases/ ; 2022KTZ004//the Chinese Medical Education Association/ ; },
mesh = {Humans ; *Vortioxetine/therapeutic use ; *Bipolar Disorder/drug therapy ; Male ; Female ; Adult ; *Valproic Acid/therapeutic use/adverse effects ; Middle Aged ; Treatment Outcome ; Prospective Studies ; Pilot Projects ; Lurasidone Hydrochloride/therapeutic use/adverse effects ; Drug Therapy, Combination ; },
abstract = {OBJECTIVES: Whether vortioxetine has a utility as an adjuvant drug in the treatment of bipolar depression remains controversial. This study aimed to validate the efficacy and safety of vortioxetine in bipolar depression.

METHODS: Patients with bipolar Ⅱ depression were enrolled in this prospective, two-center, randomized, 12-week pilot trial. The main indicator for assessing treatment effectiveness was a Montgomery-Asberg Depression Rating Scale (MADRS) of ≥50%. All eligible patients initially received four weeks of lurasidone monotherapy. Patients who responded well continued to receive this kind of monotherapy. However, no-response patients were randomly assigned to either valproate or vortioxetine treatment for eight weeks. By comprehensively comparing the results of MADRS over a period of 4‍‒‍12 weeks, a systematic analysis was conducted to determine whether vortioxetine could be used as an adjuvant drug for treating bipolar depression.

RESULTS: Thirty-seven patients responded to lurasidone monotherapy, and 60 patients were randomly assigned to the valproate or vortioxetine group for eight weeks. After two weeks of combined valproate or vortioxetine treatment, the MADRS score in the vortioxetine group was significantly lower than that in the valproate group. There was no difference in the MADRS scores between the two groups at 8 and 12 weeks. The incidence of side effects did not significantly differ between the valproate and vortioxetine groups. Importantly, three patients in the vortioxetine group appeared to switch to mania or hypomania.

CONCLUSIONS: This study suggested that lurasidone combination with vortioxetine might have potential benefits to bipolar II depression in the early stage, while disease progression should be monitored closely for the risk of switching to mania.},
}

Humans
*Vortioxetine/therapeutic use
*Bipolar Disorder/drug therapy
Male
Female
Adult
*Valproic Acid/therapeutic use/adverse effects
Middle Aged
Treatment Outcome
Prospective Studies
Pilot Projects
Lurasidone Hydrochloride/therapeutic use/adverse effects
Drug Therapy, Combination

@article {pmid39814210,
year = {2025},
author = {Kyoda, Y and Shibamori, K and Tachikawa, K and Nofuji, S and Saito, Y and Tabata, H and Shindo, T and Hashimoto, K and Kobayashi, K and Tanaka, T and Masumori, N},
title = {The change of detrusor contractility at 5 years after transurethral resection of the prostate: a single center prospective observational study.},
journal = {Urology},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.urology.2024.12.038},
pmid = {39814210},
issn = {1527-9995},
abstract = {OBJECTIVE: To prospectively assess the impact of transurethral resection of the prostate (TURP) on detrusor function using pressure flow study (PFS) at 5 years after surgery in a single center prospective non-randomized observational study.

METHODS: Sixty consecutive male patients were prospectively enrolled and underwent TURP from November 2014 to November 2018. A questionnaire survey, free uroflowmetry and PFS were performed at baseline, and 6, 24 and 60 months after surgery. We divided the age groups at 70 years and defined the younger group as those younger than 70 years old, and the elderly group as those aged 70 years or older. The primary endpoint was the change of the bladder contractility index (BCI).

RESULTS: Of the 60 patients, 39 completed the protocol. Regardless of age, the bladder outlet obstruction indices at 6, 24, and 60 months after surgery were significantly lower than before surgery (all, p<0.01). Although the BCI did not significantly change during 60 months for the entire group of 39 patients, it was significantly decreased at 60 months (85.6) after surgery compared to before surgery (102) in the elderly group (p=0.02).

CONCLUSION: We prospectively evaluated detrusor contractility up to 5 years after TURP. It was significantly reduced in the elderly, in spite of which the relief of bladder outlet obstruction was maintained for 5 years after surgery.},
}

@article {pmid39814172,
year = {2025},
author = {Wei, Q and Fan, W and Li, HF and Wanga, PS and Xu, M and Dong, HL and Yu, H and Lyu, J and Luo, WJ and Chen, DF and Ge, W and Wu, ZY},
title = {Biallelic variants in SREBF2 cause autosomal recessive spastic paraplegia.},
journal = {Journal of genetics and genomics = Yi chuan xue bao},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jgg.2025.01.004},
pmid = {39814172},
issn = {1673-8527},
abstract = {Hereditary spastic paraplegias (HSPs) refer to a genetically and clinically heterogeneous group of neurodegenerative disorders characterized by the degeneration of motor neurons. To date, a significant number of patients still have not received a definite genetic diagnosis. Therefore, identifying unreported causative genes continues to be of great importance. Here, we perform whole exome sequencing in a cohort of Chinese HSP patients. Three homozygous variants (p.L604W, p.S517F, and p.T984A) within the sterol regulatory element-binding factor 2 (SREBF2) gene are identified in one autosomal recessive family and two sporadic patients, respectively. Co-segregation is confirmed by Sanger sequencing in all available members. The three variants are rare in the public or in-house database and are predicted to be damaging. The biological impacts of variants in SREBF2 are examined by functional experiments in patient-derived fibroblasts and Drosophila. We find that the variants upregulate cellular cholesterol due to the overactivation of SREBP2, eventually impairing the autophagosomal and lysosomal functions. The overexpression of the mature form of SREBP2 leads to locomotion defects in Drosophila. Our findings identify SREBF2 as a causative gene for HSP and highlight the impairment of cholesterol as a critical pathway for HSP.},
}

@article {pmid39813939,
year = {2025},
author = {Chen, L and Yin, Z and Gu, X and Zhang, X and Cao, X and Zhang, C and Li, X},
title = {Neurophysiological data augmentation for EEG-fNIRS multimodal features based on a denoising diffusion probabilistic model.},
journal = {Computer methods and programs in biomedicine},
volume = {261},
number = {},
pages = {108594},
doi = {10.1016/j.cmpb.2025.108594},
pmid = {39813939},
issn = {1872-7565},
abstract = {BACKGROUND AND OBJECTIVE: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.

METHODS: In this study, we proposed an EEG-fNIRS data augmentation framework based on the combination of denoising diffusion probabilistic model (DDPM) and adding Gaussian noise (EFDA-CDG), for enhancing the performance of hybrid BCI systems. Firstly, we unified the temporal and spatial dimensions of EEG and fNIRS by manually extracting features and spatial mapping interpolation to create EEG-fNIRS joint distribution samples. Then, the DDPM generative model was combined with the traditional method of adding Gaussian noise to provide richer training data for the classifier. Finally, we constructed a classification module that applies EEG feature attention and fNIRS terrain attention to improve classification accuracy.

RESULTS: In order to evaluate the effectiveness of EFDA-CDG framework, experiments were conducted and fully validated on three publicly available databases and one self-collected database. In the context of a participant-dependent training approach, our method achieves accuracy rates of 82.02% for motor imagery, 91.93% for mental arithmetic, and 90.54% for n-back tasks on public databases. Additionally, our method boasts an accuracy rate of 97.82% for drug addiction discrimination task on the self-collected database.

CONCLUSIONS: EFDA-CDG framework successfully facilitates data augmentation, thereby enhancing the performance of EEG-fNIRS hybrid BCI systems.},
}

@article {pmid39811472,
year = {2024},
author = {Maibam, PC and Pei, D and Olikkal, P and Vinjamuri, RK and Kakoty, NM},
title = {Enhancing prosthetic hand control: A synergistic multi-channel electroencephalogram.},
journal = {Wearable technologies},
volume = {5},
number = {},
pages = {e18},
pmid = {39811472},
issn = {2631-7176},
abstract = {Electromyogram (EMG) has been a fundamental approach for prosthetic hand control. However it is limited by the functionality of residual muscles and muscle fatigue. Currently, exploring temporal shifts in brain networks and accurately classifying noninvasive electroencephalogram (EEG) for prosthetic hand control remains challenging. In this manuscript, it is hypothesized that the coordinated and synchronized temporal patterns within the brain network, termed as brain synergy, contain valuable information to decode hand movements. 32-channel EEGs were acquired from 10 healthy participants during hand grasp and open. Synergistic spatial distribution pattern and power spectra of brain activity were investigated using independent component analysis of EEG. Out of 32 EEG channels, 15 channels spanning the frontal, central and parietal regions were strategically selected based on the synergy of spatial distribution pattern and power spectrum of independent components. Time-domain and synergistic features were extracted from the selected 15 EEG channels. These features were employed to train a Bayesian optimizer-based support vector machine (SVM). The optimized SVM classifier could achieve an average testing accuracy of 94.39 .84% using synergistic features. The paired t-test showed that synergistic features yielded significantly higher area under curve values (p < .05) compared to time-domain features in classifying hand movements. The output of the classifier was employed for the control of the prosthetic hand. This synergistic approach for analyzing temporal activities in motor control and control of prosthetic hands have potential contributions to future research. It addresses the limitations of EMG-based approaches and emphasizes the effectiveness of synergy-based control for prostheses.},
}

@article {pmid39809040,
year = {2025},
author = {Xu, R and Allison, BZ and Zhao, X and Liang, W and Wang, X and Cichocki, A and Jin, J},
title = {Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {184},
number = {},
pages = {107124},
doi = {10.1016/j.neunet.2025.107124},
pmid = {39809040},
issn = {1879-2782},
abstract = {Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. This paper proposes a novel Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network (MS-PSA-SOC) for ERP Detection. The model integrates a multi-scale architecture, self-attention mechanism, and deep metric learning to achieve a more comprehensive, refined, and discriminative feature representation. The MS module aggregates fine-grained local features and global features with a larger receptive field within a multi-scale architecture, effectively capturing the dynamic characteristics of complex oscillatory activities in the brain at different levels of abstraction. This preserves complementary spatiotemporal representation information. The PSA module continues the multi-scale contextual modeling from the previous module and achieves adaptive recalibration of multi-scale features. By employing effective aggregation and selection mechanisms, it highlights key features while suppressing redundant information. The SOC module jointly optimizes similarity metric loss and classification loss, maintaining the feature space distribution while focusing on sample class labels. This optimization of similarity relationships between samples improves the model's generalization ability and robustness. Results from public and self-collected datasets demonstrate that the command recognition accuracy of the MS-PSA-SOC model is at least 3.1% and 2.8% higher than other advanced algorithms, achieving superior performance. Additionally, the method demonstrates a lower standard deviation across both datasets. This study also validated the network parameters based on Shannon's sampling theorem and EEG "microstates" through relevant experiments.},
}

@article {pmid39808940,
year = {2025},
author = {Gu, M and Pei, W and Gao, X and Wang, Y},
title = {Optimizing the proportion of stimulation area in a grid stimulus for user-friendly SSVEP-based BCIs.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adaa1e},
pmid = {39808940},
issn = {1741-2552},
abstract = {OBJECTIVE: Steady-state visual evoked potentials (SSVEPs) rely on the photic driving response to encode electroencephalogram (EEG) signals stably and efficiently. However, the user experience of the traditional stimulation with high-contrast flickers urgently needs to be improved. In this study, we introduce a novel paradigm of grid stimulation with weak flickering perception, distinguished by a markedly lower proportion of stimulation area in the overall pattern.

APPROACH: In an offline single-target experiment, we investigated the unique characteristics of SSVEPs evoked by varying proportions in grid stimuli within low and medium frequency bands. Based on the analysis of simulation performance across a four-class brain-computer interface (BCI) task and the evaluation of user experience questionnaires, a subset of paradigms that balance performance and comfort were selected for implementation in four-target online BCI systems.

MAIN RESULTS: Our results demonstrate that even ultra-low stimulation proportion paradigms can still evoke strong responses within specific frequency bands, effectively enhancing user experience with low and middle frequency stimuli. Notably, proportions of 0.94% and 2.10% within the 3-5 Hz range provide an optimal balance between performance and user experience. For frequencies extending up to 15 Hz, a 2.10% proportion remains ideal. At 20 Hz, slightly higher proportions of 3.75% and 8.43% maintain these benefits.

SIGNIFICANCE: These findings are crucial for advancing the development of effective and user-friendly SSVEP-based BCI systems.},
}

@article {pmid39808939,
year = {2025},
author = {Prakash, P and Lei, T and Flint, RD and Hsieh, JK and Fitzgerald, Z and Mugler, EM and Templer, J and Goldrick, MA and Tate, MC and Rosenow, JM and Glaser, JI and Slutzky, MW},
title = {Decoding speech intent from non-frontal cortical areas.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adaa20},
pmid = {39808939},
issn = {1741-2552},
abstract = {Brain-machine interfaces (BMIs) have advanced greatly in decoding speech signals originating from the speech motor cortices. Primarily, these BMIs target individuals with intact speech motor cortices but who are paralyzed by disrupted connections between frontal cortices and their articulators due to brainstem stroke or motor neuron diseases such as amyotrophic lateral sclerosis. A few studies have shown some information outside the speech motor cortices, such as in parietal and temporal lobes, that also may be useful for BMIs. The ability to use information from outside the frontal lobes could be useful not only for people with locked-in syndrome but also to people with frontal lobe damage, which can cause nonfluent aphasia or apraxia of speech. However, temporal and parietal lobes are predominantly involved in perceptive speech processing and comprehension. Therefore, to be able to use signals from these areas in a speech BMI, it is important to ascertain that they are related to production. Here, using intracranial recordings, we sought evidence for whether, when and where neural information related to speech intention could be found in the temporal and parietal cortices. Causal information enabled us to distinguish speech intent from resting state and other processes involved in language processing or working memory. Information related to speech intent was distributed widely across the temporal and parietal lobes, including superior temporal, medial temporal, angular, and supramarginal gyri. This provides evidence of a decodable production-related signal in these areas. This insight may help in designing speech brain-machine interfaces that could benefit people with locked-in syndrome, aphasia or apraxia of speech.},
}

@article {pmid39808931,
year = {2025},
author = {Russo, JS and Shiels, TA and Lin, CS and John, SE and Grayden, DB},
title = {Decoding imagined movement in people with multiple sclerosis for brain-computer interface translation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adaa1d},
pmid = {39808931},
issn = {1741-2552},
abstract = {Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A Brain-Computer Interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair. However, the limited BCI research in people with MS has been confined to exploring the P300 response and brain signals associated with attempted movement. The current study aims to expand the MS-BCI literature by highlighting the feasibility of decoding MS imagined movement. Approach. We collected electroencephalography (EEG) data from eight participants with various symptoms of MS and ten neurotypical control participants. Participants made imagined movements of the hands and feet as directed by a go no-go protocol. Binary regularised linear discriminant analysis was used to classify imagined movement vs. rest and vs. movement at individual time-frequency points. The frequency bands which provided the maximal accuracy, and the associated latency, were compared. Main Results. In all MS participants, the classification algorithm achieved above 70% accuracy in at least one imagined movement vs. rest classification and most movement vs. movement classifications. There was no significant difference between classification of limbs with weakness or paralysis to neurotypical controls. Both the MS and control groups possessed decodable information within the alpha (7-13 Hz) and beta (16-30 Hz) bands at similar latency. Significance. This study is the first to demonstrate the feasibility of decoding imagined movements in people with MS. As an alternative to the P300 response, motor imagery-based control of a BCI may also be combined with existing motor imagery therapy to supplement MS rehabilitation. These promising results merit further long term BCI studies to investigate the effect of MS progression on classification performance. .},
}