@article {pmid38614892,
year = {2024},
author = {He, X and Li, W and Ma, H},
title = {Orchestrating neuronal activity-dependent translation via the integrated stress response protein GADD34.},
journal = {Trends in neurosciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tins.2024.03.008},
pmid = {38614892},
issn = {1878-108X},
abstract = {In a recent study, Oliveira and colleagues revealed how growth arrest and DNA damage-inducible protein 34 (GADD34), an effector of the integrated stress response, initiates the translation of synaptic plasticity-related mRNAs following brain-derived neurotrophic factor (BDNF) stimulation. This work suggests that GADD34 may link transcriptional products with translation control upon neuronal activation, illuminating how protein synthesis is orchestrated in neuronal plasticity.},
}

@article {pmid38612334,
year = {2024},
author = {Knozowski, P and Nowakowski, JJ and Stawicka, AM and Dulisz, B and Górski, A},
title = {Effect of Management of Grassland on Prey Availability and Physiological Condition of Nestling of Red-Backed Shrike Lanius collurio.},
journal = {Animals : an open access journal from MDPI},
volume = {14},
number = {7},
pages = {},
pmid = {38612334},
issn = {2076-2615},
abstract = {The study aimed to determine the influence of grassland management on the potential food base of the red-backed shrike Lanius collurio and the condition of chicks in the population inhabiting semi-natural grasslands in the Narew floodplain. The grassland area was divided into three groups: extensively used meadows, intensively used meadows fertilised with mineral fertilisers, and intensively used meadows fertilised with liquid manure, and selected environmental factors that may influence food availability were determined. Using Barber traps, 1825 samples containing 53,739 arthropods were collected, and the diversity, abundance, and proportion of large arthropods in the samples were analysed depending on the grassland use type. In the bird population, the condition of the chicks was characterised by the BCI (Body Condition Index) and haematological parameters (glucose level, haemoglobin level, haematocrit, and H:L ratio). The diversity of arthropods was highest in extensively used meadows. Still, the mean abundance and proportion of arthropods over 1 cm in length differed significantly for Orthoptera, Hymenoptera, Arachne, and Carabidae between grassland use types, with the highest proportion of large arthropods and the highest abundance recorded in manure-fertilised meadows. The highest Body Condition Indexes and blood glucose levels of nestlings indicating good nestling nutrition were recorded in nests of birds associated with extensive land use. The H:L ratio as an indicator of the physiological condition of nestlings was high on manure-fertilised and extensively managed meadows, indicating stress factors associated with these environments. This suggests that consideration should be given to the effects of chemicals, such as pesticides or drug residues, that may come from slurry poured onto fields on the fitness of red-backed shrike chicks.},
}

@article {pmid38610540,
year = {2024},
author = {Clemente, L and La Rocca, M and Paparella, G and Delussi, M and Tancredi, G and Ricci, K and Procida, G and Introna, A and Brunetti, A and Taurisano, P and Bevilacqua, V and de Tommaso, M},
title = {Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain-Computer Interface Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {7},
pages = {},
pmid = {38610540},
issn = {1424-8220},
support = {POR Puglia FESR-FSE 2014-2020 - Asse I - Azione 1.4.b//Regione Puglia/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Aging ; Brain ; Esthetics ; Perception ; },
abstract = {In the field of neuroscience, brain-computer interfaces (BCIs) are used to connect the human brain with external devices, providing insights into the neural mechanisms underlying cognitive processes, including aesthetic perception. Non-invasive BCIs, such as EEG and fNIRS, are critical for studying central nervous system activity and understanding how individuals with cognitive deficits process and respond to aesthetic stimuli. This study assessed twenty participants who were divided into control and impaired aging (AI) groups based on MMSE scores. EEG and fNIRS were used to measure their neurophysiological responses to aesthetic stimuli that varied in pleasantness and dynamism. Significant differences were identified between the groups in P300 amplitude and late positive potential (LPP), with controls showing greater reactivity. AI subjects showed an increase in oxyhemoglobin in response to pleasurable stimuli, suggesting hemodynamic compensation. This study highlights the effectiveness of multimodal BCIs in identifying the neural basis of aesthetic appreciation and impaired aging. Despite its limitations, such as sample size and the subjective nature of aesthetic appreciation, this research lays the groundwork for cognitive rehabilitation tailored to aesthetic perception, improving the comprehension of cognitive disorders through integrated BCI methodologies.},
}

Humans
*Brain-Computer Interfaces
Aging
Brain
Esthetics
Perception

@article {pmid38609642,
year = {2023},
author = {Zeng, J and Zhang, Y and Xiang, Y and Liang, S and Xue, C and Zhang, J and Ran, Y and Cao, M and Huang, F and Huang, S and Deng, W and Li, T},
title = {Optimizing multi-domain hematologic biomarkers and clinical features for the differential diagnosis of unipolar depression and bipolar depression.},
journal = {Npj mental health research},
volume = {2},
number = {1},
pages = {4},
pmid = {38609642},
issn = {2731-4251},
abstract = {There is a lack of objective features for the differential diagnosis of unipolar and bipolar depression, especially those that are readily available in practical settings. We investigated whether clinical features of disease course, biomarkers from complete blood count, and blood biochemical markers could accurately classify unipolar and bipolar depression using machine learning methods. This retrospective study included 1160 eligible patients (918 with unipolar depression and 242 with bipolar depression). Patient data were randomly split into training (85%) and open test (15%) sets 1000 times, and the average performance was reported. XGBoost achieved the optimal open-test performance using selected biomarkers and clinical features-AUC 0.889, sensitivity 0.831, specificity 0.839, and accuracy 0.863. The importance of features for differential diagnosis was measured using SHapley Additive exPlanations (SHAP) values. The most informative features include (1) clinical features of disease duration and age of onset, (2) biochemical markers of albumin, low density lipoprotein (LDL), and potassium, and (3) complete blood count-derived biomarkers of white blood cell count (WBC), platelet-to-lymphocyte ratio (PLR), and monocytes (MONO). Overall, onset features and hematologic biomarkers appear to be reliable information that can be readily obtained in clinical settings to facilitate the differential diagnosis of unipolar and bipolar depression.},
}

@article {pmid38608677,
year = {2024},
author = {Wang, Y and Wang, X and Wang, L and Zheng, L and Meng, S and Zhu, N and An, X and Wang, L and Yang, J and Zheng, C and Ming, D},
title = {Dynamic prediction of goal location by coordinated representation of prefrontal-hippocampal theta sequences.},
journal = {Current biology : CB},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cub.2024.03.032},
pmid = {38608677},
issn = {1879-0445},
abstract = {Prefrontal (PFC) and hippocampal (HPC) sequences of neuronal firing modulated by theta rhythms could represent upcoming choices during spatial memory-guided decision-making. How the PFC-HPC network dynamically coordinates theta sequences to predict specific goal locations and how it is interrupted in memory impairments induced by amyloid beta (Aβ) remain unclear. Here, we detected theta sequences of firing activities of PFC neurons and HPC place cells during goal-directed spatial memory tasks. We found that PFC ensembles exhibited predictive representation of the specific goal location since the starting phase of memory retrieval, earlier than the hippocampus. High predictive accuracy of PFC theta sequences existed during successful memory retrieval and positively correlated with memory performance. Coordinated PFC-HPC sequences showed PFC-dominant prediction of goal locations during successful memory retrieval. Furthermore, we found that theta sequences of both regions still existed under Aβ accumulation, whereas their predictive representation of goal locations was weakened with disrupted spatial representation of HPC place cells and PFC neurons. These findings highlight the essential role of coordinated PFC-HPC sequences in successful memory retrieval of a precise goal location.},
}

@article {pmid38608331,
year = {2024},
author = {Inguscio, BMS and Cartocci, G and Sciaraffa, N and Nicastri, M and Giallini, I and Aricò, P and Greco, A and Babiloni, F and Mancini, P},
title = {Two are better than one: Differences in cortical EEG patterns during auditory and visual verbal working memory processing between Unilateral and Bilateral Cochlear Implanted children.},
journal = {Hearing research},
volume = {446},
number = {},
pages = {109007},
doi = {10.1016/j.heares.2024.109007},
pmid = {38608331},
issn = {1878-5891},
abstract = {Despite the proven effectiveness of cochlear implant (CI) in the hearing restoration of deaf or hard-of-hearing (DHH) children, to date, extreme variability in verbal working memory (VWM) abilities is observed in both unilateral and bilateral CI user children (CIs). Although clinical experience has long observed deficits in this fundamental executive function in CIs, the cause to date is still unknown. Here, we have set out to investigate differences in brain functioning regarding the impact of monaural and binaural listening in CIs compared with normal hearing (NH) peers during a three-level difficulty n-back task undertaken in two sensory modalities (auditory and visual). The objective of this pioneering study was to identify electroencephalographic (EEG) marker pattern differences in visual and auditory VWM performances in CIs compared to NH peers and possible differences between unilateral cochlear implant (UCI) and bilateral cochlear implant (BCI) users. The main results revealed differences in theta and gamma EEG bands. Compared with hearing controls and BCIs, UCIs showed hypoactivation of theta in the frontal area during the most complex condition of the auditory task and a correlation of the same activation with VWM performance. Hypoactivation in theta was also observed, again for UCIs, in the left hemisphere when compared to BCIs and in the gamma band in UCIs compared to both BCIs and NHs. For the latter two, a correlation was found between left hemispheric gamma oscillation and performance in the audio task. These findings, discussed in the light of recent research, suggest that unilateral CI is deficient in supporting auditory VWM in DHH. At the same time, bilateral CI would allow the DHH child to approach the VWM benchmark for NH children. The present study suggests the possible effectiveness of EEG in supporting, through a targeted approach, the diagnosis and rehabilitation of VWM in DHH children.},
}

@article {pmid38608024,
year = {2024},
author = {Abbasi, A and Rangwani, R and Bowen, DW and Fealy, AW and Danielsen, NP and Gulati, T},
title = {Cortico-cerebellar coordination facilitates neuroprosthetic control.},
journal = {Science advances},
volume = {10},
number = {15},
pages = {eadm8246},
pmid = {38608024},
issn = {2375-2548},
mesh = {Animals ; Rats ; *Cerebellum ; *Brain-Computer Interfaces ; Cell Nucleus ; Learning ; Movement ; },
abstract = {Temporally coordinated neural activity is central to nervous system function and purposeful behavior. Still, there is a paucity of evidence demonstrating how this coordinated activity within cortical and subcortical regions governs behavior. We investigated this between the primary motor (M1) and contralateral cerebellar cortex as rats learned a neuroprosthetic/brain-machine interface (BMI) task. In neuroprosthetic task, actuator movements are causally linked to M1 "direct" neurons that drive the decoder for successful task execution. However, it is unknown how task-related M1 activity interacts with the cerebellum. We observed a notable 3 to 6 hertz coherence that emerged between these regions' local field potentials (LFPs) with learning that also modulated task-related spiking. We identified robust task-related indirect modulation in the cerebellum, which developed a preferential relationship with M1 task-related activity. Inhibiting cerebellar cortical and deep nuclei activity through optogenetics led to performance impairments in M1-driven neuroprosthetic control. Together, these results demonstrate that cerebellar influence is necessary for M1-driven neuroprosthetic control.},
}

Animals
Rats
*Cerebellum
*Brain-Computer Interfaces
Cell Nucleus
Learning
Movement

@article {pmid38607193,
year = {2024},
author = {Kong, D and Chen, Y and Wang, L and Lu, Y and Luo, S and Chai, H and Chen, L},
title = {Adoption of Rehabilitation Climbing Wall Combined with Brain-computer Fusion Interface in Adolescent Idiopathic Scoliosis.},
journal = {Alternative therapies in health and medicine},
volume = {},
number = {},
pages = {},
pmid = {38607193},
issn = {1078-6791},
abstract = {BACKGROUND: As the adoption of brain-computer interface (BCI) technology in rehabilitation training is gradually maturing, the rehabilitation climbing walls combined with BCI technology are applied in adolescent idiopathic scoliosis (AIS) adoption research.

METHODS: From January 2022 to January 2023, a total of 100 AIS patients were assigned into a control group (group C, rehabilitation climbing wall training) and an observation group (group B, rehabilitation climbing wall training based on BCI technology) equally and randomly. The therapeutic effects of the patients were analyzed, including the Cobb angle, waist range of motion, and quality of life.

RESULTS: The Cobb angles of all patients after three months of treatment were obviously smaller than those preoperatively, and the Cobb angle of patients in group B was smaller than that of group C. The improvement rate of the Cobb angle of patients in group B was substantially superior to that in group C (95%CI 17.8-42.6, P = .034). Moreover, patients in groups C and B had more extensive waist flexion, tension, and left ranges. Suitable lateral regions after three months of treatment than before and lower lumbar dysfunction scores, and group B was significantly better than group C (95%CI 20.3-35.4, P = .042). After three months of treatment, all patients' general condition, physical pain, physiological function, and mental health scores were higher than those preoperatively, and the scores in group B were substantially superior to those in group C (95%CI 51.3-84.2, P = .022). Furthermore, the total effective rate of patients in group B after three months was markedly superior to that in group C (96% vs. 82%) (95%CI 79.3-97.2, P = .014).

CONCLUSION: The results of the study suggest that the rehabilitation climbing wall training method combined with brain-computer interface (BCI) technology has significant therapeutic effects on adolescent idiopathic scoliosis (AIS) patients. The intervention was found to effectively reduce the Cobb angle, increase the lumbar range of motion, improve lumbar function, and enhance the quality of life of the patients. These findings indicate that the adoption of rehabilitation climbing walls combined with BCI technology can be clinically valuable in the treatment of AIS. This approach holds promise in improving the rehabilitation outcomes for AIS patients, providing a non-invasive alternative to surgical interventions.},
}

@article {pmid38606614,
year = {2024},
author = {Xu, S and Xiao, X and Manshaii, F and Chen, J},
title = {Injectable Fluorescent Neural Interfaces for Cell-Specific Stimulating and Imaging.},
journal = {Nano letters},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.nanolett.4c00815},
pmid = {38606614},
issn = {1530-6992},
abstract = {Building on current explorations in chronic optical neural interfaces, it is essential to address the risk of photothermal damage in traditional optogenetics. By focusing on calcium fluorescence for imaging rather than stimulation, injectable fluorescent neural interfaces significantly minimize photothermal damage and improve the accuracy of neuronal imaging. Key advancements including the use of injectable microelectronics for targeted electrical stimulation and their integration with cell-specific genetically encoded calcium indicators have been discussed. These injectable electronics that allow for post-treatment retrieval offer a minimally invasive solution, enhancing both usability and reliability. Furthermore, the integration of genetically encoded fluorescent calcium indicators with injectable bioelectronics enables precise neuronal recording and imaging of individual neurons. This shift not only minimizes risks such as photothermal conversion but also boosts safety, specificity, and effectiveness of neural imaging. Embracing these advancements represents a significant leap forward in biomedical engineering and neuroscience, paving the way for advanced brain-machine interfaces.},
}

@article {pmid38606309,
year = {2024},
author = {Jeong, CH and Lim, H and Lee, J and Lee, HS and Ku, J and Kang, YJ},
title = {Attentional state-synchronous peripheral electrical stimulation during action observation induced distinct modulation of corticospinal plasticity after stroke.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1373589},
pmid = {38606309},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain computer interface-based action observation (BCI-AO) is a promising technique in detecting the user's cortical state of visual attention and providing feedback to assist rehabilitation. Peripheral nerve electrical stimulation (PES) is a conventional method used to enhance outcomes in upper extremity function by increasing activation in the motor cortex. In this study, we examined the effects of different pairings of peripheral nerve electrical stimulation (PES) during BCI-AO tasks and their impact on corticospinal plasticity.

MATERIALS AND METHODS: Our innovative BCI-AO interventions decoded user's attentive watching during task completion. This process involved providing rewarding visual cues while simultaneously activating afferent pathways through PES. Fifteen stroke patients were included in the analysis. All patients underwent a 15 min BCI-AO program under four different experimental conditions: BCI-AO without PES, BCI-AO with continuous PES, BCI-AO with triggered PES, and BCI-AO with reverse PES application. PES was applied at the ulnar nerve of the wrist at an intensity equivalent to 120% of the sensory threshold and a frequency of 50 Hz. The experiment was conducted randomly at least 3 days apart. To assess corticospinal and peripheral nerve excitability, we compared pre and post-task (post 0, post 20 min) parameters of motor evoked potential and F waves under the four conditions in the muscle of the affected hand.

RESULTS: The findings indicated that corticospinal excitability in the affected hemisphere was higher when PES was synchronously applied with AO training, using BCI during a state of attentive watching. In contrast, there was no effect on corticospinal activation when PES was applied continuously or in the reverse manner. This paradigm promoted corticospinal plasticity for up to 20 min after task completion. Importantly, the effect was more evident in patients over 65 years of age.

CONCLUSION: The results showed that task-driven corticospinal plasticity was higher when PES was applied synchronously with a highly attentive brain state during the action observation task, compared to continuous or asynchronous application. This study provides insight into how optimized BCI technologies dependent on brain state used in conjunction with other rehabilitation training could enhance treatment-induced neural plasticity.},
}

@article {pmid38606308,
year = {2024},
author = {Xue, Q and Song, Y and Wu, H and Cheng, Y and Pan, H},
title = {Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1309594},
pmid = {38606308},
issn = {1662-4548},
abstract = {INTRODUCTION: Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity.

METHODS: Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification.

RESULTS AND DISCUSSION: Achievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications.},
}

@article {pmid38604523,
year = {2024},
author = {Liu, L and Li, J and Ouyang, R and Zhou, D and Fan, C and Liang, W and Li, F and Lv, Z and Wu, X},
title = {Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain-computer interface and exoskeleton.},
journal = {Journal of neuroscience methods},
volume = {406},
number = {},
pages = {110132},
doi = {10.1016/j.jneumeth.2024.110132},
pmid = {38604523},
issn = {1872-678X},
abstract = {BACKGROUND: Traditional therapist-based rehabilitation training for patients with movement impairment is laborious and expensive. In order to reduce the cost and improve the treatment effect of rehabilitation, many methods based on human-computer interaction (HCI) technology have been proposed, such as robot-assisted therapy and functional electrical stimulation (FES). However, due to the lack of active participation of brain, these methods have limited effects on the promotion of damaged nerve remodeling.

NEW METHOD: Based on the neurofeedback training provided by the combination of brain-computer interface (BCI) and exoskeleton, this paper proposes a multimodal brain-controlled active rehabilitation system to help improve limb function. The joint control mode of steady-state visual evoked potential (SSVEP) and motor imagery (MI) is adopted to achieve self-paced control and thus maximize the degree of brain involvement, and a requirement selection function based on SSVEP design is added to facilitate communication with aphasia patients.

In addition, the Transformer is introduced as the MI decoder in the asynchronous online BCI to improve the global perception of electroencephalogram (EEG) signals and maintain the sensitivity and efficiency of the system.

RESULTS: In two multi-task online experiments for left hand, right hand, foot and idle states, subject achieves 91.25% and 92.50% best accuracy, respectively.

CONCLUSION: Compared with previous studies, this paper aims to establish a high-performance and low-latency brain-controlled rehabilitation system, and provide an independent and autonomous control mode of the brain, so as to improve the effect of neural remodeling. The performance of the proposed method is evaluated through offline and online experiments.},
}

@article {pmid38603901,
year = {2024},
author = {Shi, X and She, Q and Fang, F and Meng, M and Tan, T and Zhang, Y},
title = {Enhancing cross-subject EEG emotion recognition through multi-source manifold metric transfer learning.},
journal = {Computers in biology and medicine},
volume = {174},
number = {},
pages = {108445},
doi = {10.1016/j.compbiomed.2024.108445},
pmid = {38603901},
issn = {1879-0534},
abstract = {Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain adaptation challenges in affective brain-computer interfaces (aBCI). However, previous TL methods usually use a stationary distance, such as Euclidean distance, to quantify the distribution dissimilarity between two domains, overlooking the inherent links among similar samples, potentially leading to suboptimal feature mapping. In this study, we introduced a novel algorithm called multi-source manifold metric transfer learning (MSMMTL) to enhance the efficacy of conventional TL. Specifically, we first selected the source domain based on Mahalanobis distance to enhance the quality of the source domains and then used manifold feature mapping approach to map the source and target domains on the Grassmann manifold to mitigate data drift between domains. In this newly established shared space, we optimized the Mahalanobis metric by maximizing the inter-class distances while minimizing the intra-class distances in the target domain. Recognizing that significant distribution discrepancies might persist across different domains even on the manifold, to ensure similar distributions between the source and target domains, we further imposed constraints on both domains under the Mahalanobis metric. This approach aims to reduce distributional disparities and enhance the electroencephalogram (EEG) emotion recognition performance. In cross-subject experiments, the MSMMTL model exhibits average classification accuracies of 88.83 % and 65.04 % for SEED and DEAP, respectively, underscoring the superiority of our proposed MSMMTL over other state-of-the-art methods. MSMMTL can effectively solve the problem of individual differences in EEG-based affective computing.},
}

@article {pmid38603841,
year = {2024},
author = {De Rubis, G and Paudel, KR and Yeung, S and Mohamad, S and Sudhakar, S and Singh, SK and Gupta, G and Hansbro, PM and Chellappan, DK and Oliver, BGG and Dua, K},
title = {18-β-glycyrrhetinic acid-loaded polymeric nanoparticles attenuate cigarette smoke-induced markers of impaired antiviral response in vitro.},
journal = {Pathology, research and practice},
volume = {257},
number = {},
pages = {155295},
doi = {10.1016/j.prp.2024.155295},
pmid = {38603841},
issn = {1618-0631},
abstract = {Tobacco smoking is a leading cause of preventable mortality, and it is the major contributor to diseases such as COPD and lung cancer. Cigarette smoke compromises the pulmonary antiviral immune response, increasing susceptibility to viral infections. There is currently no therapy that specifically addresses the problem of impaired antiviral response in cigarette smokers and COPD patients, highlighting the necessity to develop novel treatment strategies. 18-β-glycyrrhetinic acid (18-β-gly) is a phytoceutical derived from licorice with promising anti-inflammatory, antioxidant, and antiviral activities whose clinical application is hampered by poor solubility. This study explores the therapeutic potential of an advanced drug delivery system encapsulating 18-β-gly in poly lactic-co-glycolic acid (PLGA) nanoparticles in addressing the impaired antiviral immunity observed in smokers and COPD patients. Exposure of BCi-NS1.1 human bronchial epithelial cells to cigarette smoke extract (CSE) resulted in reduced expression of critical antiviral chemokines (IP-10, I-TAC, MIP-1α/1β), mimicking what happens in smokers and COPD patients. Treatment with 18-β-gly-PLGA nanoparticles partially restored the expression of these chemokines, demonstrating promising therapeutic impact. The nanoparticles increased IP-10, I-TAC, and MIP-1α/1β levels, exhibiting potential in attenuating the negative effects of cigarette smoke on the antiviral response. This study provides a novel approach to address the impaired antiviral immune response in vulnerable populations, offering a foundation for further investigations and potential therapeutic interventions. Further studies, including a comprehensive in vitro characterization and in vivo testing, are warranted to validate the therapeutic efficacy of 18-β-gly-PLGA nanoparticles in respiratory disorders associated with compromised antiviral immunity.},
}

@article {pmid38602850,
year = {2024},
author = {Wang, Z and Hu, H and Zhou, T and Xu, T and Zhao, X},
title = {Average Time Consumption Per Character - a Practical Performance Metric for Generic Synchronous BCI Spellers.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3387469},
pmid = {38602850},
issn = {1558-2531},
abstract = {OBJECTIVE: The information transfer rate (ITR) is widely accepted as a performance metric for generic brain-computer interface (BCI) spellers, while it is noticeable that the communication speed given by ITR is actually an upper bound which however can never be reached in real systems. A new performance metric is therefore needed.

METHODS: In this paper, a new metric named average time consumption per character (ATCPC) is proposed. It quantifies how long it takes on average to type one character using a typical synchronous BCI speller. To analytically derive ATCPC, the real typing process is modelled with a random walk on a graph. Misclassification and backspace are carefully characterized. A close-form formula of ATCPC is obtained through computing the hitting time of the random walk. The new metric is validated through simulated typing experiments and compared with ITR.

RESULTS: Firstly, the formula and simulation show a good consistency. Secondly, ITR always tends to overestimate the communication speed, while ATCPC is more realistic.

CONCLUSION: The proposed ATCPC metric is valid.

SIGNIFICANCE: ATCPC is a qualified substitute for ITR. ATCPC also reveals the great potential of keyboard optimization to further enhance the performance of BCI spellers, which was hardly investigated before.},
}

@article {pmid38602573,
year = {2024},
author = {Waisberg, E and Ong, J and Lee, AG},
title = {Ethical Considerations of Neuralink and Brain-Computer Interfaces: Balancing Innovation and Responsibility.},
journal = {Annals of biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {38602573},
issn = {1573-9686},
abstract = {Neuralink is a neurotechnology company founded by Elon Musk in 2016, which has been quietly developing revolutionary technology allowing for ultra-high precision bidirectional communication between external devices and the brain. In this paper, we explore the multifaceted ethical considerations surrounding neural interfaces, analyzing potential societal impacts, risks, and call for a need for responsible innovation. Despite the technological, medical, medicolegal, and ethical challenges ahead, neural interface technology remains extremely promising and has the potential to create a new era of medicine.},
}

@article {pmid38601924,
year = {2024},
author = {Anger, JT and Case, LK and Baranowski, AP and Berger, A and Craft, RM and Damitz, LA and Gabriel, R and Harrison, T and Kaptein, K and Lee, S and Murphy, AZ and Said, E and Smith, SA and Thomas, DA and Valdés Hernández, MDC and Trasvina, V and Wesselmann, U and Yaksh, TL},
title = {Pain mechanisms in the transgender individual: a review.},
journal = {Frontiers in pain research (Lausanne, Switzerland)},
volume = {5},
number = {},
pages = {1241015},
pmid = {38601924},
issn = {2673-561X},
abstract = {SPECIFIC AIM: Provide an overview of the literature addressing major areas pertinent to pain in transgender persons and to identify areas of primary relevance for future research.

METHODS: A team of scholars that have previously published on different areas of related research met periodically though zoom conferencing between April 2021 and February 2023 to discuss relevant literature with the goal of providing an overview on the incidence, phenotype, and mechanisms of pain in transgender patients. Review sections were written after gathering information from systematic literature searches of published or publicly available electronic literature to be compiled for publication as part of a topical series on gender and pain in the Frontiers in Pain Research.

RESULTS: While transgender individuals represent a significant and increasingly visible component of the population, many researchers and clinicians are not well informed about the diversity in gender identity, physiology, hormonal status, and gender-affirming medical procedures utilized by transgender and other gender diverse patients. Transgender and cisgender people present with many of the same medical concerns, but research and treatment of these medical needs must reflect an appreciation of how differences in sex, gender, gender-affirming medical procedures, and minoritized status impact pain.

CONCLUSIONS: While significant advances have occurred in our appreciation of pain, the review indicates the need to support more targeted research on treatment and prevention of pain in transgender individuals. This is particularly relevant both for gender-affirming medical interventions and related medical care. Of particular importance is the need for large long-term follow-up studies to ascertain best practices for such procedures. A multi-disciplinary approach with personalized interventions is of particular importance to move forward.},
}

@article {pmid38601801,
year = {2024},
author = {Li, H and Li, H and Ma, L and Polina, D},
title = {Revealing brain's cognitive process deeply: a study of the consistent EEG patterns of audio-visual perceptual holistic.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1377233},
pmid = {38601801},
issn = {1662-5161},
abstract = {INTRODUCTION: To investigate the brain's cognitive process and perceptual holistic, we have developed a novel method that focuses on the informational attributes of stimuli.

METHODS: We recorded EEG signals during visual and auditory perceptual cognition experiments and conducted ERP analyses to observe specific positive and negative components occurring after 400ms during both visual and auditory perceptual processes. These ERP components represent the brain's perceptual holistic processing activities, which we have named Information-Related Potentials (IRPs). We combined IRPs with machine learning methods to decode cognitive processes in the brain.

RESULTS: Our experimental results indicate that IRPs can better characterize information processing, particularly perceptual holism. Additionally, we conducted a brain network analysis and found that visual and auditory perceptual holistic processing share consistent neural pathways.

DISCUSSION: Our efforts not only demonstrate the specificity, significance, and reliability of IRPs but also reveal their great potential for future brain mechanism research and BCI applications.},
}

@article {pmid38601800,
year = {2024},
author = {Chen, Y and Wang, F and Li, T and Zhao, L and Gong, A and Nan, W and Ding, P and Fu, Y},
title = {Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1391550},
pmid = {38601800},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) is a revolutionizing human-computer interaction, which has potential applications for specific individuals or groups in specific scenarios. Extensive research has been conducted on the principles and implementation methods of BCI, and efforts are currently being made to bridge the gap from research to real-world applications. However, there are inaccurate or erroneous conceptions about BCI among some members of the public, and certain media outlets, as well as some BCI researchers, developers, manufacturers, and regulators, propagate misleading or overhyped claims about BCI technology. Therefore, this article summarizes the several misconceptions and misleading propaganda about BCI, including BCI being capable of "mind-controlled," "controlling brain," "mind reading," and the ability to "download" or "upload" information from or to the brain using BCI, among others. Finally, the limitations (shortcomings) and limits (boundaries) of BCI, as well as the necessity of conducting research aimed at countering BCI systems are discussed, and several suggestions are offered to reduce misconceptions and misleading claims about BCI.},
}

@article {pmid38598676,
year = {2024},
author = {Gancio, J and Masoller, C and Tirabassi, G},
title = {Permutation entropy analysis of EEG signals for distinguishing eyes-open and eyes-closed brain states: Comparison of different approaches.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {34},
number = {4},
pages = {},
doi = {10.1063/5.0200029},
pmid = {38598676},
issn = {1089-7682},
mesh = {Humans ; Entropy ; *Brain ; *Electroencephalography/methods ; Brain Mapping/methods ; Signal Processing, Computer-Assisted ; },
abstract = {Developing reliable methodologies to decode brain state information from electroencephalogram (EEG) signals is an open challenge, crucial to implementing EEG-based brain-computer interfaces (BCIs). For example, signal processing methods that identify brain states could allow motor-impaired patients to communicate via non-invasive, EEG-based BCIs. In this work, we focus on the problem of distinguishing between the states of eyes closed (EC) and eyes open (EO), employing quantities based on permutation entropy (PE). An advantage of PE analysis is that it uses symbols (ordinal patterns) defined by the ordering of the data points (disregarding the actual values), hence providing robustness to noise and outliers due to motion artifacts. However, we show that for the analysis of multichannel EEG recordings, the performance of PE in discriminating the EO and EC states depends on the symbols' definition and how their probabilities are estimated. Here, we study the performance of PE-based features for EC/EO state classification in a dataset of N=107 subjects with one-minute 64-channel EEG recordings in each state. We analyze features obtained from patterns encoding temporal or spatial information, and we compare different approaches to estimate their probabilities (by averaging over time, over channels, or by "pooling"). We find that some PE-based features provide about 75% classification accuracy, comparable to the performance of features extracted with other statistical analysis techniques. Our work highlights the limitations of PE methods in distinguishing the eyes' state, but, at the same time, it points to the possibility that subject-specific training could overcome these limitations.},
}

Humans
Entropy
*Brain
*Electroencephalography/methods
Brain Mapping/methods
Signal Processing, Computer-Assisted

@article {pmid38598403,
year = {2024},
author = {Li, D and Wang, X and Dou, M and Zhao, Y and Cui, X and Xiang, J and Wang, B},
title = {Multi-stimulus Least-squares Transformation with Online Adaptation Scheme to Reduce Calibration Effort for SSVEP-based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3387283},
pmid = {38598403},
issn = {1558-0210},
abstract = {UNLABELLED: Steady-state visual evoked potential (SSVEP), one of the most popular electroencephalography (EEG)-based brain-computer interface (BCI) paradigms, can achieve high performance using calibration-based recognition algorithms. As calibration-based recognition algorithms are time-consuming to collect calibration data, the least-squares transformation (LST) has been used to reduce the calibration effort for SSVEP-based BCI. However, the transformation matrices constructed by current LST methods are not precise enough, resulting in large differences between the transformed data and the real data of the target subject. This ultimately leads to the constructed spatial filters and reference templates not being effective enough. To address these issues, this paper proposes multi-stimulus LST with online adaptation scheme (ms-LST-OA).

METHODS: The proposed ms-LST-OA consists of two parts. Firstly, to improve the precision of the transformation matrices, we propose the multi-stimulus LST (ms-LST) using cross-stimulus learning scheme as the cross-subject data transformation method. The ms-LST uses the data from neighboring stimuli to construct a higher precision transformation matrix for each stimulus to reduce the differences between transformed data and real data. Secondly, to further optimize the constructed spatial filters and reference templates, we use an online adaptation scheme to learn more features of the EEG signals of the target subject through an iterative process trial-by-trial.

RESULTS: ms-LST-OA performance was measured for three datasets (Benchmark Dataset, BETA Dataset, and UCSD Dataset). Using few calibration data, the ITR of ms-LST-OA achieved 210.01±10.10 bits/min, 172.31±7.26 bits/min, and 139.04±14.90 bits/min for all three datasets, respectively.

CONCLUSION: Using ms-LST-OA can reduce calibration effort for SSVEP-based BCIs.},
}

@article {pmid38598402,
year = {2024},
author = {Qin, K and Xu, R and Li, S and Wang, X and Cichocki, A and Jin, J},
title = {A Time Local Weighted Transformation Recognition Framework for Steady State Visual Evoked Potentials based Brain Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3386763},
pmid = {38598402},
issn = {1558-0210},
abstract = {Canonical correlation analysis (CCA), Multivariate synchronization index (MSI), and their extended methods have been widely used for target recognition in Brain-computer interfaces (BCIs) based on Steady State Visual Evoked Potentials (SSVEP), and covariance calculation is an important process for these algorithms. Some studies have proved that embedding time-local information into the covariance can optimize the recognition effect of the above algorithms. However, the optimization effect can only be observed from the recognition results and the improvement principle of time-local information cannot be explained. Therefore, we propose a time-local weighted transformation (TT) recognition framework that directly embeds the time-local information into the electroencephalography signal through weighted transformation. The influence mechanism of time-local information on the SSVEP signal can then be observed in the frequency domain. Low-frequency noise is suppressed on the premise of sacrificing part of the SSVEP fundamental frequency energy, the harmonic energy of SSVEP is enhanced at the cost of introducing a small amount of high-frequency noise. The experimental results show that the TT recognition framework can significantly improve the recognition ability of the algorithms and the separability of extracted features. Its enhancement effect is significantly better than the traditional time-local covariance extraction method, which has enormous application potential.},
}

@article {pmid38593021,
year = {2024},
author = {Niu, X and Lu, N and Yan, R and Luo, H},
title = {Model and Data Dual-Driven Double-Point Observation Network for Ultra-Short MI EEG Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3386565},
pmid = {38593021},
issn = {2168-2208},
abstract = {Although deep networks have succeeded in various signal classification tasks, the time sequence samples used to train the deep models are usually required to reach a certain length. Especially, in brain computer interface (BCI) research, around 3.5s-long motor imagery (MI) Electroencephalography (EEG) samples are needed to obtain satisfactory classification performance. This time-span requirement of the training samples makes real-time MI BCI systems impossible to implement based on deep networks, which restricts the related researches within laboratory and makes practical application hard to accomplish. To address this issue, a double-point observation deep network (DoNet) is developed to classify ultra-short samples buried in noise. First, an analytical solution is developed theoretically to perform ultra-short signal classification based on double-point couples. Then, a signal-noise model is constructed to study the interference of noise on classification based on double-point couples. Based on which, an independent identical distribution condition is utilized to improve the classification accuracy in a data-driven manner. Combining the theoretical model and data-driven mechanism, DoNet can construct a steady data-distribution for the double-point couples of the samples with the same label. Therefore, the conditional probability of each double-point couple of a test sample can be obtained. With a voting strategy, the samples can be accurately classified by fusing these conditional probabilities. Meanwhile, the noise interference can be suppressed. DoNet has been evaluated on two public EEG datasets. Compared to most state-of-the-art methods, the 1s-long EEG signal classification accuracy has been improved by more than 3%.},
}

@article {pmid38592090,
year = {2024},
author = {Ille, N},
title = {Orthogonal extended infomax algorithm.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad38db},
pmid = {38592090},
issn = {1741-2552},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Learning ; Normal Distribution ; },
abstract = {Objective.The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster.Approach.Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax by a fully-multiplicative orthogonal-group based update scheme of the ICA unmixing matrix, leading to an orthogonal extended infomax algorithm (OgExtInf). The computational performance of OgExtInf was compared with original extended infomax and with two fast ICA algorithms: the popular FastICA and Picard, a preconditioned limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm belonging to the family of quasi-Newton methods.Main results.OgExtInf converges much faster than original extended infomax. For small-size electroencephalogram (EEG) data segments, as used for example in online EEG processing, OgExtInf is also faster than FastICA and Picard.Significance.OgExtInf may be useful for fast and reliable ICA, e.g. in online systems for epileptic spike and seizure detection or brain-computer interfaces.},
}

*Algorithms
*Brain-Computer Interfaces
Electroencephalography
Learning
Normal Distribution

@article {pmid38590363,
year = {2024},
author = {Osuna-Orozco, R and Zhao, Y and Stealey, HM and Lu, HY and Contreras-Hernandez, E and Santacruz, SR},
title = {Adaptation and learning as strategies to maximize reward in neurofeedback tasks.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1368115},
pmid = {38590363},
issn = {1662-5161},
abstract = {INTRODUCTION: Adaptation and learning have been observed to contribute to the acquisition of new motor skills and are used as strategies to cope with changing environments. However, it is hard to determine the relative contribution of each when executing goal directed motor tasks. This study explores the dynamics of neural activity during a center-out reaching task with continuous visual feedback under the influence of rotational perturbations.

METHODS: Results for a brain-computer interface (BCI) task performed by two non-human primate (NHP) subjects are compared to simulations from a reinforcement learning agent performing an analogous task. We characterized baseline activity and compared it to the activity after rotational perturbations of different magnitudes were introduced. We employed principal component analysis (PCA) to analyze the spiking activity driving the cursor in the NHP BCI task as well as the activation of the neural network of the reinforcement learning agent.

RESULTS AND DISCUSSION: Our analyses reveal that both for the NHPs and the reinforcement learning agent, the task-relevant neural manifold is isomorphic with the task. However, for the NHPs the manifold is largely preserved for all rotational perturbations explored and adaptation of neural activity occurs within this manifold as rotations are compensated by reassignment of regions of the neural space in an angular pattern that cancels said rotations. In contrast, retraining the reinforcement learning agent to reach the targets after rotation results in substantial modifications of the underlying neural manifold. Our findings demonstrate that NHPs adapt their existing neural dynamic repertoire in a quantitatively precise manner to account for perturbations of different magnitudes and they do so in a way that obviates the need for extensive learning.},
}

@article {pmid38589229,
year = {2024},
author = {Falaki, A and Quessy, S and Dancause, N},
title = {Differential modulation of local field potentials in the primary and premotor cortices during ipsilateral and contralateral reach to grasp in macaque monkeys.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.1161-23.2024},
pmid = {38589229},
issn = {1529-2401},
abstract = {Hand movements are associated with modulations of neuronal activity across several interconnected cortical areas, including the primary motor cortex (M1), and the dorsal and ventral premotor cortices (PMd and PMv). Local field potentials (LFPs) provide a link between neuronal discharges and synaptic inputs. Our current understanding of how LFPs vary in M1, PMd, and PMv during contralateral and ipsilateral movements is incomplete. To help reveal unique features in the pattern of modulations, we simultaneously recorded LFPs in these areas in two macaque monkeys performing reach and grasp movements with either the right or left hand. The greatest effector-dependent differences were seen in M1, at low (≤ 13 Hz) and gamma frequencies. In premotor areas, differences related to hand use were only present in low frequencies. PMv exhibited the greatest increase in low frequencies during instruction cues and the smallest effector-dependent modulation during movement execution. In PMd, delta oscillations were greater during contralateral reach and grasp, and beta activity increased during contralateral grasp. In contrast, beta oscillations decreased in M1 and PMv. These results suggest that while M1 primarily exhibits effector-specific LFP activity, premotor areas compute more effector-independent aspects of the task requirements, particularly during movement preparation for PMv and production for PMd. The generation of precise hand movements likely relies on the combination of complementary information contained in the unique pattern of neural modulations contained in each cortical area. Accordingly, integrating LFPs from premotor areas and M1 could enhance the performance and robustness of brain-machine interfaces.Significance Statement We compared local field potentials (LFPs) from the primary motor cortex (M1), the dorsal and ventral premotor cortices (PMd and PMv) while monkeys performed reach and grasp with the contralateral or ipsilateral hand. In general, hand-related differences were greater in M1 than in premotor areas. During both contralateral and ipsilateral trials, LFPs were more similar when comparing the two premotor areas than comparing M1 to PMd or PMv. However, the pattern of modulations in each area had unique features. The combination of these signals is likely essential to support the flexibility and complexity of unilateral hand movements. Our results help to understand the neural substrate that allows cortical areas to concurrently contribute to different aspects of movement planning and production.},
}

@article {pmid38587944,
year = {2024},
author = {Carrara, I and Papadopoulo, T},
title = {Classification of BCI-EEG Based on the Augmented Covariance Matrix.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3386219},
pmid = {38587944},
issn = {1558-2531},
abstract = {OBJECTIVE: Electroencephalography signals are recorded as multidimensional datasets. We propose a new framework based on the augmented covariance that stems from an autoregressive model to improve motor imagery classification.

METHODS: From the autoregressive model can be derived the Yule-Walker equations, which show the emergence of a symmetric positive definite matrix: the augmented covariance matrix. The state-of the art for classifying covariance matrices is based on Riemannian Geometry. A fairly natural idea is therefore to apply this Riemannian Geometry based approach to these augmented covariance matrices. The methodology for creating the augmented covariance matrix shows a natural connection with the delay embedding theorem proposed by Takens for dynamical systems. Such an embedding method is based on the knowledge of two parameters: the delay and the embedding dimension, respectively related to the lag and the order of the autoregressive model. This approach provides new methods to compute the hyper-parameters in addition to standard grid search.

RESULTS: The augmented covariance matrix performed ACMs better than any state-of-the-art methods. We will test our approach on several datasets and several subjects using the MOABB framework, using both within-session and cross-session evaluation.

CONCLUSION: The improvement in results is due to the fact that the augmented covariance matrix incorporates not only spatial but also temporal information. As such, it contains information on the nonlinear components of the signal through the embedding procedure, which allows the leveraging of dynamical systems algorithms.

SIGNIFICANCE: These results extend the concepts and the results of the Riemannian distance based classification algorithm.},
}

@article {pmid38586334,
year = {2024},
author = {Curà, F and Sesana, R and Corsaro, L and Dugand, MM},
title = {An Active Thermography approach for materials characterisation of thermal management systems for Lithium-ion batteries.},
journal = {Heliyon},
volume = {10},
number = {7},
pages = {e28587},
pmid = {38586334},
issn = {2405-8440},
abstract = {The aim of this work is an alternative non destructive technique for estimating the thermal properties of four different Thermal Management System (TMS) materials. More in detail, a thermographic setup realized with the Active Thermography approach (AT) is utilized for the purpose and the data elaboration follows the ISO 18755 Standard. As well known, Phase Changes Materials (PCMs) represent an innovative solution in the Thermal Management System (TMS) of Lithium-Ion batteries and, during the years, many solutions were developed to improve its thermal properties. As a matter of fact, parameters such as the internal temperature or heat exchanges impact on both efficiency and safety of the whole battery system. Consequently, the thermal conductivity was often chosen as a performance indicator of Thermal Management System (TMS) materials. In this work, both thermal diffusivity and thermal conductivity were estimated in two different testing conditions, respectively at room temperature and higher temperature conditions. The Active Thermography (AT) technique proposed in this activity has satisfactory estimated both thermal diffusivity and thermal conductivity of Thermal Management System (TMS) materials. An analytical model was also developed to reproduce the temperature experimental profiles. Finally, results obtained with AT approach were compared to those available from commercial datasheet and literature.},
}

@article {pmid38586195,
year = {2024},
author = {Ma, P and Dong, C and Lin, R and Liu, H and Lei, D and Chen, X and Liu, H},
title = {A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1306283},
pmid = {38586195},
issn = {1662-4548},
abstract = {BACKGROUND: The development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals.

OBJECTIVE: This paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task.

METHODS: The signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL.

RESULTS: For Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm's performance was found to be optimal.

CONCLUSION: The feature fusion of DTF network and graph theory features enhanced CSP algorithm's performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model's sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.},
}

@article {pmid38586146,
year = {2024},
author = {Shuqfa, Z and Lakas, A and Belkacem, AN},
title = {Increasing accessibility to a large brain-computer interface dataset: Curation of physionet EEG motor movement/imagery dataset for decoding and classification.},
journal = {Data in brief},
volume = {54},
number = {},
pages = {110181},
pmid = {38586146},
issn = {2352-3409},
abstract = {A reliable motor imagery (MI) brain-computer interface (BCI) requires accurate decoding, which in turn requires model calibration using electroencephalography (EEG) signals from subjects executing or imagining the execution of movements. Although the PhysioNet EEG Motor Movement/Imagery Dataset is currently the largest EEG dataset in the literature, relatively few studies have used it to decode MI trials. In the present study, we curated and cleaned this dataset to store it in an accessible format that is convenient for quick exploitation, decoding, and classification using recent integrated development environments. We dropped six subjects owing to anomalies in EEG recordings and pre-possessed the rest, resulting in 103 subjects spanning four MI and four motor execution tasks. The annotations were coded to correspond to different tasks using numerical values. The resulting dataset is stored in both MATLAB structure and CSV files to ensure ease of access and organization. We believe that improving the accessibility of this dataset will help EEG-based MI-BCI decoding and classification, enabling more reliable real-life applications. The convenience and ease of access of this dataset may therefore lead to improvements in cross-subject classification and transfer learning.},
}

@article {pmid38586082,
year = {2024},
author = {Mueller, NN and Kim, Y and Ocoko, MYM and Dernelle, P and Kale, I and Patwa, S and Hermoso, AC and Chirra, D and Capadona, JR and Hess-Dunning, A},
title = {Effects of Micromachining on Anti-oxidant Elution from a Mechanically-Adaptive Polymer.},
journal = {Journal of micromechanics and microengineering : structures, devices, and systems},
volume = {34},
number = {3},
pages = {},
pmid = {38586082},
issn = {0960-1317},
support = {I01 RX003083/RX/RRD VA/United States ; },
abstract = {Intracortical microelectrodes (IMEs) can be used to restore motor and sensory function as a part of brain-computer interfaces in individuals with neuromusculoskeletal disorders. However, the neuroinflammatory response to IMEs can result in their premature failure, leading to reduced therapeutic efficacy. Mechanically-adaptive, resveratrol-eluting (MARE) neural probes target two mechanisms believed to contribute to the neuroinflammatory response by reducing the mechanical mismatch between the brain tissue and device, as well as locally delivering an antioxidant therapeutic. To create the mechanically-adaptive substrate, a dispersion, casting, and evaporation method is used, followed by a microfabrication process to integrate functional recording electrodes on the material. Resveratrol release experiments were completed to generate a resveratrol release profile and demonstrated that the MARE probes are capable of long-term controlled release. Additionally, our results showed that resveratrol can be degraded by laser-micromachining, an important consideration for future device fabrication. Finally, the electrodes were shown to have a suitable impedance for single-unit neural recording and could record single units in vivo.},
}

@article {pmid38585364,
year = {2024},
author = {Ben Pazi, H and Jahashan, S and Har Nof, S and Zibman, S and Yanai-Kohelet, O and Prigan, L and Intrator, N and Bornstein, NM and Ribo, M},
title = {Pre-hospital stroke monitoring past, present, and future: a perspective.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1341170},
pmid = {38585364},
issn = {1664-2295},
abstract = {Integrated brain-machine interface signifies a transformative advancement in neurological monitoring and intervention modalities for events such as stroke, the leading cause of disability. Historically, stroke management relied on clinical evaluation and imaging. While today's stroke landscape integrates artificial intelligence for proactive clinical decision-making, mainly in imaging and stroke detection, it depends on clinical observation for early detection. Cardiovascular monitoring and detection systems, which have become standard throughout healthcare and wellness settings, provide a model for future cerebrovascular monitoring and detection. This commentary reviews the progression of continuous stroke monitoring, spotlighting contemporary innovations and prospective avenues, and emphasizes the influential roles of cutting-edge technologies in shaping stroke care.},
}

@article {pmid38585226,
year = {2023},
author = {Yuvaraj, M and Raja, P and David, A and Burdet, E and Skm, V and Balasubramanian, S},
title = {A systematic investigation of detectors for low signal-to-noise ratio EMG signals.},
journal = {F1000Research},
volume = {12},
number = {},
pages = {429},
pmid = {38585226},
issn = {2046-1402},
mesh = {Humans ; Electromyography ; Signal-To-Noise Ratio ; *Signal Processing, Computer-Assisted ; Algorithms ; *Stroke ; },
abstract = {BACKGROUND: Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown.

METHODS: This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy.

RESULTS: The results indicate that the Modified Hodges detector - a simplified version of the threshold-based Hodges detector introduced in the current study - was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges.

CONCLUSIONS: Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.},
}

Humans
Electromyography
Signal-To-Noise Ratio
*Signal Processing, Computer-Assisted
Algorithms
*Stroke

@article {pmid38584867,
year = {2024},
author = {Khan, AYY and Anjum, A and Qadri, HM},
title = {Ethical tightrope: Navigating neuro-ethics in brain computer interface (BCI) technology.},
journal = {Brain & spine},
volume = {4},
number = {},
pages = {102800},
pmid = {38584867},
issn = {2772-5294},
}

@article {pmid38581031,
year = {2024},
author = {Ferrero, L and Soriano-Segura, P and Navarro, J and Jones, O and Ortiz, M and Iáñez, E and Azorín, JM and Contreras-Vidal, JL},
title = {Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {21},
number = {1},
pages = {48},
pmid = {38581031},
issn = {1743-0003},
support = {FPU19/03165//Ministry of Science, Innovation and Universities through the Aid for the Training of University Teachers/ ; PID2021-124111OB-C31//MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe/ ; },
mesh = {Humans ; *Deep Learning ; *Exoskeleton Device ; *Brain-Computer Interfaces ; Algorithms ; Lower Extremity ; Electroencephalography/methods ; },
abstract = {BACKGROUND: This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will.

METHODS: A total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants' neural activity using the second deep learning approach for the decoding.

RESULTS: The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance.

CONCLUSION: This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study's discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.},
}

Humans
*Deep Learning
*Exoskeleton Device
*Brain-Computer Interfaces
Algorithms
Lower Extremity
Electroencephalography/methods

@article {pmid38580923,
year = {2024},
author = {Allonen, S and Aittoniemi, J and Vuorialho, M and Närhi, L and Panula, K and Vuento, R and Honkaniemi, J},
title = {Streptococcus intermedius causing primary bacterial ventriculitis in a patient with severe periodontitis - a case report.},
journal = {BMC neurology},
volume = {24},
number = {1},
pages = {112},
pmid = {38580923},
issn = {1471-2377},
support = {EVO; 1326/2010//the State Research Funding of Vaasa Hospital District/ ; },
mesh = {Male ; Humans ; Middle Aged ; Streptococcus intermedius ; *Cerebral Ventriculitis/complications/diagnostic imaging/drug therapy ; Anti-Bacterial Agents/therapeutic use ; *Meningitis/diagnosis ; *Central Nervous System Bacterial Infections ; *Periodontitis/complications/drug therapy ; },
abstract = {BACKGROUND: Streptococcus intermedius is a member of the S. anginosus group and is part of the normal oral microbiota. It can cause pyogenic infections in various organs, primarily in the head and neck area, including brain abscesses and meningitis. However, ventriculitis due to periodontitis has not been reported previously.

CASE PRESENTATION: A 64-year-old male was admitted to the hospital with a headache, fever and later imbalance, blurred vision, and general slowness. Neurological examination revealed nuchal rigidity and general clumsiness. Meningitis was suspected, and the patient was treated with dexamethasone, ceftriaxone and acyclovir. A brain computer tomography (CT) scan was normal, and cerebrospinal fluid (CSF) Gram staining and bacterial cultures remained negative, so the antibacterial treatment was discontinued. Nine days after admission, the patient's condition deteriorated. The antibacterial treatment was restarted, and a brain magnetic resonance imaging revealed ventriculitis. A subsequent CT scan showed hydrocephalus, so a ventriculostomy was performed. In CSF Gram staining, chains of gram-positive cocci were observed. Bacterial cultures remained negative, but a bacterial PCR detected Streptococcus intermedius. An orthopantomography revealed advanced periodontal destruction in several teeth and periapical abscesses, which were subsequently operated on. The patient was discharged in good condition after one month.

CONCLUSIONS: Poor dental health can lead to life-threatening infections in the central nervous system, even in a completely healthy individual. Primary bacterial ventriculitis is a diagnostic challenge, which may result in delayed treatment and increased mortality.},
}

Male
Humans
Middle Aged
Streptococcus intermedius
*Cerebral Ventriculitis/complications/diagnostic imaging/drug therapy
Anti-Bacterial Agents/therapeutic use
*Meningitis/diagnosis
*Central Nervous System Bacterial Infections
*Periodontitis/complications/drug therapy

@article {pmid38580626,
year = {2024},
author = {Chen, F and Zheng, J and Wang, L and Krajbich, I},
title = {Attribute latencies causally shape intertemporal decisions.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {2948},
pmid = {38580626},
issn = {2041-1723},
support = {2148982//National Science Foundation (NSF)/ ; 72002195//National Natural Science Foundation of China (National Science Foundation of China)/ ; 71871199//National Natural Science Foundation of China (National Science Foundation of China)/ ; 72371226//National Natural Science Foundation of China (National Science Foundation of China)/ ; STI 2030-Major Projects 2021ZD0200409//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; },
mesh = {Humans ; Animals ; Mice ; Time Factors ; *Delay Discounting ; Reward ; Reaction Time ; Choice Behavior/physiology ; },
abstract = {Intertemporal choices - decisions that play out over time - pervade our life. Thus, how people make intertemporal choices is a fundamental question. Here, we investigate the role of attribute latency (the time between when people start to process different attributes) in shaping intertemporal preferences using five experiments with choices between smaller-sooner and larger-later rewards. In the first experiment, we identify attribute latencies using mouse-trajectories and find that they predict individual differences in choices, response times, and changes across time constraints. In the other four experiments we test the causal link from attribute latencies to choice, staggering the display of the attributes. This changes attribute latencies and intertemporal preferences. Displaying the amount information first makes people more patient, while displaying time information first does the opposite. These findings highlight the importance of intra-choice dynamics in shaping intertemporal choices and suggest that manipulating attribute latency may be a useful technique for nudging.},
}

Humans
Animals
Mice
Time Factors
*Delay Discounting
Reward
Reaction Time
Choice Behavior/physiology

@article {pmid38580047,
year = {2024},
author = {Wang, C and Sun, Y and Xing, Y and Liu, K and Xu, K},
title = {Role of electrophysiological activity and interactions of lateral habenula in the development of depression-like behavior in a chronic restraint stress model.},
journal = {Brain research},
volume = {},
number = {},
pages = {148914},
doi = {10.1016/j.brainres.2024.148914},
pmid = {38580047},
issn = {1872-6240},
abstract = {Closed-loop deep brain stimulation (DBS) system offers a promising approach for treatment-resistant depression, but identifying universally accepted electrophysiological biomarkers for closed-loop DBS systems targeting depression is challenging. There is growing evidence suggesting a strong association between the lateral habenula (LHb) and depression. Here, we took LHb as a key target, utilizing multi-site local field potentials (LFPs) to study the acute and chronic changes in electrophysiology, functional connectivity, and brain network characteristics during the formation of a chronic restraint stress (CRS) model. Furthermore, our model combining the electrophysiological changes of LHb and interactions between LHb and other potential targets of depression can effectively distinguish depressive states, offering a new way for developing effective closed-loop DBS strategies.},
}

@article {pmid38579958,
year = {2024},
author = {Lo, YT and Lim, MJR and Kok, CY and Wang, S and Blok, SZ and Ang, TY and Ng, VYP and Rao, JP and Chua, KSG},
title = {Neural interface-based motor neuroprosthesis in post-stroke upper limb neurorehabilitation: An individual patient data meta-analysis.},
journal = {Archives of physical medicine and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.apmr.2024.04.001},
pmid = {38579958},
issn = {1532-821X},
abstract = {OBJECTIVE: To determine the efficacy of neural interface-, including brain-computer interface (BCI), based neurorehabilitation through conventional and individual patient data (IPD) meta-analysis, and to assess clinical parameters associated with positive response to neural interface-based neurorehabilitation.

DATA SOURCES: PubMed, EMBASE, and Cochrane Library databases up to February 2022 were reviewed.

STUDY SELECTION: Studies using neural interface-controlled physical effectors (FES and/or powered exoskeletons) and reported Fugl-Meyer Assessment-upper extremity (FMA-UE) scores were identified. This meta-analysis was prospectively registered on PROSPERO (#CRD42022312428). PRISMA guidelines were followed.

DATA EXTRACTION: Change in FMA-UE scores were pooled to estimate the mean effect size. Subgroup analyses were performed on clinical parameters and neural interface parameters with both study-level variables and IPD.

DATA SYNTHESIS: Forty-six studies containing 617 patients were included. Twenty-nine studies involving 214 patients reported IPD. FMA-UE score increased by a mean of 5.23 (95% CI: 3.85 to 6.61). Systems that used motor attempt resulted in greater FMA-UE gain than motor imagery, as did training lasting >4 versus ≤4 weeks. On IPD analysis, the mean time-to-improvement above MCID was 12 weeks (95% CI: 7 to not reached). At 6 months, 58% improved above MCID (95% CI: 41 to 70%). Patients with severe impairment (p=0.042) and age >50 years (p=0.0022) correlated with the failure to improve above the MCID on univariate log-rank tests. However, these factors were only borderline significant on multivariate Cox analysis (HR 0.15, p = 0.08 and HR 0.47, p = 0.06, respectively).

CONCLUSION: Neural interface-based motor rehabilitation resulted in significant though modest reductions in post-stroke impairment and should be considered for wider applications in stroke neurorehabilitation.},
}

@article {pmid38579696,
year = {2024},
author = {Ali, YH and Bodkin, KL and Rigotti-Thompson, M and Patel, K and Card, NS and Bhaduri, B and Nason-Tomaszewski, SR and Mifsud, DM and Hou, X and Nicolas, C and Allcroft, S and Hochberg, L and Au Yong, N and Stavisky, SD and Miller, LE and Brandman, D and Pandarinath, C},
title = {BRAND: A platform for closed-loop experiments with deep network models.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad3b3a},
pmid = {38579696},
issn = {1741-2552},
abstract = {OBJECTIVE: Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g., Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g., C and C++).

APPROACH: To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termed nodes, which communicate with each other in a graph via streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.

MAIN RESULTS: In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1-millisecond chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 milliseconds of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041) performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems.

SIGNIFICANCE: By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments. .},
}

@article {pmid38578854,
year = {2024},
author = {Lin, PJ and Li, W and Zhai, X and Li, Z and Sun, J and Xu, Q and Pan, Y and Ji, L and Li, C},
title = {Explainable deep-learning prediction for brain-computer interfaces supported lower extremity motor gains based on multi-state fusion.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3384498},
pmid = {38578854},
issn = {1558-0210},
abstract = {Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.},
}

@article {pmid38577666,
year = {2024},
author = {Liu, F and Zheng, H and Ma, S and Zhang, W and Liu, X and Chua, Y and Shi, L and Zhao, R},
title = {Advancing brain-inspired computing with hybrid neural networks.},
journal = {National science review},
volume = {11},
number = {5},
pages = {nwae066},
pmid = {38577666},
issn = {2053-714X},
abstract = {Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain-computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network (HNN), which integrates computer-science-oriented artificial neural networks (ANNs) with neuroscience-oriented spiking neural networks (SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.},
}

@article {pmid38576451,
year = {2024},
author = {Ning, M and Duwadi, S and Yücel, MA and von Lühmann, A and Boas, DA and Sen, K},
title = {fNIRS dataset during complex scene analysis.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1329086},
pmid = {38576451},
issn = {1662-5161},
support = {T32 DC013017/DC/NIDCD NIH HHS/United States ; },
}

@article {pmid38572293,
year = {2024},
author = {},
title = {Correction to: Transfer learning promotes acquisition of individual BCI skills.},
journal = {PNAS nexus},
volume = {3},
number = {4},
pages = {pgae137},
doi = {10.1093/pnasnexus/pgae137},
pmid = {38572293},
issn = {2752-6542},
abstract = {[This corrects the article DOI: 10.1093/pnasnexus/pgae076.].},
}

@article {pmid38572110,
year = {2024},
author = {Wang, L and Hong, W and Zhu, H and He, Q and Yang, B and Wang, J and Weng, Q},
title = {Macrophage senescence in health and diseases.},
journal = {Acta pharmaceutica Sinica. B},
volume = {14},
number = {4},
pages = {1508-1524},
pmid = {38572110},
issn = {2211-3835},
abstract = {Macrophage senescence, manifested by the special form of durable cell cycle arrest and chronic low-grade inflammation like senescence-associated secretory phenotype, has long been considered harmful. Persistent senescence of macrophages may lead to maladaptation, immune dysfunction, and finally the development of age-related diseases, infections, autoimmune diseases, and malignancies. However, it is a ubiquitous, multi-factorial, and dynamic complex phenomenon that also plays roles in remodeled processes, including wound repair and embryogenesis. In this review, we summarize some general molecular changes and several specific biomarkers during macrophage senescence, which may bring new sight to recognize senescent macrophages in different conditions. Also, we take an in-depth look at the functional changes in senescent macrophages, including metabolism, autophagy, polarization, phagocytosis, antigen presentation, and infiltration or recruitment. Furthermore, some degenerations and diseases associated with senescent macrophages as well as the mechanisms or relevant genetic regulations of senescent macrophages are integrated, not only emphasizing the possibility of regulating macrophage senescence to benefit age-associated diseases but also has an implication on the finding of potential targets or drugs clinically.},
}

@article {pmid38570593,
year = {2024},
author = {van Stuijvenberg, OC and Broekman, MLD and Wolff, SEC and Bredenoord, AL and Jongsma, KR},
title = {Developer perspectives on the ethics of AI-driven neural implants: a qualitative study.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {7880},
pmid = {38570593},
issn = {2045-2322},
support = {17619//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; },
mesh = {*Artificial Intelligence ; Reproducibility of Results ; Qualitative Research ; Focus Groups ; *Cochlear Implants ; },
abstract = {Convergence of neural implants with artificial intelligence (AI) presents opportunities for the development of novel neural implants and improvement of existing neurotechnologies. While such technological innovation carries great promise for the restoration of neurological functions, they also raise ethical challenges. Developers of AI-driven neural implants possess valuable knowledge on the possibilities, limitations and challenges raised by these innovations; yet their perspectives are underrepresented in academic literature. This study aims to explore perspectives of developers of neurotechnology to outline ethical implications of three AI-driven neural implants: a cochlear implant, a visual neural implant, and a motor intention decoding speech-brain-computer-interface. We conducted semi-structured focus groups with developers (n = 19) of AI-driven neural implants. Respondents shared ethically relevant considerations about AI-driven neural implants that we clustered into three themes: (1) design aspects; (2) challenges in clinical trials; (3) impact on users and society. Developers considered accuracy and reliability of AI-driven neural implants conditional for users' safety, authenticity, and mental privacy. These needs were magnified by the convergence with AI. Yet, the need for accuracy and reliability may also conflict with potential benefits of AI in terms of efficiency and complex data interpretation. We discuss strategies to mitigate these challenges.},
}

*Artificial Intelligence
Reproducibility of Results
Qualitative Research
Focus Groups
*Cochlear Implants

@article {pmid38570113,
year = {2024},
author = {Sun, WB and Fu, JX and Chen, YL and Li, HF and Wu, ZY and Chen, DF},
title = {Both gain- and loss-of-function variants of KCNA1 are associated with paroxysmal kinesignic dyskinesia.},
journal = {Journal of genetics and genomics = Yi chuan xue bao},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jgg.2024.03.013},
pmid = {38570113},
issn = {1673-8527},
abstract = {KCNA1 is the coding gene for Kv1.1 voltage-gated potassium channel α subunit. Three variants of KCNA1 have been reported to manifest as paroxysmal kinesignic dyskinesia (PKD), but the correlation between them remains unclear due to the phenotypic complexity of KCNA1 variants as well as the rarity of PKD cases. Using the whole exome sequencing followed by Sanger sequencing, we screen potential pathogenic KCNA1 variants in patients clinically diagnosed with paroxysmal movement disorders and identify three previously unreported missense variants of KCNA1 in three unrelated Chinese families. The proband of one family (c.496G>A, p.A166T) manifests as episodic ataxia type 1, and the other two (c.877G>A, p.V293I; and c.1112C>A, p.T371A) manifest as PKD. The pathogenicity of these variants is confirmed by functional studies, suggesting that p.A166T and p.T371A cause a loss-of-function of the channel, while p.V293I leads to a gain-of-function with the property of voltage-dependent gating and activation kinetic affected. By reviewing the locations of PKD-manifested KCNA1 variants in Kv1.1 protein, we find that these variants tend to cluster around the pore domain, which is similar to epilepsy. Thus, our study strengthens the correlation between KCNA1 variants and PKD and provides more information on genotype-phenotype correlations of KCNA1 channelopathy.},
}

@article {pmid38565846,
year = {2024},
author = {Li, H and Li, Z and Yuan, X and Tian, Y and Ye, W and Zeng, P and Li, XM and Guo, F},
title = {Dynamic encoding of temperature in the central circadian circuit coordinates physiological activities.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {2834},
pmid = {38565846},
issn = {2041-1723},
mesh = {Animals ; Circadian Rhythm/physiology ; Temperature ; Sleep/physiology ; Drosophila ; *Circadian Clocks/physiology ; *Drosophila Proteins/genetics ; Drosophila melanogaster/physiology ; },
abstract = {The circadian clock regulates animal physiological activities. How temperature reorganizes circadian-dependent physiological activities remains elusive. Here, using in-vivo two-photon imaging with the temperature control device, we investigated the response of the Drosophila central circadian circuit to temperature variation and identified that DN1as serves as the most sensitive temperature-sensing neurons. The circadian clock gate DN1a's diurnal temperature response. Trans-synaptic tracing, connectome analysis, and functional imaging data reveal that DN1as bidirectionally targets two circadian neuronal subsets: activity-related E cells and sleep-promoting DN3s. Specifically, behavioral data demonstrate that the DN1a-E cell circuit modulates the evening locomotion peak in response to cold temperature, while the DN1a-DN3 circuit controls the warm temperature-induced nocturnal sleep reduction. Our findings systematically and comprehensively illustrate how the central circadian circuit dynamically integrates temperature and light signals to effectively coordinate wakefulness and sleep at different times of the day, shedding light on the conserved neural mechanisms underlying temperature-regulated circadian physiology in animals.},
}

Animals
Circadian Rhythm/physiology
Temperature
Sleep/physiology
Drosophila
*Circadian Clocks/physiology
*Drosophila Proteins/genetics
Drosophila melanogaster/physiology

@article {pmid38565100,
year = {2024},
author = {Li, W and Li, H and Sun, X and Kang, H and An, S and Wang, G and Gao, Z},
title = {Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad3986},
pmid = {38565100},
issn = {1741-2552},
abstract = {OBJECTIVE: The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore, there is a significant limitation in the generalization performance of EEG models due to the substantial inter-individual variability observed in EEG signals.

APPROACH: To address these issues, we propose a novel self-supervised contrastive learning framework for decoding motor imagery (MI) signals in cross-subject scenarios. Specifically, we design an encoder combining convolutional neural network and attention mechanism. In the contrastive learning training stage, the network undergoes training with the pretext task of data augmentation to minimize the distance between pairs of homologous transformations while simultaneously maximizing the distance between pairs of heterologous transformations. It enhances the amount of data utilized for training and improves the network's ability to extract deep features from original signals without relying on the true labels of the data.

MAIN RESULTS: To evaluate our framework's efficacy, we conduct extensive experiments on three public MI datasets: BCI IV IIa, BCI IV IIb, and HGD datasets. The proposed method achieves cross-subject classification accuracies of 67.32%, 82.34%, and 81.13% on the three datasets, demonstrating superior performance compared to existing methods.

SIGNIFICANCE: Therefore, this method has great promise for improving the performance of cross-subject transfer learning in MI-based BCI systems.},
}

@article {pmid38563704,
year = {2024},
author = {Yang, Q and Wu, B and Castagnola, E and Pwint, MY and Williams, N and Vazquez, AL and Cui, XT},
title = {Integrated Microprism and Microelectrode Array for Simultaneous Electrophysiology and Two-Photon Imaging Across all Cortical Layers.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e2302362},
doi = {10.1002/adhm.202302362},
pmid = {38563704},
issn = {2192-2659},
abstract = {Cerebral neural electronics play a crucial role in neuroscience research with increasing translational applications such as brain-computer interface for sensory input and motor output restoration. While widely utilized for decades, our understandings of the cellular mechanisms underlying this technology remains limited. Although two-photon microscopy (TPM) has shown great promise in imaging superficial neural electrodes, its application to deep-penetrating electrodes is unclear. Here, we introduce a novel device integrating transparent microelectrode arrays (MEAs) with glass microprisms, enabling electrophysiology recording and stimulation alongside TPM imaging across all cortical layers in a vertical plane. Tested in Thy1-GCaMP6 mice for over 4 months, our integrated device demonstrated the capability for multisite electrophysiological recording and simultaneous TPM calcium imaging. As a proof of concept, we investigated the impact of microstimulation amplitude, frequency, and depth on neural activation patterns throughout cortical layers using our setup. With future improvements in material stability and single unit yield, our multimodal tool can greatly expand integrated electrophysiology and optical imaging from the superficial brain to the entire cortical column, opening new avenues for neuroscience research and neurotechnology development. This article is protected by copyright. All rights reserved.},
}

@article {pmid38562772,
year = {2024},
author = {Rajeswaran, P and Payeur, A and Lajoie, G and Orsborn, AL},
title = {Assistive sensory-motor perturbations influence learned neural representations.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.03.20.585972},
pmid = {38562772},
abstract = {Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Population dimensionality remained constant or increased with learning, counter to trends with non-adaptive BCIs. Yet, over time, task information was contained in a smaller subset of neurons or population modes. Moreover, task information was ultimately stored in neural modes that occupied a small fraction of the population variance. An artificial neural network model suggests the adaptive decoders contribute to forming these compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations, like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.},
}

@article {pmid37935217,
year = {2024},
author = {Venu, K and Natesan, P},
title = {Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {69},
number = {2},
pages = {125-140},
pmid = {37935217},
issn = {1862-278X},
mesh = {*Deep Learning ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Imagination ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVES: To design and develop an approach named HC + SMA-SSA scheme for classifying motor imagery task.

METHODS: The offered model employs a new method for classifying motor imagery task. Initially, down sampling is deployed to pre-process the incoming signal. Subsequently, "Modified Stockwell Transform (ST) and common spatial pattern (CSP) based features are extracted". Then, optimal channel selection is made by a novel hybrid optimization model named as Spider Monkey Assisted SSA (SMA-SSA). Here, "Long Short Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BI-GRU)" models are used for final classification, whose outcomes are averaged at the end. At last, the improvement of SMA-SSA based model is proven over different metrics.

RESULTS: A superior sensitivity of 0.939 is noted for HC + SMA-SSA that was higher over HC with no optimization and proposed with traditional ST.

CONCLUSIONS: The proposed method achieved effective classification performance in terms of performance measures.},
}

*Deep Learning
Electroencephalography/methods
*Brain-Computer Interfaces
Imagination
Algorithms
Signal Processing, Computer-Assisted

@article {pmid38566861,
year = {2019},
author = {Fontaine, AK and Segil, JL and Caldwell, JH and Weir, RFF},
title = {Real-Time Prosthetic Digit Actuation by Optical Read-out of Activity-Dependent Calcium Signals in an Ex Vivo Peripheral Nerve.},
journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering},
volume = {2019},
number = {},
pages = {143-146},
pmid = {38566861},
issn = {1948-3546},
support = {OT2 OD023852/OD/NIH HHS/United States ; },
abstract = {Improved neural interfacing strategies are needed for the full articulation of advanced prostheses. To address limitations of existing control interface designs, the work of our laboratory has presented an optical approach to reading activity from individual nerve fibers using activity-dependent calcium transients. Here, we demonstrate the feasibility of such signals to control prosthesis actuation by using the axonal fluorescence signal in an ex vivo mouse nerve to drive a prosthetic digit in real-time. Additionally, signals of varying action potential frequency are streamed post hoc to the prosthesis, showing graded motor output and the potential for proportional neural control. This proof-of-concept work is a novel demonstration of the functional use of activity-dependent optical read-out in the nerve.},
}

@article {pmid38562360,
year = {2024},
author = {Ilchev, B and Chervenkov, V and Valchev, N and Nakov, V and Minchev, T and Vassilev, G and Tsvetanov, T and Laleva, L and Milev, M and Spiriev, T},
title = {Interdisciplinary Successful Revascularization of Traumatic Occlusion of the Right Common Carotid Artery.},
journal = {Cureus},
volume = {16},
number = {3},
pages = {e55395},
pmid = {38562360},
issn = {2168-8184},
abstract = {Blunt carotid artery injury (BCI) poses a rare yet severe threat following vascular trauma, often leading to significant morbidity and mortality. We present a case of a 33-year-old male who suffered complete thrombotic occlusion of the right common carotid artery (CCA) following a workplace accident. Clinical evaluation revealed profound neurological deficits, prompting multidisciplinary surgical intervention guided by the Denver criteria (Grade I - disruption inside the vessel that results in a narrowing of the lumen by less than 25%; Grade II - dissection or intramural hematoma causing greater than 25% stenosis; Grade III - comprises pseudoaneurysm formation; Grade IV - causes total vessel occlusion; Grade V - describes vessel transection with extravasation). Surgical exploration unveiled extensive arterial damage, necessitating thrombectomy, primary repair, and double-layered patch angioplasty using an autologous saphenous vein. Postoperative recovery was uneventful, with the restoration of pulsatile blood flow confirmed by Doppler ultrasound. Three-month follow-up demonstrated patent arterial reconstruction and improved cerebral perfusion, despite the persistent neurological deficits. Our case underscores the challenges in diagnosing and managing BCI, advocating for a tailored approach based on injury severity and neurological status. While conservative management remains standard, surgical intervention offers a viable option in select cases, particularly those with complete vessel occlusion and neurological compromise. Long-term surveillance is imperative to assess the durability of arterial reconstruction and monitor for recurrent thromboembolic events. Further research is warranted to refine management algorithms and elucidate optimal treatment strategies in this rare but critical vascular pathology.},
}

@article {pmid38560190,
year = {2024},
author = {Akuthota, S and K, R and Ravichander, J},
title = {Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN.},
journal = {Heliyon},
volume = {10},
number = {7},
pages = {e27198},
pmid = {38560190},
issn = {2405-8440},
abstract = {This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks. The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF's mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process. The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a & 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%.},
}

@article {pmid38560116,
year = {2024},
author = {Chen, D},
title = {Improved empirical mode decomposition bagging RCSP combined with Fisher discriminant method for EEG feature extraction and classification.},
journal = {Heliyon},
volume = {10},
number = {7},
pages = {e28235},
pmid = {38560116},
issn = {2405-8440},
abstract = {BACKGROUND: Traditional Common Spatial Pattern (CSP) algorithms for Electroencephalogram (EEG) signal classification are sensitive to noise and can produce low accuracy in small sample datasets.

NEW METHOD: To solve the problem, an improved Empirical Mode Decomposition (EMD) Bagging Regularized CSP (RCSP) algorithm is proposed. It filters EEG signals through improved EMD, inhibits high-frequency noise, retains effective information in the characteristic frequency band, and uses Bagging algorithm for data reconstruction. Feature extraction is performed with regularization of spatial patterns and Fisher linear discriminant analysis for feature classification. T-test is used for classification.

RESULTS: The improved EMD Bagging RCSP algorithm has improved accuracy and robustness compared to CSP and its derivatives. The average classification rate is increased by about 6%, demonstrating the effectiveness and correctness of the proposed algorithm.Comparison with existing methods: The proposed algorithm outperforms CSP and its derivatives by retaining effective information and inhibiting high-frequency noise in small sample EEG datasets.

CONCLUSIONS: The proposed EMD Bagging RCSP algorithm provides a reliable and effective method for EEG signal classification and can be used in various applications, including brain-computer interfaces and clinical EEG diagnosis.},
}

@article {pmid38558011,
year = {2024},
author = {Chen, D and Zhao, Z and Zhang, S and Chen, S and Wu, X and Shi, J and Liu, N and Pan, C and Tang, Y and Meng, C and Zhao, X and Tao, B and Liu, W and Chen, D and Ding, H and Zhang, P and Tang, Z},
title = {Evolving Therapeutic Landscape of Intracerebral Hemorrhage: Emerging Cutting-Edge Advancements in Surgical Robots, Regenerative Medicine, and Neurorehabilitation Techniques.},
journal = {Translational stroke research},
volume = {},
number = {},
pages = {},
pmid = {38558011},
issn = {1868-601X},
support = {2022B37//Research Fund of Tongji Hospital/ ; 92148206//National Natural Science Foundation of China/ ; },
abstract = {Intracerebral hemorrhage (ICH) is the most serious form of stroke and has limited available therapeutic options. As knowledge on ICH rapidly develops, cutting-edge techniques in the fields of surgical robots, regenerative medicine, and neurorehabilitation may revolutionize ICH treatment. However, these new advances still must be translated into clinical practice. In this review, we examined several emerging therapeutic strategies and their major challenges in managing ICH, with a particular focus on innovative therapies involving robot-assisted minimally invasive surgery, stem cell transplantation, in situ neuronal reprogramming, and brain-computer interfaces. Despite the limited expansion of the drug armamentarium for ICH over the past few decades, the judicious selection of more efficacious therapeutic modalities and the exploration of multimodal combination therapies represent opportunities to improve patient prognoses after ICH.},
}

@article {pmid38557253,
year = {2024},
author = {Van Horn, AL and Burgess, JR},
title = {From Blunt Cardiac Injury to Heart Transplant Following Motorcycle Collision.},
journal = {The American surgeon},
volume = {},
number = {},
pages = {31348241241699},
doi = {10.1177/00031348241241699},
pmid = {38557253},
issn = {1555-9823},
abstract = {Traumatic coronary artery occlusion and dissection is an exceedingly rare complication of blunt cardiac injury (BCI), though it has been previously noted in a number of case reports. However, it can also lead to heart transplant, which to our knowledge has not been previously described in the literature. We present a case of a healthy 24-year-old man without significant past medical history who was in a motorcycle accident, resulting in sternal fracture and BCI. He was ultimately found to have thrombotic occlusion and dissection of his left anterior descending artery (LAD), requiring mechanical thrombectomy and drug-eluting stent, as well as subsequent hospitalizations and operations due to various complications. It was suspected that he went into ventricular fibrillation and had a second motorcycle collision, resulting in cardiogenic shock. Ultimately, his progression of ischemic cardiomyopathy and mitral regurgitation led to the need for heart transplant. Blunt cardiac injury with myocardial contusion has such a broad range of pathologies. It is essential that patients with these injury patterns raise a high level of suspicion for BCI and are followed closely with appropriate diagnostic testing and rapid intervention for best possible outcomes.},
}

@article {pmid38557034,
year = {2024},
author = {Ling, W and Shang, X and Yu, C and Li, C and Xu, K and Feng, L and Wei, Y and Tang, T and Huang, X},
title = {Miniaturized Implantable Fluorescence Probes Integrated with Metal-Organic Frameworks for Deep Brain Dopamine Sensing.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.4c00632},
pmid = {38557034},
issn = {1936-086X},
abstract = {Continuously monitoring neurotransmitter dynamics can offer profound insights into neural mechanisms and the etiology of neurological diseases. Here, we present a miniaturized implantable fluorescence probe integrated with metal-organic frameworks (MOFs) for deep brain dopamine sensing. The probe is assembled from physically thinned light-emitting diodes (LEDs) and phototransistors, along with functional surface coatings, resulting in a total thickness of 120 μm. A fluorescent MOF that specifically binds dopamine is introduced, enabling a highly sensitive dopamine measurement with a detection limit of 79.9 nM. A compact wireless circuit weighing only 0.85 g is also developed and interfaced with the probe, which was later applied to continuously monitor real-time dopamine levels during deep brain stimulation in rats, providing critical information on neurotransmitter dynamics. Cytotoxicity tests and immunofluorescence analysis further suggest a favorable biocompatibility of the probe for implantable applications. This work presents fundamental principles and techniques for integrating fluorescent MOFs and flexible electronics for brain-computer interfaces and may provide more customized platforms for applications in neuroscience, disease tracing, and smart diagnostics.},
}

@article {pmid38555287,
year = {2024},
author = {Wei, M and Xu, K and Tang, B and Li, J and Yun, Y and Zhang, P and Wu, Y and Bao, K and Lei, K and Chen, Z and Ma, H and Sun, C and Liu, R and Li, M and Li, L and Lin, H},
title = {Monolithic back-end-of-line integration of phase change materials into foundry-manufactured silicon photonics.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {2786},
pmid = {38555287},
issn = {2041-1723},
support = {91950204, 62105287, 61975179, and 92150302//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Monolithic integration of novel materials without modifying the existing photonic component library is crucial to advancing heterogeneous silicon photonic integrated circuits. Here we show the introduction of a silicon nitride etch stop layer at select areas, coupled with low-loss oxide trench, enabling incorporation of functional materials without compromising foundry-verified device reliability. As an illustration, two distinct chalcogenide phase change materials (PCMs) with remarkable nonvolatile modulation capabilities, namely Sb2Se3 and Ge2Sb2Se4Te1, were monolithic back-end-of-line integrated, offering compact phase and intensity tuning units with zero-static power consumption. By employing these building blocks, the phase error of a push-pull Mach-Zehnder interferometer optical switch could be reduced with a 48% peak power consumption reduction. Mirco-ring filters with >5-bit wavelength selective intensity modulation and waveguide-based >7-bit intensity-modulation broadband attenuators could also be achieved. This foundry-compatible platform could open up the possibility of integrating other excellent optoelectronic materials into future silicon photonic process design kits.},
}

@article {pmid38554856,
year = {2024},
author = {Bader, ER and Boro, AD and Killian, NJ and Eskandar, EN},
title = {A method for precisely timed, on-demand intracranial stimulation using the RNS device.},
journal = {Brain stimulation},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.brs.2024.03.020},
pmid = {38554856},
issn = {1876-4754},
}

@article {pmid38554787,
year = {2024},
author = {Chunduri, V and Aoudni, Y and Khan, S and Aziz, A and Rizwan, A and Deb, N and Keshta, I and Soni, M},
title = {Multi-Scale Spatiotemporal Attention Network for Neuron based Motor Imagery EEG Classification.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110128},
doi = {10.1016/j.jneumeth.2024.110128},
pmid = {38554787},
issn = {1872-678X},
abstract = {BACKGROUND: In recent times, the expeditious expansion of Brain-Computer Interface (BCI) technology in neuroscience, which relies on electroencephalogram (EEG) signals associated with motor imagery, has yielded outcomes that rival conventional approaches, notably due to the triumph of deep learning. Nevertheless, the task of developing and training a comprehensive network to extract the underlying characteristics of motor imagining EEG data continues to pose challenges.

NEW METHOD: This paper presents a multi-scale spatiotemporal self-attention (SA) network model that relies on an attention mechanism. This model aims to classify motor imagination EEG signals into four classes (left hand, right hand, foot, tongue/rest) by considering the temporal and spatial properties of EEG. It is employed to autonomously allocate greater weights to channels linked to motor activity and lesser weights to channels not related to movement, thus choosing the most suitable channels. Neuron utilises parallel multi-scale Temporal Convolutional Network (TCN) layers to extract feature information in the temporal domain at various scales, effectively eliminating temporal domain noise.

RESULTS: The suggested model achieves accuracies of 79.26%, 85.90%, and 96.96% on the BCI competition datasets IV-2a, IV-2b, and HGD, respectively.

In terms of single-subject classification accuracy, this strategy demonstrates superior performance compared to existing methods.

CONCLUSION: The results indicate that the proposed strategy exhibits favourable performance, resilience, and transfer learning capabilities.},
}

@article {pmid38552161,
year = {2024},
author = {Wang, W and Zhou, H and Xu, Z and Li, Z and Zhang, L and Wan, P},
title = {Flexible Conformally Bioadhesive MXene Hydrogel Electronics for Machine Learning-Facilitated Human-Interactive Sensing.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e2401035},
doi = {10.1002/adma.202401035},
pmid = {38552161},
issn = {1521-4095},
abstract = {Wearable epidermic electronics assembled from conductive hydrogels are attracting various research attention for their seamless integration with human body for conformally real-time health monitoring, clinical diagnostics and medical treatment, and human-interactive sensing. Nevertheless, it remains a tremendous challenge to simultaneously achieve conformally bioadhesive epidermic electronics with remarkable self-adhesiveness, reliable ultraviolet (UV)-protection ability, and admirable sensing performance for high-fidelity epidermal electrophysiological signals monitoring, along with timely photothermal therapeutic performances after medical diagnostic sensing, as well as efficient antibacterial activity and reliable hemostatic effect for potential medical therapy. Herein, a conformally bioadhesive hydrogel-based epidermic sensor, featuring superior self-adhesiveness and excellent UV-protection performance, is developed by dexterously assembling conducting MXene nanosheets network with biological hydrogel polymer network for conformally stably attaching onto human skin for high-quality recording of various epidermal electrophysiological signals with high signal-to-noise ratios (SNR) and low interfacial impedance for intelligent medical diagnosis and smart human-machine interface. Moreover, a smart sign language gesture recognition platform based on collected EMG signals are designed for hassle-free communication with hearing-impaired people with the help of advanced machine learning algorithms. Meanwhile, the bioadhesive MXene hydrogel possesses reliable antibacterial capability, excellent biocompatibility and effective hemostasis properties for promising bacterial-infected wound bleeding. This article is protected by copyright. All rights reserved.},
}

@article {pmid38550646,
year = {2024},
author = {Bossi, F and Ciardo, F and Mostafaoui, G},
title = {Editorial: Neurocognitive features of human-robot and human-machine interaction.},
journal = {Frontiers in psychology},
volume = {15},
number = {},
pages = {1394970},
pmid = {38550646},
issn = {1664-1078},
}

@article {pmid38550567,
year = {2024},
author = {Racz, FS and Kumar, S and Kaposzta, Z and Alawieh, H and Liu, DH and Liu, R and Czoch, A and Mukli, P and Millán, JDR},
title = {Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1271831},
pmid = {38550567},
issn = {1662-4548},
abstract = {Riemannian geometry-based classification (RGBC) gained popularity in the field of brain-computer interfaces (BCIs) lately, due to its ability to deal with non-stationarities arising in electroencephalography (EEG) data. Domain adaptation, however, is most often performed on sample covariance matrices (SCMs) obtained from EEG data, and thus might not fully account for components affecting covariance estimation itself, such as regional trends. Detrended cross-correlation analysis (DCCA) can be utilized to estimate the covariance structure of such signals, yet it is computationally expensive in its original form. A recently proposed online implementation of DCCA, however, allows for its fast computation and thus makes it possible to employ DCCA in real-time applications. In this study we propose to replace the SCM with the DCCA matrix as input to RGBC and assess its effect on offline and online BCI performance. First we evaluated the proposed decoding pipeline offline on previously recorded EEG data from 18 individuals performing left and right hand motor imagery (MI), and benchmarked it against vanilla RGBC and popular MI-detection approaches. Subsequently, we recruited eight participants (with previous BCI experience) who operated an MI-based BCI (MI-BCI) online using the DCCA-enhanced Riemannian decoder. Finally, we tested the proposed method on a public, multi-class MI-BCI dataset. During offline evaluations the DCCA-based decoder consistently and significantly outperformed the other approaches. Online evaluation confirmed that the DCCA matrix could be computed in real-time even for 22-channel EEG, as well as subjects could control the MI-BCI with high command delivery (normalized Cohen's κ: 0.7409 ± 0.1515) and sample-wise MI detection (normalized Cohen's κ: 0.5200 ± 0.1610). Post-hoc analysis indicated characteristic connectivity patterns under both MI conditions, with stronger connectivity in the hemisphere contralateral to the MI task. Additionally, fractal scaling exponent of neural activity was found increased in the contralateral compared to the ipsilateral motor cortices (C4 and C3 for left and right MI, respectively) in both classes. Combining DCCA with Riemannian geometry-based decoding yields a robust and effective decoder, that not only improves upon the SCM-based approach but can also provide relevant information on the neurophysiological processes behind MI.},
}

@article {pmid38547834,
year = {2024},
author = {Abbott, JR and Jeakle, EN and Haghighi, P and Usoro, JO and Sturgill, BS and Wu, Y and Geramifard, N and Radhakrishna, R and Patnaik, S and Nakajima, S and Hess, J and Mehmood, Y and Devata, V and Vijayakumar, G and Sood, A and Doan Thai, TT and Dogra, K and Hernandez-Reynoso, AG and Pancrazio, JJ and Cogan, SF},
title = {Planar amorphous silicon carbide microelectrode arrays for chronic recording in rat motor cortex.},
journal = {Biomaterials},
volume = {308},
number = {},
pages = {122543},
doi = {10.1016/j.biomaterials.2024.122543},
pmid = {38547834},
issn = {1878-5905},
abstract = {Chronic implantation of intracortical microelectrode arrays (MEAs) capable of recording from individual neurons can be used for the development of brain-machine interfaces. However, these devices show reduced recording capabilities under chronic conditions due, at least in part, to the brain's foreign body response (FBR). This creates a need for MEAs that can minimize the FBR to possibly enable long-term recording. A potential approach to reduce the FBR is the use of MEAs with reduced cross-sectional geometries. Here, we fabricated 4-shank amorphous silicon carbide (a-SiC) MEAs and implanted them into the motor cortex of seven female Sprague-Dawley rats. Each a-SiC MEA shank was 8 μm thick by 20 μm wide and had sixteen sputtered iridium oxide film (SIROF) electrodes (4 per shank). A-SiC was chosen as the fabrication base for its high chemical stability, good electrical insulation properties, and amenability to thin film fabrication. Electrochemical analysis and neural recordings were performed weekly for 4 months. MEAs were characterized pre-implantation in buffered saline and in vivo using electrochemical impedance spectroscopy and cyclic voltammetry at 50 mV/s and 50,000 mV/s. Neural recordings were analyzed for single unit activity. At the end of the study, animals were sacrificed for immunohistochemical analysis. We observed statistically significant, but small, increases in 1 and 30 kHz impedance values and 50,000 mV/s charge storage capacity over the 16-week implantation period. Slow sweep 50 mV/s CV and 1 Hz impedance did not significantly change over time. Impedance values increased from 11.6 MΩ to 13.5 MΩ at 1 Hz, 1.2 MΩ-2.9 MΩ at 1 kHz, and 0.11 MΩ-0.13 MΩ at 30 kHz over 16 weeks. The median charge storage capacity of the implanted electrodes at 50 mV/s was 58.1 mC/cm[2] on week 1 and 55.9 mC/cm[2] on week 16, and at 50,000 mV/s, 4.27 mC/cm[2] on week 1 and 5.93 mC/cm[2] on week 16. Devices were able to record neural activity from 92% of all active channels at the beginning of the study, At the study endpoint, a-SiC devices were still recording single-unit activity on 51% of electrochemically active electrode channels. In addition, we observed that the signal-to-noise ratio experienced a small decline of -0.19 per week. We also classified observed units as fast and slow repolarizing based on the trough-to-peak time. Although the overall presence of single units declined, fast and slow repolarizing units declined at a similar rate. At recording electrode depth, immunohistochemistry showed minimal tissue response to the a-SiC devices, as indicated by statistically insignificant differences in activated glial cell response between implanted brains slices and contralateral sham slices at 150 μm away from the implant location, as evidenced by GFAP staining. NeuN staining revealed the presence of neuronal cell bodies close to the implantation site, again statistically not different from a contralateral sham slice. These results warrant further investigation of a-SiC MEAs for future long-term implantation neural recording studies.},
}

@article {pmid38545737,
year = {2024},
author = {Ergün, E and Aydemir, Ö and Korkmaz, OE},
title = {Investigating the informative brain region in multiclass electroencephalography and near infrared spectroscopy based BCI system using band power based features.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-16},
doi = {10.1080/10255842.2024.2333924},
pmid = {38545737},
issn = {1476-8259},
abstract = {In recent years, various brain imaging techniques have been used as input signals for brain-computer interface (BCI) systems. Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are two prominent techniques in this field, each with its own advantages and limitations. As a result, there is a growing tendency to integrate these methods in a hybrid within BCI systems. The primary aim of this study is to identify highly functional brain regions within an EEG + NIRS-based BCI system. To achieve this, the research focused on identifying EEG electrodes positioned in different brain lobes and then investigating the functionality of each lobe. The methodology involved segmenting the EEG + NIRS dataset into 2.4 s time windows, and then extracting band-power based features from these segmented signals. A classification algorithm, specifically the k-nearest neighbor algorithm, was then used to classify the features. The result was a remarkable classification accuracy (CA) of 95.54%±1.31 when using the active brain region within the hybrid model. These results underline the effectiveness of the proposed approach, as it outperformed both standalone EEG and NIRS modalities in terms of CA by 5.19% and 40.90%, respectively. Furthermore, the results confirm the considerable potential of the method in classifying EEG + NIRS signals recorded during tasks such as reading text while scrolling in different directions, including right, left, up and down. This research heralds a promising step towards enhancing the capabilities of BCI systems by harnessing the synergistic power of EEG and NIRS technologies.},
}

@article {pmid38544185,
year = {2024},
author = {Albán-Escobar, M and Navarrete-Arroyo, P and De la Cruz-Guevara, DR and Tobar-Quevedo, J},
title = {Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {6},
pages = {},
pmid = {38544185},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Robotic Surgical Procedures ; Electroencephalography/methods ; *Self-Help Devices ; Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {This paper explores the potential benefits of integrating a brain-computer interface (BCI) utilizing the visual-evoked potential paradigm (SSVEP) with a six-degrees-of-freedom (6-DOF) robotic arm to enhance rehabilitation tools. The SSVEP-BCI employs electroencephalography (EEG) as a method of measuring neural responses inside the occipital lobe in reaction to pre-established visual stimulus frequencies. The BCI offline and online studies yielded accuracy rates of 75% and 83%, respectively, indicating the efficacy of the system in accurately detecting and capturing user intent. The robotic arm achieves planar motion by utilizing a total of five control frequencies. The results of this experiment exhibited a high level of precision and consistency, as indicated by the recorded values of ±0.85 and ±1.49 cm for accuracy and repeatability, respectively. Moreover, during the performance tests conducted with the task of constructing a square within each plane, the system demonstrated accuracy of 79% and 83%. The use of SSVEP-BCI and a robotic arm together shows promise and sets a solid foundation for the development of assistive technologies that aim to improve the health of people with amyotrophic lateral sclerosis, spina bifida, and other related diseases.},
}

Humans
*Brain-Computer Interfaces
*Robotic Surgical Procedures
Electroencephalography/methods
*Self-Help Devices
Evoked Potentials, Visual
Photic Stimulation

@article {pmid38541720,
year = {2024},
author = {Pais-Vieira, C and Figueiredo, JG and Perrotta, A and Matos, D and Aguiar, M and Ramos, J and Gato, M and Poleri, T and Pais-Vieira, M},
title = {Activation of a Rhythmic Lower Limb Movement Pattern during the Use of a Multimodal Brain-Computer Interface: A Case Study of a Clinically Complete Spinal Cord Injury.},
journal = {Life (Basel, Switzerland)},
volume = {14},
number = {3},
pages = {},
pmid = {38541720},
issn = {2075-1729},
support = {UIDP/04501/2020, UIDB/04279/2020, FCT/IF/00098/2015, project CISUC -UID/CEC/00326/2020, and via the doctoral scholarship 2023.02051.BD//Fundação para a Ciência e Tecnologia/ ; 95/2016//Bial (Portugal)/ ; (grant agreement No. 779963)//European Union's Horizon 2020 Research and Innovation Programme, via an Open Call issued and executed under Project EUROBENCH (grant agreement No. 779963) Thertact-Fb and Thertact- NEXT/ ; MC-12-2018//SANTA CASA Prémios Neurociências Melo e Castro/ ; },
abstract = {Brain-computer interfaces (BCIs) that integrate virtual reality with tactile feedback are increasingly relevant for neurorehabilitation in spinal cord injury (SCI). In our previous case study employing a BCI-based virtual reality neurorehabilitation protocol, a patient with complete T4 SCI experienced reduced pain and emergence of non-spastic lower limb movements after 10 sessions. However, it is still unclear whether these effects can be sustained, enhanced, and replicated, as well as the neural mechanisms that underlie them. The present report outlines the outcomes of extending the previous protocol with 24 more sessions (14 months, in total). Clinical, behavioral, and neurophysiological data were analyzed. The protocol maintained or reduced pain levels, increased self-reported quality of life, and was frequently associated with the appearance of non-spastic lower limb movements when the patient was engaged and not experiencing stressful events. Neural activity analysis revealed that changes in pain were encoded in the theta frequency band by the left frontal electrode F3. Examination of the lower limbs revealed alternating movements resembling a gait pattern. These results suggest that sustained use of this BCI protocol leads to enhanced quality of life, reduced and stable pain levels, and may result in the emergence of rhythmic patterns of lower limb muscle activity reminiscent of gait.},
}

@article {pmid38539656,
year = {2024},
author = {Yao, X and Li, T and Ding, P and Wang, F and Zhao, L and Gong, A and Nan, W and Fu, Y},
title = {Emotion Classification Based on Transformer and CNN for EEG Spatial-Temporal Feature Learning.},
journal = {Brain sciences},
volume = {14},
number = {3},
pages = {},
pmid = {38539656},
issn = {2076-3425},
support = {62376112, 82172058, 81771926, 61763022, and 62006246.//National Natural Science Foundation of China/ ; },
abstract = {OBJECTIVES: The temporal and spatial information of electroencephalogram (EEG) signals is crucial for recognizing features in emotion classification models, but it excessively relies on manual feature extraction. The transformer model has the capability of performing automatic feature extraction; however, its potential has not been fully explored in the classification of emotion-related EEG signals. To address these challenges, the present study proposes a novel model based on transformer and convolutional neural networks (TCNN) for EEG spatial-temporal (EEG ST) feature learning to automatic emotion classification.

METHODS: The proposed EEG ST-TCNN model utilizes position encoding (PE) and multi-head attention to perceive channel positions and timing information in EEG signals. Two parallel transformer encoders in the model are used to extract spatial and temporal features from emotion-related EEG signals, and a CNN is used to aggregate the EEG's spatial and temporal features, which are subsequently classified using Softmax.

RESULTS: The proposed EEG ST-TCNN model achieved an accuracy of 96.67% on the SEED dataset and accuracies of 95.73%, 96.95%, and 96.34% for the arousal-valence, arousal, and valence dimensions, respectively, for the DEAP dataset.

CONCLUSIONS: The results demonstrate the effectiveness of the proposed ST-TCNN model, with superior performance in emotion classification compared to recent relevant studies.

SIGNIFICANCE: The proposed EEG ST-TCNN model has the potential to be used for EEG-based automatic emotion recognition.},
}

@article {pmid38539622,
year = {2024},
author = {Kuang, M and Zhan, Z and Gao, S},
title = {Natural Image Reconstruction from fMRI Based on Node-Edge Interaction and Multi-Scale Constraint.},
journal = {Brain sciences},
volume = {14},
number = {3},
pages = {},
pmid = {38539622},
issn = {2076-3425},
support = {2019SCUH0007//Sichuan University Innovation Spark Project/ ; },
abstract = {Reconstructing natural stimulus images using functional magnetic resonance imaging (fMRI) is one of the most challenging problems in brain decoding and is also the crucial component of a brain-computer interface. Previous methods cannot fully exploit the information about interactions among brain regions. In this paper, we propose a natural image reconstruction method based on node-edge interaction and a multi-scale constraint. Inspired by the extensive information interactions in the brain, a novel graph neural network block with node-edge interaction (NEI-GNN block) is presented, which can adequately model the information exchange between brain areas via alternatively updating the nodes and edges. Additionally, to enhance the quality of reconstructed images in terms of both global structure and local detail, we employ a multi-stage reconstruction network that restricts the reconstructed images in a coarse-to-fine manner across multiple scales. Qualitative experiments on the generic object decoding (GOD) dataset demonstrate that the reconstructed images contain accurate structural information and rich texture details. Furthermore, the proposed method surpasses the existing state-of-the-art methods in terms of accuracy in the commonly used n-way evaluation. Our approach achieves 82.00%, 59.40%, 45.20% in n-way mean squared error (MSE) evaluation and 83.50%, 61.80%, 46.00% in n-way structural similarity index measure (SSIM) evaluation, respectively. Our experiments reveal the importance of information interaction among brain areas and also demonstrate the potential for developing visual-decoding brain-computer interfaces.},
}

@article {pmid38539605,
year = {2024},
author = {Gu, X and Jiang, L and Chen, H and Li, M and Liu, C},
title = {Exploring Brain Dynamics via EEG and Steady-State Activation Map Networks in Music Composition.},
journal = {Brain sciences},
volume = {14},
number = {3},
pages = {},
pmid = {38539605},
issn = {2076-3425},
support = {2023YFC3604100//the National Key Research and Development Program of China/ ; YG2021169//the Art Planning Program of Jiangxi Province/ ; YS22247//Jiangxi University Humanities and Social Science Program/ ; },
abstract = {In recent years, the integration of brain-computer interface technology and neural networks in the field of music generation has garnered widespread attention. These studies aimed to extract individual-specific emotional and state information from electroencephalogram (EEG) signals to generate unique musical compositions. While existing research has focused primarily on brain regions associated with emotions, this study extends this research to brain regions related to musical composition. To this end, a novel neural network model incorporating attention mechanisms and steady-state activation mapping (SSAM) was proposed. In this model, the self-attention module enhances task-related information in the current state matrix, while the extended attention module captures the importance of state matrices over different time frames. Additionally, a convolutional neural network layer is used to capture spatial information. Finally, the ECA module integrates the frequency information learned by the model in each of the four frequency bands, mapping these by learning their complementary frequency information into the final attention representation. Evaluations conducted on a dataset specifically constructed for this study revealed that the model surpassed representative models in the emotion recognition field, with recognition rate improvements of 1.47% and 3.83% for two different music states. Analysis of the attention matrix indicates that the left frontal lobe and occipital lobe are the most critical brain regions in distinguishing between 'recall and creation' states, while FP1, FPZ, O1, OZ, and O2 are the electrodes most related to this state. In our study of the correlations and significances between these areas and other electrodes, we found that individuals with musical training exhibit more extensive functional connectivity across multiple brain regions. This discovery not only deepens our understanding of how musical training can enhance the brain's ability to work in coordination but also provides crucial guidance for the advancement of brain-computer music generation technologies, particularly in the selection of key brain areas and electrode configurations. We hope our research can guide the work of EEG-based music generation to create better and more personalized music.},
}

@article {pmid38539602,
year = {2024},
author = {Niu, C and Yan, Z and Yin, K and Zhou, S},
title = {Identification and Verification of Error-Related Potentials Based on Cerebellar Targets.},
journal = {Brain sciences},
volume = {14},
number = {3},
pages = {},
pmid = {38539602},
issn = {2076-3425},
support = {62293552//National Natural Science Foundation of China/ ; },
abstract = {The error-related potential (ErrP) is a weak explicit representation of the human brain for individual wrong behaviors. Previously, ErrP-related research usually focused on the design of automatic correction and the error correction mechanisms of high-risk pipeline-type judgment systems. Mounting evidence suggests that the cerebellum plays an important role in various cognitive processes. Thus, this study introduced cerebellar information to enhance the online classification effect of error-related potentials. We introduced cerebellar regional characteristics and improved discriminative canonical pattern matching (DCPM) in terms of data training and model building. In addition, this study focused on the application value and significance of cerebellar error-related potential characterization in the selection of excellent ErrP-BCI subjects (brain-computer interface). Here, we studied a specific ErrP, the so-called feedback ErrP. Thirty participants participated in this study. The comparative experiments showed that the improved DCPM classification algorithm proposed in this paper improved the balance accuracy by approximately 5-10% compared with the original algorithm. In addition, a correlation analysis was conducted between the error-related potential indicators of each brain region and the classification effect of feedback ErrP-BCI data, and the Fisher coefficient of the cerebellar region was determined as the quantitative screening index of the subjects. The screened subjects were superior to other subjects in the performance of the classification algorithm, and the performance of the classification algorithm was improved by up to 10%.},
}

@article {pmid38539585,
year = {2024},
author = {Wu, S and Bhadra, K and Giraud, AL and Marchesotti, S},
title = {Adaptive LDA Classifier Enhances Real-Time Control of an EEG Brain-Computer Interface for Decoding Imagined Syllables.},
journal = {Brain sciences},
volume = {14},
number = {3},
pages = {},
pmid = {38539585},
issn = {2076-3425},
support = {#51NF40_180888/SNSF_/Swiss National Science Foundation/Switzerland ; },
abstract = {Brain-Computer Interfaces (BCIs) aim to establish a pathway between the brain and an external device without the involvement of the motor system, relying exclusively on neural signals. Such systems have the potential to provide a means of communication for patients who have lost the ability to speak due to a neurological disorder. Traditional methodologies for decoding imagined speech directly from brain signals often deploy static classifiers, that is, decoders that are computed once at the beginning of the experiment and remain unchanged throughout the BCI use. However, this approach might be inadequate to effectively handle the non-stationary nature of electroencephalography (EEG) signals and the learning that accompanies BCI use, as parameters are expected to change, and all the more in a real-time setting. To address this limitation, we developed an adaptive classifier that updates its parameters based on the incoming data in real time. We first identified optimal parameters (the update coefficient, UC) to be used in an adaptive Linear Discriminant Analysis (LDA) classifier, using a previously recorded EEG dataset, acquired while healthy participants controlled a binary BCI based on imagined syllable decoding. We subsequently tested the effectiveness of this optimization in a real-time BCI control setting. Twenty healthy participants performed two BCI control sessions based on the imagery of two syllables, using a static LDA and an adaptive LDA classifier, in randomized order. As hypothesized, the adaptive classifier led to better performances than the static one in this real-time BCI control task. Furthermore, the optimal parameters for the adaptive classifier were closely aligned in both datasets, acquired using the same syllable imagery task. These findings highlight the effectiveness and reliability of adaptive LDA classifiers for real-time imagined speech decoding. Such an improvement can shorten the training time and favor the development of multi-class BCIs, representing a clear interest for non-invasive systems notably characterized by low decoding accuracies.},
}

@article {pmid38538229,
year = {2024},
author = {Andrade, K and Houmani, N and Guieysse, T and Razafimahatratra, S and Klarsfeld, A and Dreyfus, G and Dubois, B and Vialatte, F and Medani, T},
title = {Self-Modulation of Gamma-Band Synchronization through EEG-Neurofeedback Training in the Elderly.},
journal = {Journal of integrative neuroscience},
volume = {23},
number = {3},
pages = {67},
doi = {10.31083/j.jin2303067},
pmid = {38538229},
issn = {0219-6352},
support = {//URGOTECH/ ; },
mesh = {Humans ; Aged ; *Neurofeedback/methods ; Electroencephalography ; Brain/physiology ; Cognition/physiology ; *Alzheimer Disease/therapy ; Biomarkers ; },
abstract = {BACKGROUND: Electroencephalography (EEG) stands as a pivotal non-invasive tool, capturing brain signals with millisecond precision and enabling real-time monitoring of individuals' mental states. Using appropriate biomarkers extracted from these EEG signals and presenting them back in a neurofeedback loop offers a unique avenue for promoting neural compensation mechanisms. This approach empowers individuals to skillfully modulate their brain activity. Recent years have witnessed the identification of neural biomarkers associated with aging, underscoring the potential of neuromodulation to regulate brain activity in the elderly.

METHODS AND OBJECTIVES: Within the framework of an EEG-based brain-computer interface, this study focused on three neural biomarkers that may be disturbed in the aging brain: Peak Alpha Frequency, Gamma-band synchronization, and Theta/Beta ratio. The primary objectives were twofold: (1) to investigate whether elderly individuals with subjective memory complaints can learn to modulate their brain activity, through EEG-neurofeedback training, in a rigorously designed double-blind, placebo-controlled study; and (2) to explore potential cognitive enhancements resulting from this neuromodulation.

RESULTS: A significant self-modulation of the Gamma-band synchronization biomarker, critical for numerous higher cognitive functions and known to decline with age, and even more in Alzheimer's disease (AD), was exclusively observed in the group undergoing EEG-neurofeedback training. This effect starkly contrasted with subjects receiving sham feedback. While this neuromodulation did not directly impact cognitive abilities, as assessed by pre- versus post-training neuropsychological tests, the high baseline cognitive performance of all subjects at study entry likely contributed to this result.

CONCLUSION: The findings of this double-blind study align with a key criterion for successful neuromodulation, highlighting the significant potential of Gamma-band synchronization in such a process. This important outcome encourages further exploration of EEG-neurofeedback on this specific neural biomarker as a promising intervention to counter the cognitive decline that often accompanies brain aging and, eventually, to modify the progression of AD.},
}

Humans
Aged
*Neurofeedback/methods
Electroencephalography
Brain/physiology
Cognition/physiology
*Alzheimer Disease/therapy
Biomarkers

@article {pmid38538143,
year = {2024},
author = {Wei 魏赣超, G and Tajik Mansouri زینب تاجیک منصوری, Z and Wang 王晓婧, X and Stevenson, IH},
title = {Calibrating Bayesian decoders of neural spiking activity.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.2158-23.2024},
pmid = {38538143},
issn = {1529-2401},
abstract = {Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, that provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine: 1) decoding the direction of grating stimuli from spike recordings in primary visual cortex in monkeys, 2) decoding movement direction from recordings in primary motor cortex in monkeys, 3) decoding natural images from multi-region recordings in mice, and 4) decoding position from hippocampal recordings in rats. For each setting we characterize the overconfidence, and we describe a possible method to correct miscalibration post-hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain machine interfaces that more accurately reflect confidence levels when identifying external variables.Significance Statement Bayesian decoding is a statistical technique for making probabilistic predictions about external stimuli or movements based on recordings of neural activity. These predictions may be useful for robust brain machine interfaces or for understanding perceptual or behavioral confidence. However, the probabilities produced by these models do not always match the observed outcomes. Just as a weather forecast predicting a 50% chance of rain may not accurately correspond to an outcome of rain 50% of the time, Bayesian decoders of neural activity can be miscalibrated as well. Here we identify and measure miscalibration of Bayesian decoders for neural spiking activity in a range of experimental settings. We compare multiple statistical models and demonstrate how overconfidence can be corrected.},
}

@article {pmid38538056,
year = {2024},
author = {Brannigan, J and McClanahan, A and Hui, F and Fargen, KM and Pinter, N and Oxley, TJ},
title = {Superior cortical venous anatomy for endovascular device implantation: a systematic review.},
journal = {Journal of neurointerventional surgery},
volume = {},
number = {},
pages = {},
doi = {10.1136/jnis-2023-021434},
pmid = {38538056},
issn = {1759-8486},
abstract = {Endovascular electrode arrays provide a minimally invasive approach to access intracranial structures for neural recording and stimulation. These arrays are currently used as brain-computer interfaces (BCIs) and are deployed within the superior sagittal sinus (SSS), although cortical vein implantation could improve the quality and quantity of recorded signals. However, the anatomy of the superior cortical veins is heterogenous and poorly characterised. MEDLINE and Embase databases were systematically searched from inception to December 15, 2023 for studies describing the anatomy of the superior cortical veins. A total of 28 studies were included: 19 cross-sectional imaging studies, six cadaveric studies, one intraoperative anatomical study and one review. There was substantial variability in cortical vein diameter, length, confluence angle, and location relative to the underlying cortex. The mean number of SSS branches ranged from 11 to 45. The vein of Trolard was most often reported as the largest superior cortical vein, with a mean diameter ranging from 2.1 mm to 3.3 mm. The mean vein of Trolard was identified posterior to the central sulcus. One study found a significant age-related variability in cortical vein diameter and another identified myoendothelial sphincters at the base of the cortical veins. Cortical vein anatomical data are limited and inconsistent. The vein of Trolard is the largest tributary vein of the SSS; however, its relation to the underlying cortex is variable. Variability in cortical vein anatomy may necessitate individualized pre-procedural planning of training and neural decoding in endovascular BCI. Future focus on the relation to the underlying cortex, sulcal vessels, and vessel wall anatomy is required.},
}

@article {pmid38537269,
year = {2024},
author = {Soldado-Magraner, J and Antonietti, A and French, J and Higgins, N and Young, MJ and Larrivee, D and Monteleone, R},
title = {Applying the IEEE BRAIN neuroethics framework to intra-cortical brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad3852},
pmid = {38537269},
issn = {1741-2552},
abstract = {Brain-computer interfaces (BCIs) are neuroprosthetic devices that allow for direct interaction between brains and machines. These types of neurotechnologies have recently experienced a strong drive in research and development, given, in part, that they promise to restore motor and communication abilities in individuals experiencing severe paralysis. While a rich literature analyzes the ethical, legal, and sociocultural implications (ELSCI) of these novel neurotechnologies, engineers, clinicians and BCI practitioners often do not have enough exposure to these topics. Here, we present the IEEE Neuroethics Framework, an international, multiyear, iterative initiative aimed at developing a robust, accessible set of considerations for diverse stakeholders. Using the framework, we provide practical examples of ELSCI considerations for BCI neurotechnologies. We focus on invasive technologies, and in particular, devices that are implanted intra-cortically for medical research applications. We demonstrate the utility of our framework in exposing a wide range of implications across different intra-cortical BCI technology modalities and conclude with recommendations on how to utilize this knowledge in the development and application of ethical guidelines for BCI neurotechnologies.},
}

@article {pmid38537268,
year = {2024},
author = {Suematsu, N and Vazquez, AL and Kozai, TDY},
title = {Activation and depression of neural and hemodynamic responses induced by the intracortical microstimulation and visual stimulation in the mouse visual cortex.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad3853},
pmid = {38537268},
issn = {1741-2552},
abstract = {OBJECTIVE: Intracortical microstimulation can be an effective method for restoring sensory perception in contemporary brain-machine interfaces. However, the mechanisms underlying better control of neuronal responses remain poorly understood, as well as the relationship between neuronal activity and other concomitant phenomena occurring around the stimulation site.

APPROACH: Different microstimulation frequencies were investigated in vivo on Thy1-GCaMP6s mice using widefield and two-photon imaging to evaluate the evoked excitatory neural responses across multiple spatial scales as well as the induced hemodynamic responses. Specifically, we quantified stimulation-induced neuronal activation and depression in the mouse visual cortex and measured hemodynamic oxyhemoglobin and deoxyhemoglobin signals using mesoscopic-scale widefield imaging. Main results. Our calcium imaging findings revealed a preference for lower-frequency stimulation in driving stronger neuronal activation. A depressive response following the neural activation preferred a slightly higher frequency stimulation compared to the activation. Hemodynamic signals exhibited a comparable spatial spread to neural calcium signals. Oxyhemoglobin concentration around the stimulation site remained elevated during the post-activation (depression) period. Somatic and neuropil calcium responses measured by two-photon microscopy showed similar dependence on stimulation parameters, although the magnitudes measured in soma was greater than in neuropil. Furthermore, higher-frequency stimulation induced a more pronounced activation in soma compared to neuropil, while depression was predominantly induced in soma irrespective of stimulation frequencies.

SIGNIFICANCE: These results suggest that the mechanism underlying depression differs from activation, requiring ample oxygen supply, and affecting neurons. Our findings provide a novel understanding of evoked excitatory neuronal activity induced by intracortical microstimulation and offer insights into neuro-devices that utilize both activation and depression phenomena to achieve desired neural responses. .},
}

@article {pmid38536681,
year = {2024},
author = {Liang, G and Cao, D and Wang, J and Zhang, Z and Wu, Y},
title = {EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3382226},
pmid = {38536681},
issn = {1558-0210},
abstract = {The motor imagery brain-computer interface (MI-BCI) based on electroencephalography (EEG) is a widely used human-machine interface paradigm. However, due to the non-stationarity and individual differences among subjects in EEG signals, the decoding accuracy is limited, affecting the application of the MI-BCI. In this paper, we propose the EISATC-Fusion model for MI EEG decoding, consisting of inception block, multi-head self-attention (MSA), temporal convolutional network (TCN), and layer fusion. Specifically, we design a DS Inception block to extract multi-scale frequency band information. And design a new cnnCosMSA module based on CNN and cos attention to solve the attention collapse and improve the interpretability of the model. The TCN module is improved by the depthwise separable convolution to reduces the parameters of the model. The layer fusion consists of feature fusion and decision fusion, fully utilizing the features output by the model and enhances the robustness of the model. We improve the two-stage training strategy for model training. Early stopping is used to prevent model overfitting, and the accuracy and loss of the validation set are used as indicators for early stopping. The proposed model achieves within-subject classification accuracies of 84.57% and 87.58% on BCI Competition IV Datasets 2a and 2b, respectively. And the model achieves cross-subject classification accuracies of 67.42% and 71.23% (by transfer learning) when training the model with two sessions and one session of Dataset 2a, respectively. The interpretability of the model is demonstrated through weight visualization method.},
}

@article {pmid38533741,
year = {2024},
author = {Siu, C and Aoude, M and Andersen, J and Adams, KD},
title = {The lived experiences of play and the perspectives of disabled children and their parents surrounding brain-computer interfaces.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-10},
doi = {10.1080/17483107.2024.2333884},
pmid = {38533741},
issn = {1748-3115},
abstract = {Brain-computer interfaces (BCI) offer promise to the play of children with significant physical impairments, as BCI technology can enable disabled children to control computer devices, toys, and robots using only their brain signals. However, there is little research on the unique needs of disabled children when it comes to BCI-enabled play. Thus, this paper explored the lived experiences of play for children with significant physical impairments and examined how BCI could potentially be implemented into disabled children's play experiences by applying a social model of childhood disability. Descriptive qualitative methodology was employed by conducting four semi-structured interviews with two children with significant physical impairments and their parents. We found that disabled children's play can be interpreted as passive or active depending on one's definition and perceptions surrounding play. Moreover, disabled children continue to face physical, economic, and technological barriers in their play, as well as play restrictions from physical impairments. We urge that future research should strive to directly hear from disabled children themselves, as their perspectives may differ from their parents' views. Also, future BCI development should strive to incorporate video games, recreational and entertainment applications/platforms, toys and switch-adapted toys, and power wheelchairs to better support the play of children with significant physical impairments.Implications for RehabilitationAssistive technology research should strive to examine the social, infrastructural, and environmental barriers that continue to disable and restrict participation for disabled children and their families through applying a social model of childhood disability and other holistic frameworks that look beyond individual factorsFuture research that examines the needs and lives of disabled children should strive to directly seek the opinions and perspectives of disabled children themselvesBrain-computer interface development should strive to incorporate video games, recreational and entertainment applications/platforms, toys and switch-adapted toys, and power wheelchairs to better support the play of children with significant physical impairments.},
}

@article {pmid38533483,
year = {2024},
author = {Wang, G and Tang, J and Yin, Z and Yu, S and Shi, X and Hao, X and Zhao, Z and Pan, Y and Li, S},
title = {The neurocomputational signature of decision-making for unfair offers in females under acute psychological stress.},
journal = {Neurobiology of stress},
volume = {30},
number = {},
pages = {100622},
pmid = {38533483},
issn = {2352-2895},
abstract = {Stress is a crucial factor affecting social decision-making. However, its impacts on the behavioral and neural processes of females' unfairness decision-making remain unclear. Combining computational modeling and functional near-infrared spectroscopy (fNIRS), this study attempted to illuminate the neurocomputational signature of unfairness decision-making in females. We also considered the effect of trait stress coping styles. Forty-four healthy young females (20.98 ± 2.89 years) were randomly assigned to the stress group (n = 21) and the control group (n = 23). Acute psychosocial stress was induced by the Trier Social Stress Test (TSST), and participants then completed the one-shot ultimatum game (UG) as responders. The results showed that acute psychosocial stress reduced the adaptability to fairness and lead to more random decision-making responses. Moreover, in the stress group, a high level of negative coping style predicted more deterministic decision. fNIRS results showed that stress led to an increase of oxy-hemoglobin (HbO) peak in the right temporoparietal junction (rTPJ), while decreased the activation of left middle temporal gyrus (lMTG) when presented the moderately unfair (MU) offers. This signified more involvement of the mentalization and the inhibition of moral processing. Moreover, individuals with higher negative coping scores showed more deterministic decision behaviors under stress. Taken together, our study emphasizes the role of acute psychosocial stress in affecting females' unfairness decision-making mechanisms in social interactions, and provides evidences for the "tend and befriend" pattern based on a cognitive neuroscience perspec.},
}

@article {pmid38532987,
year = {2024},
author = {Hu, S and Ng, CH and Mann, JJ},
title = {Editorial: Linking treatment target identification to biological mechanisms underlying mood disorders - Volume II.},
journal = {Frontiers in psychiatry},
volume = {15},
number = {},
pages = {1385955},
doi = {10.3389/fpsyt.2024.1385955},
pmid = {38532987},
issn = {1664-0640},
}

@article {pmid38532608,
year = {2024},
author = {Assi, DS and Huang, H and Karthikeyan, V and Theja, VCS and de Souza, MM and Roy, VAL},
title = {Topological Quantum Switching enabled Neuroelectronic Synaptic Modulators for Brain Computer Interface.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e2306254},
doi = {10.1002/adma.202306254},
pmid = {38532608},
issn = {1521-4095},
abstract = {Aging and genetic-related disorders in the human brain lead to impairment of daily cognitive functions. Due to their neural synaptic complexity and the current limits of knowledge, reversing these disorders remains a substantial challenge for Brain-Computer Interfaces (BCI). In this work, we provide a solution to potentially override aging and neurological disorder-related cognitive function loss in the human brain through the application of our quantum synaptic device. To illustrate this point, we design and develop a quantum topological insulator (QTI) Bi2Se2Te-based synaptic neuroelectronic device, where the electric field-induced tunable topological surface edge states and quantum switching properties make them a premier option for establishing artificial synaptic neuromodulation approaches. Leveraging these unique quantum synaptic properties, our developed synaptic device provides the capability to neuromodulate distorted neural signals, leading to the reversal of age-related disorders via BCI. With the synaptic neuroelectronic characteristics of our device, we demonstrate excellent efficacy in treating cognitive neural dysfunctions through modulated neuromorphic stimuli. As a proof of concept, we demonstrate real-time neuromodulation of electroencephalogram (EEG) deduced distorted event-related potentials (ERP) by modulation of our synaptic device array. This article is protected by copyright. All rights reserved.},
}

@article {pmid38531360,
year = {2024},
author = {Losey, DM and Hennig, JA and Oby, ER and Golub, MD and Sadtler, PT and Quick, KM and Ryu, SI and Tyler-Kabara, EC and Batista, AP and Yu, BM and Chase, SM},
title = {Learning leaves a memory trace in motor cortex.},
journal = {Current biology : CB},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cub.2024.03.003},
pmid = {38531360},
issn = {1879-0445},
abstract = {How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex. This memory trace coexisted with proficient performance under the familiar map, primarily by altering neural activity in dimensions that did not impact behavior. Forming memory traces might be how the brain is able to provide for the joint learning of multiple behaviors without interference.},
}

@article {pmid38531054,
year = {2024},
author = {Kesarwani, M and Kincaid, Z and Azhar, M and Azam, M},
title = {Enhanced MAPK signaling induced by CSF3Rmutants confers dependence to DUSP1 for leukemic transformation.},
journal = {Blood advances},
volume = {},
number = {},
pages = {},
doi = {10.1182/bloodadvances.2023010830},
pmid = {38531054},
issn = {2473-9537},
abstract = {Elevated MAPK and the JAK-STAT signaling play pivotal roles in the pathogenesis of chronic neutrophilic leukemia (CNL) and atypical chronic myeloid leukemia (aCML). While inhibitors targeting these pathways effectively suppress the diseases, they fall short in providing enduring remission, largely attributed to cytostatic nature of these drugs. Even combinations of these drugs are ineffective in achieving sustained remission. Enhanced MAPK signaling besides promoting proliferation and survival triggers a pro-apoptotic response. Consequently, malignancies reliant on elevated MAPK signaling employ MAPK-feedback regulators to intricately modulate the signaling output, prioritizing proliferation and survival while dampening the apoptotic stimuli. Herein, we demonstrate that enhanced MAPK signaling in CSF3R (Granulocyte-colony stimulating factor receptor)-driven leukemia upregulates the expression of Dual specificity phosphatase 1 (DUSP1) to suppress the apoptotic stimuli crucial for leukemogenesis. Consequently, genetic deletion of Dusp1 in mice conferred synthetic lethality to CSF3R-induced leukemia. Mechanistically, DUSP1 depletion in leukemic context causes activation of JNK1/2 that results in induced expression of BIM and P53 while suppressing the expression BCL2 that selectively triggers apoptotic response in leukemic cells. Pharmacological inhibition of DUSP1 by BCI (a DUSP1 inhibitor) alone lacked anti-leukemic activity due to ERK1/2 rebound caused by off-target inhibition of DUSP6. Consequently, a combination of BCI with a MEK inhibitor successfully cured CSF3R-induced leukemia in a preclinical mouse model. Our findings underscore the pivotal role of DUSP1 in leukemic transformation driven by enhanced MAPK signaling and advocate for the development of a selective DUSP1 inhibitor for curative treatment outcomes.},
}

@article {pmid38530872,
year = {2024},
author = {Meng, L and He, L and Chen, M and Huang, Y},
title = {The compensation effect of competence frustration and its behavioral manifestations.},
journal = {PsyCh journal},
volume = {},
number = {},
pages = {},
doi = {10.1002/pchj.746},
pmid = {38530872},
issn = {2046-0260},
support = {2021ZGL004//Shanghai Philosophy and Social Science Planning Project/ ; 72271165//National Natural Science Foundation of China/ ; },
abstract = {The frustration of competence, one of the three basic psychological needs proposed by self-determination theory, has been widely demonstrated to negatively influence one's motivation and well-being in both work and life. However, research on the recovery mechanism of competence is still in the nascent stage. In this study, a two-stage behavioral experiment was conducted to examine the restoration of competence and the potential moderating role of resilience. Results showed that individuals who were asked to recall experience of competence frustration performed better on subsequent tasks, manifesting their behavioral efforts of competence restoration. However, resilience does not play a significant moderating role in competence restoration. Through convergent behavioral evidence, findings of this study demonstrate the compensation effect of competence frustration.},
}

@article {pmid38529662,
year = {2024},
author = {Lein, A and Baumgartner, WD and Riss, D and Gstöttner, W and Landegger, LD and Liu, DT and Thurner, T and Vyskocil, E and Brkic, FF},
title = {Early Results With the New Active Bone-Conduction Hearing Implant: A Systematic Review and Meta-Analysis.},
journal = {Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery},
volume = {},
number = {},
pages = {},
doi = {10.1002/ohn.728},
pmid = {38529662},
issn = {1097-6817},
abstract = {OBJECTIVE: The bone conduction implant (BCI) 602 is a new transcutaneous BCI with smaller dimensions. However, limited patient numbers restrict the statistical power and generalizability of the current studies. The present systematic review and meta-analysis summarize early audiological and medical outcomes of adult and pediatric patients implanted with the BCI 602 due to mixed or conductive hearing loss.

DATA SOURCE: Following the Preferred Reporting items for Systematic Reviews and Meta-analyses guidelines, 108 studies were reviewed, and 6 (5.6%) were included in the meta-analysis.

REVIEW METHOD: The data on study and patient characteristics, surgical outcomes, and audiological test results were extracted from each article. Meta-analysis employed the fixed-effect and random-effects models to analyze the mean differences (MDs) between pre- and postoperative performances.

RESULTS: In total, 116 patients were evaluated, including 64 (55%) adult and 52 (45%) pediatric patients. No intraoperative adverse events were reported, while postoperative complications were reported in 2 (3.1%) adult and 2 (3.8%) pediatric patients. Studies consistently showed significant improvements in audiological outcomes, quality of life, and sound localization in the aided condition. In the meta-analysis, we observed a significant difference in the unaided compared to the aided condition in sound field thresholds (n = 112; MD, -27.05 dB; P < 0.01), signal-to-noise ratio (n = 96; MD, -6.35 dB; P < 0.01), and word recognition scores (n = 96; MD, 68.89%; P < 0.01).

CONCLUSION: The implantation of the BCI 602 was associated with minimal surgical complications and excellent audiological outcomes for both the pediatric and the adult cohort. Therefore, our analysis indicates a high level of safety and reliability. Further research should focus on direct comparisons with other BCIs and long-term functional outcomes.},
}

@article {pmid38529269,
year = {2024},
author = {Kothe, C and Hanada, G and Mullen, S and Mullen, T},
title = {On decoding of rapid motor imagery in a diverse population using a high-density NIRS device.},
journal = {Frontiers in neuroergonomics},
volume = {5},
number = {},
pages = {1355534},
pmid = {38529269},
issn = {2673-6195},
abstract = {INTRODUCTION: Functional near-infrared spectroscopy (fNIRS) aims to infer cognitive states such as the type of movement imagined by a study participant in a given trial using an optical method that can differentiate between oxygenation states of blood in the brain and thereby indirectly between neuronal activity levels. We present findings from an fNIRS study that aimed to test the applicability of a high-density (>3000 channels) NIRS device for use in short-duration (2 s) left/right hand motor imagery decoding in a diverse, but not explicitly balanced, subject population. A side aim was to assess relationships between data quality, self-reported demographic characteristics, and brain-computer interface (BCI) performance, with no subjects rejected from recruitment or analysis.

METHODS: BCI performance was quantified using several published methods, including subject-specific and subject-independent approaches, along with a high-density fNIRS decoder previously validated in a separate study.

RESULTS: We found that decoding of motor imagery on this population proved extremely challenging across all tested methods. Overall accuracy of the best-performing method (the high-density decoder) was 59.1 +/- 6.7% after excluding subjects where almost no optode-scalp contact was made over motor cortex and 54.7 +/- 7.6% when all recorded sessions were included. Deeper investigation revealed that signal quality, hemodynamic responses, and BCI performance were all strongly impacted by the hair phenotypical and demographic factors under investigation, with over half of variance in signal quality explained by demographic factors alone.

DISCUSSION: Our results contribute to the literature reporting on challenges in using current-generation NIRS devices on subjects with long, dense, dark, and less pliable hair types along with the resulting potential for bias. Our findings confirm the need for increased focus on these populations, accurate reporting of data rejection choices across subject intake, curation, and final analysis in general, and signal a need for NIRS optode designs better optimized for the general population to facilitate more robust and inclusive research outcomes.},
}

@article {pmid38527459,
year = {2024},
author = {Li, G and Lan, L and He, T and Tang, Z and Liu, S and Li, Y and Huang, Z and Guan, Y and Li, X and Zhang, Y and Lai, HY},
title = {Comprehensive Assessment of Ischemic Stroke in Nonhuman Primates: Neuroimaging, Behavioral, and Serum Proteomic Analysis.},
journal = {ACS chemical neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1021/acschemneuro.3c00826},
pmid = {38527459},
issn = {1948-7193},
abstract = {Ischemic strokes, prevalence and impactful, underscore the necessity of advanced research models closely resembling human physiology. Our study utilizes nonhuman primates (NHPs) to provide a detailed exploration of ischemic stroke, integrating neuroimaging data, behavioral outcomes, and serum proteomics to elucidate the complex interplay of factors involved in stroke pathophysiology. We observed a consistent pattern in infarct volume, peaking at 1-month postmiddle cerebral artery occlusion (MCAO) and then stabilized. This pattern was strongly correlated to notable changes in motor function and working memory performance. Using diffusion tensor imaging (DTI), we detected significant alterations in fractional anisotropy (FA) and mean diffusivity (MD) values, signaling microstructural changes in the brain. These alterations closely correlated with the neurological and cognitive deficits that we observed, highlighting the sensitivity of DTI metrics in stroke assessment. Behaviorally, the monkeys exhibited a reliance on their unaffected limb for compensatory movements, a common response to stroke impairment. This adaptation, along with consistent DTI findings, suggests a significant impact of stroke on motor function and spatial perception. Proteomic analysis through MS/MS functional enrichment identified two distinct groups of proteins with significant changes post-MCAO. Notably, MMP9, THBS1, MB, PFN1, and YWHAZ were identified as potential biomarkers and therapeutic targets for ischemic stroke. Our results underscore the complex nature of stroke and advocate for an integrated approach, combining neuroimaging, behavioral studies, and proteomics, for advancing our understanding and treatment of this condition.},
}

@article {pmid38526885,
year = {2024},
author = {Qin, Y and Zhang, W and Tao, X},
title = {TBEEG: A Two-Branch Manifold Domain Enhanced Transformer Algorithm for Learning EEG Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3380595},
pmid = {38526885},
issn = {1558-0210},
abstract = {The electroencephalogram-based (EEG) brain-computer interface (BCI) has garnered significant attention in recent research. However, the practicality of EEG remains constrained by the lack of efficient EEG decoding technology. The challenge lies in effectively translating intricate EEG into meaningful, generalizable information. EEG signal decoding primarily relies on either time domain or frequency domain information. There lacks a method capable of simultaneously and effectively extracting both time and frequency domain features, as well as efficiently fuse these features. Addressing these limitations, a two-branch Manifold Domain enhanced transformer algorithm is designed to holistically capture EEG's spatio-temporal information. Our method projects the time-domain information of EEG signals into the Riemannian spaces to fully decode the time dependence of EEG signals. Using wavelet transform, the time domain information is converted into frequency domain information, and the spatial information contained in the frequency domain information of EEG signal is mined through the spectrogram. The effectiveness of the proposed TBEEG algorithm is validated on BCIC-IV-2a dataset and MAMEM-SSVEP-II datasets.},
}

@article {pmid38523306,
year = {2024},
author = {Zolfaghari, S and Yousefi Rezaii, T and Meshgini, S},
title = {Applying Common Spatial Pattern and Convolutional Neural Network to Classify Movements via EEG Signals.},
journal = {Clinical EEG and neuroscience},
volume = {},
number = {},
pages = {15500594241234836},
doi = {10.1177/15500594241234836},
pmid = {38523306},
issn = {2169-5202},
abstract = {Developing an electroencephalography (EEG)-based brain-computer interface (BCI) system is crucial to enhancing the control of external prostheses by accurately distinguishing various movements through brain signals. This innovation can provide comfortable circumstances for the populace who have movement disabilities. This study combined the most prospering methods used in BCI systems, including one-versus-rest common spatial pattern (OVR-CSP) and convolutional neural network (CNN), to automatically extract features and classify eight different movements of the shoulder, wrist, and elbow via EEG signals. The number of subjects who participated in the experiment was 10, and their EEG signals were recorded while performing movements at fast and slow speeds. We used preprocessing techniques before transforming EEG signals into another space by OVR-CSP, followed by sending signals into the CNN architecture consisting of four convolutional layers. Moreover, we extracted feature vectors after applying OVR-CSP and considered them as inputs to KNN, SVM, and MLP classifiers. Then, the performance of these classifiers was compared with the CNN method. The results demonstrated that the classification of eight movements using the proposed CNN architecture obtained an average accuracy of 97.65% for slow movements and 96.25% for fast movements in the subject-independent model. This method outperformed other classifiers with a substantial difference; ergo, it can be useful in improving BCI systems for better control of prostheses.},
}

@article {pmid38523252,
year = {2024},
author = {Deng, L and Wei, W and Qiao, C and Yin, Y and Li, X and Yu, H and Jian, L and Ma, X and Zhao, L and Wang, Q and Deng, W and Guo, W and Li, T},
title = {Dynamic aberrances of substantia nigra-relevant coactivation patterns in first-episode treatment-naïve patients with schizophrenia.},
journal = {Psychological medicine},
volume = {},
number = {},
pages = {1-11},
doi = {10.1017/S0033291724000655},
pmid = {38523252},
issn = {1469-8978},
abstract = {BACKGROUND: Although dopaminergic disturbances are well-known in schizophrenia, the understanding of dopamine-related brain dynamics remains limited. This study investigates the dynamic coactivation patterns (CAPs) associated with the substantia nigra (SN), a key dopaminergic nucleus, in first-episode treatment-naïve patients with schizophrenia (FES).

METHODS: Resting-state fMRI data were collected from 84 FES and 94 healthy controls (HCs). Frame-wise clustering was implemented to generate CAPs related to SN activation or deactivation. Connectome features of each CAP were derived using an edge-centric method. The occurrence for each CAP and the balance ratio for antagonistic CAPs were calculated and compared between two groups, and correlations between temporal dynamic metrics and symptom burdens were explored.

RESULTS: Functional reconfigurations in CAPs exhibited significant differences between the activation and deactivation states of SN. During SN activation, FES more frequently recruited a CAP characterized by activated default network, language network, control network, and the caudate, compared to HCs (F = 8.54, FDR-p = 0.030). Moreover, FES displayed a tilted balance towards a CAP featuring SN-coactivation with the control network, caudate, and thalamus, as opposed to its antagonistic CAP (F = 7.48, FDR-p = 0.030). During SN deactivation, FES exhibited increased recruitment of a CAP with activated visual and dorsal attention networks but decreased recruitment of its opposing CAP (F = 6.58, FDR-p = 0.034).

CONCLUSION: Our results suggest that neuroregulatory dysfunction in dopaminergic pathways involving SN potentially mediates aberrant time-varying functional reorganizations in schizophrenia. This finding enriches the dopamine hypothesis of schizophrenia from the perspective of brain dynamics.},
}

@article {pmid38520132,
year = {2024},
author = {Jeong, HJ and Lee, H and Choo, MS and Cho, SY and Jeong, SJ and Oh, SJ},
title = {Effect of detrusor underactivity on surgical outcomes of holmium laser enucleation of the prostate.},
journal = {BJU international},
volume = {},
number = {},
pages = {},
doi = {10.1111/bju.16346},
pmid = {38520132},
issn = {1464-410X},
abstract = {OBJECTIVE: To evaluate the effect of detrusor underactivity (DUA) on the postoperative outcomes of holmium laser enucleation of the prostate (HoLEP) in patients with benign prostatic hyperplasia (BPH).

PATIENTS AND METHODS: Patients with BPH who underwent HoLEP between January 2018 and December 2022 were enrolled in this prospective database study. Patients were divided into DUA (bladder contractility index [BCI] <100) and non-DUA (BCI ≥100) groups. Objective (maximum urinary flow rate [Qmax], post-void residual urine volume [PVR]) and subjective outcomes (International Prostate Symptom Score [IPSS], Overactive Bladder Symptom Score [OABSS], satisfaction with treatment question [STQ], overall response assessment [ORA], and willingness to undergo surgery question [WUSQ]) were compared between the two groups before surgery, and at 3 and 6 months after HoLEP.

RESULTS: A total of 689 patients, with a mean (standard deviation [SD]) age of 69.8 (7.1) years, were enrolled. The mean (SD) BCI in the non-DUA (325 [47.2%]) and DUA (364 [52.8%]) groups was 123.4 (21.4) and 78.6 (14.2), respectively. Both objective (Qmax and PVR) and subjective (IPSS, IPSS-quality of life, and OABSS) outcomes after surgery significantly improved in both groups. The Qmax was lower in the DUA than in the non-DUA group postoperatively. At 6 months postoperatively, the total IPSS was higher in the DUA than in the non-DUA group. There were no significant differences in surgical complications between the two groups. Responses to the STQ, ORA, and WUSQ at 6 months postoperatively demonstrated that the patients were satisfied with the surgery (90.5% in the DUA group; 95.2% in the non-DUA group), their symptoms improved with surgery (95.9% in the DUA group; 100.0% in the non-DUA group), and they were willing to undergo surgery again (95.9% in the DUA group; 97.9% in the non-DUA group). There were no significant differences in the responses to the STQ and WUSQ between the two groups.

CONCLUSION: Our midterm results demonstrated that patients with BPH and DUA showed minimal differences in clinical outcomes after HoLEP compared to those without DUA. The overall satisfaction was high in the DUA group.},
}

@article {pmid38520009,
year = {2024},
author = {Zou, P and Liu, C and Zhang, Y and Wei, C and Liu, X and Xu, S and Ling, Q and Chen, Z and Du, G and Yuan, X},
title = {Transurethral surgical treatment for benign prostatic hyperplasia with detrusor underactivity: a systematic review and meta-analysis.},
journal = {Systematic reviews},
volume = {13},
number = {1},
pages = {93},
pmid = {38520009},
issn = {2046-4053},
mesh = {Male ; Humans ; *Prostatic Hyperplasia/complications/surgery ; Quality of Life ; *Urinary Bladder, Underactive/surgery/etiology ; Treatment Outcome ; *Transurethral Resection of Prostate/adverse effects/methods ; },
abstract = {BACKGROUND: The efficacy of surgical treatment for benign prostatic hyperplasia (BPH) patients with detrusor underactivity (DU) remains controversial.

METHODS: To summarize relevant evidence, three databases (PubMed, Embase, and Web of Science) were searched from database inception to May 1, 2023. Transurethral surgical treatment modalities include transurethral prostatectomy (TURP), photoselective vaporization of the prostate (PVP), and transurethral incision of the prostate (TUIP). The efficacy of the transurethral surgical treatment was assessed according to maximal flow rate on uroflowmetry (Qmax), International Prostate Symptom Score (IPSS), postvoid residual (PVR), quality of life (QoL), voided volume, bladder contractility index (BCI) and maximal detrusor pressure at maximal flow rate (PdetQmax). Pooled mean differences (MDs) were used as summary statistics for comparison. The quality of enrolled studies was evaluated by using the Newcastle-Ottawa Scale. Sensitivity analysis and funnel plots were applied to assess possible biases.

RESULTS: In this study, 10 studies with a total of 1142 patients enrolled. In BPH patients with DU, within half a year, significant improvements in Qmax (pooled MD, 4.79; 95% CI, 2.43-7.16; P < 0.05), IPSS(pooled MD, - 14.29; 95%CI, - 16.67-11.90; P < 0.05), QoL (pooled MD, - 1.57; 95% CI, - 2.37-0.78; P < 0.05), voided volume (pooled MD, 62.19; 95% CI, 17.91-106.48; P < 0.05), BCI (pooled MD, 23.59; 95% CI, 8.15-39.04; P < 0.05), and PdetQmax (pooled MD, 28.62; 95% CI, 6.72-50.52; P < 0.05) were observed after surgery. In addition, after more than 1 year, significant improvements were observed in Qmax (pooled MD, 6.75; 95%CI, 4.35-9.15; P < 0.05), IPSS(pooled MD, - 13.76; 95%CI, - 15.17-12.35; P < 0.05), PVR (pooled MD, - 179.78; 95%CI, - 185.12-174.44; P < 0.05), QoL (pooled MD, - 2.61; 95%CI, - 3.12-2.09; P < 0.05), and PdetQmax (pooled MD, 27.94; 95%CI, 11.70-44.19; P < 0.05). Compared with DU patients who did not receive surgery, DU patients who received surgery showed better improvement in PVR (pooled MD, 137.00; 95%CI, 6.90-267.10; P < 0.05) and PdetQmax (pooled MD, - 8.00; 95%CI, - 14.68-1.32; P < 0.05).

CONCLUSIONS: Our meta-analysis results showed that transurethral surgery can improve the symptoms of BPH patients with DU. Surgery also showed advantages over pharmacological treatment for BPH patients with DU.

PROSPERO CRD42023415188.},
}

Male
Humans
*Prostatic Hyperplasia/complications/surgery
Quality of Life
*Urinary Bladder, Underactive/surgery/etiology
Treatment Outcome
*Transurethral Resection of Prostate/adverse effects/methods

@article {pmid38518778,
year = {2024},
author = {Li, XY and Zhang, SY and Hong, YZ and Chen, ZG and Long, Y and Yuan, DH and Zhao, JJ and Tang, SS and Wang, H and Hong, H},
title = {TGR5-mediated lateral hypothalamus-dCA3-dorsolateral septum circuit regulates depressive-like behavior in male mice.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2024.02.019},
pmid = {38518778},
issn = {1097-4199},
abstract = {Although bile acids play a notable role in depression, the pathological significance of the bile acid TGR5 membrane-type receptor in this disorder remains elusive. Using depression models of chronic social defeat stress and chronic restraint stress in male mice, we found that TGR5 in the lateral hypothalamic area (LHA) predominantly decreased in GABAergic neurons, the excitability of which increased in depressive-like mice. Upregulation of TGR5 or inhibition of GABAergic excitability in LHA markedly alleviated depressive-like behavior, whereas down-regulation of TGR5 or enhancement of GABAergic excitability facilitated stress-induced depressive-like behavior. TGR5 also bidirectionally regulated excitability of LHA GABAergic neurons via extracellular regulated protein kinases-dependent Kv4.2 channels. Notably, LHA GABAergic neurons specifically innervated dorsal CA3 (dCA3) CaMKIIα neurons for mediation of depressive-like behavior. LHA GABAergic TGR5 exerted antidepressant-like effects by disinhibiting dCA3 CaMKIIα neurons projecting to the dorsolateral septum (DLS). These findings advance our understanding of TGR5 and the LHA[GABA]→dCA3[CaMKIIα]→DLS[GABA] circuit for the development of potential therapeutic strategies in depression.},
}

@article {pmid38518426,
year = {2024},
author = {Caillet, AH and Phillips, ATM and Modenese, L and Farina, D},
title = {NeuroMechanics: Electrophysiological and computational methods to accurately estimate the neural drive to muscles in humans in vivo.},
journal = {Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology},
volume = {76},
number = {},
pages = {102873},
doi = {10.1016/j.jelekin.2024.102873},
pmid = {38518426},
issn = {1873-5711},
abstract = {The ultimate neural signal for muscle control is the neural drive sent from the spinal cord to muscles. This neural signal comprises the ensemble of action potentials discharged by the active spinal motoneurons, which is transmitted to the innervated muscle fibres to generate forces. Accurately estimating the neural drive to muscles in humans in vivo is challenging since it requires the identification of the activity of a sample of motor units (MUs) that is representative of the active MU population. Current electrophysiological recordings usually fail in this task by identifying small MU samples with over-representation of higher-threshold with respect to lower-threshold MUs. Here, we describe recent advances in electrophysiological methods that allow the identification of more representative samples of greater numbers of MUs than previously possible. This is obtained with large and very dense arrays of electromyographic electrodes. Moreover, recently developed computational methods of data augmentation further extend experimental MU samples to infer the activity of the full MU pool. In conclusion, the combination of new electrode technologies and computational modelling allows for an accurate estimate of the neural drive to muscles and opens new perspectives in the study of the neural control of movement and in neural interfacing.},
}

@article {pmid38518365,
year = {2024},
author = {McNamara, IN and Wellman, SM and Li, L and Eles, JR and Savya, S and Sohal, HS and Angle, MR and Kozai, TDY},
title = {Electrode sharpness and insertion speed reduce tissue damage near high-density penetrating arrays.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad36e1},
pmid = {38518365},
issn = {1741-2552},
abstract = {OBJECTIVE: Over the past decade, neural electrodes have played a crucial role in bridging biological tissues with electronic and robotic devices. This study focuses on evaluating the optimal tip profile and insertion speed for effectively implanting Paradromics' high-density Fine Microwire Arrays (FμA) prototypes into the primary visual cortex (V1) of mice and rats, addressing the challenges associated with the "bed-of-nails" effect and tissue dimpling.

APPROACH: Tissue response was assessed by investigating the impact of electrodes on the blood-brain barrier (BBB) and cellular damage, with a specific emphasis on tailored insertion strategies to minimize tissue disruption during electrode implantation.

MAIN RESULTS: Electro-sharpened arrays demonstrated a marked reduction in cellular damage within 50 μm of the electrode tip compared to blunt and angled arrays. Histological analysis revealed that slow insertion speeds led to greater BBB compromise than fast and pneumatic methods. Successful single-unit recordings validated the efficacy of the optimized electro-sharpened arrays in capturing neural activity.

SIGNIFICANCE: These findings underscore the critical role of tailored insertion strategies in minimizing tissue damage during electrode implantation, highlighting the suitability of electro-sharpened arrays for long-term implant applications. This research contributes to a deeper understanding of the complexities associated with high-channel-count microelectrode array implantation, emphasizing the importance of meticulous assessment and optimization of key parameters for effective integration and minimal tissue disruption. By elucidating the interplay between insertion parameters and tissue response, our study lays a strong foundation for the development of advanced implantable devices with a reduction in reactive gliosis and improved performance in neural recording applications.},
}

@article {pmid38517720,
year = {2024},
author = {Han, Y and Ke, Y and Wang, R and Wang, T and Ming, D},
title = {Enhancing SSVEP-BCI Performance under Fatigue State Using Dynamic Stopping Strategy.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3380635},
pmid = {38517720},
issn = {1558-0210},
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have gained significant attention in recent years due to their advantages, including high communication rates, short calibration times, and high signal-to-noise ratios. However, one of the major challenges in practical application is performance degradation due to fatigue during continuous and prolonged use. This study aims to mitigate the negative effects of fatigue on SSVEP-BCI performance by adjusting data lengths and updating detection models using dynamic stopping strategy. Two 16-target SSVEP-BCIs were used for data collection, one using low-frequency stimulation and the other using high-frequency stimulation. A self-recorded fatigue dataset from twenty-four subjects was used for extensive evaluation studies. The simulated online experiment demonstrated that the proposed methods outperformed the conventional fixed stopping strategy in terms of classification accuracy, information transfer rate, and selection time, regardless of stimulus frequency. In conclusion, the proposed methods can improve the performance of the SSVEP-BCI in fatigue state and provide superior performance over extended periods of sustained use.},
}

@article {pmid38514730,
year = {2024},
author = {Myszor, IT and Lapka, K and Hermannsson, K and Rekha, RS and Bergman, P and Gudmundsson, GH},
title = {Bile acid metabolites enhance expression of cathelicidin antimicrobial peptide in airway epithelium through activation of the TGR5-ERK1/2 pathway.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {6750},
pmid = {38514730},
issn = {2045-2322},
mesh = {Humans ; *Bile Acids and Salts/metabolism ; *Cathelicidins/metabolism ; MAP Kinase Signaling System ; Receptors, G-Protein-Coupled/metabolism ; Epithelium/metabolism ; Lithocholic Acid/pharmacology/metabolism ; },
abstract = {Signals for the maintenance of epithelial homeostasis are provided in part by commensal bacteria metabolites, that promote tissue homeostasis in the gut and remote organs as microbiota metabolites enter the bloodstream. In our study, we investigated the effects of bile acid metabolites, 3-oxolithocholic acid (3-oxoLCA), alloisolithocholic acid (AILCA) and isolithocholic acid (ILCA) produced from lithocholic acid (LCA) by microbiota, on the regulation of innate immune responses connected to the expression of host defense peptide cathelicidin in lung epithelial cells. The bile acid metabolites enhanced expression of cathelicidin at low concentrations in human bronchial epithelial cell line BCi-NS1.1 and primary bronchial/tracheal cells (HBEpC), indicating physiological relevance for modulation of innate immunity in airway epithelium by bile acid metabolites. Our study concentrated on deciphering signaling pathways regulating expression of human cathelicidin, revealing that LCA and 3-oxoLCA activate the surface G protein-coupled bile acid receptor 1 (TGR5, Takeda-G-protein-receptor-5)-extracellular signal-regulated kinase (ERK1/2) cascade, rather than the nuclear receptors, aryl hydrocarbon receptor, farnesoid X receptor and vitamin D3 receptor in bronchial epithelium. Overall, our study provides new insights into the modulation of innate immune responses by microbiota bile acid metabolites in the gut-lung axis, highlighting the differences in epithelial responses between different tissues.},
}

Humans
*Bile Acids and Salts/metabolism
*Cathelicidins/metabolism
MAP Kinase Signaling System
Receptors, G-Protein-Coupled/metabolism
Epithelium/metabolism
Lithocholic Acid/pharmacology/metabolism