@article {pmid39437806,
year = {2024},
author = {Zuo, M and Yu, B and Sui, L},
title = {Classification of EEG evoked in 2D and 3D virtual reality: traditional machine learning vs. deep learning.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ad89c5},
pmid = {39437806},
issn = {2057-1976},
abstract = {Backgrounds Virtual reality (VR) simulates real-life events and scenarios, widely used in education, entertainment, and medicine. VR can be presented in two or three dimensions (2D or 3D), and 3D VR produces a more realistic and immersive experience. Previous research has revealed that the electroencephalogram (EEG) induced by 3D VR has a different profile from that of 2D VR, manifesting in many aspects, such as the power of brain rhythm, brain activation, and brain functional connectivity. However, studies on how to classify EEG in 2D and 3D VR were limited. Methods 64-channel EEG was recorded, while visual stimuli were given in 2D and 3D VR. The classification of these recorded EEG signals was done using two machine learning methods: the traditional method and the deep learning method. In the traditional machine learning classification, EEG features of power spectral density (PSD) and common spatial patterns (CSP) were extracted, and three classification algorithms, support vector machines (SVM), K-nearest neighbors (KNN), and random forests (RF), were used. A specialized convolutional neural network, EEGNet, was used in the deep learning classification. These classification approaches were compared with respect to their classification performance. Results In aspects of four performance evaluations for classification, accuracy, precision, recall, and F1-score, respectively, classification using the deep learning method is better than the traditional machine learning approaches. Classification accuracy with deep learning with EEGNet could reach up to 97.86%. Conclusions The classification performance of 2D and 3D VR-induced EEG can be achieved with EEGNet-based deep learning, outperforming conventional machine learning approaches. Given the role of EEGNet, which is designed for EEG-based brain-computer interfaces (BCI), better performance classification of EEG in 2D and 3D VR environments might be predicted to be helpful for the application of 3D VR in BCI. .},
}

@article {pmid39435615,
year = {2024},
author = {Song, X and Li, R and Chu, X and Li, Q and Li, R and Li, Q and Tong, K and Gu, X and Ming, D},
title = {Multilevel analysis of the central-peripheral-target organ pathway: contributing to recovery after peripheral nerve injury.},
journal = {Neural regeneration research},
volume = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-24-00641},
pmid = {39435615},
issn = {1673-5374},
abstract = {Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities. Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites, neglecting multilevel pathological analysis of the overall nervous system and target organs. This has led to restrictions on current therapeutic approaches. In this paper, we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective, covering the central nervous system, peripheral nervous system, and target organs. After peripheral nerve injury, the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves; changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord. The nerve will undergo axonal regeneration, activation of Schwann cells, inflammatory response, and vascular system regeneration at the injury site. Corresponding damage to target organs can occur, including skeletal muscle atrophy and sensory receptor disruption. We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury. The main current treatments are conducted passively and include physical factor rehabilitation, pharmacological treatments, cell-based therapies, and physical exercise. However, most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway. Therefore, we should further explore multilevel treatment options that produce effective, long-lasting results, perhaps requiring a combination of passive (traditional) and active (novel) treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.},
}

@article {pmid39435350,
year = {2024},
author = {Beauchemin, N and Charland, P and Karran, A and Boasen, J and Tadson, B and Sénécal, S and Léger, PM},
title = {Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1416683},
pmid = {39435350},
issn = {1662-5161},
abstract = {Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner's mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner's cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann-Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI's potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts.},
}

@article {pmid39434212,
year = {2024},
author = {Ahmadi-Dastgerdi, N and Hosseini-Nejad, H and Alinejad-Rokny, H},
title = {A Hardware-Efficient Novelty-Aware Spike Sorting Approach for Brain-Implantable Microsystems.},
journal = {International journal of neural systems},
volume = {34},
number = {12},
pages = {2450067},
doi = {10.1142/S0129065724500679},
pmid = {39434212},
issn = {1793-6462},
mesh = {*Brain-Computer Interfaces ; *Action Potentials/physiology ; *Brain/physiology ; Neurons/physiology ; Signal Processing, Computer-Assisted ; Humans ; Algorithms ; },
abstract = {Unsupervised spike sorting, a vital processing step in real-time brain-implantable microsystems, is faced with the prominent challenge of managing nonstationarity in neural signals. In long-term recordings, spike waveforms gradually change and new source neurons are likely to become activated. Adaptive spike sorters combined with on-implant training units effectively process the nonstationary signals at the cost of high hardware resource utilization. On the other hand, static approaches, while being hardware-friendly, are subjected to decreased processing performance in such recordings where the neural signal characteristics gradually change. To strike a balance between the hardware cost and processing performance, this study proposes a hardware-efficient novelty-aware spike sorting approach that is capable of dealing with both variated spike waveforms and spike waveforms generated from new source neurons. Its improved hardware efficiency compared to adaptive ones and capability of dealing with nonstationary signals make it attractive for implantable applications. The proposed novelty-aware spike sorting especially would be a good fit for brain-computer interfaces where long-term, real-time interaction with the brain is required, and the available on-implant hardware resources are limited. Our unsupervised spike sorting benefits from a novelty detection process to deal with neural signal variations. It tracks the spike features so that in case of detecting an unexpected change (novelty detection) both on and off-implant parameters are updated to preserve the performance in new state. To make the proposed approach agile enough to be suitable for brain implants, the on-implant computations are reduced while the computational burden is realized off-implant. The performance of our proposed approach is evaluated using both synthetic and real datasets. The results demonstrate that, in the mean, it is capable of detecting 94.31% of novel spikes (wave-drifted or emerged spikes) with a classification accuracy (CA) of 96.31%. Moreover, an FPGA prototype of the on-implant circuit is implemented and tested. It is shown that in comparison to the OSORT algorithm, a pivotal spike sorting method, our spike sorting provides a higher CA at significantly lower hardware resources. The proposed circuit is also implemented in a 180-nm standard CMOS process, achieving a power consumption of 1.78[Formula: see text][Formula: see text] per channel and a chip area of 0.07[Formula: see text]mm[2] per channel.},
}

*Brain-Computer Interfaces
*Action Potentials/physiology
*Brain/physiology
Neurons/physiology
Signal Processing, Computer-Assisted
Humans
Algorithms

@article {pmid39433844,
year = {2024},
author = {Pun, TK and Khoshnevis, M and Hosman, T and Wilson, GH and Kapitonava, A and Kamdar, F and Henderson, JM and Simeral, JD and Vargas-Irwin, CE and Harrison, MT and Hochberg, LR},
title = {Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.},
journal = {Communications biology},
volume = {7},
number = {1},
pages = {1363},
pmid = {39433844},
issn = {2399-3642},
support = {N2864C, A2295R, A2827R, A3803R//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; T32MH115895//U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)/ ; UH2NS095548, U01NS098968//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01DC017844, R01DC014034//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Quadriplegia/physiopathology ; Adult ; Female ; Middle Aged ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method, "MINDFUL", to measure instabilities in neural data for useful long-term iBCI, without needing labels of user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.},
}

Humans
*Brain-Computer Interfaces
Male
Quadriplegia/physiopathology
Adult
Female
Middle Aged

@article {pmid39433597,
year = {2024},
author = {Candrea, DN and Shah, S and Luo, S and Angrick, M and Rabbani, Q and Coogan, C and Milsap, GW and Nathan, KC and Wester, BA and Anderson, WS and Rosenblatt, KR and Uchil, A and Clawson, L and Maragakis, NJ and Vansteensel, MJ and Tenore, FV and Ramsey, NF and Fifer, MS and Crone, NE},
title = {A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling.},
journal = {Communications medicine},
volume = {4},
number = {1},
pages = {207},
pmid = {39433597},
issn = {2730-664X},
support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) can restore communication for movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command click detectors provide a basic yet highly functional capability.

METHODS: We sought to test the performance and long-term stability of click decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis. We trained the participant's click detector using a small amount of training data (<44 min across 4 days) collected up to 21 days prior to BCI use, and then tested it over a period of 90 days without any retraining or updating.

RESULTS: Using a click detector to navigate a switch scanning speller interface, the study participant can maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation can interrupt usage of a fixed model, a new click detector can achieve comparable performance despite being trained with even less data (<15 min, within 1 day).

CONCLUSIONS: These results demonstrate that a click detector can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.},
}

@article {pmid39433073,
year = {2024},
author = {Zeng, P and Fan, L and Luo, Y and Shen, H and Hu, D},
title = {Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8963},
pmid = {39433073},
issn = {1741-2552},
abstract = {The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been proposed to eliminate noise from EEG signals, but most of these methods focus solely on signal denoising itself, disregarding the impact on subsequent tasks, which deviates from the original intention of EEG denoising. The main objective of this study is to optimize EEG denoising models with a purpose of improving the performance of BCI tasks. Approach. To this end, we proposed an innovative Task-Oriented EEG Denoising Generative Adversarial Network (TOED-GAN) method. This network utilizes the generator of GAN to decompose and reconstruct clean signals from the raw EEG signals, and the discriminator to learn to distinguish the generated signals from the true clean signals, resulting in a remarkable increase of the signal-to-noise ratio (SNR) by simultaneously enhancing task-related components and removing task-irrelevant noise from the original contaminated signals. Main results. We evaluated the performance of the model on a public dataset and a self-collected dataset respectively, with canonical correlation analysis (CCA) classification tasks of the steady-state visual evoked potential (SSVEP) based BCI. Experimental results demonstrate that TOED-GAN exhibits excellent performance in removing EEG noise and improving performance for SSVEP-BCI, with accuracy improvement rates reaching 18.47% and 21.33% in contrast to the baseline methods of convolutional neural networks, respectively Significance. This work proves that the proposed TOED-GAN, as an EEG denoising method tailored for SSVEP tasks, contributes to enhancing the performance of BCIs in practical application scenarios.},
}

@article {pmid39433072,
year = {2024},
author = {Guetschel, P and Ahmadi, S and Tangermann, M},
title = {Review of deep representation learning techniques for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8962},
pmid = {39433072},
issn = {1741-2552},
abstract = {In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial interest. This review synthesizes empirical findings from a collection of articles using deep representation learning techniques for BCI decoding, to provide a comprehensive analysis of the current state-of-the-art. Each article was scrutinized based on three criteria: (1) the deep representation learning technique employed, (2) the underlying motivation for its utilization, and (3) the approaches adopted for characterizing the learned representations. Among the 81 articles finally reviewed in depth, our analysis reveals a predominance of 31 articles using autoencoders. We identified 13 studies employing self-supervised learning (SSL) techniques, among which ten were published in 2022 or later, attesting to the relative youth of the field. However, at the time being, none of these have led to standard foundation models that are picked up by the BCI community. Likewise, only a few studies have introspected their learned representations. We observed that the motivation in most studies for using representation learning techniques is for solving transfer learning tasks, but we also found more specific motivations such as to learn robustness or invariances, as an algorithmic bridge, or finally to uncover the structure of the data. Given the potential of foundation models to effectively tackle these challenges, we advocate for a continued dedication to the advancement of foundation models specifically designed for EEG signal decoding by using SSL techniques. We also underline the imperative of establishing specialized benchmarks and datasets to facilitate the development and continuous improvement of such foundation models.},
}

@article {pmid39433071,
year = {2024},
author = {Chen, X and Meng, L and Xu, Y and Wu, D},
title = {Adversarial artifact detection in EEG-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8964},
pmid = {39433071},
issn = {1741-2552},
abstract = {OBJECTIVE: Machine learning has achieved significant success in electroencephalogram (EEG) based brain-computer interfaces (BCIs), with most existing research focusing on improving the decoding accuracy. However, recent studies have shown that EEG-based BCIs are vulnerable to adversarial attacks, where small perturbations added to the input can cause misclassification. Detecting adversarial examples is crucial for both understanding this phenomenon and developing effective defense strategies.

APPROACH: This paper, for the first time, explores adversarial detection in EEG-based BCIs. We extend several popular adversarial detection approaches from computer vision to BCIs. Two new Mahalanobis distance based adversarial detection approaches, and three cosine distance based adversarial detection approaches, are also proposed, which showed promising performance in detecting three kinds of white-box attacks.

MAIN RESULTS: We evaluated the performance of eight adversarial detection approaches on three EEG datasets, three neural networks, and four types of adversarial attacks. Our approach achieved an area under the curve (AUC) score of up to 99.99% in detecting white-box attacks. Additionally, we assessed the transferability of different adversarial detectors to unknown attacks.

SIGNIFICANCE: Through extensive experiments, we found that white-box attacks may be easily detected, and differences exist in the distributions of different types of adversarial examples. Our work should facilitate understanding the vulnerability of existing BCI models and developing more secure BCIs in the future.},
}

@article {pmid39432989,
year = {2024},
author = {Yang, H and Zhu, Z and Ni, S and Wang, X and Nie, Y and Tao, C and Zou, D and Jiang, W and Zhao, Y and Zhou, Z and Sun, L and Li, M and Tao, TH and Liu, K and Wei, X},
title = {Silk fibroin-based bioelectronic devices for high-sensitivity, stable, and prolonged in vivo recording.},
journal = {Biosensors & bioelectronics},
volume = {267},
number = {},
pages = {116853},
doi = {10.1016/j.bios.2024.116853},
pmid = {39432989},
issn = {1873-4235},
abstract = {Silk fibroin, recognized for its biocompatibility and modifiable properties, has significant potential in bioelectronics. Traditional silk bioelectronic devices, however, face rapid functional losses in aqueous or in vivo environments due to high water absorption of silk fibroin, which leads to expansion, structural damage, and conductive failure. In this study, we developed a novel approach by creating oriented crystallization (OC) silk fibroin through physical modification of the silk protein. This advancement enabled the fabrication of electronic interfaces for chronic biopotential recording. A pre-stretching treatment of the silk membrane allowed for tunable molecular orientation and crystallization, markedly enhancing its aqueous stability, biocompatibility, and electronic shielding capabilities. The OC devices demonstrated robust performance in sensitive detection and motion tracking of cutaneous electrical signals, long-term (over seven days) electromyographic signal acquisition in live mice with high signal-to-noise ratio (SNR >20), and accurate detection of high-frequency oscillations (HFO) in epileptic models (200-500 Hz). This work not only improves the structural and functional integrity of silk fibroin but also extends its application in durable bioelectronics and interfaces suited for long-term physiological environments.},
}

@article {pmid39432777,
year = {2024},
author = {Jiang, L and Qi, X and Lai, M and Zhou, J and Yuan, M and You, J and Liu, Q and Pan, J and Zhao, L and Ying, M and Ji, J and Li, K and Zhang, Y and Pan, W and He, Q and Yang, B and Cao, J},
title = {WDR20 prevents hepatocellular carcinoma senescence by orchestrating the simultaneous USP12/46-mediated deubiquitination of c-Myc.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {121},
number = {44},
pages = {e2407904121},
doi = {10.1073/pnas.2407904121},
pmid = {39432777},
issn = {1091-6490},
support = {2021YFA1300604//National Key R&D Program of China/ ; 82304514//Youth Fund of the National Natural Science Foundation of China/ ; LQ23H310004//Zhejiang Provincial Natural Science Foundation of China/ ; 226-2023-00059//the Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Carcinoma, Hepatocellular/metabolism/genetics/pathology ; *Liver Neoplasms/metabolism/genetics/pathology ; Animals ; *Proto-Oncogene Proteins c-myc/metabolism/genetics ; *Cellular Senescence ; Mice ; *Ubiquitination ; *Ubiquitin Thiolesterase/metabolism/genetics ; Cell Line, Tumor ; Cell Proliferation ; Gene Expression Regulation, Neoplastic ; Mice, Transgenic ; Carrier Proteins ; },
abstract = {The dysfunction of the ubiquitin-proteasome system (UPS) facilitates the malignant progression of hepatocellular carcinoma (HCC). While targeting the UPS for HCC therapy has been proposed, identifying effective targets has been challenging. In this study, we conducted a focused screen of siRNA libraries targeting UPS-related WD40 repeat (WDR) proteins and found that silencing WDR20, a deubiquitinating enzyme activating factor, selectively inhibited the proliferation of HCC cells without affecting normal hepatocytes. Moreover, the downregulation of WDR20 expression induced HCC cellular senescence and suppressed tumor progression in xenograft, sleeping beauty transposon/transposase, and hydrodynamic tail vein injection-induced HCC models, and Alb-Cre[+]/MYC[+] HCC transgenic mouse models. Mechanistically, we found that WDR20 silencing disturbed the protein stability of c-Myc, orchestrating the simultaneous USP12/46-mediated deubiquitination of c-Myc, thereby promoting the transcriptional activation of CDKN1A. Further investigation revealed a positive coexpression of WDR20 and c-Myc in a tissue microarray with 88 HCC clinical samples. By employing three patient-derived organoids from individuals with HCC, we have validated the decrease in c-Myc expression and the significant induction of senescence and growth inhibition following silencing of WDR20. This study not only uncovers the biological function of WDR20 and elucidates the molecular mechanism underlying its negative regulation of HCC cellular senescence but also highlight the potential of WDR20 as a promising target for HCC therapy.},
}

Humans
*Carcinoma, Hepatocellular/metabolism/genetics/pathology
*Liver Neoplasms/metabolism/genetics/pathology
Animals
*Proto-Oncogene Proteins c-myc/metabolism/genetics
*Cellular Senescence
Mice
*Ubiquitination
*Ubiquitin Thiolesterase/metabolism/genetics
Cell Line, Tumor
Cell Proliferation
Gene Expression Regulation, Neoplastic
Mice, Transgenic
Carrier Proteins

@article {pmid39430565,
year = {2024},
author = {Ratasukharom, N and Niwitpong, SA and Niwitpong, S},
title = {Estimation methods for the variance of Birnbaum-Saunders distribution containing zero values with application to wind speed data in Thailand.},
journal = {PeerJ},
volume = {12},
number = {},
pages = {e18272},
pmid = {39430565},
issn = {2167-8359},
mesh = {Thailand ; *Wind ; Models, Statistical ; Monte Carlo Method ; Air Pollution/analysis ; Humans ; Particulate Matter/analysis ; Confidence Intervals ; Computer Simulation ; },
abstract = {Thailand is currently grappling with a severe problem of air pollution, especially from small particulate matter (PM), which poses considerable threats to public health. The speed of the wind is pivotal in spreading these harmful particles across the atmosphere. Given the inherently unpredictable wind speed behavior, our focus lies in establishing the confidence interval (CI) for the variance of wind speed data. To achieve this, we will employ the delta-Birnbaum-Saunders (delta-BirSau) distribution. This statistical model allows for analyzing wind speed data and offers valuable insights into its variability and potential implications for air quality. The intervals are derived from ten different methods: generalized confidence interval (GCI), bootstrap confidence interval (BCI), generalized fiducial confidence interval (GFCI), and normal approximation (NA). Specifically, we apply GCI, BCI, and GFCI while considering the estimation of the proportion of zeros using the variance stabilized transformation (VST), Wilson, and Hannig methods. To evaluate the performance of these methods, we conduct a simulation study using Monte Carlo simulations in the R statistical software. The study assesses the coverage probabilities and average widths of the proposed confidence intervals. The simulation results reveal that GFCI based on the Wilson method is optimal for small sample sizes, GFCI based on the Hannig method excels for medium sample sizes, and GFCI based on the VST method stands out for large sample sizes. To further validate the practical application of these methods, we employ daily wind speed data from an industrial area in Prachin Buri and Rayong provinces, Thailand.},
}

Thailand
*Wind
Models, Statistical
Monte Carlo Method
Air Pollution/analysis
Humans
Particulate Matter/analysis
Confidence Intervals
Computer Simulation

@article {pmid39429223,
year = {2024},
author = {Zhu, L and Xu, M and Huang, A and Zhang, J and Tan, X},
title = {Multi-source domain transfer network based on subdomain adaptation and minimum class confusion for EEG emotion recognition.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/10255842.2024.2417212},
pmid = {39429223},
issn = {1476-8259},
abstract = {Electroencephalogram (EEG) signals, which objectively reflect the state of the brain, are widely favored in emotion recognition research. However, the presence of cross-session and cross-subject variation in EEG signals has hindered the practical implementation of EEG-based emotion recognition technologies. In this article, we propose a multi-source domain transfer method based on subdomain adaptation and minimum class confusion (MS-SAMCC) in response to the addressed issue. First, we introduce the mix-up data augmentation technique to generate augmented samples. Next, we propose a minimum class confusion subdomain adaptation method (MCCSA) as a sub-module of the multi-source domain adaptation module. This approach enables global alignment between each source domain and the target domain, while also achieving alignment among individual subdomains within them. Additionally, we employ minimum class confusion (MCC) as a regularizer for this sub-module. We performed experiments on SEED, SEED IV, and FACED datasets. In the cross-subject experiments, our method achieved mean classification accuracies of 87.14% on SEED, 63.24% on SEED IV, and 42.07% on FACED. In the cross-session experiments, our approach obtained average classification accuracies of 94.20% on SEED and 71.66% on SEED IV. These results demonstrate that the MS-SAMCC approach proposed in this study can effectively address EEG-based emotion recognition tasks.},
}

@article {pmid39429090,
year = {2024},
author = {Wang, D and Zhou, H and Zhou, XL and Hu, YZ and Xu, HZ and Fu, JF},
title = {[Research advances of food addiction and obesity in children].},
journal = {Zhonghua er ke za zhi = Chinese journal of pediatrics},
volume = {62},
number = {11},
pages = {1121-1124},
doi = {10.3760/cma.j.cn112140-20240415-00268},
pmid = {39429090},
issn = {0578-1310},
}

@article {pmid39428886,
year = {2024},
author = {Ruiz, S and Valera, L and Ramos, P and Sitaram, R},
title = {Neurorights in the Constitution: from neurotechnology to ethics and politics.},
journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
volume = {379},
number = {1915},
pages = {20230098},
doi = {10.1098/rstb.2023.0098},
pmid = {39428886},
issn = {1471-2970},
support = {//National Agency for Research and Development Millennium Science Initiative/ ; //Fondo Nacional de Desarrollo Científico y Tecnológico/ ; },
mesh = {Chile ; Humans ; *Politics ; *Neurosciences/ethics ; Neuroimaging/ethics ; Brain-Computer Interfaces/ethics ; Neurofeedback ; },
abstract = {Neuroimaging technologies such as brain-computer interfaces and neurofeedback have evolved rapidly as new tools for cognitive neuroscience and as potential clinical interventions. However, along with these developments, concern has grown based on the fear of the potential misuse of neurotechnology. In October 2021, Chile became the first country to include neurorights in its Constitution. The present article is divided into two parts. In the first section, we describe the path followed by neurorights that led to its inclusion in the Chilean Constitution, and the neurotechnologies usually involved in neurorights discussions. In the second part, we discuss two potential problems of neurorights. We begin by pointing out some epistemological concerns regarding neurorights, mainly referring to the ambiguity of the concepts used in neurolegislations, the difficult relationship between neuroscience and politics and the weak reasons for urgency in legislating. We then describe the dangers of overprotective laws in medical research, based on the detrimental effect of recent legislation in Chile and the potential risk posed by neurorights to the benefits of neuroscience development. This article aims to engage with the scientific community interested in neurotechnology and neurorights in an interdisciplinary reflection of the potential consequences of neurorights.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.},
}

Chile
Humans
*Politics
*Neurosciences/ethics
Neuroimaging/ethics
Brain-Computer Interfaces/ethics
Neurofeedback

@article {pmid39428881,
year = {2024},
author = {Sulzer, J and Papageorgiou, TD and Goebel, R and Hendler, T},
title = {Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation.},
journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
volume = {379},
number = {1915},
pages = {20230081},
doi = {10.1098/rstb.2023.0081},
pmid = {39428881},
issn = {1471-2970},
mesh = {*Neurofeedback/methods ; Humans ; *Brain/physiology ; Neurotransmitter Agents/metabolism ; Cognition ; },
abstract = {Neurofeedback (NF) is endogenous neuromodulation of circumscribed brain circuitry. While its use of real-time brain activity in a closed-loop system is similar to brain-computer interfaces, instead of controlling an external device like the latter, the goal of NF is to change a targeted brain function. In this special issue on NF, we present current and future methods for extracting and manipulating neural function, how these methods may reveal new insights about brain function, applications, and rarely discussed ethical considerations of guiding and interpreting the brain activity of others. Together, the articles in this issue outline the possibilities of NF use and impact in the real world, poising to influence the development of more effective and personalized NF protocols, improving the understanding of underlying psychological and neurological mechanisms and enhancing treatment precision for various neurological and psychiatric conditions.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.},
}

*Neurofeedback/methods
Humans
*Brain/physiology
Neurotransmitter Agents/metabolism
Cognition

@article {pmid39428875,
year = {2024},
author = {Sitaram, R and Sanchez-Corzo, A and Vargas, G and Cortese, A and El-Deredy, W and Jackson, A and Fetz, E},
title = {Mechanisms of brain self-regulation: psychological factors, mechanistic models and neural substrates.},
journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
volume = {379},
number = {1915},
pages = {20230093},
doi = {10.1098/rstb.2023.0093},
pmid = {39428875},
issn = {1471-2970},
support = {//National Institute of Health/ ; //ATR Computational Neuroscience Laboratories/ ; },
mesh = {Humans ; *Neurofeedback ; *Brain/physiology ; *Self-Control ; Cognition/physiology ; Animals ; Brain-Computer Interfaces ; Models, Neurological ; },
abstract = {While neurofeedback represents a promising tool for neuroscience and a brain self-regulation approach to psychological rehabilitation, the field faces several problems and challenges. Current research has shown great variability and even failure among human participants in learning to self-regulate target features of brain activity with neurofeedback. A better understanding of cognitive mechanisms, psychological factors and neural substrates underlying self-regulation might help improve neurofeedback's scientific and clinical practices. This article reviews the current understanding of the neural mechanisms of brain self-regulation by drawing on findings from human and animal studies in neurofeedback, brain-computer/machine interfaces and neuroprosthetics. In this article, we look closer at the following topics: cognitive processes and psychophysiological factors affecting self-regulation, theoretical models and neural substrates underlying self-regulation, and finally, we provide an outlook on the outstanding gaps in knowledge and technical challenges. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.},
}

Humans
*Neurofeedback
*Brain/physiology
*Self-Control
Cognition/physiology
Animals
Brain-Computer Interfaces
Models, Neurological

@article {pmid39428037,
year = {2024},
author = {V, HM and Begum, BS},
title = {Towards Imagined Speech: Identification of Brain States from EEG Signals for BCI-based Communication Systems.},
journal = {Behavioural brain research},
volume = {},
number = {},
pages = {115295},
doi = {10.1016/j.bbr.2024.115295},
pmid = {39428037},
issn = {1872-7549},
abstract = {BACKGROUND: The electroencephalogram (EEG) based brain-computer interface (BCI) system employing imagined speech serves as a mechanism for decoding EEG signals to facilitate control over external devices or communication with the external world at the moment the user desires. To effectively deploy such BCIs, it is imperative to accurately discern various brain states from continuous EEG signals when users initiate word imagination.

NEW METHOD: This study involved the acquisition of EEG signals from 15 subjects engaged in four states: resting, listening, imagined speech, and actual speech, each involving a predefined set of 10 words. The EEG signals underwent preprocessing, segmentation, spatio-temporal and spectral analysis of each state, and functional connectivity analysis using the phase locking value (PLV) method. Subsequently, five features were extracted from the frequency and time-frequency domains. Classification tasks were performed using four machine learning algorithms in both pair-wise and multiclass scenarios, considering subject-dependent and subject-independent data.

RESULTS: In the subject-dependent scenario, the random forest (RF) classifier achieved a maximum accuracy of 94.60% for pairwise classification, while the artificial neural network (ANN) classifier achieved a maximum accuracy of 66.92% for multiclass classification. In the subject-independent scenario, the random forest (RF) classifier achieved maximum accuracies of 81.02% for pairwise classification and 55.58% for multiclass classification. Moreover, EEG signals were classified based on frequency bands and brain lobes, revealing that the theta (θ) and delta (δ) bands, as well as the frontal and temporal lobes, are sufficient for distinguishing between brain states.

CONCLUSION: The findings promise to develop a system capable of automatically segmenting imagined speech segments from continuous EEG signals.},
}

@article {pmid39426564,
year = {2024},
author = {Sleziona, D and Ely, DR and Thommes, M},
title = {Mechanisms of drug release from a melt-milled, poorly soluble drug substance.},
journal = {Journal of pharmaceutical sciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.xphs.2024.10.016},
pmid = {39426564},
issn = {1520-6017},
abstract = {Increasing the dissolution kinetics of low aqueous soluble drugs is one of the main priorities in drug formulation. New strategies must be developed, which should consider the two main dissolution mechanisms: surface reaction and diffusion. One promising tool is the so-called solid crystal suspension, a solid dispersion consisting of purely crystalline substances. In this concept, reducing the drug particle size and embedding the particles in a hydrophilic excipient increases the dissolution kinetics. Therefore, a solid crystal suspension containing submicron drug particles was produced via a modified stirred media milling process. A geometrical phase-field approach was used to model the dissolution behavior of the drug particles. A carrier material, xylitol, and the model drug substance, griseofulvin, were ground in a pearl mill. The in-vitro dissolution profile of the product was modeled to gain a deep physical understanding of the dissolution process. The used numerical tool has the potential to be a valuable approach for predicting the dissolution behavior of newly developed formulation strategies.},
}

@article {pmid39425602,
year = {2024},
author = {Mahalungkar, SP and Shrivastava, R and Angadi, S},
title = {A brief survey on human activity recognition using motor imagery of EEG signals.},
journal = {Electromagnetic biology and medicine},
volume = {},
number = {},
pages = {1-16},
doi = {10.1080/15368378.2024.2415089},
pmid = {39425602},
issn = {1536-8386},
abstract = {Human being's biological processes and psychological activities are jointly connected to the brain. So, the examination of human activity is more significant for the well-being of humans. There are various models for brain activity detection considering neuroimaging for attaining decreased time requirement, increased control commands, and enhanced accuracy. Motor Imagery (MI)-based Brain-Computer Interface (BCI) systems create a way in which the brain can interact with the environment by processing Electroencephalogram (EEG) signals. Human Activity Recognition (HAR) deals with identifying the physiological activities of human beings based on sensory signals. This survey reviews the different methods available for HAR based on MI-EEG signals. A total of 50 research articles based on HAR from EEG signals are considered in this survey. This survey discusses the challenges faced by various techniques for HAR. Moreover, the papers are assessed considering various parameters, techniques, publication year, performance metrics, utilized tools, employed databases, etc. There were many techniques developed to solve the problem of HAR and they are classified as Machine Learning (ML) and Deep Learning (DL)models. At last, the research gaps and limitations of the techniques were discussed that contribute to developing an effective HAR.},
}

@article {pmid39424369,
year = {2024},
author = {Berling, D and Baroni, L and Chaffiol, A and Gauvain, G and Picaud, S and Antolík, J},
title = {Optogenetic stimulation recruits cortical neurons in a morphology-dependent manner.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.1215-24.2024},
pmid = {39424369},
issn = {1529-2401},
abstract = {Single-photon optogenetics enables precise, cell-type-specific modulation of neuronal circuits, making it a crucial tool in neuroscience. Its miniaturization in the form of fully implantable wide-field stimulator arrays enables long-term interrogation of cortical circuits and bares promise for Brain-Machine Interfaces for sensory and motor function restoration. However, achieving selective activation of functional cortical representations poses a challenge, as studies show that targeted optogenetic stimulation results in activity spread beyond one functional domain. While recurrent network mechanisms contribute to activity spread, here we demonstrate with detailed simulations of isolated pyramidal neurons from cat of unknown sex that already neuron morphology causes a complex spread of optogenetic activity at the scale of one cortical column. Since the shape of a neuron impacts its optogenetic response, we find that a single stimulator at the cortical surface recruits a complex spatial distribution of neurons that can be inhomogeneous and vary with stimulation intensity and neuronal morphology across layers. We explore strategies to enhance stimulation precision, finding that optimizing stimulator optics may offer more significant improvements than preferentially somatic expression of the opsin through genetic targeting. Our results indicate that, with the right optical setup, single-photon optogenetics can precisely activate isolated neurons at the scale of functional cortical domains spanning several hundred micrometers.Significance Statement Sensory features, such as the position or orientation of a visual stimulus, are mapped onto the surface of cortex as functional domains. Their selective activation, that may enable eliciting complex percepts, is intensively pursued for basic science and clinical applications. However, delivery of light into one functional domain in optogenetically transfected cortex results in complex, widespread neuronal activity, spreading beyond the targeted domain. Our computational study reveals that neuron morphology contributes to this diffuse response in a cortical-layer and intensity-dependent manner. We show that enhancing the stimulator optics is more effective than soma-targeting of the opsin in increasing spatial precision of stimulation. Our simulations provide insights for designing optogenetic stimulation protocols and hardware to achieve selective activation of functional domains.},
}

@article {pmid39423832,
year = {2024},
author = {Karpowicz, BM and Bhaduri, B and Nason-Tomaszewski, SR and Jacques, BG and Ali, YH and Flint, RD and Bechefsky, PH and Hochberg, LR and Au Yong, N and Slutzky, MW and Pandarinath, C},
title = {Reducing power requirements for high-accuracy decoding in iBCIs.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad88a4},
pmid = {39423832},
issn = {1741-2552},
abstract = {OBJECTIVE: Current intracortical brain-computer interfaces (iBCIs) rely predominantly on threshold crossings ("spikes") for decoding neural activity into a control signal for an external device. Spiking data can yield high accuracy online control during complex behaviors; however, its dependence on high-sampling-rate data collection can pose challenges. An alternative signal for iBCI decoding is the local field potential (LFP), a continuous-valued signal that can be acquired simultaneously with spiking activity. However, LFPs are seldom used alone for online iBCI control as their decoding performance has yet to achieve parity with spikes.

APPROACH: Here, we present a strategy to improve the performance of LFP-based decoders by first training a neural dynamics model to use LFPs to reconstruct the firing rates underlying spiking data, and then decoding from the estimated rates. We test these models on previously-collected macaque data during center-out and random-target reaching tasks as well as data collected from a human iBCI participant during attempted speech.

MAIN RESULTS: In all cases, training models from LFPs enables firing rate reconstruction with accuracy comparable to spiking-based dynamics models. In addition, LFP-based dynamics models enable decoding performance exceeding that of LFPs alone and approaching that of spiking-based models. In all applications except speech, LFP-based dynamics models also facilitate decoding accuracy exceeding that of direct decoding from spikes.

SIGNIFICANCE: Because LFP-based dynamics models operate on lower bandwidth and with lower sampling rate than spiking models, our findings indicate that iBCI devices can be designed to operate with lower power requirements than devices dependent on recorded spiking activity, without sacrificing high-accuracy decoding.},
}

@article {pmid39423831,
year = {2024},
author = {Carrara, I and Aristimunha, B and Corsi, MC and de Camargo, RY and Chevallier, S and Papadopoulo, T},
title = {Geometric neural network based on phase space for BCI-EEG decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad88a2},
pmid = {39423831},
issn = {1741-2552},
abstract = {\textbf{Objective:}
The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This is particularly true for BCI, where the brain activity is decoded to control external devices without requiring muscle control. Electroencephalography (EEG) is a widely adopted choice for designing BCI systems due to its non-invasive and cost-effective nature and excellent temporal resolution. Still, it comes at the expense of limited training data, poor signal-to-noise, and a large variability across and within-subject recordings. Finally, setting up a BCI system with many electrodes takes a long time, hindering the widespread adoption of reliable DL architectures in BCIs outside research laboratories. To improve adoption, we need to improve user comfort using, for instance, reliable algorithms that operate with few electrodes. \textbf{Approach:}
Our research aims to develop a DL algorithm that delivers effective results with a limited number of electrodes. Taking advantage of the Augmented Covariance Method and the framework of SPDNet, we propose the \method{}
architecture and analyze its performance and the interpretability of the results. The evaluation is conducted on 5-fold cross-validation, using only three electrodes positioned above the Motor Cortex. The methodology was tested on nearly 100 subjects from several open-source datasets using the Mother Of All BCI Benchmark (MOABB) framework. \textbf{Main results:}
The results of our \method{}
demonstrate that the augmented approach combined with the SPDNet significantly outperforms all the current state-of-the-art DL architecture in MI decoding. \textbf{Significance:}
This new architecture is explainable and with a low number of trainable parameters.},
}

@article {pmid39423829,
year = {2024},
author = {Valle, C and Mendez-Orellana, C and Herff, C and Rodriguez-Fernandez, M},
title = {Identification of perceived sentences using deep neural networks in EEG.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad88a3},
pmid = {39423829},
issn = {1741-2552},
abstract = {Decoding speech from brain activity can enable communication for individuals with speech disorders. Deep neural networks have shown great potential for speech decoding applications. However, the limited availability of large datasets containing neural recordings from speech-impaired subjects poses a challenge. Leveraging data from healthy participants can mitigate this limitation and expedite the development of speech neuroprostheses while minimizing the need for patient-specific training data. Approach. In this study, we collected a substantial dataset consisting of recordings from 56 healthy participants using 64 EEG channels. Multiple neural networks were trained to classify perceived sentences in the Spanish language using subject-independent, mixed-subjects, and fine-tuning approaches. The dataset has been made publicly available to foster further research in this area. Main results. Our results demonstrate a remarkable level of accuracy in distinguishing sentence identity across 30 classes, showcasing the feasibility of training Deep Neural Networks (DNNs) to decode sentence identity from perceived speech using EEG. Notably, the subject-independent approach rendered accuracy comparable to the mixed-subjects approach, although with higher variability among subjects. Additionally, our fine-tuning approach yielded even higher accuracy, indicating an improved capability to adapt to individual subject characteristics, which enhances performance. This suggests that DNNs have effectively learned to decode universal features of brain activity across individuals while also being adaptable to specific participant data. Furthermore, our analyses indicate that EEGNet and DeepConvNet exhibit comparable performance, outperforming ShallowConvNet for sentence identity decoding. Finally, our Grad-CAM visualization analysis identifies key areas influencing the network's predictions, offering valuable insights into the neural processes underlying language perception and comprehension. Significance. These findings advance our understanding of EEG-based speech perception decoding and hold promise for the development of speech neuroprostheses, particularly in scenarios where subjects cannot provide their own training data.},
}

@article {pmid39423826,
year = {2024},
author = {Chen, X and Li, S and Tu, Y and Wang, Z and Wu, D},
title = {User-wise perturbations for user identity protection in EEG-based BCIs.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad88a5},
pmid = {39423826},
issn = {1741-2552},
abstract = {OBJECTIVE: An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less attention has been paid to the ethics of BCIs. Aside from task-specific information, EEG signals also contain rich private information, e.g., user identity, emotion, disorders, etc., which should be protected.

APPROACH: We show for the first time that adding user-wise perturbations can make identity information in EEG unlearnable. We propose four types of user-wise privacy-preserving perturbations, i.e., random noise, synthetic noise, error minimization noise, and error maximization noise. After adding the proposed perturbations to EEG training data, the user identity information in the data becomes unlearnable, while the BCI task information remains unaffected.

MAIN RESULTS: Experiments on six EEG datasets using three neural network classifiers and various traditional machine learning models demonstrated the robustness and practicability of the proposed perturbations.

SIGNIFICANCE: Our research shows the feasibility of hiding user identity information in EEG data without impacting the primary BCI task information.},
}

@article {pmid39423083,
year = {2024},
author = {Kostoglou, K and Muller-Putz, GR},
title = {Motor-Related EEG Analysis Using a Pole Tracking Approach.},
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.2024.3483294},
pmid = {39423083},
issn = {1558-0210},
abstract = {This study introduces an alternative approach to electroencephalography (EEG) time-frequency analysis based on time-varying autoregressive (TV-AR) models in a cascade configuration to independently monitor key EEG spectral components. The method is evaluated for its neurophysiological interpretation and effectiveness in motor-related brain-computer interface (BCI) applications. Specifically, we assess the ability of the tracked EEG poles to discriminate between rest, movement execution (ME) and movement imagination (MI) in healthy subjects, as well as movement attempts (MA) in individuals with spinal cord injury (SCI). Our results show that pole tracking effectively captures broad changes in EEG dynamics, such as transitions between rest and movement-related states. It outperformed traditional EEG-based features, increasing detection accuracy for ME by an average of 4.1% (with individual improvements reaching as high as 15%) and MI by an average of 4.5% (up to 13.8%) compared to time-domain low-frequency EEG features. Similarly, compared to alpha/beta band power, the method improved ME detection by an average of 5.9% (up to 10.4%) and MI by an average of 4.3% (up to 10.2%), with results averaged across 15 healthy participants. In one participant with SCI, pole tracking improved MA detection by 12.9% over low-frequency EEG features and 4.8% over alpha/beta band power. However, its ability to distinguish finer movement details within specific movement types was limited. Additionally, the temporal evolution of the extracted pole tracking features revealed event-related desynchronization phenomena, typically observed during ME, MA and MI, as well as increases in frequency, which are of neurophysiological interest.},
}

@article {pmid39421856,
year = {2024},
author = {Klee, D and Memmott, T and Oken, B},
title = {The Effect of Jittered Stimulus Onset Interval on Electrophysiological Markers of Attention in a Brain-Computer Interface Rapid Serial Visual Presentation Paradigm.},
journal = {Signals},
volume = {5},
number = {1},
pages = {18-39},
pmid = {39421856},
issn = {2624-6120},
abstract = {Brain responses to discrete stimuli are modulated when multiple stimuli are presented in sequence. These alterations are especially pronounced when the time course of an evoked response overlaps with responses to subsequent stimuli, such as in a rapid serial visual presentation (RSVP) paradigm used to control a brain-computer interface (BCI). The present study explored whether the measurement or classification of select brain responses during RSVP would improve through application of an established technique for dealing with overlapping stimulus presentations, known as irregular or "jittered" stimulus onset interval (SOI). EEG data were collected from 24 healthy adult participants across multiple rounds of RSVP calibration and copy phrase tasks with varying degrees of SOI jitter. Analyses measured three separate brain signals sensitive to attention: N200, P300, and occipitoparietal alpha attenuation. Presentation jitter visibly reduced intrusion of the SSVEP, but in general, it did not positively or negatively affect attention effects, classification, or system performance. Though it remains unclear whether stimulus overlap is detrimental to BCI performance overall, the present study demonstrates that single-trial classification approaches may be resilient to rhythmic intrusions like SSVEP that appear in the averaged EEG.},
}

@article {pmid39421849,
year = {2024},
author = {Deng, L and Tang, H and Roy, K},
title = {Editorial: Understanding and bridging the gap between neuromorphic computing and machine learning, volume II.},
journal = {Frontiers in computational neuroscience},
volume = {18},
number = {},
pages = {1455530},
pmid = {39421849},
issn = {1662-5188},
}

@article {pmid39421767,
year = {2024},
author = {Wang, Z and Xiao, X and Zhou, Z and Chen, Y and Xia, T and Sheng, X and Han, Y and Gong, W and Si, K},
title = {FLUID: a fluorescence-friendly lipid-compatible ultrafast clearing method.},
journal = {Biomedical optics express},
volume = {15},
number = {10},
pages = {5609-5624},
pmid = {39421767},
issn = {2156-7085},
abstract = {Many clearing methods achieve high transparency by removing lipid components from tissues, which damages microstructure and limits their application in lipid research. As for methods which preserve lipid, it is difficult to balance transparency, fluorescence preservation and clearing speed. In this study, we propose a rapid water-based clearing method that is fluorescence-friendly and preserves lipid components. FLUID allows for preservation of endogenous fluorescence over 60 days. It shows negligible tissue distortion and is compatible with various types of fluorescent labeling and tissue staining methods. High quality imaging of human brain tissue and compatibility with pathological staining demonstrated the potential of our method for three-dimensional (3D) biopsy and clinical pathological diagnosis.},
}

@article {pmid39421626,
year = {2024},
author = {Mokienko, OA},
title = {Brain-Computer Interfaces with Intracortical Implants for Motor and Communication Functions Compensation: Review of Recent Developments.},
journal = {Sovremennye tekhnologii v meditsine},
volume = {16},
number = {1},
pages = {78-89},
pmid = {39421626},
issn = {2309-995X},
mesh = {*Brain-Computer Interfaces ; Humans ; Electrodes, Implanted ; Prostheses and Implants ; },
abstract = {Brain-computer interfaces allow the exchange of data between the brain and an external device, bypassing the muscular system. Clinical studies of invasive brain-computer interface technologies have been conducted for over 20 years. During this time, there has been a continuous improvement of approaches to neuronal signal processing in order to improve the quality of control of external devices. Currently, brain-computer interfaces with intracortical implants allow completely paralyzed patients to control robotic limbs for self-service, use a computer or a tablet, type text, and reproduce speech at an optimal speed. Studies of invasive brain-computer interfaces regularly provide new fundamental data on functioning of the central nervous system. In recent years, breakthrough discoveries and achievements have been annually made in this sphere. This review analyzes the results of clinical experiments of brain-computer interfaces with intracortical implants, provides information on the stages of this technology development, its main discoveries and achievements.},
}

*Brain-Computer Interfaces
Humans
Electrodes, Implanted
Prostheses and Implants

@article {pmid39421603,
year = {2024},
author = {Ye, M and Yang, C and Cheng, JX and Lee, HJ and Jiang, Y and Shi, L},
title = {Editorial: Neuromodulation technology: advances in optics and acoustics.},
journal = {Frontiers in cellular neuroscience},
volume = {18},
number = {},
pages = {1494457},
pmid = {39421603},
issn = {1662-5102},
}

@article {pmid39420525,
year = {2024},
author = {Shang, S and Shi, Y and Zhang, Y and Liu, M and Zhang, H and Wang, P and Zhuang, L},
title = {Artificial intelligence for brain disease diagnosis using electroencephalogram signals.},
journal = {Journal of Zhejiang University. Science. B},
volume = {25},
number = {10},
pages = {914-940},
doi = {10.1631/jzus.B2400103},
pmid = {39420525},
issn = {1862-1783},
support = {2021ZD0200405//the National Key Research and Development Project of China/ ; 62271443, 32250008 and 82330064//the National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Artificial Intelligence ; *Brain Diseases/diagnosis/physiopathology ; *Brain-Computer Interfaces ; Algorithms ; Machine Learning ; Brain/physiology ; Deep Learning ; Signal Processing, Computer-Assisted ; },
abstract = {Brain signals refer to electrical signals or metabolic changes that occur as a consequence of brain cell activity. Among the various non-invasive measurement methods, electroencephalogram (EEG) stands out as a widely employed technique, providing valuable insights into brain patterns. The deviations observed in EEG reading serve as indicators of abnormal brain activity, which is associated with neurological diseases. Brain‒computer interface (BCI) systems enable the direct extraction and transmission of information from the human brain, facilitating interaction with external devices. Notably, the emergence of artificial intelligence (AI) has had a profound impact on the enhancement of precision and accuracy in BCI technology, thereby broadening the scope of research in this field. AI techniques, encompassing machine learning (ML) and deep learning (DL) models, have demonstrated remarkable success in classifying and predicting various brain diseases. This comprehensive review investigates the application of AI in EEG-based brain disease diagnosis, highlighting advancements in AI algorithms.},
}

Humans
*Electroencephalography/methods
*Artificial Intelligence
*Brain Diseases/diagnosis/physiopathology
*Brain-Computer Interfaces
Algorithms
Machine Learning
Brain/physiology
Deep Learning
Signal Processing, Computer-Assisted

@article {pmid39419976,
year = {2024},
author = {Chen, D and Zhao, Z and Shi, J and Li, S and Xu, X and Wu, Z and Tang, Y and Liu, N and Zhou, W and Ni, C and Ma, B and Wang, J and Zhang, J and Huang, L and You, Z and Zhang, P and Tang, Z},
title = {Harnessing the sensing and stimulation function of deep brain-machine interfaces: a new dawn for overcoming substance use disorders.},
journal = {Translational psychiatry},
volume = {14},
number = {1},
pages = {440},
pmid = {39419976},
issn = {2158-3188},
support = {92148206//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82071330//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Substance-Related Disorders/therapy ; *Brain-Computer Interfaces ; *Deep Brain Stimulation/methods ; Brain/physiopathology ; Behavior, Addictive/therapy/physiopathology ; },
abstract = {Substance use disorders (SUDs) imposes profound physical, psychological, and socioeconomic burdens on individuals, families, communities, and society as a whole, but the available treatment options remain limited. Deep brain-machine interfaces (DBMIs) provide an innovative approach by facilitating efficient interactions between external devices and deep brain structures, thereby enabling the meticulous monitoring and precise modulation of neural activity in these regions. This pioneering paradigm holds significant promise for revolutionizing the treatment landscape of addictive disorders. In this review, we carefully examine the potential of closed-loop DBMIs for addressing SUDs, with a specific emphasis on three fundamental aspects: addictive behaviors-related biomarkers, neuromodulation techniques, and control policies. Although direct empirical evidence is still somewhat limited, rapid advancements in cutting-edge technologies such as electrophysiological and neurochemical recordings, deep brain stimulation, optogenetics, microfluidics, and control theory offer fertile ground for exploring the transformative potential of closed-loop DBMIs for ameliorating symptoms and enhancing the overall well-being of individuals struggling with SUDs.},
}

Humans
*Substance-Related Disorders/therapy
*Brain-Computer Interfaces
*Deep Brain Stimulation/methods
Brain/physiopathology
Behavior, Addictive/therapy/physiopathology

@article {pmid39419932,
year = {2024},
author = {Deak, F},
title = {Alzheimer's disease and other memory disorders in the age of AI: reflection and perspectives on the 120th anniversary of the birth of Dr. John von Neumann.},
journal = {GeroScience},
volume = {},
number = {},
pages = {},
pmid = {39419932},
issn = {2509-2723},
support = {NIH NIA R01 AG062655/AG/NIA NIH HHS/United States ; SAGA23-1142437/ALZ/Alzheimer's Association/United States ; },
abstract = {Two themes are coming to the forefront in this decade: Cognitive impairment of an aging population and the quantum leap in developing artificial intelligence (AI). Both can be described as growing exponentially and presenting serious challenges. Although many questions have been addressed about the dangers of AI, we want to go beyond the fearful aspects of this topic and focus on the possible contribution of AI to solve the problem of chronic disorders of the elderly leading to cognitive impairment, like Alzheimer's disease, Parkinson's disease, and Lewy body dementia. Our second goal is to look at the ways in which modern neuroscience can influence the future design of computers and the development of AI. We wish to honor the memory of Dr. John von Neumann, who came up with many breakthrough details of the first electronic computer. Remarkably, Dr. von Neumann dedicated his last book to the comparison of the human brain and the computer as it stood in those years of the mid-1950s. We will point out how his ideas are more relevant than ever in the age of supercomputers, AI and brain implants.},
}

@article {pmid39419108,
year = {2024},
author = {de Seta, V and Colamarino, E and Pichiorri, F and Savina, G and Patarini, F and Riccio, A and Cincotti, F and Mattia, D and Toppi, J},
title = {Brain and Muscle derived features to discriminate simple hand motor tasks for a rehabilitative BCI: comparative study on healthy and post-stroke individuals.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8838},
pmid = {39419108},
issn = {1741-2552},
abstract = {Brain-Computer Interfaces targeting post-stroke recovery of the upper limb employ mainly electroencephalography to decode movement-related brain activation. Recently hybrid systems including muscular activity were introduced. We compared the motor task discrimination abilities of three different features, namely event-related desynchronization/synchronization (ERD/ERS) and movement-related cortical potential (MRCP) as brain-derived features and cortico-muscular coherence (CMC) as a hybrid brain-muscle derived feature, elicited in 13 healthy subjects and 13 stroke patients during the execution/attempt of two simple hand motor tasks (finger extension and grasping) commonly employed in upper limb rehabilitation protocols. Approach. We employed a three-way statistical design to investigate whether their ability to discriminate the two movements follows a specific temporal evolution along the movement execution and is eventually different among the three features and between the two groups. We also investigated the differences in performance at the single-subject level. Main results. The ERD/ERS and the CMC-based classification showed similar temporal evolutions of the performance with a significant increase in accuracy during the execution phase while MRCP-based accuracy peaked at movement onset. Such temporal dynamics were similar but slower in stroke patients when the movements were attempted with the affected hand. Moreover, CMC outperformed the two brain features in healthy subjects and stroke patients when performing the task with their unaffected hand, whereas a higher variability across subjects was observed in patients performing the tasks with their affected hand. Interestingly, brain features performed better in this latter condition with respect to healthy subjects. Significance. Our results provide hints to improve the design of Brain-Computer Interfaces for post-stroke rehabilitation, emphasizing the need for personalized approaches tailored to patients' characteristics and to the intended rehabilitative target.},
}

@article {pmid39419104,
year = {2024},
author = {Afonso, M and Sánchez Cuesta, FJ and González Zamorano, Y and Romero, JP and Vourvopoulos, A},
title = {Investigating the synergistic neuromodulation effect of bilateral rTMS and VR brain-computer interfaces training in chronic stroke patients.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8836},
pmid = {39419104},
issn = {1741-2552},
abstract = {OBJECTIVE: Stroke is a major cause of adult disability worldwide, resulting in motor impairments. To regain motor function, patients undergo rehabilitation, typically involving repetitive movement training. For those who lack volitional movement, novel technology-based approaches have emerged that directly involve the central nervous system, through neuromodulation techniques such as transcranial magnetic stimulation (TMS), and closed-loop neurofeedback like brain-computer interfaces (BCIs). This, can be augmented through proprioceptive feedback delivered many times by embodied virtual reality (VR). Nonetheless, despite a growing body of research demonstrating the individual efficacy of each technique, there is limited information on their combined effects.

APPROACH: In this study, we analyzed the Electroencephalographic (EEG) signals acquired from 10 patients with more than 4 months since stroke during a longitudinal intervention with repetitive TMS followed by VR-BCI training. From the EEG, the Event Related Desynchronization (ERD) and Individual Alpha Frequency (IAF) were extracted, evaluated over time and correlated with clinical outcome.

RESULTS: Every patient's clinical outcome improved after treatment, and ERD magnitude increased during simultaneous rTMS and VR-BCI. Additionally, IAF values showed a significant correlation with clinical outcome, nonetheless, no relationship was found between differences in ERD pre- post- intervention with the clinical improvement.

SIGNIFICANCE: This study furnishes empirical evidence supporting the efficacy of the joint action of rTMS and VR-BCI in enhancing patient recovery. It also suggests a relationship between IAF and rehabilitation outcomes, that could potentially serve as a retrievable biomarker for stroke recovery.},
}

@article {pmid39419091,
year = {2024},
author = {Liu, J and Younk, R and Drahos, LM and Nagrale, SS and Yadav, S and Widge, AS and Shoaran, M},
title = {Neural decoding and feature selection methods for closed-loop control of avoidance behavior.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad8839},
pmid = {39419091},
issn = {1741-2552},
abstract = {Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors. Approach. We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance. Main results. Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring <310 ms for training, <0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU. Significance. Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation. .},
}

@article {pmid39419024,
year = {2024},
author = {Agudelo-Toro, A and Michaels, JA and Sheng, WA and Scherberger, H},
title = {Accurate neural control of a hand prosthesis by posture-related activity in the primate grasping circuit.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2024.09.018},
pmid = {39419024},
issn = {1097-4199},
abstract = {Brain-computer interfaces (BCIs) have the potential to restore hand movement for people with paralysis, but current devices still lack the fine control required to interact with objects of daily living. Following our understanding of cortical activity during arm reaches, hand BCI studies have focused primarily on velocity control. However, mounting evidence suggests that posture, and not velocity, dominates in hand-related areas. To explore whether this signal can causally control a prosthesis, we developed a BCI training paradigm centered on the reproduction of posture transitions. Monkeys trained with this protocol were able to control a multidimensional hand prosthesis with high accuracy, including execution of the very intricate precision grip. Analysis revealed that the posture signal in the target grasping areas was the main contributor to control. We present, for the first time, neural posture control of a multidimensional hand prosthesis, opening the door for future interfaces to leverage this additional information channel.},
}

@article {pmid39416663,
year = {2024},
author = {Tang, Q and Yang, X and Sun, M and He, M and Sa, R and Zhang, K and Zhu, B and Li, T},
title = {Research trends and hotspots of post-stroke upper limb dysfunction: a bibliometric and visualization analysis.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1449729},
pmid = {39416663},
issn = {1664-2295},
abstract = {BACKGROUND: The global prevalence of stroke has been increasing. Motor dysfunction is observed in approximately 55 to 75% of stroke patients, with upper limb impairment affecting around 85% of them. Following upper limb dysfunction, the body's recovery time is not only slower compared to the lower limbs, but the restoration of its fine motor skills is significantly more challenging, greatly impacting the daily lives of patients. Consequently, there is an increasing urgency for study on the upper limb function in stroke.

METHODS: A search was conducted in the Web of Science Core Collection: Science Citation Index Expanded (SCI-Expanded) database for material published from January 1, 2004 to December 31, 2023. We included all relevant literature reports and conducted an analysis of annual publications, countries/regions, institutions, journals, co-cited references, and keywords using the software packages CiteSpace, VOSviewer, and Bibliometrix R. Next, we succinctly outlined the research trends and hotspots in post-stroke upper limb dysfunction.

RESULTS: This analysis comprised 1,938 articles from 1,897 institutions, 354 journals, and 53 countries or regions. A yearly rise in the production of publications was noted. The United States is the foremost nation on the issue. Northwestern University has the most amounts of papers compared to all other institutions. The journal Neurorehabilitation and Neural Repair is a highly significant publication in this field, with Catherine E. Lang serving as the principal author. The majority of the most-cited references focus on subjects such as the reliability and validity of assessment instruments, RCT of therapies, systematic reviews, and meta-analyses. The intervention measures primarily comprise three types of high-frequency phrases that are related, as determined by keyword analysis: intelligent rehabilitation, physical factor therapy, and occupational therapy. Current areas of focus in research include randomized clinical trials, neurorehabilitation, and robot-assisted therapy.

CONCLUSION: Current research has shown a growing interest in studying upper limb function assessment, occupational therapy, physical therapy, robot-assisted therapy, virtual reality, brain-computer interface, telerehabilitation, cortical reorganisation, and neural plasticity. These topics have become popular and are expected to be the focus of future research.},
}

@article {pmid39416032,
year = {2024},
author = {Lee, JY and Lee, S and Mishra, A and Yan, X and McMahan, B and Gaisford, B and Kobashigawa, C and Qu, M and Xie, C and Kao, JC},
title = {Non-invasive brain-machine interface control with artificial intelligence copilots.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.10.09.615886},
pmid = {39416032},
issn = {2692-8205},
abstract = {Motor brain-machine interfaces (BMIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the last two decades, BMIs face key obstacles to clinical viability. Invasive BMIs achieve proficient cursor and robotic arm control but require neurosurgery, posing significant risk to patients. Non-invasive BMIs do not have neurosurgical risk, but achieve lower performance, sometimes being prohibitively frustrating to use and preventing widespread adoption. We take a step toward breaking this performance-risk tradeoff by building performant non-invasive BMIs. The critical limitation that bounds decoder performance in non-invasive BMIs is their poor neural signal-to-noise ratio. To overcome this, we contribute (1) a novel EEG decoding approach and (2) artificial intelligence (AI) copilots that infer task goals and aid action completion. We demonstrate that with this "AI-BMI," in tandem with a new adaptive decoding approach using a convolutional neural network (CNN) and ReFIT-like Kalman filter (KF), healthy users and a paralyzed participant can autonomously and proficiently control computer cursors and robotic arms. Using an AI copilot improves goal acquisition speed by up to 4.3 × in the standard center-out 8 cursor control task and enables users to control a robotic arm to perform the sequential pick-and-place task, moving 4 randomly placed blocks to 4 randomly chosen locations. As AI copilots improve, this approach may result in clinically viable non-invasive AI-BMIs.},
}

@article {pmid39415112,
year = {2024},
author = {Guo, X and Kong, L and Wen, Y and Chen, L and Hu, S},
title = {Impact of second-generation antipsychotics monotherapy or combined therapy in cytokine, lymphocyte subtype, and thyroid antibodies for schizophrenia: a retrospective study.},
journal = {BMC psychiatry},
volume = {24},
number = {1},
pages = {695},
pmid = {39415112},
issn = {1471-244X},
support = {2021C03107//the Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//the Leading Talent of Scientific and Technological Innovation - "Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//the Innovation team for precision diagnosis and treatment of major brain diseases/ ; 226-2022-00193, 226-2022-00002//the Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Schizophrenia/drug therapy/immunology/blood ; Male ; Female ; Retrospective Studies ; Adult ; *Antipsychotic Agents/therapeutic use ; *Cytokines/blood/immunology ; Middle Aged ; *Autoantibodies/blood/immunology ; *Drug Therapy, Combination ; Lymphocyte Subsets/immunology/drug effects ; Iodide Peroxidase/immunology ; },
abstract = {BACKGROUND: Schizophrenia (SCZ) shares high clinical relevance with the immune system, and the potential interactions of psychopharmacological drugs with the immune system are still an overlooked area. Here, we aimed to identify whether the second-generation antipsychotics (SGA) monotherapy or combined therapy of SGA with other psychiatric medications influence the routine blood immunity biomarkers of patients with SCZ.

METHODS: Medical records of inpatients with SCZ from January 2019 to June 2023 were retrospectively screened from June 2023 to August 2023. The demographic data and peripheral levels of cytokines (IL-2, IL-4, IL-6, TNF-α, INF-γ, and IL-17 A), lymphocyte subtype proportions (CD3+, CD4+, CD8 + T-cell, and natural killer (NK) cells), and thyroid autoimmune antibodies (thyroid peroxidase antibody (TPOAb), and antithyroglobulin antibody (TGAb)) were collected and analyzed.

RESULTS: 30 drug-naïve patients, 64 SGA monotherapy (20 for first-episode SCZ, 44 for recurrent SCZ) for at least one week, 39 combined therapies for recurrent SCZ (18 with antidepressant, 10 with benzodiazepine, and 11 with mood stabilizer) for at least two weeks, and 23 used to receive SGA monotherapy (had withdrawn for at least two weeks) were included despite specific medication. No difference in cytokines was found between the SGA monotherapy sub-groups (p > 0.05). Of note, SGA monotherapy appeared to induce a down-regulation of IFN-γ in both first (mean [95% confidence interval]: 1.08 [0.14-2.01] vs. 4.60 [2.11-7.08], p = 0.020) and recurrent (1.88 [0.71-3.05] vs. 4.60 [2.11-7.08], p = 0.027) episodes compared to drug-naïve patients. However, the lymphocyte proportions and thyroid autoimmune antibodies remained unchanged after at least two weeks of SGA monotherapy (p > 0.05). In combined therapy groups, results mainly resembled the SGA monotherapy for recurrent SCZ (p > 0.05).

CONCLUSION: The study demonstrated that SGA monotherapy possibly achieved its comfort role via modulating IFN-γ, and SGA combined therapy showed an overall resemblance to monotherapy.},
}

Humans
*Schizophrenia/drug therapy/immunology/blood
Male
Female
Retrospective Studies
Adult
*Antipsychotic Agents/therapeutic use
*Cytokines/blood/immunology
Middle Aged
*Autoantibodies/blood/immunology
*Drug Therapy, Combination
Lymphocyte Subsets/immunology/drug effects
Iodide Peroxidase/immunology

@article {pmid39413730,
year = {2024},
author = {Wang, J and Chen, ZS},
title = {Closed-loop neural interfaces for pain: Where do we stand?.},
journal = {Cell reports. Medicine},
volume = {5},
number = {10},
pages = {101662},
doi = {10.1016/j.xcrm.2024.101662},
pmid = {39413730},
issn = {2666-3791},
mesh = {Humans ; Animals ; *Chronic Pain/therapy/physiopathology ; Pain Management/methods ; Brain-Computer Interfaces ; },
abstract = {Advances in closed-loop neural interfaces and neuromodulation have offered a potentially effective and non-addictive treatment for chronic pain. These interfaces link neural sensors with device outputs to provide temporally precise stimulation. We discuss challenges and trends of state-of-the-art neural interfaces for treating pain in animal models and human pilot trials.},
}

Humans
Animals
*Chronic Pain/therapy/physiopathology
Pain Management/methods
Brain-Computer Interfaces

@article {pmid39413689,
year = {2024},
author = {Rebouillat, B and Barascud, N and Kouider, S},
title = {Partial awareness during voluntary endogenous decision.},
journal = {Consciousness and cognition},
volume = {125},
number = {},
pages = {103769},
doi = {10.1016/j.concog.2024.103769},
pmid = {39413689},
issn = {1090-2376},
abstract = {Despite our feeling of control over decisions, our ability to consciously access choices before execution remains debated. Recent research reveals prospective access to intention to act, allowing potential vetoes of impending decisions. However, whether the content of impending decision can be accessed remain debated. Here we track neural signals during participants' early deliberation in free decisions. Participants chose freely between two options but sometimes had to reject their current decision just before execution. The initially preferred option, tracked in real time, significantly predicts the upcoming choice, but remain mostly outside of conscious awareness. Participants often display overconfidence in their access to this content. Instead, confidence is associated with a neural marker of self-initiated decision, indicating a qualitative confusion in the confidence evaluation process. Our results challenge the notion of complete agency over choices, suggesting inflated awareness of forthcoming decisions and providing insights into metacognitive processes in free decision-making.},
}

@article {pmid39413360,
year = {2024},
author = {Trott, J and Slaymaker, C and Niznik, G and Althoff, T and Netherton, B},
title = {Brain Computer Interfaces: An Introduction for Clinical Neurodiagnostic Technologists.},
journal = {The Neurodiagnostic journal},
volume = {},
number = {},
pages = {1-14},
doi = {10.1080/21646821.2024.2408501},
pmid = {39413360},
issn = {2164-6821},
abstract = {Brain-computer interface (BCI) is a term used to describe systems that translate biological information into commands that can control external devices such as computers, prosthetics, and other machinery. While BCI is used in military applications, home control systems, and a wide array of entertainment, much of its modern interest and funding can be attributed to its utility in the medical community, where it has rapidly propelled advancements in the restoration or replacement of critical functions robbed from victims of disease, stroke, and traumatic injury. BCI devices can allow patients to move prosthetic limbs, operate devices such as wheelchairs or computers, and communicate through writing and speech-generating devices. In this article, we aim to provide an introductory summary of the historical context and modern growing utility of BCI, with specific interest in igniting the conversation of where and how the neurodiagnostics community and its associated parties can embrace and contribute to the world of BCI.},
}

@article {pmid39412979,
year = {2024},
author = {Zhang, Y and Yu, Y and Li, H and Wu, A and Zeng, LL and Hu, D},
title = {MASER: Enhancing EEG Spatial Resolution with State Space Modeling.},
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.2024.3481886},
pmid = {39412979},
issn = {1558-0210},
abstract = {Consumer-grade Electroencephalography (EEG) devices equipped with few electrodes often suffer from low spatial resolution, hindering the accurate capture of intricate brain activity patterns. To address this issue, we propose MASER, a novel super-resolution approach for EEG recording. In MASER, we design the eMamba block for extracting EEG features based on the principles of state space models (SSMs). We further stack eMamba blocks to form a low-resolution feature extractor and a high-resolution signal predictor, which enhances the feature representation. During the training of MASER, we fully consider the characteristics of multidimensional biological series signals, incorporating a smoothness constraint loss to achieve more consistent high-resolution reconstructions. MASER pioneers EEG-oriented state space modeling, effectively capturing the temporal dynamics and latent states, thereby revealing complex neural interactions over time. Extensive experiments show that the proposed MASER outperforms the state-of-the-art methods in super-resolution quality on two public EEG datasets, with normalized mean square error reduced by 16.25% and Pearson correlation improved by 1.13%. Moreover, a case study of motor imagery recognition highlights the advantages conferred by high-resolution EEG signals. With a 4x increase in spatial resolution by MASER, the recognition accuracy improves by 5.74%, implying a significant performance elevation in brain-computer interface (BCI) command mapping. By enhancing the spatial resolution of EEG signals, MASER makes EEG-based applications more accessible, reducing cost and setup time while maintaining high performance across various domains such as gaming, education, and healthcare.},
}

@article {pmid39409506,
year = {2024},
author = {Xu, H and Haider, W and Aziz, MZ and Sun, Y and Yu, X},
title = {Transforming Motor Imagery Analysis: A Novel EEG Classification Framework Using AtSiftNet Method.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {19},
pages = {},
doi = {10.3390/s24196466},
pmid = {39409506},
issn = {1424-8220},
support = {52172387//National Natural Science Foundation of China/ ; U2033202, U1333119//Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China/ ; ILA22032-1A//Fundamental Research Funds for the Central Universities/ ; 2022Z071052001//Aeronautical Science Foundation of China/ ; 2022JGZ14//Northwestern Polytechnical University/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Principal Component Analysis ; *Machine Learning ; *Imagination/physiology ; Neural Networks, Computer ; Algorithms ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350×18). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946%), Mutual Information (i.e., 98.902%), Independent Component Analysis (i.e., 99.62%), and Principal Component Analysis (i.e., 98.884%) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89%. The experiments' findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain-Computer Interfaces (BCI).},
}

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

@article {pmid39409418,
year = {2024},
author = {Borirakarawin, M and Siribunyaphat, N and Aung, ST and Punsawad, Y},
title = {The Development of a Multicommand Tactile Event-Related Potential-Based Brain-Computer Interface Utilizing a Low-Cost Wearable Vibrotactile Stimulator.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {19},
pages = {},
doi = {10.3390/s24196378},
pmid = {39409418},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods/instrumentation ; Male ; *Wearable Electronic Devices ; *Touch/physiology ; Adult ; Female ; *Vibration ; Evoked Potentials/physiology ; Young Adult ; Event-Related Potentials, P300/physiology ; },
abstract = {A tactile event-related potential (ERP)-based brain-computer interface (BCI) system is an alternative for enhancing the control and communication abilities of quadriplegic patients with visual or auditory impairments. Hence, in this study, we proposed a tactile stimulus pattern using a vibrotactile stimulator for a multicommand BCI system. Additionally, we observed a tactile ERP response to the target from random vibrotactile stimuli placed in the left and right wrist and elbow positions to create commands. An experiment was conducted to explore the location of the proposed vibrotactile stimulus and to verify the multicommand tactile ERP-based BCI system. Using the proposed features and conventional classification methods, we examined the classification efficiency of the four commands created from the selected EEG channels. The results show that the proposed vibrotactile stimulation with 15 stimulus trials produced a prominent ERP response in the Pz channels. The average classification accuracy ranged from 61.9% to 79.8% over 15 stimulus trials, requiring 36 s per command in offline processing. The P300 response in the parietal area yielded the highest average classification accuracy. The proposed method can guide the development of a brain-computer interface system for physically disabled people with visual or auditory impairments to control assistive and rehabilitative devices.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/methods/instrumentation
Male
*Wearable Electronic Devices
*Touch/physiology
Adult
Female
*Vibration
Evoked Potentials/physiology
Young Adult
Event-Related Potentials, P300/physiology

@article {pmid39409405,
year = {2024},
author = {Silvoni, S and Occhigrossi, C and Di Giorgi, M and Lulé, D and Birbaumer, N},
title = {Brain Function, Learning, and Role of Feedback in Complete Paralysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {19},
pages = {},
doi = {10.3390/s24196366},
pmid = {39409405},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Learning/physiology ; *Brain/physiology/physiopathology ; Paralysis/physiopathology/psychology ; Communication ; Feedback ; },
abstract = {The determinants and driving forces of communication abilities in the locked-in state are poorly understood so far. Results from an experimental-clinical study on a completely paralyzed person involved in communication sessions after the implantation of a microelectrode array were retrospectively analyzed. The aim was to focus on the prerequisites and determinants for learning to control a brain-computer interface for communication in paralysis. A comparative examination of the communication results with the current literature was carried out in light of an ideomotor theory of thinking. We speculate that novel skill learning took place and that several aspects of the wording of sentences during the communication sessions reflect preserved cognitive and conscious processing. We also present some speculations on the operant learning procedure used for communication, which argues for the reformulation of the previously postulated hypothesis of the extinction of response planning and goal-directed ideas in the completely locked-in state. We highlight the importance of feedback and reinforcement in the thought-action-consequence associative chain necessary to maintain purposeful communication. Finally, we underline the necessity to consider the psychosocial context of patients and the duration of complete immobilization as determinants of the 'extinction of thinking' theory and to identify the actual barriers preventing communication in these patients.},
}

Humans
*Brain-Computer Interfaces
*Learning/physiology
*Brain/physiology/physiopathology
Paralysis/physiopathology/psychology
Communication
Feedback

@article {pmid39409342,
year = {2024},
author = {Liu, M and Liu, Y and Feleke, AG and Fei, W and Bi, L},
title = {Neural Signature and Decoding of Unmanned Aerial Vehicle Operators in Emergency Scenarios Using Electroencephalography.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {19},
pages = {},
doi = {10.3390/s24196304},
pmid = {39409342},
issn = {1424-8220},
support = {JCKY2022602C024//Basic Research Plan/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Adult ; Evoked Potentials/physiology ; Brain/physiology ; Aircraft ; Young Adult ; Female ; Emergencies ; },
abstract = {Brain-computer interface (BCI) offers a novel means of communication and control for individuals with disabilities and can also enhance the interactions between humans and machines for the broader population. This paper explores the brain neural signatures of unmanned aerial vehicle (UAV) operators in emergencies and develops an operator's electroencephalography (EEG) signals-based detection method for UAV emergencies. We found regularity characteristics similar to classic event-related potential (ERP) components like visual mismatch negativity (vMMN) and contingent negative variation (CNV). Source analysis revealed a sequential activation of the occipital, temporal, and frontal lobes following the onset of emergencies, corresponding to the processing of attention, emotion, and motor intention triggered by visual stimuli. Furthermore, an online detection system was implemented and tested. Experimental results showed that the system achieved an average accuracy of over 88% in detecting emergencies with a detection latency of 431.95 ms from the emergency onset. This work lays a foundation for understanding the brain activities of operators in emergencies and developing an EEG-based detection method for emergencies to assist UAV operations.},
}

Humans
*Electroencephalography/methods
*Brain-Computer Interfaces
Male
Adult
Evoked Potentials/physiology
Brain/physiology
Aircraft
Young Adult
Female
Emergencies

@article {pmid39409201,
year = {2024},
author = {Shokri, R and Koolivand, Y and Shoaei, O and Caviglia, DD and Aiello, O},
title = {A Reconfigurable, Nonlinear, Low-Power, VCO-Based ADC for Neural Recording Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {19},
pages = {},
doi = {10.3390/s24196161},
pmid = {39409201},
issn = {1424-8220},
mesh = {*Analog-Digital Conversion ; Humans ; Signal-To-Noise Ratio ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Brain/physiology ; Neurons/physiology ; Equipment Design ; Nonlinear Dynamics ; },
abstract = {Neural recording systems play a crucial role in comprehending the intricacies of the brain and advancing treatments for neurological disorders. Within these systems, the analog-to-digital converter (ADC) serves as a fundamental component, converting the electrical signals from the brain into digital data that can be further processed and analyzed by computing units. This research introduces a novel nonlinear ADC designed specifically for spike sorting in biomedical applications. Employing MOSFET varactors and voltage-controlled oscillators (VCOs), this ADC exploits the nonlinear capacitance properties of MOSFET varactors, achieving a parabolic quantization function that digitizes the noise with low resolution and the spikes with high resolution, effectively suppressing the background noise present in biomedical signals. This research aims to develop a reconfigurable, nonlinear voltage-controlled oscillator (VCO)-based ADC, specifically designed for implantable neural recording systems used in neuroprosthetics and brain-machine interfaces. The proposed design enhances the signal-to-noise ratio and reduces power consumption, making it more efficient for real-time neural data processing. By improving the performance and energy efficiency of these devices, the research contributes to the development of more reliable medical technologies for monitoring and treating neurological disorders. The quantization step of the ADC spans from 44.8 mV in the low-amplitude range to 1.4 mV in the high-amplitude range. The circuit was designed and simulated utilizing a 180 nm CMOS process; however, no physical prototype has been fabricated at this stage. Post-layout simulations confirm the expected performance. Occupying a silicon area is 0.09 mm[2]. Operating at a sampling frequency of 16 kS/s and a supply voltage of 1 volt, this ADC consumes 62.4 µW.},
}

*Analog-Digital Conversion
Humans
Signal-To-Noise Ratio
Signal Processing, Computer-Assisted
Brain-Computer Interfaces
Brain/physiology
Neurons/physiology
Equipment Design
Nonlinear Dynamics

@article {pmid39405625,
year = {2024},
author = {Kasprzyk-Hordern, B and Jagadeesan, K and Sims, N and Farkas, K and Proctor, K and Bagnall, J and Robertson, M and Jones, DL and Wade, MJ},
title = {Multi-biomarker approach for estimating population size in a national-scale wastewater-based epidemiology study.},
journal = {Water research},
volume = {268},
number = {Pt A},
pages = {122527},
doi = {10.1016/j.watres.2024.122527},
pmid = {39405625},
issn = {1879-2448},
abstract = {This study identifies biochemical markers (BCIs) that can be used as population markers in wastewater-based epidemiology (WBE) and compares their estimates with other established population size estimation (PE) methods, including census data (PECEN). Several groups of BCIs (64 targets: genetic and chemical markers) were investigated in an intercity study, including 10 cities/towns within England equating to a population of ∼7 million people. Several selection criteria were applied to identify the best BCIs to provide robust estimation of population size at a catchment level: (1) excellent performance with analytical methods; (2) excellent fit of the linear regression model which indicates PE-driven BCI daily loads; (3) low temporal variability in usage; (4) human-linked origin. Only a few tested BCIs showed a strong positive linear correlation between daily BCI loads and PE indicating their low spatiotemporal variability. These are: cimetidine, clarithromycin, metformin, cotinine, bezafibrate, metronidazole and hydroxymetronidazole, diclofenac, and benzophenone 1. However, only high/long term usage pharmaceuticals: cimetidine and metformin as well as cotinine (metabolite of nicotine) performed well when tested in two independent datasets and catchments accounting for both spatial and temporal scales. Strong seasonal usage trends were observed for antihistamines, NSAIDs (anti-inflammatories), antibiotics and UV filters, invalidating them as PE markers. Key conclusions from the study are: (1) Cimetidine is the best performing BCI; (2) Chemical markers outperform genetic markers as PE BCIs; (3) Water utility PE estimates (PEWW) align well with PECEN and PEBCI values; (4) Ammonium/orthophosphate as well as viral PE markers suffer from high temporal variability, hence, they are not recommended as PEBCI markers, and, most importantly, (5) PEBCI calibration/validation at the country/region level is advised in order to establish the best PE markers suited for local/national needs and accounting for site/region specific uncertainties.},
}

@article {pmid39402900,
year = {2024},
author = {Zhang, X and Pei, X and Shi, Y and Yang, Y and Bai, X and Chen, T and Zhao, Y and Yang, Q and Ye, J and Leng, X and Yang, Q and Bai, R and Wang, Y and Sui, B},
title = {Unveiling connections between venous disruption and cerebral small vessel disease using diffusion tensor image analysis along perivascular space (DTI-ALPS): A 7T MRI study.},
journal = {International journal of stroke : official journal of the International Stroke Society},
volume = {},
number = {},
pages = {17474930241293966},
doi = {10.1177/17474930241293966},
pmid = {39402900},
issn = {1747-4949},
abstract = {BACKGROUND: Cerebral venous disruption is one of the characteristic findings in cerebral small vessel disease (CSVD), and its disruption may impede perivascular glymphatic drainage. And lower diffusivity along perivascular space (DTI-ALPS) index has been suggested to be with the presence and severity of CSVD. However, the relationships between venous disruption, DTI-ALPS index, and CSVD neuroimaging features remain unclear.

AIMS: To investigate the association between venous integrity and perivascular diffusion activity, and explore the mediating role of DTI-ALPS index between venous disruption and CSVD imaging features.

METHODS: In this cross-sectional study, 31 patients (mean age, 59.0 ± 9.9 years) were prospectively enrolled and underwent 7T MR imaging. DTI-ALPS index was measured to quantify the perivascular diffusivity. The visibility and continuity of deep medullary veins (DMVs) were evaluated based on a brain region-based visual score on high-resolution susceptibility weighted imaging. White matter hyperintensity (WMH) and perivascular space (PVS) were assessed using qualitative and quantitative methods. Linear regression and mediation analysis were performed to analyze the relationships among DMV scores, DTI-ALPS index, and CSVD features.

RESULTS: The DTI-ALPS index was significantly associated with the parietal DMV score [β = -0.573, p-corrected = 0.004]. Parietal DMV score was associated with WMH volume [β = 0.463, p-corrected = 0.013] and PVS volume in basal ganglia (β = 0.415, p-corrected = 0.028]. Mediation analyses showed that DTI-ALPS index manifested a full mediating effect on the association between parietal DMV score and WMH (indirect effect = 0.115, Pm=43.1%), as well as between parietal DMV score and PVS volume in basal ganglia (indirect effect = 0.161, Pm=42.8%).

CONCLUSIONS: Cerebral venous disruption is associated with glymphatic activity, and with WMH and PVS volumes. Our results suggest cerebral venous integrity may play a critical role in preserving perivascular glymphatic activity; while disruption of small veins may impair the perivascular diffusivity, thereby contributing to the development of WMH and PVS enlargement.},
}

@article {pmid39369803,
year = {2024},
author = {Jin, C and Li, Y and Yin, Y and Ma, T and Hong, W and Liu, Y and Li, N and Zhang, X and Gao, JH and Zhang, X and Zha, R},
title = {The dorsomedial prefrontal cortex promotes self-control by inhibiting the egocentric perspective.},
journal = {NeuroImage},
volume = {301},
number = {},
pages = {120879},
doi = {10.1016/j.neuroimage.2024.120879},
pmid = {39369803},
issn = {1095-9572},
abstract = {The dorsomedial prefrontal cortex (dmPFC) plays a crucial role in social cognitive functions, including perspective-taking. Although perspective-taking has been linked to self-control, the mechanism by which the dmPFC might facilitate self-control remains unclear. Using the multimodal neuroimaging dataset from the Human Connectome Project (Study 1, N =978 adults), we established a reliable association between the dmPFC and self-control, as measured by discounting rate-the tendency to prefer smaller, immediate rewards over larger, delayed ones. Experiments (Study 2, N = 36 adults) involving high-definition transcranial direct current stimulation showed that anodal stimulation of the dmPFC reduces the discounting of delayed rewards and decreases the congruency effect in egocentric but not allocentric perspective in the visual perspective-taking tasks. These findings suggest that the dmPFC promotes self-control by inhibiting the egocentric perspective, offering new insights into the neural underpinnings of self-control and perspective-taking, and opening new avenues for interventions targeting disorders characterized by impaired self-regulation.},
}

@article {pmid39401512,
year = {2024},
author = {Eisma, YB and Van Vliet, ST and Nederveen, A and de Winter, J},
title = {Assessing the Influence of Visual Stimulus Properties on Steady-State Visually Evoked Potentials and Pupil Diameter.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ad865d},
pmid = {39401512},
issn = {2057-1976},
abstract = {Steady-State Visual Evoked Potentials (SSVEPs) are brain responses measurable via electroencephalography (EEG) in response to continuous visual stimulation at a constant frequency. SSVEPs have been instrumental in advancing our understanding of human vision and attention, as well as in the development of brain-computer interfaces (BCIs). Ongoing questions remain about which type of visual stimulus causes the most potent SSVEP response. The current study investigated the effects of color, size, and flicker frequency on the signal-to-noise ratio of SSVEPs, complemented by pupillary light reflex measurements obtained through an eye-tracker. Six participants were presented with visual stimuli that differed in terms of color (white, red, green), shape (circles, squares, triangles), size (10,000 to 30,000 pixels), flicker frequency (8 to 25 Hz), and grouping (one stimulus at a time vs. four stimuli presented in a 2×2 matrix to simulate a BCI). The results indicated that larger stimuli elicited stronger SSVEP responses and more pronounced pupil constriction. Additionally, the results revealed an interaction between stimulus color and flicker frequency, with red being more effective at lower frequencies and white at higher frequencies. Future SSVEP research could focus on the recommended waveform, interactions between SSVEP and power grid frequency, a wider range of flicker frequencies, a larger sample of participants, and a systematic comparison of the information transfer obtained through SSVEPs, pupil diameter, and eye movements.},
}

@article {pmid39400422,
year = {2024},
author = {Vírseda-Chamorro, M and Salinas-Casado, J and Adot-Zurbano, JM and Méndez-Rubio, S and Moreno-Sierra, J},
title = {Can We Differentiate Between Organic and Functional Bladder Outlet Obstruction in Males With Parkinson's Disease?.},
journal = {Neurourology and urodynamics},
volume = {},
number = {},
pages = {},
doi = {10.1002/nau.25599},
pmid = {39400422},
issn = {1520-6777},
support = {//The authors received no specific funding for this work./ ; },
abstract = {OBJECTIVES: To determine the type of bladder outlet obstruction (BOO) in patients with Parkinson's disease (PD).

MATERIAL AND METHOD: A case-control study was carried out in 46 patients divided into two groups. Group 1 formed by 23 PD patients with BOO (a URA parameter ≥ 29 cm H2O). Group 2 formed by 23 patients with benign prostatic hyperplasia (BPH) and compressive obstruction (an opening pressure > 35 cm H2O) and URA parameter ≥ 29 cm H2O). Both groups underwent a pressure-flow study to calculate Dynamic Urethral Resistance Relationship (DURR) patterns. Based on previous research, we describe two types of DURR pattern. Pattern A typical of dynamic or functional obstruction and pattern B typical of static or organic obstruction.

RESULTS: We found that PD patients had a significantly higher frequency of pattern A (70%) than BPH patients (4%). Other significant differences between groups were age (greater in PD group), bladder compliance (greater in PD group), maximum flow rate [Qmax (greater in BPH group)], maximum detrusor pressure [Pmax (greater in BPH group)], detrusor pressure at maximum flow rate [PQmax (greater in BPH group)], opening detrusor pressure (greater in BPH group), and the bladder contractility parameters BCI and Wmax (greater in BPH group). There were no significant differences in perineal voiding electromyography (EMG) activity between groups nor relationship between voiding EMG activity and the type of DURR pattern.

CONCLUSIONS: Our results are consistent with the usefulness of the DURR pattern to differentiate between functional and organic BOO in PD patients. Most PD patients have functional obstruction although a minority has organic obstruction consistent with BPH.},
}

@article {pmid39399473,
year = {2024},
author = {Zhang, K and Zhou, W and Yu, H and Pang, M and Gao, H and Anwar, F and Yu, K and Zhou, Z and Guo, F and Liu, X and Ming, D},
title = {Insights on pathophysiology of hydrocephalus rats induced by kaolin injection.},
journal = {FASEB bioAdvances},
volume = {6},
number = {9},
pages = {351-364},
pmid = {39399473},
issn = {2573-9832},
abstract = {Hydrocephalus can affect brain function and motor ability. Current treatments mostly involve invasive surgeries, with a high risk of postoperative infections and failure. A successful animal model plays a significant role in developing new treatments for hydrocephalus. Hydrocephalus was induced in Sprague-Dawley rats by injecting 25% kaolin into the subarachnoid space at the cerebral convexities with different volumes of 30, 60 and 90 μL. Magnetic resonance imaging (MRI) was performed 1 month and 4 months after kaolin injection. The behavioral performance was assessed weekly, lasting for 7 weeks. The histopathological analyses were conducted to the lateral ventricles by hematoxylin-eosin (HE) staining. Transcriptomic analysis was used between Normal Pressure Hydrocephalus (NPH) patients and hydrocephalus rats. MRI showed a progressive enlargement of ventricles in hydrocephalus group. Kaolin-60 μL and kaolin-90 μL groups showed larger ventricular size, higher anxiety level, bigger decline in body weight, motor ability and cognitive competence. These symptoms may be due to higher-grade inflammatory infiltrate and the damage of the structure of ependymal layer of the ventricles, indicated by HE staining. The overlap upregulated genes and pathways mainly involve immunity and inflammation. Transcriptomic revealed shared pathogenic genes CD40, CD44, CXCL10, and ICAM1 playing a dominance role. 60 μL injection might be recommended for the establishment of hydrocephalus animal model, with a high successful rate and high stability. The hydrocephalus model was able to resemble the inflammatory mechanism and behavioral performance observed in human NPH patients, providing insights for identifying therapeutic targets for hydrocephalus.},
}

@article {pmid39398473,
year = {2024},
author = {Hanada, GM and Kalabic, M and Ferris, DP},
title = {Mobile brain-body imaging data set of indoor treadmill walking and outdoor walking with a visual search task.},
journal = {Data in brief},
volume = {57},
number = {},
pages = {110968},
doi = {10.1016/j.dib.2024.110968},
pmid = {39398473},
issn = {2352-3409},
abstract = {To fully understand brain processes in the real world, it is necessary to record and quantitatively analyse brain processes during real world human experiences. Mobile electroencephalography (EEG) and physiological data sensors provide new opportunities for studying humans outside of the laboratory. The purpose of this study was to document data from high-density EEG and mobile physiological sensors while humans performed a visual search task both on a treadmill in a laboratory setting and overground in a natural outdoor setting. The data set includes 49 young, healthy participants on an outdoor arboretum path and on a treadmill in a laboratory with a large virtual reality screen. The data provide a valuable research tool for scientists interested in signal processing, electrocortical brain processes, mobile brain imaging, and brain-computer interfaces based on mobile EEG. Given the comparison data between laboratory and real world conditions, researchers can test the viability of new processing algorithms across conditions or investigate changes in electrocortical activity related to behavioural dynamics coded into the data.},
}

@article {pmid39397592,
year = {2024},
author = {Deepika, D and Rekha, G},
title = {A hybrid capsule attention-based convolutional bi-GRU method for multi-class mental task classification based brain-computer Interface.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-17},
doi = {10.1080/10255842.2024.2410221},
pmid = {39397592},
issn = {1476-8259},
abstract = {Electroencephalography analysis is critical for brain computer interface research. The primary goal of brain-computer interface is to establish communication between impaired people and others via brain signals. The classification of multi-level mental activities using the brain-computer interface has recently become more difficult, which affects the accuracy of the classification. However, several deep learning-based techniques have attempted to identify mental tasks using multidimensional data. The hybrid capsule attention-based convolutional bidirectional gated recurrent unit model was introduced in this study as a hybrid deep learning technique for multi-class mental task categorization. Initially, the obtained electroencephalography data is pre-processed with a digital low-pass Butterworth filter and a discrete wavelet transform to remove disturbances. The spectrally adaptive common spatial pattern is used to extract characteristics from pre-processed electroencephalography data. The retrieved features were then loaded into the suggested classification model, which was used to extract the features deeply and classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using a dung beetle optimization approach. Finally, the proposed classifier is assessed for several types of mental task classification using the provided dataset. The simulation results are compared with the existing state-of-the-art techniques in terms of accuracy, precision, recall, etc. The accuracy obtained using the proposed approach is 97.87%, which is higher than that of the other existing methods.},
}

@article {pmid39397321,
year = {2024},
author = {Gokhale, SM and Bhatia, M},
title = {Lymphoscintigraphy With SPECT-CT in Detecting the Site of Chyle Leak in Postoperative Patient.},
journal = {Clinical nuclear medicine},
volume = {},
number = {},
pages = {},
doi = {10.1097/RLU.0000000000005496},
pmid = {39397321},
issn = {1536-0229},
abstract = {Here is a case of chyle leak post McKeown esophagectomy. Lymphoscintigraphy with 99mTc-filtered sulfur colloid revealed tracer accumulation along the thoracic duct and in the left hemithorax. Precise localization of leak was done by SPECT-CT imaging. This enabled timely surgical intervention and reduced further morbidity. This procedure is not only precise but also cost-effective as compared with the other available investigations.},
}

@article {pmid39397043,
year = {2024},
author = {Shi, C and Jiang, J and Li, C and Chen, C and Jian, W and Song, J},
title = {Precision-induced localized molten liquid metal stamps for damage-free transfer printing of ultrathin membranes and 3D objects.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {8839},
pmid = {39397043},
issn = {2041-1723},
support = {U21A20502//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12225209//National Natural Science Foundation of China (National Science Foundation of China)/ ; U20A6001//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12321002//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12302214//National Natural Science Foundation of China (National Science Foundation of China)/ ; LQ23A020006//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {Transfer printing, a crucial technique for heterogeneous integration, has gained attention for enabling unconventional layouts and high-performance electronic systems. Elastomer stamps are typically used for transfer printing, where localized heating for elastomer stamp can effectively control the transfer process. A key challenge is the potential damage to ultrathin membranes from the contact force of elastic stamps, especially with fragile inorganic nanomembranes. Herein, we present a precision-induced localized molten technique that employs either laser-induced transient heating or hotplate-induced directional heating to precisely melt solid gallium (Ga). By leveraging the fluidity of localized molten Ga, which provides gentle contact force and exceptional conformal adaptability, this technique avoids damage to fragile thin films and improves operational reliability compared to fully liquefied Ga stamps. Furthermore, the phase transition of Ga provides a reversible adhesion with high adhesion switchability. Once solidified, the Ga stamp hardens and securely adheres to the micro/nano-membrane during the pick-up process. The solidified stamp also exhibits the capability to maneuver arbitrarily shaped objects by generating a substantial grip force through the interlocking effects. Such a robust, damage-free, simply operable protocol illustrates its promising capabilities in transfer printing diverse ultrathin membranes and objects on complex surfaces for developing high-performance unconventional electronics.},
}

@article {pmid39396768,
year = {2024},
author = {Zhang, J and Wang, L and Guo, H and Kong, S and Li, W and He, Q and Ding, L and Yang, B},
title = {The role of Tim-3 blockade in the tumor immune microenvironment beyond T cells.},
journal = {Pharmacological research},
volume = {},
number = {},
pages = {107458},
doi = {10.1016/j.phrs.2024.107458},
pmid = {39396768},
issn = {1096-1186},
abstract = {Numerous preclinical studies have demonstrated the inhibitory function of T cell immunoglobulin mucin domain-containing protein 3 (Tim-3) on T cells as an inhibitory receptor, leading to the clinical development of anti-Tim-3 blocking antibodies. However, recent studies have shown that Tim-3 is expressed not only on T cells but also on multiple cell types in the tumor microenvironment (TME), including dendritic cells (DCs), natural killer (NK) cells, macrophages, and tumor cells. Therefore, Tim-3 blockade in the immune microenvironment not only affect the function of T cells but also influence the functions of other cells. For example, Tim-3 blockade can enhance the ability of DCs to regulate innate and adaptive immunity. The role of Tim-3 blockade in NK cells function is controversial, as it can enhance the antitumor function of NK cells under certain conditions while having the opposite effect in other situations. Additionally, Tim-3 blockade can promote the reversal of macrophage polarization from the M2 phenotype to the M1 phenotype. Furthermore, Tim-3 blockade can inhibit tumor development by suppressing the proliferation and metastasis of tumor cells. In summary, increasing evidence has shown that Tim-3 in other cell types also plays a critical role in the efficacy of anti-Tim-3 therapy. Understanding the function of anti-Tim-3 therapy in non-T cells can help elucidate the diverse responses observed in clinical patients, leading to better development of relevant therapeutic strategies. This review aims to discuss the role of Tim-3 in the TME and emphasize the impact of Tim-3 blockade in the tumor immune microenvironment beyond T cells.},
}

@article {pmid39396567,
year = {2024},
author = {Su, H and Zhan, G and Lin, Y and Wang, L and Jia, J and Zhang, L and Gan, Z and Kang, X},
title = {Analysis of brain network differences in the active, motor imagery, and passive stoke rehabilitation paradigms based on the task-state EEG.},
journal = {Brain research},
volume = {},
number = {},
pages = {149261},
doi = {10.1016/j.brainres.2024.149261},
pmid = {39396567},
issn = {1872-6240},
abstract = {Different movement paradigms have varying effects on stroke rehabilitation, and their mechanisms of action on the brain are not fully understood. This study aims to investigate disparities in brain network and functional connectivity of three movement paradigms (active, motor imagery, passive) on stroke recovery. EEG signals were recorded from 11 S patients (SP) and 13 healthy controls (HC) during fist clenching and opening tasks under the three paradigms. Brain networks were constructed to analyze alterations in brain network connectivity, node strength (NS), clustering coefficients (CC), characteristic path length (CPL), and small-world index(S). Our findings revealed increased activity in the contralateral motor area in SP and higher activity in the ipsilateral motor area in HC. In the beta band, SP exhibited significantly higher CC in motor imagery (MI) than in active and passive tasks. Furthermore, the small world index of SP during MI tasks in the beta band was significantly smaller than in the active and passive tasks. NS in the gamma band for SP during the MI paradigm was significantly higher than in the active and passive paradigms. These findings suggest reorganization within both ipsilateral and contralateral motor areas of stroke patients during MI tasks, providing evidence for neural restructuring. Collectively, these findings contribute to a deeper understanding of task-state brain network changes and the rehabilitative mechanism of MI on motor function.},
}

@article {pmid39387251,
year = {2024},
author = {Zhang, W and Bai, L and Xu, W and Liu, J and Chen, Y and Lin, W and Lu, H and Wang, B and Luo, B and Peng, G and Zhang, K and Shen, C},
title = {Sirt6 Mono-ADP-Ribosylates YY1 to Promote Dystrophin Expression for Neuromuscular Transmission.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2406390},
doi = {10.1002/advs.202406390},
pmid = {39387251},
issn = {2198-3844},
support = {LZ22C110002//Natural Science Foundation of Zhejiang Province/ ; 2021YFA1101100//National Key R&D Program of China/ ; 2022YFF1000500//National Key R&D Program of China/ ; 32271031//National Natural Science Foundation of China/ ; 82230038//National Natural Science Foundation of China/ ; 31871203//National Natural Science Foundation of China/ ; 32071032//National Natural Science Foundation of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; },
abstract = {The degeneration of the neuromuscular junction (NMJ) and the decline in motor function are common features of aging, but the underlying mechanisms have remained largely unclear. This study reveals that Sirt6 is reduced in aged mouse muscles. Ablation of Sirt6 in skeletal muscle causes a reduction of Dystrophin levels, resulting in premature NMJ degeneration, compromised neuromuscular transmission, and a deterioration in motor performance. Mechanistic studies show that Sirt6 negatively regulates the stability of the Dystrophin repressor YY1 (Yin Yang 1). Specifically, Sirt6 mono-ADP-ribosylates YY1, causing its disassociation from the Dystrophin promoter and allowing YY1 to bind to the SMURF2 E3 ligase, leading to its degradation. Importantly, supplementation with nicotinamide mononucleotide (NMN) enhances the mono-ADP-ribosylation of YY1 and effectively delays NMJ degeneration and the decline in motor function in elderly mice. These findings provide valuable insights into the intricate mechanisms underlying NMJ degeneration during aging. Targeting Sirt6 could be a potential therapeutic approach to mitigate the detrimental effects on NMJ degeneration and improve motor function in the elderly population.},
}

@article {pmid39396023,
year = {2024},
author = {Zhou, H and Hong, T and Chen, X and Su, C and Teng, B and Xi, W and Cadet, JL and Yang, Y and Geng, F and Hu, Y},
title = {Glutamate concentration of medial prefrontal cortex is inversely associated with addictive behaviors: a translational study.},
journal = {Translational psychiatry},
volume = {14},
number = {1},
pages = {433},
pmid = {39396023},
issn = {2158-3188},
support = {81971245//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {*Glutamic Acid/metabolism ; *Prefrontal Cortex/metabolism/physiopathology ; Male ; Animals ; Rats ; Humans ; *Internet Addiction Disorder/physiopathology/metabolism ; *Behavior, Addictive/physiopathology/metabolism ; *Drug-Seeking Behavior/physiology ; Adult ; *Methamphetamine ; Young Adult ; Gyrus Cinguli/metabolism/physiopathology ; Female ; Disease Models, Animal ; Rats, Sprague-Dawley ; Translational Research, Biomedical ; Self Administration ; },
abstract = {In both preclinical and clinical settings, dysregulated frontostriatal circuits have been identified as the underlying neural substrates of compulsive seeking/taking behaviors manifested in substance use disorders and behavioral addictions including internet gaming disorder (IGD). However, the neurochemical substrates for these disorders remain elusive. The lack of comprehensive cognitive assessments in animal models has hampered our understanding of neural plasticity in addiction from these models. In this study, combining data from a rat model of compulsive taking/seeking and human participants with various levels of IGD severity, we investigated the relationship between regional glutamate (Glu) concentration and addictive behaviors. We found that Glu levels were significantly lower in the prelimbic cortex (PrL) of rats after 20-days of methamphetamine self-administration (SA), compared to controls. Glu concentration after a punishment phase negatively correlated with acute drug-seeking behavior. In addition, changes in Glu levels from a drug naïve state to compulsive drug taking patterns negatively correlated with drug-seeking during both acute and prolonged abstinence. The human data revealed a significant negative correlation between Glu concentration in the dorsal anterior cingulate cortex (dACC), the human PrL counterpart, and symptoms of IGD. Interestingly, there was a positive correlation between Glu levels in the dACC and self-control, as well as mindful awareness. Further analysis revealed that the dACC Glu concentration mediated the relationship between self-control/mindful awareness and IGD symptoms. These results provide convergent evidence for a protective role of dACC/PrL in addiction, suggesting interventions to enhance dACC glutamatergic functions as a potential strategy for addiction prevention and treatment.},
}

*Glutamic Acid/metabolism
*Prefrontal Cortex/metabolism/physiopathology
Male
Animals
Rats
Humans
*Internet Addiction Disorder/physiopathology/metabolism
*Behavior, Addictive/physiopathology/metabolism
*Drug-Seeking Behavior/physiology
Adult
*Methamphetamine
Young Adult
Gyrus Cinguli/metabolism/physiopathology
Female
Disease Models, Animal
Rats, Sprague-Dawley
Translational Research, Biomedical
Self Administration

@article {pmid39395910,
year = {2024},
author = {Fan, J and Wang, X and Xu, H},
title = {Sex-Differential Neural Circuits and Behavioral Responses for Empathy.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {39395910},
issn = {1995-8218},
}

@article {pmid39394849,
year = {2024},
author = {Li, Z and Meng, M},
title = {An SCA-based classifier for motor imagery EEG classification.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/10255842.2024.2414069},
pmid = {39394849},
issn = {1476-8259},
abstract = {Efficient and accurate multi-class classification of electroencephalogram (EEG) signals poses a significant challenge in the development of motor imagery-based brain-computer interface (MI-BCI). Drawing inspiration from the sine cosine algorithm (SCA), a widely employed swarm intelligence algorithm for optimization problems, we proposed a novel population-based classification algorithm for EEG signals in this article. To fully leverage the characteristics contained in EEG signals, multi-scale sub-signals were constructed in terms of temporal windows and spectral bands simultaneously, and the common spatial pattern (CSP) features were extracted from each sub-signal. Subsequently, we integrated the multi-center optimal vectors mechanism into the classical SCA, resulting in the development of a multi-center SCA (MCSCA) classifier. During the classification stage, the label was assigned to the test trials by evaluating the Euclidean distance between their feature vectors and each optimal vector in MCSCA. Additionally, the weights of feature vectors were exploited to select the sub-signal of specific temporal windows and spectral bands for feature reduction, thereby declining computational effort and eliminating data redundancy. To validate the performance of the MCSCA classifier, we conducted four-class classification experiments using the BCI Competition IV dataset 2a, achieving an average classification accuracy of 71.89%. The experimental results show that the proposed algorithm offers a novel and effective approach for EEG classification.},
}

@article {pmid39391754,
year = {2024},
author = {Salari, V and O'Connor, R and Rodrigues, S and Oblak, D},
title = {Editorial: New approaches in Brain-Machine Interfaces with implants.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1485472},
pmid = {39391754},
issn = {1662-4548},
}

@article {pmid39391265,
year = {2024},
author = {Ren, C and Li, X and Gao, Q and Pan, M and Wang, J and Yang, F and Duan, Z and Guo, P and Zhang, Y},
title = {The effect of brain-computer interface controlled functional electrical stimulation training on rehabilitation of upper limb after stroke: a systematic review and meta-analysis.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1438095},
pmid = {39391265},
issn = {1662-5161},
abstract = {INTRODUCTION: Several clinical studies have demonstrated that brain-computer interfaces (BCIs) controlled functional electrical stimulation (FES) facilitate neurological recovery in patients with stroke. This review aims to evaluate the effectiveness of BCI-FES training on upper limb functional recovery in stroke patients.

METHODS: PubMed, Embase, Cochrane Library, Science Direct and Web of Science were systematically searched from inception to October 2023. Randomized controlled trials (RCTs) employing BCI-FES training were included. The methodological quality of the RCTs was assessed using the PEDro scale. Meta-analysis was conducted using RevMan 5.4.1 and STATA 18.

RESULTS: The meta-analysis comprised 290 patients from 10 RCTs. Results showed a moderate effect size in upper limb function recovery through BCI-FES training (SMD = 0.50, 95% CI: 0.26-0.73, I[2] = 0%, p < 0.0001). Subgroup analysis revealed that BCI-FES training significantly enhanced upper limb motor function in BCI-FES vs. FES group (SMD = 0.37, 95% CI: 0.00-0.74, I[2] = 21%, p = 0.05), and the BCI-FES + CR vs. CR group (SMD = 0.61, 95% CI: 0.28-0.95, I[2] = 0%, p = 0.0003). Moreover, BCI-FES training demonstrated effectiveness in both subacute (SMD = 0.56, 95% CI: 0.25-0.87, I[2] = 0%, p = 0.0004) and chronic groups (SMD = 0.42, 95% CI: 0.05-0.78, I[2] = 45%, p = 0.02). Subgroup analysis showed that both adjusting (SMD = 0.55, 95% CI: 0.24-0.87, I[2] = 0%, p = 0.0006) and fixing (SMD = 0.43, 95% CI: 0.07-0.78, I[2] = 46%, p = 0.02). BCI thresholds before training significantly improved motor function in stroke patients. Both motor imagery (MI) (SMD = 0.41 95% CI: 0.12-0.71, I[2] = 13%, p = 0.006) and action observation (AO) (SMD = 0.73, 95% CI: 0.26-1.20, I[2] = 0%, p = 0.002) as mental tasks significantly improved upper limb function in stroke patients.

DISCUSSION: BCI-FES has significant immediate effects on upper limb function in subacute and chronic stroke patients, but evidence for its long-term impact remains limited. Using AO as the mental task may be a more effective BCI-FES training strategy.

Identifier: CRD42023485744, https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023485744.},
}

@article {pmid39391047,
year = {2024},
author = {Jang, M and Hays, M and Yu, WH and Lee, C and Caragiulo, P and Ramkaj, A and Wang, P and Phillips, AJ and Vitale, N and Tandon, P and Yan, P and Mak, PI and Chae, Y and Chichilnisky, EJ and Murmann, B and Muratore, DG},
title = {A 1024-Channel 268 nW/pixel 36×36 μm[2]/channel Data-Compressive Neural Recording IC for High-Bandwidth Brain-Computer Interfaces.},
journal = {IEEE journal of solid-state circuits},
volume = {59},
number = {4},
pages = {1123-1136},
pmid = {39391047},
issn = {0018-9200},
abstract = {This paper presents a data-compressive neural recording IC for single-cell resolution high-bandwidth brain-computer interfaces. The IC features wired-OR lossy compression during digitization, thus preventing data deluge and massive data movement. By discarding unwanted baseline samples of the neural signals, the output data rate is reduced by 146× on average while allowing the reconstruction of spike samples. The recording array consists of pulse position modulation-based active digital pixels with a global single-slope analog-to-digital conversion scheme, which enables a low-power and compact pixel design with significantly simple routing and low array readout energy. Fabricated in a 28-nm CMOS process, the neural recording IC features 1024 channels (i.e., 32 × 32 array) with a pixel pitch of 36 μm that can be directly matched to a high-density microelectrode array. The pixel achieves 7.4 μVrms input-referred noise with a -3 dB bandwidth of 300-Hz to 5-kHz while consuming only 268 nW from a single 1-V supply. The IC achieves the smallest area per channel (36 × 36 μm[2]) and the highest energy efficiency among the state-of-the-art neural recording ICs published to date.},
}

@article {pmid39389052,
year = {2024},
author = {Zheng, Z and Liu, Y and Mu, R and Guo, X and Feng, Y and Guo, C and Yang, L and Qiu, W and Zhang, Q and Yang, W and Dong, Z and Qiu, S and Dong, Y and Cui, Y},
title = {A small population of stress-responsive neurons in the hypothalamus-habenula circuit mediates development of depression-like behavior in mice.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2024.09.012},
pmid = {39389052},
issn = {1097-4199},
abstract = {Accumulating evidence has shown that various brain functions are associated with experience-activated neuronal ensembles. However, whether such neuronal ensembles are engaged in the pathogenesis of stress-induced depression remains elusive. Utilizing activity-dependent viral strategies in mice, we identified a small population of stress-responsive neurons, primarily located in the middle part of the lateral hypothalamus (mLH) and the medial part of the lateral habenula (LHbM). These neurons serve as "starter cells" to transmit stress-related information and mediate the development of depression-like behaviors during chronic stress. Starter cells in the mLH and LHbM form dominant connections, which are selectively potentiated by chronic stress. Silencing these connections during chronic stress prevents the development of depression-like behaviors, whereas activating these connections directly elicits depression-like behaviors without stress experience. Collectively, our findings dissect a core functional unit within the LH-LHb circuit that mediates the development of depression-like behaviors in mice.},
}

@article {pmid39386879,
year = {2024},
author = {Huang, Y and Yang, L and Yang, L and Xu, Z and Li, M and Shang, Z},
title = {Microstimulation-based path tracking control of pigeon robots through parameter adaptive strategy.},
journal = {Heliyon},
volume = {10},
number = {19},
pages = {e38113},
pmid = {39386879},
issn = {2405-8440},
abstract = {Research on animal robots utilizing neural electrical stimulation is a significant focus within the field of neuro-control, though precise behavior control remains challenging. This study proposes a parameter-adaptive strategy to achieve accurate path tracking. First, the mapping relationship between neural electrical stimulation parameters and corresponding behavioral responses is comprehensively quantified. Next, adjustment rules related to the parameter-adaptive control strategy are established to dynamically generate different stimulation patterns. A parameter-adaptive path tracking control strategy (PAPTCS), based on fuzzy control principles, is designed for the precise path tracking tasks of pigeon robots in open environments. The results indicate that altering stimulation parameter levels significantly affects turning angles, with higher UPN and PTN inducing changes in the pigeons' motion state. In experimental scenarios, the average control efficiency of this system was 82.165%. This study provides a reference method for the precise control of pigeon robot behavior, contributing to research on accurate target path tracking.},
}

@article {pmid39385316,
year = {2024},
author = {Wang, D and Guo, X and Huang, Q and Wang, Z and Chen, J and Hu, S},
title = {Efficacy and Safety of Transcranial Direct Current Stimulation as an Add-On Trial Treatment for Acute Bipolar Depression Patients With Suicidal Ideation.},
journal = {CNS neuroscience & therapeutics},
volume = {30},
number = {10},
pages = {e70077},
doi = {10.1111/cns.70077},
pmid = {39385316},
issn = {1755-5949},
support = {81971271//National Natural Science Foundation of China/ ; 82201675//National Natural Science Foundation of China/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//Innovation Team for Precision Diagnosis and Treatment of Major Brain Diseases/ ; LQ20H090012//Natural Science Foundation of Zhejiang Province/ ; 2020KY134//Health and Family Planning Commission of Zhejiang Province/ ; },
mesh = {Humans ; *Bipolar Disorder/therapy/psychology ; Male ; Female ; *Transcranial Direct Current Stimulation/methods ; Adult ; Double-Blind Method ; *Suicidal Ideation ; Middle Aged ; Treatment Outcome ; Psychiatric Status Rating Scales ; Quetiapine Fumarate/therapeutic use ; Young Adult ; Antipsychotic Agents/therapeutic use/adverse effects ; Combined Modality Therapy/methods ; },
abstract = {AIMS: Bipolar depression poses an overwhelming suicide risk. We aimed to examine the efficacy and safety of transcranial direct current stimulation (tDCS) combined with quetiapine in bipolar patients as a suicidal intervention.

METHODS: In a single-center, double-blind, treatment-naive bipolar depression patients with suicidal ideation were randomly assigned to quetiapine in combination with either active (n = 16) or sham (n = 15) tDCS over the left dorsolateral prefrontal cortex for three consecutive weeks. The 30-min, 2-mA tDCS was conducted twice a day on the weekday of the first week and then once a day on the weekdays of the two following weeks. Primary efficacy outcome measure was the change in the Beck Scale for Suicidal Ideation (BSSI). Secondary outcomes included changes on the 17-item Hamilton Depression Rating Scale (HDRS-17) and Montgomery-Asberg Depression Rating Scale (MADRS). Outcome was evaluated on Day 3 and weekend. Safety outcome was based on the reported adverse reactions.

RESULTS: Active tDCS was superior to sham tDCS on the BSSI at Day 3 and tended to sustain every weekend during the treatment process, compared to baseline. However, no difference between active and sham in HDRS-17 and MADRS was found. Response and remission rate also supported the antisuicide effect of tDCS, with higher response and remission rate in BSSI, but no antidepressant effect, compared to sham, over time. Regarding safety, active tDCS was well tolerated and all the adverse reactions reported were mild and limited to transient scalp discomfort.

CONCLUSION: The tDCS was effective as an antisuicide treatment for acute bipolar depression patients with suicidal ideation, with minimal side effects reported.},
}

Humans
*Bipolar Disorder/therapy/psychology
Male
Female
*Transcranial Direct Current Stimulation/methods
Adult
Double-Blind Method
*Suicidal Ideation
Middle Aged
Treatment Outcome
Psychiatric Status Rating Scales
Quetiapine Fumarate/therapeutic use
Young Adult
Antipsychotic Agents/therapeutic use/adverse effects
Combined Modality Therapy/methods

@article {pmid39368606,
year = {2024},
author = {Takemi, M and Tia, B and Kosugi, A and Castagnola, E and Ansaldo, A and Ricci, D and Fadiga, L and Ushiba, J and Iriki, A},
title = {Posture-dependent modulation of marmoset cortical motor maps detected via rapid multichannel epidural stimulation.},
journal = {Neuroscience},
volume = {560},
number = {},
pages = {263-271},
doi = {10.1016/j.neuroscience.2024.09.047},
pmid = {39368606},
issn = {1873-7544},
abstract = {Recent neuroimaging and electrophysiological studies have suggested substantial short-term plasticity in the topographic maps of the primary motor cortex (M1). However, previous methods lack the temporal resolution to detect rapid modulation of these maps, particularly in naturalistic conditions. To address this limitation, we previously developed a rapid stimulation mapping procedure with implanted cortical surface electrodes. In this study, employing our previously established procedure, we examined rapid topographical changes in forelimb M1 motor maps in three awake male marmoset monkeys. The results revealed that although the hotspot (the location in M1 that elicited a forelimb muscle twitch with the lowest stimulus intensity) remained constant across postures, the stimulus intensity required to elicit the forelimb muscle twitch in the perihotspot region and the size of motor representations were posture-dependent. Hindlimb posture was particularly effective in inducing these modulations. The angle of the body axis relative to the gravitational vertical line did not alter the motor maps. These results provide a proof of concept that a rapid stimulation mapping system with chronically implanted cortical electrodes can capture the dynamic regulation of forelimb motor maps in natural conditions. Moreover, they suggest that posture is a crucial variable to be controlled in future studies of motor control and cortical plasticity. Further exploration is warranted into the neural mechanisms regulating forelimb muscle representations in M1 by the hindlimb sensorimotor state.},
}

@article {pmid39384601,
year = {2024},
author = {Li, D and Li, K and Xia, Y and Dong, J and Lu, R},
title = {Joint hybrid recursive feature elimination based channel selection and ResGCN for cross session MI recognition.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {23549},
pmid = {39384601},
issn = {2045-2322},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; *Neural Networks, Computer ; Imagination ; Brain/physiology ; },
abstract = {In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively.},
}

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

@article {pmid39383883,
year = {2024},
author = {Ottenhoff, MC and Verwoert, M and Goulis, S and Wagner, L and van Dijk, JP and Kubben, PL and Herff, C},
title = {Global motor dynamics - invariant neural representations of motor behavior in distributed brain-wide recordings.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad851c},
pmid = {39383883},
issn = {1741-2552},
abstract = {OBJECTIVE: Motor-related neural activity is more widespread than previously thought, as pervasive brain-wide neural correlates of motor behavior have been reported in various animal species. Brain-wide movement-related neural activity have been observed in individual brain areas in humans as well, but it is unknown to what extent global patterns exist.

APPROACH: Here, we use a decoding approach to capture and characterize brain-wide neural correlates of movement. We recorded invasive electrophysiological data from stereotactic electroencephalographic electrodes implanted in eight epilepsy patients who performed both an executed and imagined grasping task. Combined, these electrodes cover the whole brain, including deeper structures such as the hippocampus, insula and basal ganglia. We extract a low-dimensional representation and classify movement from rest trials using a Riemannian decoder.

MAIN RESULTS: We reveal global neural dynamics that are predictive across tasks and participants. Using an ablation analysis, we demonstrate that these dynamics remain remarkably stable under loss of information. Similarly, the dynamics remain stable across participants, as we were able to predict movement across participants using transfer learning.

SIGNIFICANCE: Our results show that decodable global motor-related neural dynamics exist within a low-dimensional space. The dynamics are predictive of movement, nearly brain-wide and present in all our participants. The results broaden the scope to brain-wide investigations, and may allow combining datasets of multiple participants with varying electrode locations or calibrationless neural decoder.},
}

@article {pmid39383715,
year = {2024},
author = {Li, K and Qian, L and Zhang, C and Li, R and Zeng, J and Xue, C and Deng, W},
title = {Deep transcranial magnetic stimulation for treatment-resistant obsessive-compulsive disorder: A meta-analysis of randomized-controlled trials.},
journal = {Journal of psychiatric research},
volume = {180},
number = {},
pages = {96-102},
doi = {10.1016/j.jpsychires.2024.09.043},
pmid = {39383715},
issn = {1879-1379},
abstract = {BACKGROUND: Deep transcranial magnetic stimulation (dTMS), an advancement of transcranial magnetic stimulation, was created to reach wider and possibly more profound regions of the brain. At present, there is insufficient high-quality evidence to support the effectiveness and safety of dTMS in treating obsessive-compulsive disorder (OCD).

OBJECTIVE: This study used a meta-analysis to evaluate the effectiveness and safety of dTMS for treating OCD.

METHODS: Four randomized controlled trials were found by searching PubMed, Embase, Web of Science, and Cochrane Library up to February 2024. The fixed effects meta-analysis model was used for the purpose of data merging in Stata17. The risk ratio (RR) value was used as the measure of effect size to compare response rates and dropout rates between active and sham dTMS.

RESULTS: The meta-analysis included four randomized-controlled trials involving 252 patients with treatment-resistant OCD. Active dTMS showed a notably greater rate of response on the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) in comparison to sham dTMS after treatment (Y-BOCS: RR = 3.71, 95% confidence interval [CI] 2.06 to 6.69) and at the one-month follow-up (Y-BOCS: RR = 2.60, 95% CI 1.59 to 4.26). Subgroup analysis revealed that active dTMS with H-coils was more effective than sham dTMS (RR = 3.57, 95%CI 1.93 to 6.60). No serious adverse events were documented in the studies that were included.

CONCLUSION: The findings suggest that dTMS demonstrates notable efficacy and safety in treating patients with treatment-resistant OCD compared to sham dTMS, with sustained effectiveness noted throughout the one-month post-treatment period.},
}

@article {pmid39381774,
year = {2024},
author = {Pilacinski, A and Christ, L and Boshoff, M and Iossifidis, I and Adler, P and Miro, M and Kuhlenkötter, B and Klaes, C},
title = {Human in the collaborative loop: a strategy for integrating human activity recognition and non-invasive brain-machine interfaces to control collaborative robots.},
journal = {Frontiers in neurorobotics},
volume = {18},
number = {},
pages = {1383089},
pmid = {39381774},
issn = {1662-5218},
abstract = {Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method's potential benefits and implications for HRC.},
}

@article {pmid39379912,
year = {2024},
author = {Chen, B and Dong, J and Guo, W and Li, T},
title = {Sex-specific associations between levels of high-sensitivity C-reactive protein and severity of depression: retrospective cross-sectional analysis of inpatients in China.},
journal = {BMC psychiatry},
volume = {24},
number = {1},
pages = {667},
pmid = {39379912},
issn = {1471-244X},
support = {82230046//Key Project of the National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: We aimed to clarify the controversial relationship between levels of high-sensitivity C-reactive protein (hs-CRP) and severity of depression in men and women.

METHODS: Medical records were retrospectively analyzed for 1,236 inpatients at our medical center who were diagnosed with depression at discharge between January 2018 and August 2022. Depression severity was assessed during hospitalization using the 24-item Hamilton Depression Rating Scale. Potential associations between severity scores and hs-CRP levels were explored using multivariate linear regression as well as smooth curve fitting to detect non-linear patterns.

RESULTS: In male patients, hs-CRP levels between 2.00 mg/L and 10.00 mg/L showed a non-linear association with depression severity overall (fully adjusted β = 1.69, 95% CI 0.65 to 2.72), as well as with severity of specific symptoms such as hopelessness, sluggishness, and cognitive disturbance. In female patients, hs-CRP levels showed a linear association with severity of cognitive disturbance (fully adjusted β = 0.07, 95% CI 0.01 to 0.12). These results remained significant after adjusting for age, body mass index, diabetes, hypertension, history of drinking, history of smoking, and estradiol levels.

DISCUSSION: Levels of hs-CRP show sex-specific associations with depression severity, particularly levels between 2.00 and 10.00 mg/L in men. These findings may help develop personalized anti-inflammatory treatments for depression, particularly for men with hs-CRP levels of 2.00-10.00 mg/L.},
}

@article {pmid39378126,
year = {2024},
author = {Li, J and Wu, W and Chen, J and Xu, Z and Yang, B and He, Q and Yang, X and Yan, H and Luo, P},
title = {Development and safety of investigational and approved drugs targeting the RAS function regulation in RAS mutant cancers.},
journal = {Toxicological sciences : an official journal of the Society of Toxicology},
volume = {},
number = {},
pages = {},
doi = {10.1093/toxsci/kfae129},
pmid = {39378126},
issn = {1096-0929},
abstract = {The RAS gene family holds a central position in controlling key cellular activities such as migration, survival, metabolism, and other vital biological processes. The activation of RAS signaling cascades is instrumental in the development of various cancers. Although several RAS inhibitors have gained approval from the United States Food and Drug Administration (FDA) for their substantial antitumor effects, their widespread and severe adverse reactions significantly curtail their practical usage in the clinic. Thus, there exists a pressing need for a comprehensive understanding of these adverse events, ensuring the clinical safety of RAS inhibitors through the establishment of precise management guidelines, suitable intermittent dosing schedules, and innovative combination regimens. This review centers on the evolution of RAS inhibitors in cancer therapy, delving into the common adverse effects associated with these inhibitors, their underlying mechanisms, and the potential strategies for mitigation.},
}

@article {pmid39376540,
year = {2024},
author = {Giove, F and Zuo, XN and Calhoun, VD},
title = {Editorial: Insights in brain imaging methods: 2023.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1488845},
doi = {10.3389/fnins.2024.1488845},
pmid = {39376540},
issn = {1662-4548},
}

@article {pmid39375394,
year = {2024},
author = {Katoozian, D and Hosseini-Nejad, H and Dehaqani, MA},
title = {A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {23291},
pmid = {39375394},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Animals ; *Algorithms ; *Macaca mulatta ; Neurons/physiology ; Male ; Humans ; },
abstract = {In the field of Brain Machine Interface (BMI), the process of translating motor intention into a machine command is denoted as decoding. However, despite recent advancements, decoding remains a formidable challenge within BMI. The utilization of current decoding algorithms in the field of BMI often involves computational complexity and requires the use of computers. This is primarily due to the reliance on mathematical models to address the decoding issue and perform subsequent output calculations. Unfortunately, computers are not feasible for implantable BMI systems due to their size and power consumption. To address this predicament, this study proposes a pioneering approach inspired by hyperdimensional computing. This approach first involves identifying the pattern of each stimulus by considering the normal firing rate distribution of each neuron. Subsequently, the newly observed firing pattern for each input is compared with the patterns detected at each moment for each neuron. The algorithm, which shares similarities with hyperdimensional computing, identifies the most similar pattern as the final output. This approach reduces the dependence on mathematical models. The efficacy of this method is assessed through the utilization of an authentic dataset acquired from the Frontal Eye Field (FEF) of two male rhesus monkeys. The output space encompasses eight possible angles. The results demonstrate an accuracy rate of 51.5% while exhibiting significantly low computational complexity, involving a mere 2050 adder operators. Furthermore, the proposed algorithm is implemented on a field-programmable gate array (FPGA) and as an ASIC designe in a standard CMOS 180 nm technology, underscoring its suitability for real-time implantable BMI applications. The implementation required only 2.3 Kbytes of RAM, occupied an area of 2.2 mm[2], and consumed 9.32 µW at a 1.8 V power supply. Consequently, the proposed solution represents an accurate, low computational complexity, hardware-friendly, and real-time approach.},
}

*Brain-Computer Interfaces
Animals
*Algorithms
*Macaca mulatta
Neurons/physiology
Male
Humans

@article {pmid39374625,
year = {2024},
author = {Abbasi, MAA and Abbasi, HF and Yu, X and Aziz, MZ and Yih, NTJY and Fan, Z},
title = {E-SAT: An extreme learning machine based self attention approach for decoding motor imagery EEG in subject-specific tasks.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad83f4},
pmid = {39374625},
issn = {1741-2552},
abstract = {The advancements in Brain-Computer Interface (BCI) have substantially evolved people's lives by enabling direct communication between the human brain and external peripheral devices. In recent years, the integration of machine larning (ML) and deep learning (DL) models have considerably imrpoved the performances of BCIs for decoding the motor imagery (MI) tasks. However, there still exist several limitations, e.g., extensive training time and high sensitivity to noises or outliers with those existing models, which largely hinder the rapid developments of BCIs. To address such issues, this paper proposes a novel extreme learning machine (ELM) based self-attention (E-SAT) mechanism to enhance subject-specific classification performances. Specifically, for E-SAT, ELM is employed both to imrpove self-attention module generalization ability for feature extraction and to optimize the model's parameter initialization process. Meanwhile, the extracted features are also classified using ELM, and the end-to-end ELM based setup is used to evaluate E-SAT performances on different MI EEG signals. Extensive experiments with different datasets, such as BCI Competition III Dataset IV-a, IV-b and BCI Competition IV Datasets 1,2a,2b,3, are conducted to verify the effectiveness of proposed E-SAT strategy. Results show that E-SAT outperforms several state-of-the-art (SOTA) existing methods in subject-specific classification on all the datasets, with an average classification accuracy of 99.8%,99.1%,98.9%,75.8%, 90.8%, and 95.4%, being achieved for each datasets, respectively. The experimental results not only show outstanding performance of E-SAT in feature extractions, but also demonstrate that it helps achieves the best results among nine other robust ones. In addition, results in this study also demonstrate that E-SAT achieves exceptional performance in both binary and multi-class classification tasks, as well as for noisy and non-noisy datatsets. .},
}

@article {pmid39374272,
year = {2024},
author = {Lv, R and Chang, W and Yan, G and Nie, W and Zheng, L and Guo, B and Sadiq, MT},
title = {A novel recognition and classification approach for motor imagery based on spatio-temporal features.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3464550},
pmid = {39374272},
issn = {2168-2208},
abstract = {Motor imagery, as a paradigm of brainmachine interfaces, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-machine interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.},
}

@article {pmid39373084,
year = {2024},
author = {Kong, F and He, F and Chisholm, RA},
title = {High beta diversity of gaps contributes to plot-level tree diversity in a tropical forest.},
journal = {Ecology},
volume = {},
number = {},
pages = {e4443},
doi = {10.1002/ecy.4443},
pmid = {39373084},
issn = {1939-9170},
support = {//China Scholarship Council/ ; },
abstract = {Canopy gaps are widely recognized as being crucial for maintaining the diversity of forest tree communities. But empirical studies have found mixed results because the differences in diversity between individual gaps and non-gaps are often small and statistically undetectable. One overlooked factor, however, is how small individual gap versus non-gap differences may accumulate across sites and potentially have a large effect on forest diversity at the plot scale. Our study investigated sapling richness, density, and composition in 124 treefall gaps, and 200 non-gap sites in the 50-ha tropical forest plot at Barro Colorado Island (BCI), Panama. Additionally, we analyzed species accumulation curves to understand how species richness increases with increasing stem numbers. We observed that sapling richness and density were only slightly higher in gaps 7 years after formation and statistically indistinguishable from non-gaps after 12 years. However, species accumulation curves across multiple gaps were substantially higher than those across non-gaps. Species composition showed small differences between individual gaps and non-gaps but differed significantly between collections of gaps and non-gaps. Specifically, 55 species specialized in 7-year-old gaps compared with 24 in non-gaps; of these, 23 gap-specialized species and zero non-gap species were pioneers. Our results indicate that tree species richness is higher in gaps because of both higher stem density and the presence of gap-specialized species. Our study has finally provided compelling evidence to support the idea that gaps enhance the overall diversity of tropical forest tree communities.},
}

@article {pmid39372246,
year = {2024},
author = {Sakel, M and Saunders, K and Ozolins, C and Biswas, R},
title = {Feasibility and Safety of a Home-based Electroencephalogram Neurofeedback Intervention to Reduce Chronic Neuropathic Pain: A Cohort Clinical Trial.},
journal = {Archives of rehabilitation research and clinical translation},
volume = {6},
number = {3},
pages = {100361},
pmid = {39372246},
issn = {2590-1095},
abstract = {OBJECTIVE: To evaluate the feasibility, safety, and potential health benefits of an 8-week home-based neurofeedback intervention.

DESIGN: Single-group preliminary study.

SETTING: Community-based.

PARTICIPANTS: Nine community dwelling adults with chronic neuropathic pain, 6 women and 3 men, with an average age of 51.9 years (range, 19-78 years) and with a 7-day average minimum pain score of 4 of 10 on the visual analog pain scale.

INTERVENTIONS: A minimum of 5 neurofeedback sessions per week (40min/session) for 8 consecutive weeks was undertaken with a 12-week follow-up baseline electroencephalography recording period.

MAIN OUTCOME MEASURES: Primary feasibility outcomes: accessibility, tolerability, safety (adverse events and resolution), and human and information technology (IT) resources required. Secondary outcomes: pain, sensitization, catastrophization, anxiety, depression, sleep, health-related quality of life, electroencephalographic activity, and simple participant feedback.

RESULTS: Of the 23 people screened, 11 were eligible for recruitment. One withdrew and another completed insufficient sessions for analysis, which resulted in 9 datasets analyzed. Three participants withdrew from the follow-up baselines, leaving 6 who completed the entire trial protocol. Thirteen adverse events were recorded and resolved: 1 was treatment-related, 4 were equipment-related, and 8 were administrative-related (eg, courier communication issues). The human and IT resources necessary for trial implementation were identified. There were also significant improvements in pain levels, depression, and anxiety. Six of 9 participants perceived minimal improvement or no change in symptoms after the trial, and 5 of 9 participants were satisfied with the treatment received.

CONCLUSIONS: It is feasible and safe to conduct a home-based trial of a neurofeedback intervention for people with chronic neuropathic pain, when the human and IT resources are provided and relevant governance processes are followed. Improvements in secondary outcomes merit investigation with a randomized controlled trial.},
}

@article {pmid39371523,
year = {2024},
author = {Jin, W and Zhu, X and Qian, L and Wu, C and Yang, F and Zhan, D and Kang, Z and Luo, K and Meng, D and Xu, G},
title = {Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review.},
journal = {Frontiers in computational neuroscience},
volume = {18},
number = {},
pages = {1431815},
pmid = {39371523},
issn = {1662-5188},
abstract = {Brain-computer interfaces (BCIs) represent a groundbreaking approach to enabling direct communication for individuals with severe motor impairments, circumventing traditional neural and muscular pathways. Among the diverse array of BCI technologies, electroencephalogram (EEG)-based systems are particularly favored due to their non-invasive nature, user-friendly operation, and cost-effectiveness. Recent advancements have facilitated the development of adaptive bidirectional closed-loop BCIs, which dynamically adjust to users' brain activity, thereby enhancing responsiveness and efficacy in neurorehabilitation. These systems support real-time modulation and continuous feedback, fostering personalized therapeutic interventions that align with users' neural and behavioral responses. By incorporating machine learning algorithms, these BCIs optimize user interaction and promote recovery outcomes through mechanisms of activity-dependent neuroplasticity. This paper reviews the current landscape of EEG-based adaptive bidirectional closed-loop BCIs, examining their applications in the recovery of motor and sensory functions, as well as the challenges encountered in practical implementation. The findings underscore the potential of these technologies to significantly enhance patients' quality of life and social interaction, while also identifying critical areas for future research aimed at improving system adaptability and performance. As advancements in artificial intelligence continue, the evolution of sophisticated BCI systems holds promise for transforming neurorehabilitation and expanding applications across various domains.},
}

@article {pmid39371161,
year = {2024},
author = {Angrick, M and Luo, S and Rabbani, Q and Joshi, S and Candrea, DN and Milsap, GW and Gordon, CR and Rosenblatt, K and Clawson, L and Maragakis, N and Tenore, FV and Fifer, MS and Ramsey, NF and Crone, NE},
title = {Real-time detection of spoken speech from unlabeled ECoG signals: A pilot study with an ALS participant.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.09.18.24313755},
pmid = {39371161},
abstract = {Objective . Brain-Computer Interfaces (BCIs) hold significant promise for restoring communication in individuals with partial or complete loss of the ability to speak due to paralysis from amyotrophic lateral sclerosis (ALS), brainstem stroke, and other neurological disorders. Many of the approaches to speech decoding reported in the BCI literature have required time-aligned target representations to allow successful training - a major challenge when translating such approaches to people who have already lost their voice. Approach . In this pilot study, we made a first step toward scenarios in which no ground truth is available. We utilized a graph-based clustering approach to identify temporal segments of speech production from electrocorticographic (ECoG) signals alone. We then used the estimated speech segments to train a voice activity detection (VAD) model using only ECoG signals. We evaluated our approach using held-out open-loop recordings of a single dysarthric clinical trial participant living with ALS, and we compared the resulting performance to previous solutions trained with ground truth acoustic voice recordings. Main results . Our approach achieves a median error rate of around 0.5 seconds with respect to the actual spoken speech. Embedded into a real-time BCI, our approach is capable of providing VAD results with a latency of only 10 ms. Significance . To the best of our knowledge, our results show for the first time that speech activity can be predicted purely from unlabeled ECoG signals, a crucial step toward individuals who cannot provide this information anymore due to their neurological condition, such as patients with locked-in syndrome. Clinical Trial Information . ClinicalTrials.gov, registration number NCT03567213 .},
}

@article {pmid39369514,
year = {2024},
author = {Wang, Y and Han, M and Jing, L and Jia, Q and Lv, S and Xu, Z and Liu, J and Cai, X},
title = {Enhanced neural activity detection with microelectrode arrays modified by drug-loaded calcium alginate/chitosan hydrogel.},
journal = {Biosensors & bioelectronics},
volume = {267},
number = {},
pages = {116837},
doi = {10.1016/j.bios.2024.116837},
pmid = {39369514},
issn = {1873-4235},
abstract = {Microelectrode arrays (MEAs) are pivotal brain-machine interface devices that facilitate in situ and real-time detection of neurophysiological signals and neurotransmitter data within the brain. These capabilities are essential for understanding neural system functions, treating brain disorders, and developing advanced brain-machine interfaces. To enhance the performance of MEAs, this study developed a crosslinked hydrogel coating of calcium alginate (CA) and chitosan (CS) loaded with the anti-inflammatory drug dexamethasone sodium phosphate (DSP). By modifying the MEAs with this hydrogel and various conductive nanomaterials, including platinum nanoparticles (PtNPs) and poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS), the electrical properties and biocompatibility of the electrodes were optimized. The hydrogel coating matches the mechanical properties of brain tissue more effectively and, by actively releasing anti-inflammatory drugs, significantly reduces post-implantation tissue inflammation, extends the electrodes' lifespan, and enhances the quality of neural activity detection. Additionally, this modification ensures high sensitivity and specificity in the detection of dopamine (DA), displaying high-quality dual-mode neural activity during in vivo testing and revealing significant functional differences between neuron types under various physiological states (anesthetized and awake). Overall, this study showcases the significant application value of bioactive hydrogels as excellent nanobiointerfaces and drug delivery carriers for long-term neural monitoring. This approach has the potential to enhance the functionality and acceptance of brain-machine interface devices in medical practice and has profound implications for future neuroscience research and the development of strategies for treating neurological diseases.},
}

@article {pmid39369011,
year = {2024},
author = {Peng, Z and Tong, L and Shi, W and Xu, L and Huang, X and Li, Z and Yu, X and Meng, X and He, X and Lv, S and Yang, G and Hao, H and Jiang, T and Miao, X and Ye, L},
title = {Multifunctional human visual pathway-replicated hardware based on 2D materials.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {8650},
pmid = {39369011},
issn = {2041-1723},
support = {62222404, 62304084 and 92248304//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Visual Pathways/physiology ; *Retina/physiology ; *Brain-Computer Interfaces ; Visual Cortex/physiology ; Tungsten/chemistry ; Robotics/instrumentation ; Selenium/chemistry ; Artificial Intelligence ; },
abstract = {Artificial visual system empowered by 2D materials-based hardware simulates the functionalities of the human visual system, leading the forefront of artificial intelligence vision. However, retina-mimicked hardware that has not yet fully emulated the neural circuits of visual pathways is restricted from realizing more complex and special functions. In this work, we proposed a human visual pathway-replicated hardware that consists of crossbar arrays with split floating gate 2D tungsten diselenide (WSe2) unit devices that simulate the retina and visual cortex, and related connective peripheral circuits that replicate connectomics between the retina and visual cortex. This hardware experimentally displays advanced multi-functions of red-green color-blindness processing, low-power shape recognition, and self-driven motion tracking, promoting the development of machine vision, driverless technology, brain-computer interfaces, and intelligent robotics.},
}

Humans
*Visual Pathways/physiology
*Retina/physiology
*Brain-Computer Interfaces
Visual Cortex/physiology
Tungsten/chemistry
Robotics/instrumentation
Selenium/chemistry
Artificial Intelligence

@article {pmid39368632,
year = {2024},
author = {Hu, J and Chen, C and Wu, M and Zhang, J and Meng, F and Li, T and Luo, B},
title = {Assessing consciousness in acute coma using name-evoked responses.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111091},
doi = {10.1016/j.brainresbull.2024.111091},
pmid = {39368632},
issn = {1873-2747},
abstract = {Detecting consciousness in clinically unresponsive patients remains a significant challenge. Existing studies demonstrate that electroencephalography (EEG) can detect brain responses in behaviorally unresponsive patients, indicating potential for consciousness detection. However, most of this evidence is based on chronic patients, and there is a lack of studies focusing on acute coma cases. This study aims to detect signs of residual consciousness in patients with acute coma by using bedside EEG and electromyography (EMG) during an auditory oddball paradigm. We recruited patients with acute brain injury (either traumatic brain injury or cardiac arrest) who were admitted to the intensive care unit within two weeks after injury, with a Glasgow Coma Scale (GCS) score of 8 or below. Auditory stimuli included the patients' own names and other common names (referred to as standard names), spoken by the patients' relatives, delivered under two conditions: passive listening (where patients were instructed that sounds would be played) and active listening (where patients were asked to move hands when heard their own names). Brain and muscle activity were recorded using EEG and EMG during the auditory paradigm. Event-related potentials (ERP) and EMG spectra were analyzed and compared between responses to the subject's own name and other standard names in both passive and active listening conditions. A total of 22 patients were included in the final analysis. Subjects exhibited enhanced ERP responses when exposed to their own names, particularly during the active listening task. Compared to standard names or passive listening, distinct differences in brain network connectivity and increased EMG responses were detected during active listening to their own names. These findings suggest the presence of residual consciousness, offering the potential for assessing consciousness in behaviorally unresponsive patients.},
}

@article {pmid39366386,
year = {2024},
author = {Zhang, L and Wang, HL and Zhang, YF and Mao, XT and Wu, TT and Huang, ZH and Jiang, WJ and Fan, KQ and Liu, DD and Yang, B and Zhuang, MH and Huang, GM and Liang, Y and Zhu, SJ and Zhong, JY and Xu, GY and Li, XM and Cao, Q and Li, YY and Jin, J},
title = {Stress triggers irritable bowel syndrome with diarrhea through a spermidine-mediated decline in type I interferon.},
journal = {Cell metabolism},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cmet.2024.09.002},
pmid = {39366386},
issn = {1932-7420},
abstract = {Irritable bowel syndrome with diarrhea (IBS-D) is a common and chronic gastrointestinal disorder that is characterized by abdominal discomfort and occasional diarrhea. The pathogenesis of IBS-D is thought to be related to a combination of factors, including psychological stress, abnormal muscle contractions, and inflammation and disorder of the gut microbiome. However, there is still a lack of comprehensive analysis of the logical regulatory correlation among these factors. In this study, we found that stress induced hyperproduction of xanthine and altered the abundance and metabolic characteristics of Lactobacillus murinus in the gut. Lactobacillus murinus-derived spermidine suppressed the basal expression of type I interferon (IFN)-α in plasmacytoid dendritic cells by inhibiting the K63-linked polyubiquitination of TRAF3. The reduction in IFN-α unrestricted the contractile function of colonic smooth muscle cells, resulting in an increase in bowel movement. Our findings provided a theoretical basis for the pathological mechanism of, and new drug targets for, stress-exposed IBS-D.},
}

@article {pmid39366088,
year = {2024},
author = {Pan, Y and Sequestro, M and Golkar, A and Olsson, A},
title = {Handholding reduces the recovery of threat memories and magnifies prefrontal hemodynamic responses.},
journal = {Behaviour research and therapy},
volume = {183},
number = {},
pages = {104641},
doi = {10.1016/j.brat.2024.104641},
pmid = {39366088},
issn = {1873-622X},
abstract = {Human touch is a powerful means of social and affective regulation, promoting safety behaviors. Yet, despite its importance across human contexts, it remains unknown how touch can promote the learning of new safety memories and what neural processes underlie such effects. The current study used measures of peripheral physiology and brain activity to examine the effects of interpersonal touch during safety learning (extinction) on the recovery of previously learned threat. We observed that handholding during extinction significantly reduced threat recovery, which was reflected in enhanced prefrontal hemodynamic responses. This effect was absent when learners were instructed to hold a rubber ball, independent of the presence of their partners. Our findings indicate that social touch contributes to safety learning, potentially influencing threat memories via prefrontal circuitry.},
}

@article {pmid39367153,
year = {2024},
author = {Miroshnikov, A and Yakovlev, L and Syrov, N and Vasilyev, A and Berkmush-Antipova, A and Golovanov, F and Kaplan, A},
title = {Differential Hemodynamic Responses to Motor and Tactile Imagery: Insights from Multichannel fNIRS Mapping.},
journal = {Brain topography},
volume = {38},
number = {1},
pages = {4},
pmid = {39367153},
issn = {1573-6792},
support = {21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; },
mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; *Imagination/physiology ; Male ; Female ; Adult ; *Hemodynamics/physiology ; Young Adult ; *Brain Mapping/methods ; Touch Perception/physiology ; Touch/physiology ; Somatosensory Cortex/physiology/diagnostic imaging ; Brain/physiology/diagnostic imaging ; Motor Cortex/physiology/diagnostic imaging ; },
abstract = {Tactile and motor imagery are crucial components of sensorimotor functioning and cognitive neuroscience research, yet the neural mechanisms of tactile imagery remain underexplored compared to motor imagery. This study employs multichannel functional near-infrared spectroscopy (fNIRS) combined with image reconstruction techniques to investigate the neural hemodynamics associated with tactile (TI) and motor imagery (MI). In a study of 15 healthy participants, we found that MI elicited significantly greater hemodynamic responses (HRs) in the precentral area compared to TI, suggesting the involvement of different cortical areas involved in two different types of sensorimotor mental imagery. Concurrently, the HRs in S1 and parietal areas exhibited comparable patterns in both TI and MI. During MI, both motor and somatosensory areas demonstrated comparable HRs. However, in TI, somatosensory activation was observed to be more pronounced. Our results highlight the distinctive neural profiles of motor versus tactile imagery and indicate fNIRS technique to be sensitive for this. This distinction is significant for fundamental understanding of sensorimotor integration and for developing advanced neurotechnologies, including imagery-based brain-computer interfaces (BCIs) that can differentiate between different types of mental imagery.},
}

Humans
*Spectroscopy, Near-Infrared/methods
*Imagination/physiology
Male
Female
Adult
*Hemodynamics/physiology
Young Adult
*Brain Mapping/methods
Touch Perception/physiology
Touch/physiology
Somatosensory Cortex/physiology/diagnostic imaging
Brain/physiology/diagnostic imaging
Motor Cortex/physiology/diagnostic imaging

@article {pmid39366955,
year = {2024},
author = {Ullah, R and Xue, C and Wang, S and Qin, Z and Rauf, N and Zhan, S and Khan, NU and Shen, Y and Zhou, YD and Fu, J},
title = {Alternate-day fasting delays pubertal development in normal-weight mice but prevents high-fat diet-induced obesity and precocious puberty.},
journal = {Nutrition & diabetes},
volume = {14},
number = {1},
pages = {82},
pmid = {39366955},
issn = {2044-4052},
support = {82350410491//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82370863//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *Diet, High-Fat/adverse effects ; *Puberty, Precocious/etiology/prevention & control ; Female ; Mice ; *Fasting ; *Obesity/prevention & control/etiology ; Sexual Maturation/physiology ; Pregnancy ; Mice, Inbred C57BL ; Growth Hormone/blood ; },
abstract = {BACKGROUND/OBJECTIVES: Childhood obesity, particularly in girls, is linked to early puberty onset, heightening risks for adult-onset diseases. Addressing childhood obesity and precocious puberty is vital to mitigate societal burdens. Despite existing costly and invasive medical interventions, introducing lifestyle-based alternatives is essential. Our study investigates alternate-day fasting's (ADF) impact on pubertal development in normal-weight and high-fat diet (HFD)-induced obese female mice.

METHODS: Four groups of female mice were utilized, with dams initially fed control chow during and before pregnancy. Post-parturition, two groups continued on control chow, while two switched to an HFD. Offspring diets mirrored maternal exposure. One control and one HFD group were subjected to ADF. Morphometry and hormone analyses at various time points were performed.

RESULTS: Our findings demonstrate that ADF in normal-weight mice led to reduced body length, weight, uterine, and ovarian weights, accompanied by delayed puberty and lower levels of sex hormones and growth hormone (GH). Remarkably, GH treatment effectively prevented ADF-induced growth reduction but did not prevent delayed puberty. Conversely, an HFD increased body length, induced obesity and precocious puberty, and altered sex hormones and leptin levels, which were counteracted by ADF regimen. Our data indicate ADF's potential in managing childhood obesity and precocious puberty.

CONCLUSIONS: ADF reduced GH and sex hormone levels, contributing to reduced growth and delayed puberty, respectively. Therefore, parents of normal-weight children should be cautious about prolonged overnight fasting. ADF prevented HFD-induced obesity and precocious puberty, offering an alternative to medical approaches; nevertheless, further studies are needed for translation into clinical practice.},
}

Animals
*Diet, High-Fat/adverse effects
*Puberty, Precocious/etiology/prevention & control
Female
Mice
*Fasting
*Obesity/prevention & control/etiology
Sexual Maturation/physiology
Pregnancy
Mice, Inbred C57BL
Growth Hormone/blood

@article {pmid39365711,
year = {2024},
author = {Wang, Y and Wang, J and Wang, W and Su, J and Bunterngchit, C and Hou, ZG},
title = {TFTL: A Task-Free Transfer Learning Strategy for EEG-based Cross-Subject & Cross-Dataset Motor Imagery BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3474049},
pmid = {39365711},
issn = {1558-2531},
abstract = {OBJECTIVE: Motor imagery-based brain-computer interfaces (MI-BCIs) have been playing an increasingly vital role in neural rehabilitation. However, the long-term task-based calibration required for enhanced model performance leads to an unfriendly user experience, while the inadequacy of EEG data hinders the performance of deep learning models. To address these challenges, a task-free transfer learning strategy (TFTL) for EEG-based cross-subject & cross-dataset MI-BCI is proposed for calibration time reduction and multi-center data co-modeling.

METHODS: TFTL strategy consists of data alignment, shared feature extractor, and specific classifiers, in which the label predictor for MI tasks classification, as well as domain and dataset discriminator for inter-subject variability reduction are concurrently optimized for knowledge transfer from subjects across different datasets to the target subject. Moreover, only resting data of the target subject is used for subject-specific model construction to achieve task-free.

RESULTS: We employed three deep learning methods (ShallowConvNet, EEGNet, and TCNet-Fusion) as baseline approaches to evaluate the effectiveness of the proposed strategy on five datasets (BCIC IV Dataset 2a, Dataset 1, Physionet MI, Dreyer 2023, and OpenBMI). The results demonstrate a significant improvement with the inclusion of the TFTL strategy compared to the baseline methods, reaching a maximum enhancement of 15.67% with a statistical significance (p=2.4e-5<0.05). Moreover, task-free resulted in MI trials needed for calibration being 0 for all datasets, which significantly alleviated the calibration burden for patients before usage.

CONCLUSION/SIGNIFICANCE: The proposed TFTL strategy effectively addresses challenges posed by prolonged calibration periods and insufficient EEG data, thus promoting MI-BCI from laboratory to clinical application.},
}

@article {pmid39362975,
year = {2024},
author = {Yu, H and Cao, W and Fang, T and Jin, J and Pei, G},
title = {EEG β oscillations in aberrant data perception under cognitive load modulation.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {22995},
pmid = {39362975},
issn = {2045-2322},
support = {LGG21G010002//Zhejiang Provincial Natural Science Foundation of China/ ; LQ22C090007//Zhejiang Provincial Natural Science Foundation of China/ ; 72271166//National Natural Science Foundation of China/ ; 72401263//National Natural Science Foundation of China/ ; 2023KFKT003//Open Research Project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior, Shanghai International Studies University/ ; },
mesh = {Humans ; Male ; *Cognition/physiology ; Female ; *Electroencephalography ; Adult ; Young Adult ; Decision Making/physiology ; Beta Rhythm/physiology ; Brain/physiology ; Perception/physiology ; },
abstract = {Data-driven decision making (DDDM) is becoming an indispensable component of work across various fields, and the perception of aberrant data (PAD) has emerged as an essential skill. Nonetheless, the neural processing mechanisms underpinning PAD remain incompletely elucidated. Direct evidence linking neural oscillations to PAD is currently lacking, and the impact of cognitive load remains ambiguous. We address this issue using EEG time-frequency analysis. Data were collected from 21 healthy participants. The experiment employed a 2 (low vs. high cognitive load) × 2 [PAD+ (aberrant data accurately identified as aberrant) vs. PAD- (non-aberrant data correctly recognized as normal)] within-subject laboratory design. Results indicate that upper β band oscillations (26-30 Hz) were significantly enhanced in the PAD + condition compared to PAD-, with consistent activity observed in the frontal (p < 0.001, [Formula: see text] = 0.41) and parietal lobes (p = 0.028, [Formula: see text] = 0.22) within the 300-350 ms time window. Additionally, as cognitive load increased, the time window of β oscillations for distinguishing PAD+ from PAD- shifted earlier. This study enriches our understanding of the PAD neural basis by exploring the distribution of neural oscillation frequencies, decision-making neural circuits, and the windowing effect induced by cognitive load. These findings have significant implications for elucidating the pathological mechanisms of neurodegenerative disorders, as well as in the initial screening, intervention, and treatment of diseases.},
}

Humans
Male
*Cognition/physiology
Female
*Electroencephalography
Adult
Young Adult
Decision Making/physiology
Beta Rhythm/physiology
Brain/physiology
Perception/physiology

@article {pmid39358539,
year = {2024},
author = {Drew, L},
title = {United States sets the pace for implantable brain-computer interfaces.},
journal = {Nature},
volume = {634},
number = {8032},
pages = {S8-S10},
doi = {10.1038/d41586-024-03046-5},
pmid = {39358539},
issn = {1476-4687},
mesh = {Humans ; *Brain-Computer Interfaces/statistics & numerical data/trends ; *Electrodes, Implanted/statistics & numerical data/trends ; United States ; },
}

Humans
*Brain-Computer Interfaces/statistics & numerical data/trends
*Electrodes, Implanted/statistics & numerical data/trends
United States

@article {pmid39358411,
year = {2024},
author = {Andreu-Sánchez, C and Martín-Pascual, MÁ and Gruart, A and Delgado-García, JM},
title = {Differences in Mu rhythm when seeing grasping/motor actions in a real context versus on screens.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {22921},
pmid = {39358411},
issn = {2045-2322},
support = {PID2021-122446NB-100//Ministerio de Ciencia e Innovación/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovación/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovación/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovación/ ; BIO-122//Junta de Andalucía/ ; BIO-122//Junta de Andalucía/ ; },
mesh = {Humans ; Male ; Female ; Adult ; *Electroencephalography/methods ; *Somatosensory Cortex/physiology ; *Hand Strength/physiology ; Young Adult ; Psychomotor Performance/physiology ; Photic Stimulation ; Brain Waves/physiology ; Visual Perception/physiology ; Brain-Computer Interfaces ; Motor Activity/physiology ; },
abstract = {Mu rhythm (∼8-12 Hz) in the somatosensory cortex has traditionally been linked with doing and seeing motor activities. Here, we aimed to learn how the medium (physical or screened) in which motor actions are seen could impact on that specific brain rhythm. To do so, we presented to 40 participants the very same narrative content both in a one-shot movie with no cuts and in a real theatrical performance. We recorded subjects' brain activities with electroencephalographic (EEG) procedures, and analyzed Mu rhythm present in left (C3) and right (C4) somatosensory areas in relation to the 24 motor activities included in each visual stimulus (screen vs. reality) (24 motor and grasping actions x 40 participants x 2 conditions = 1920 trials). We found lower Mu spectral power in the somatosensory area after the onset of the motor actions in real performance than on-screened content, more pronounced in the left hemisphere. In our results, the sensorimotor Mu-ERD (event-related desynchronization) was stronger during the real-world observation compared to screen observation. This could be relevant in research areas where the somatosensory cortex is important, such as online learning, virtual reality, or brain-computer interfaces.},
}

Humans
Male
Female
Adult
*Electroencephalography/methods
*Somatosensory Cortex/physiology
*Hand Strength/physiology
Young Adult
Psychomotor Performance/physiology
Photic Stimulation
Brain Waves/physiology
Visual Perception/physiology
Brain-Computer Interfaces
Motor Activity/physiology

@article {pmid39358021,
year = {2024},
author = {Graczyk, E and Hutchison, B and Valle, G and Bjanes, D and Gates, D and Raspopovic, S and Gaunt, R},
title = {Clinical Applications and Future Translation of Somatosensory Neuroprostheses.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {44},
number = {40},
pages = {},
pmid = {39358021},
issn = {1529-2401},
mesh = {Humans ; *Somatosensory Cortex/physiology ; Feedback, Sensory/physiology ; Translational Research, Biomedical/trends/methods ; Neural Prostheses ; Brain-Computer Interfaces/trends ; Electric Stimulation/methods ; Prostheses and Implants/trends ; },
abstract = {Somatosensory neuroprostheses restore, replace, or enhance tactile and proprioceptive feedback for people with sensory impairments due to neurological disorders or injury. Somatosensory neuroprostheses typically couple sensor inputs from a wearable device, prosthesis, robotic device, or virtual reality system with electrical stimulation applied to the somatosensory nervous system via noninvasive or implanted interfaces. While prior research has mainly focused on technology development and proof-of-concept studies, recent acceleration of clinical studies in this area demonstrates the translational potential of somatosensory neuroprosthetic systems. In this review, we provide an overview of neurostimulation approaches currently undergoing human testing and summarize recent clinical findings on the perceptual, functional, and psychological impact of somatosensory neuroprostheses. We also cover current work toward the development of advanced stimulation paradigms to produce more natural and informative sensory feedback. Finally, we provide our perspective on the remaining challenges that need to be addressed prior to translation of somatosensory neuroprostheses.},
}

Humans
*Somatosensory Cortex/physiology
Feedback, Sensory/physiology
Translational Research, Biomedical/trends/methods
Neural Prostheses
Brain-Computer Interfaces/trends
Electric Stimulation/methods
Prostheses and Implants/trends

@article {pmid39353205,
year = {2024},
author = {Zhang, Y and Xing, H and Li, J and Han, F and Fan, S and Zhang, Y},
title = {Bioinspired Artificial Intelligent Nociceptive Alarm System Based on Fibrous Biomemristors.},
journal = {ACS sensors},
volume = {},
number = {},
pages = {},
doi = {10.1021/acssensors.4c01568},
pmid = {39353205},
issn = {2379-3694},
abstract = {With the advancement of modern medical and brain-computer interface devices, flexible artificial nociceptors with tactile perception hold significant scientific importance and exhibit great potential in the fields of wearable electronic devices and biomimetic robots. Here, a bioinspired artificial intelligent nociceptive alarm system integrating sensing monitoring and transmission functions is constructed using a silk fibroin (SF) fibrous memristor. This memristor demonstrates high stability, low operating power, and the capability to simulate synaptic plasticity. As a result, an artificial pressure nociceptor based on the SF fibrous memristor can detect both fast and chronic pain and provide a timely alarm in the event of a fall or prolonged immobility of the carrier. Further, an array of artificial pressure nociceptors not only monitors the pressure distribution across various parts of the carrier but also provides direct feedback on the extent of long-term pressure to the carrier. This work holds significant implications for medical support in biological carriers or targeted maintenance of electronic carriers.},
}