@article {pmid39243826,
year = {2024},
author = {Shen, B and Yao, Q and Li, W and Dong, S and Zhang, H and Zhao, Y and Pan, Y and Jiang, X and Li, D and Chen, Y and Yan, J and Zhang, W and Zhu, Q and Zhang, D and Zhang, L and Wu, Y},
title = {Dynamic cerebellar and sensorimotor network compensation in tremor-dominated Parkinson's disease.},
journal = {Neurobiology of disease},
volume = {201},
number = {},
pages = {106659},
doi = {10.1016/j.nbd.2024.106659},
pmid = {39243826},
issn = {1095-953X},
abstract = {AIM: Parkinson's disease (PD) tremor is associated with dysfunction in the basal ganglia (BG), cerebellum (CB), and sensorimotor networks (SMN). We investigated tremor-related static functional network connectivity (SFNC) and dynamic functional network connectivity (DFNC) in PD patients.

METHODS: We analyzed the resting-state functional MRI data of 21 tremor-dominant Parkinson's disease (TDPD) patients and 29 healthy controls. We compared DFNC and SFNC between the three networks and assessed their associations with tremor severity.

RESULTS: TDPD patients exhibited increased SFNC between the SMN and BG networks. In addition, they spent more mean dwell time (MDT) in state 2, characterized by sparse connections, and less MDT in state 4, indicating stronger connections. Furthermore, enhanced DFNC between the CB and SMN was observed in state 2. Notably, the MDT of state 2 was positively associated with tremor scores.

CONCLUSION: The enhanced dynamic connectivity between the CB and SMN in TDPD patients suggests a potential compensatory mechanism. However, the tendency to remain in a state of sparse connectivity may contribute to the severity of tremor symptoms.},
}

@article {pmid39242446,
year = {2024},
author = {Imath, M and Ragavendran, C},
title = {Letter to editor: "Neuralink's brain implant: a vision for enhanced human-machine integration".},
journal = {Neurosurgical review},
volume = {47},
number = {1},
pages = {566},
doi = {10.1007/s10143-024-02806-1},
pmid = {39242446},
issn = {1437-2320},
mesh = {Humans ; *Brain-Computer Interfaces ; Brain/surgery ; },
}

Humans
*Brain-Computer Interfaces
Brain/surgery

@article {pmid39241437,
year = {2024},
author = {Choo, S and Park, H and Jung, JY and Flores, K and Nam, CS},
title = {Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {180},
number = {},
pages = {106665},
doi = {10.1016/j.neunet.2024.106665},
pmid = {39241437},
issn = {1879-2782},
abstract = {In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers for specific mental tasks is critical for BCI performance. The classifiers are developed by machine learning (ML) and deep learning (DL) techniques, requiring a large dataset for training to build reliable and accurate models. However, collecting large enough EEG datasets is difficult due to intra-/inter-subject variabilities and experimental costs. This leads to the data scarcity problem, which causes overfitting issues to training samples, resulting in reducing generalization performance. To solve the EEG data scarcity problem and improve the performance of the EEG classifiers, we propose a novel EEG data augmentation (DA) framework using conditional generative adversarial networks (cGANs). An experimental study is implemented with two public EEG datasets, including motor imagery (MI) tasks (BCI competition IV IIa and III IVa), to validate the effectiveness of the proposed EEG DA method for the EEG classifiers. To evaluate the proposed cGAN-based DA method, we tested eight EEG classifiers for the experiment, including traditional MLs and state-of-the-art DLs with three existing EEG DA methods. Experimental results showed that most DA methods with proper DA proportion in the training dataset had higher classification performances than without DA. Moreover, applying the proposed DA method showed superior classification performance improvement than the other DA methods. This shows that the proposed method is a promising EEG DA method for enhancing the performances of the EEG classifiers in MI-based BCIs.},
}

@article {pmid39240852,
year = {2024},
author = {Yamashiro, K and Matsumoto, N and Ikegaya, Y},
title = {Diffusion model-based image generation from rat brain activity.},
journal = {PloS one},
volume = {19},
number = {9},
pages = {e0309709},
pmid = {39240852},
issn = {1932-6203},
mesh = {Animals ; Rats ; *Brain/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; Male ; Electroencephalography/methods ; Neural Networks, Computer ; Models, Neurological ; },
abstract = {Brain-computer interface (BCI) technology has gained recognition in various fields, including clinical applications, assistive technology, and human-computer interaction research. BCI enables communication, control, and monitoring of the affective/cognitive states of users. Recently, BCI has also found applications in the artistic field, enabling real-time art composition using brain activity signals, and engaging performers, spectators, or an entire audience with brain activity-based artistic environments. Existing techniques use specific features of brain activity, such as the P300 wave and SSVEPs, to control drawing tools, rather than directly reflecting brain activity in the output image. In this study, we present a novel approach that uses a latent diffusion model, a type of deep neural network, to generate images directly from continuous brain activity. We demonstrate this technology using local field potentials from the neocortex of freely moving rats. This system continuously converted the recorded brain activity into images. Our end-to-end method for generating images from brain activity opens new possibilities for creative expression and experimentation. Notably, our results show that the generated images successfully reflect the dynamic and stochastic nature of the underlying neural activity, providing a unique procedure for visualization of brain function.},
}

Animals
Rats
*Brain/physiology/diagnostic imaging
*Brain-Computer Interfaces
Male
Electroencephalography/methods
Neural Networks, Computer
Models, Neurological

@article {pmid39240342,
year = {2024},
author = {Yang, D and Sun, Q and Li, W and Wang, Y and Qian, H and Li, D},
title = {Efficiency of HoLEP in patients with detrusor underactivity and renal dysfunction secondary to BPO.},
journal = {World journal of urology},
volume = {42},
number = {1},
pages = {509},
pmid = {39240342},
issn = {1433-8726},
support = {20214Y012//General Project of Shanghai Changning District Health Commission/ ; 202040140//Surface Project of Shanghai Health Commission/ ; },
mesh = {Humans ; Male ; *Lasers, Solid-State/therapeutic use ; Aged ; *Prostatic Hyperplasia/complications/surgery ; *Urinary Bladder, Underactive/physiopathology ; Prospective Studies ; Middle Aged ; *Urinary Bladder Neck Obstruction/surgery/etiology ; Treatment Outcome ; Laser Therapy/methods ; Prostatectomy/methods/adverse effects ; Kidney Diseases/surgery/complications ; },
abstract = {PURPOSE: The purpose of this study was to assess the bladder and renal functional outcomes of holmium laser enucleation of the prostate (HoLEP) in patients with benign prostatic obstruction (BPO) complicated by detrusor underactivity (DU) and secondary renal dysfunction.

METHODS: Thirty-one patients were included in this prospective study. Eligible patients had urinary retention, a bladder outlet obstruction index (BOOI) greater than 40, a bladder contractility index (BCI) less than 100, abnormal renal function at the initial diagnosis (serum creatinine > 132 µmol/L) and a renal pelvis anteroposterior diameter (PRAPD) > 1.5 cm bilaterally. All patients underwent HoLEP in a routine manner and were evaluated preoperatively and at 1, 3 and 6 months after surgery. The baseline characteristics of the patients, perioperative data, postoperative outcomes and complications were assessed.

RESULTS: Significant improvement was observed in the international prostate symptom score (IPSS), quality of life (QoL) score, maximal urinary flow rate (Qmax), post-void residual volume (PVR), Scr and RPAPD at the 6-month follow-up. Bladder wall thickness (BWT) exhibited a decreasing trend but did not significantly differ from the preoperative values. No grade 3 or higher adverse events occurred, and grade 3 and lower complications were treated conservatively. Three patients required reinsertion of indwelling catheters, and they were able to void spontaneously after two weeks of catheterisation training and medication treatment.

CONCLUSION: HoLEP is an effective treatment for men with BPO accompanied by DU and consequent renal function impairment. Patients are able to regain spontaneous voiding. Both bladder and renal functions were preserved and improved.},
}

Humans
Male
*Lasers, Solid-State/therapeutic use
Aged
*Prostatic Hyperplasia/complications/surgery
*Urinary Bladder, Underactive/physiopathology
Prospective Studies
Middle Aged
*Urinary Bladder Neck Obstruction/surgery/etiology
Treatment Outcome
Laser Therapy/methods
Prostatectomy/methods/adverse effects
Kidney Diseases/surgery/complications

@article {pmid39237192,
year = {2024},
author = {Pons, JL and Reys, V and Grand, F and Moreau, V and Gracy, J and Exner, TE and Labesse, G},
title = {@TOME 3.0: Interfacing Protein Structure Modeling and Ligand Docking.},
journal = {Journal of molecular biology},
volume = {436},
number = {17},
pages = {168704},
doi = {10.1016/j.jmb.2024.168704},
pmid = {39237192},
issn = {1089-8638},
mesh = {Ligands ; *Molecular Docking Simulation ; *Proteins/chemistry/metabolism ; *Protein Conformation ; Binding Sites ; Protein Binding ; Software ; Drug Design ; Models, Molecular ; },
abstract = {Knowledge of protein-ligand complexes is essential for efficient drug design. Virtual docking can bring important information on putative complexes but it is still far from being simultaneously fast and accurate. Receptors are flexible and adapt to the incoming small molecules while docking is highly sensitive to small conformational deviations. Conformation ensemble is providing a mean to simulate protein flexibility. However, modeling multiple protein structures for many targets is seldom connected to ligand screening in an efficient and straightforward manner. @TOME-3 is an updated version of our former pipeline @TOME-2, in which protein structure modeling is now directly interfaced with flexible ligand docking. Sequence-sequence profile comparisons identify suitable PDB templates for structure modeling and ligands from these templates are used to deduce binding sites to be screened. In addition, bound ligand can be used as pharmacophoric restraint during the virtual docking. The latter is performed by PLANTS while the docking poses are analysed through multiple chemoinformatics functions. This unique combination of tools allows rapid and efficient ligand docking on multiple receptor conformations in parallel. @TOME-3 is freely available on the web at https://atome.cbs.cnrs.fr.},
}

Ligands
*Molecular Docking Simulation
*Proteins/chemistry/metabolism
*Protein Conformation
Binding Sites
Protein Binding
Software
Drug Design
Models, Molecular

@article {pmid39237038,
year = {2024},
author = {Chen, S and Wang, Y and Lin, X and Sun, X and Li, W and Ma, W},
title = {Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks.},
journal = {Journal of neuroscience methods},
volume = {411},
number = {},
pages = {110276},
doi = {10.1016/j.jneumeth.2024.110276},
pmid = {39237038},
issn = {1872-678X},
abstract = {BACKGROUND: Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging due to physiological differences between subjects. Domain gap, which refers to the different distributions of EEG data at different subjects, has attracted great attention for cross-subject emotion recognition.

This study focuses on narrowing the domain gap between subjects through the emotional frequency bands and the relationship information between EEG channels. Emotional frequency band features represent the energy distribution of EEG data in different frequency ranges, while relationship information between EEG channels provides spatial distribution information about EEG data.

NEW METHOD: To achieve this, this paper proposes a model called the Frequency Band Attention Graph convolutional Adversarial neural Network (FBAGAN). This model includes three components: a feature extractor, a classifier, and a discriminator. The feature extractor consists of a layer with a frequency band attention mechanism and a graph convolutional neural network. The mechanism effectively extracts frequency band information by assigning weights and Graph Convolutional Networks can extract relationship information between EEG channels by modeling the graph structure. The discriminator then helps minimize the gap in the frequency information and relationship information between the source and target domains, improving the model's ability to generalize.

RESULTS: The FBAGAN model is extensively tested on the SEED, SEED-IV, and DEAP datasets. The accuracy and standard deviation scores are 88.17% and 4.88, respectively, on the SEED dataset, and 77.35% and 3.72 on the SEED-IV dataset. On the DEAP dataset, the model achieves 69.64% for Arousal and 65.18% for Valence. These results outperform most existing models.

CONCLUSIONS: The experiments indicate that FBAGAN effectively addresses the challenges of transferring EEG channel domain and frequency band domain, leading to improved performance.},
}

@article {pmid39236139,
year = {2024},
author = {Yang, X and Zhu, Z and Jiang, G and Wu, D and He, A and Wang, J},
title = {DC-ASTGCN: EEG Emotion Recognition Based on Fusion Deep Convolutional and Adaptive Spatio-temporal Graph Convolutional Networks.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3449083},
pmid = {39236139},
issn = {2168-2208},
abstract = {Thanks to advancements in artificial intelligence and brain-computer interface (BCI) research, there has been increasing attention towards emotion recognition techniques based on electro encephalogram (EEG) recently. The complexity of EEG data poses a challenge when it comes to accurately classifying emotions by integrating time, frequency, and spatial domain features. To address this challenge, this paper proposes a fusion model called DC-ASTGCN, which combines the strengths of deep convolutional neural network (DCNN) and adaptive spatiotemporal graphic convolutional neural network (ASTGCN) to comprehensively analyze and understand EEG signals. The DCNN focuses on extracting frequency-domain and local spatial features from EEG signals to identify brain region activity patterns, while the ASTGCN, with its spatiotemporal attention mechanism and adaptive brain topology layer, reveals the functional connectivity features between brain regions in different emotional states. This integration significantly enhances the model's ability to understand and recognize emotional states. Extensive experiments conducted on the DEAP and SEED datasets demonstrate that the DC-ASTGCN model outperforms existing state-of the-art methods in terms of emotion recognition accuracy.},
}

@article {pmid39236133,
year = {2024},
author = {Rong, F and Yang, B and Guan, C},
title = {Decoding Multi-Class Motor Imagery from Unilateral Limbs Using EEG Signals.},
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.2024.3454088},
pmid = {39236133},
issn = {1558-0210},
abstract = {The EEG is a widely utilized neural signal source, particularly in motor imagery-based brain-computer interface (MI-BCI), offering distinct advantages in applications like stroke rehabilitation. Current research predominantly concentrates on the bilateral limbs paradigm and decoding, but the use scenarios for stroke rehabilitation are typically for unilateral upper limbs. There is a significant challenge to decoding unilateral MI of multitasks due to the overlapped spatial neural activities of the tasks. This study aims to formulate a novel MI-BCI experimental paradigm for unilateral limbs with multitasks. The paradigm encompasses four imagined movement directions: top-bottom, left-right, top right-bottom left, and top left-bottom right. Forty-six healthy subjects participated in this experiment. Commonly used machine learning techniques, such as FBCSP, EEGNet, deepConvNet, and FBCNet, were employed for evaluation. To improve decoding accuracy, we propose an MVCA method that introduces temporal convolution and attention mechanism to effectively capture temporal features from multiple perspectives. With the MVCA model, we have achieved 40.6% and 64.89% classification accuracies for the four-class and two-class scenarios (top right-bottom left and top left-bottom right), respectively. Conclusion: This is the first study demonstrating that motor imagery of multiple directions in unilateral limbs can be decoded. In particular, decoding two directions, right top to left bottom and left top to right bottom, provides the best accuracy, which sheds light on future studies. This study advances the development of the MI-BCI paradigm, offering preliminary evidence for the feasibility of decoding multiple directional information from EEG. This, in turn, enhances the dimensions of MI control commands.},
}

@article {pmid39234682,
year = {2024},
author = {Longo, UG and Marino, M and de Sire, A and Ruiz-Iban, MA and D'Hooghe, P},
title = {The bioinductive collagen implant yields positive histological, clinical and MRI outcomes in the management of rotator cuff tears: A systematic review.},
journal = {Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA},
volume = {},
number = {},
pages = {},
doi = {10.1002/ksa.12429},
pmid = {39234682},
issn = {1433-7347},
abstract = {PURPOSE: The aim of this study is to report and discuss the outcomes of clinical, histological and animal studies exploring the application of bio-inductive collagen implants (BCIs) to partial and full-thickness rotator cuff tears (PT- and FT-RCTs) in addition to reporting on cost-related factors.

METHODS: Review of literature was performed using the PRISMA guidelines. A systematic electronic literature search was conducted using the CENTRAL, CINAHL, Cochrane Library, EBSCOhost, EMBASE and Google Scholar bibliographic databases. Microsoft Excel was used to create tables onto which extracted data were recorded. Tables were organized based on the research statement formulated using the PICO approach. No statistical analysis was performed.

RESULTS: Nine studies evaluated clinical and MRI outcomes of BCI augmentation for FT-RCTs, seven evaluated similar outcomes when applied to PT-RCTs, two additional studies were case reports and three studies assessed application to FT- and PT-RCTs without stratification of results, one of which also reported on histological data. Two studies reported on histological data alone, and finally, two reported on healthcare costs. BCI augmentation, alone and combined with rotator cuff repair (RCR), displays generally good histological, postoperative clinical and MRI outcomes for PT- and FT-RCT treatment. Recent economic analyses seem to be in favour of the use of this procedure, when selected and applied for appropriate patient populations.

CONCLUSION: Several studies have shown promising results of BCI application to PT- and FT-RCTs, both concomitantly and independently from RCR. Investigations report promising histological characteristics, improved clinical outcomes, increased tendon thickness, reduced defect size and lower re-tear rates.

LEVEL OF EVIDENCE: Level IV.},
}

@article {pmid39234592,
year = {2024},
author = {Tajmirriahi, M and Rabbani, H},
title = {A Review of EEG-based Localization of Epileptic Seizure Foci: Common Points with Multimodal Fusion of Brain Data.},
journal = {Journal of medical signals and sensors},
volume = {14},
number = {},
pages = {19},
pmid = {39234592},
issn = {2228-7477},
abstract = {Unexpected seizures significantly decrease the quality of life in epileptic patients. Seizure attacks are caused by hyperexcitability and anatomical lesions of special regions of the brain, and cognitive impairments and memory deficits are their most common concomitant effects. In addition to seizure reduction treatments, medical rehabilitation involving brain-computer interfaces and neurofeedback can improve cognition and quality of life in patients with focal epilepsy in most cases, in particular when resective epilepsy surgery has been considered treatment in drug-resistant epilepsy. Source estimation and precise localization of epileptic foci can improve such rehabilitation and treatment. Electroencephalography (EEG) monitoring and multimodal noninvasive neuroimaging techniques such as ictal/interictal single-photon emission computerized tomography (SPECT) imaging and structural magnetic resonance imaging are common practices for the localization of epileptic foci and have been studied in several kinds of researches. In this article, we review the most recent research on EEG-based localization of seizure foci and discuss various methods, their advantages, limitations, and challenges with a focus on model-based data processing and machine learning algorithms. In addition, we survey whether combined analysis of EEG monitoring and neuroimaging techniques, which is known as multimodal brain data fusion, can potentially increase the precision of the seizure foci localization. To this end, we further review and summarize the key parameters and challenges of processing, fusion, and analysis of multiple source data, in the framework of model-based signal processing, for the development of a multimodal brain data analyzing system. This article has the potential to be used as a valuable resource for neuroscience researchers for the development of EEG-based rehabilitation systems based on multimodal data analysis related to focal epilepsy.},
}

@article {pmid39234406,
year = {2024},
author = {Lakshminarayanan, K and Madathil, D and Murari, BM and Shah, R},
title = {Editorial: Recent advancements in brain-computer interfaces-based limb rehabilitation.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1466450},
doi = {10.3389/fnhum.2024.1466450},
pmid = {39234406},
issn = {1662-5161},
}

@article {pmid39234182,
year = {2024},
author = {Si, Y and Wang, Z and Xu, G and Wang, Z and Xu, T and Zhou, T and Hu, H},
title = {Group-member selection for RSVP-based collaborative brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1402154},
pmid = {39234182},
issn = {1662-4548},
abstract = {OBJECTIVE: The brain-computer interface (BCI) systems based on rapid serial visual presentation (RSVP) have been widely utilized for the detection of target and non-target images. Collaborative brain-computer interface (cBCI) effectively fuses electroencephalogram (EEG) data from multiple users to overcome the limitations of low single-user performance in single-trial event-related potential (ERP) detection in RSVP-based BCI systems. In a multi-user cBCI system, a superior group mode may lead to better collaborative performance and lower system cost. However, the key factors that enhance the collaboration capabilities of multiple users and how to further use these factors to optimize group mode remain unclear.

APPROACH: This study proposed a group-member selection strategy to optimize the group mode and improve the system performance for RSVP-based cBCI. In contrast to the conventional grouping of collaborators at random, the group-member selection strategy enabled pairing each user with a better collaborator and allowed tasks to be done with fewer collaborators. Initially, we introduced the maximum individual capability and maximum collaborative capability (MIMC) to select optimal pairs, improving the system classification performance. The sequential forward floating selection (SFFS) combined with MIMC then selected a sub-group, aiming to reduce the hardware and labor expenses in the cBCI system. Moreover, the hierarchical discriminant component analysis (HDCA) was used as a classifier for within-session conditions, and the Euclidean space data alignment (EA) was used to overcome the problem of inter-trial variability for cross-session analysis.

MAIN RESULTS: In this paper, we verified the effectiveness of the proposed group-member selection strategy on a public RSVP-based cBCI dataset. For the two-user matching task, the proposed MIMC had a significantly higher AUC and TPR and lower FPR than the common random grouping mode and the potential group-member selection method. Moreover, the SFFS with MIMC enabled a trade-off between maintaining performance and reducing the number of system users.

SIGNIFICANCE: The results showed that our proposed MIMC effectively optimized the group mode, enhanced the classification performance in the two-user matching task, and could reduce the redundant information by selecting the sub-group in the RSVP-based multi-user cBCI systems.},
}

@article {pmid39233125,
year = {2024},
author = {Mao, T and Guo, B and Quan, P and Deng, Y and Chai, Y and Xu, J and Jiang, C and Zhang, Q and Lu, Y and Goel, N and Basner, M and Dinges, DF and Rao, H},
title = {Morning resting hypothalamus-dorsal striatum connectivity predicts individual differences in diurnal sleepiness accumulation.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {120833},
doi = {10.1016/j.neuroimage.2024.120833},
pmid = {39233125},
issn = {1095-9572},
abstract = {While the significance of obtaining restful sleep at night and maintaining daytime alertness is well recognized for human performance and overall well-being, substantial variations exist in the development of sleepiness during diurnal waking periods. Despite the established roles of the hypothalamus and striatum in sleep-wake regulation, the specific contributions of this neural circuit in regulating individual sleep homeostasis remain elusive. This study utilized resting-state functional magnetic resonance imaging (fMRI) and mathematical modeling to investigate the role of hypothalamus-striatum connectivity in subjective sleepiness variation in a cohort of 71 healthy adults under strictly controlled in-laboratory conditions. Mathematical modeling results revealed remarkable individual differences in subjective sleepiness accumulation patterns measured by the Karolinska Sleepiness Scale (KSS). Brain imaging data demonstrated that morning hypothalamic connectivity to the dorsal striatum significantly predicts the individual accumulation of subjective sleepiness from morning to evening, while no such correlation was observed for the hypothalamus-ventral striatum connectivity. These findings underscore the distinct roles of hypothalamic connectivity to the dorsal and ventral striatum in individual sleep homeostasis, suggesting that hypothalamus-dorsal striatum circuit may be a promising target for interventions mitigating excessive sleepiness and promoting alertness.},
}

@article {pmid39232389,
year = {2024},
author = {Li, N and Chen, S and Wu, Z and Dong, J and Wang, J and Lei, Y and Mo, J and Wei, W and Li, T},
title = {Secular trends in the prevalence of schizophrenia among different age, period and cohort groups between 1990 and 2019.},
journal = {Asian journal of psychiatry},
volume = {101},
number = {},
pages = {104192},
doi = {10.1016/j.ajp.2024.104192},
pmid = {39232389},
issn = {1876-2026},
abstract = {BACKGROUND: Schizophrenia remains a major public health challenge, and designing efforts to manage it requires understanding its prevalence over time at different geographic scales and population groups.

METHODS: Drawing on data from the Global Burden of Disease study 2019, annual percentage change of schizophrenia was assessed across different age, period and cohort groups at different geographic scales from 1990 to 2019. We examined associations of prevalence with the sociodemographic index.

RESULTS: Global prevalence of schizophrenia in 2019 was 23.60 million (95 % uncertainty interval: 20.23-27.15), with China, India, the USA and Indonesia accounting for 50.72 % of it. Global prevalence increased slightly from 1990 to 2019, with an annual percentage change of 0.03 % (95 % confidence interval 0.01-0.05). Regions with intermediate sociodemographic index accounted for greater proportion of prevalence increasing than regions with high index. Prevalence decreased among those born after 1979 in regions with intermediate sociodemographic index, whereas it consistently improved among all birth cohorts in regions with low index. Regardless of sociodemographic index, prevalence was highest among individuals 30-59 years old than younger or older groups.

CONCLUSIONS: Prevalence of schizophrenia has shown small increases globally over the last three decades. The burden of disease is heavier in relatively less affluent regions, and it disproportionately affects individuals 30-59 years in all regions. Meanwhile, for regions with lower sociodemographic indices, the recent increasing burden among birth cohorts is more pronounced. These findings may help guide futural design of measures to manage or prevent schizophrenia in communities at higher risk.},
}

@article {pmid39232069,
year = {2024},
author = {Shang, L and Si, H and Wang, H and Pan, T and Liu, H and Li, Y and Qiu, J and Xu, M},
title = {Research on fatigue detection of flight trainees based on face EMF feature model combination with PSO-CNN algorithm.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {20641},
pmid = {39232069},
issn = {2045-2322},
support = {NJ2024029//Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics/ ; NJ2024029//Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics/ ; No. U2033202//Joint Fund of the National Natural Science Foundation of China and Civil Aviation Administration of China/ ; No. U2033202//Joint Fund of the National Natural Science Foundation of China and Civil Aviation Administration of China/ ; No. NS2022094//the Fundamental Research Funds for the Central Universities/ ; No. NS2022094//the Fundamental Research Funds for the Central Universities/ ; No.SYJS202207Y//the Experimental Technology Research and Development" project of Nanjing University of Aeronautics and Astronautics Project/ ; No.SYJS202207Y//the Experimental Technology Research and Development" project of Nanjing University of Aeronautics and Astronautics Project/ ; No. 202101042005//the first batch of industry-university-research cooperative collaborative education projects of the Ministry of Education in 2021/ ; No.ZDGB2021024//the Nanjing University of Aeronautics and Astronautics PhD short-term visiting scholar project/ ; },
mesh = {Humans ; *Fatigue/diagnosis ; *Algorithms ; *Neural Networks, Computer ; Aircraft ; Pilots ; Face ; Machine Learning ; Accidents, Aviation ; },
abstract = {Even though the capability of aircraft manufacturing has improved, human factors still play a pivotal role in flight accidents. For example, fatigue-related accidents are a common factor in human-led accidents. Hence, pilots' precise fatigue detections could help increase the flight safety of airplanes. The article suggests a model to recognize fatigue by implementing the convolutional neural network (CNN) by implementing flight trainees' face attributions. First, the flight trainees' face attributions are derived by a method called the land-air call process when the flight simulation is run. Then, sixty-eight points of face attributions are detected by employing the Dlib package. Fatigue attribution points were derived based on the face attribution points to construct a model called EMF to detect face fatigue. Finally, the proposed PSO-CNN algorithm is implemented to learn and train the dataset, and the network algorithm achieves a recognition ratio of 93.9% on the test set, which can efficiently pinpoint the flight trainees' fatigue level. Also, the reliability of the proposed algorithm is validated by comparing two machine learning models.},
}

Humans
*Fatigue/diagnosis
*Algorithms
*Neural Networks, Computer
Aircraft
Pilots
Face
Machine Learning
Accidents, Aviation

@article {pmid39231981,
year = {2024},
author = {Wang, Q and Sun, RY and Hu, JX and Sun, YH and Li, CY and Huang, H and Wang, H and Li, XM},
title = {Hypothalamic-hindbrain circuit for consumption-induced fear regulation.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {7728},
pmid = {39231981},
issn = {2041-1723},
support = {82090031//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82090030//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31900723//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82001186//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2019TQ0278//China Postdoctoral Science Foundation/ ; },
mesh = {Animals ; *Fear/physiology ; Male ; Mice ; Hypothalamus/physiology/metabolism ; Relaxin/metabolism ; Neurons/physiology/metabolism ; Mice, Inbred C57BL ; Neural Pathways/physiology ; Eating/physiology ; Conditioning, Classical/physiology ; Hypothalamic Area, Lateral/physiology/metabolism ; },
abstract = {To ensure survival, animals must sometimes suppress fear responses triggered by potential threats during feeding. However, the mechanisms underlying this process remain poorly understood. In the current study, we demonstrated that when fear-conditioned stimuli (CS) were presented during food consumption, a neural projection from lateral hypothalamic (LH) GAD2 neurons to nucleus incertus (NI) relaxin-3 (RLN3)-expressing neurons was activated, leading to a reduction in CS-induced freezing behavior in male mice. LH[GAD2] neurons established excitatory connections with the NI. The activity of this neural circuit, including NI[RLN3] neurons, attenuated CS-induced freezing responses during food consumption. Additionally, the lateral mammillary nucleus (LM), which received NI[RLN3] projections, along with RLN3 signaling in the LM, mediated the decrease in freezing behavior. Collectively, this study identified an LH[GAD2]-NI[RLN3]-LM circuit involved in modulating fear responses during feeding, thereby enhancing our understanding of how animals coordinate nutrient intake with threat avoidance.},
}

Animals
*Fear/physiology
Male
Mice
Hypothalamus/physiology/metabolism
Relaxin/metabolism
Neurons/physiology/metabolism
Mice, Inbred C57BL
Neural Pathways/physiology
Eating/physiology
Conditioning, Classical/physiology
Hypothalamic Area, Lateral/physiology/metabolism

@article {pmid39231469,
year = {2024},
author = {Baberwal, SS and Magre, LA and Gunawardhana, KRSD and Parkinson, M and Ward, T and Coyle, S},
title = {Motor imagery with cues in virtual reality, audio and screen.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad775e},
pmid = {39231469},
issn = {1741-2552},
abstract = {Training plays a significant role in motor imagery (MI), particularly in applications such as Motor Imagery-based Brain-Computer Interface (MIBCI) systems and rehabilitation systems. Previous studies have investigated the intricate relationship between cues and MI signals. However, the medium of presentation still remains an emerging area to be explored, as possible factors to enhance Motor Imagery signals.. Approach: We hypothesise that the medium used for cue presentation can significantly influence both performance and training outcomes in MI tasks. To test this hypothesis, we designed and executed an experiment implementing no- feedback MI. Our investigation focused on three distinct cue presentation mediums -audio, screen, and virtual reality(VR) headsets-all of which have potential implications for BCI use in the Activities of Daily Lives. Main Results: The results of our study uncovered notable variations in MI signals depending on the medium of cue presentation, where the analysis is based on 3 EEG channels. To substantiate our findings, we employed a comprehensive approach, utilizing various evaluation metrics including Event- Related Synchronisation(ERS)/Desynchronisation(ERD), Feature Extraction (using Recursive Feature Elimination (RFE)), Machine Learning methodologies (using Ensemble Learning), and participant Questionnaires. All the approaches signify that Motor Imagery signals are enhanced when presented in VR, followed by audio, and lastly screen. Applying a Machine Learning approach across all subjects, the mean cross-validation accuracy (Mean ± Std. Error) was 69.24 ± 3.12, 68.69 ± 3.3 and 66.1±2.59 when for the VR, audio-based, and screen-based instructions respectively. Significance: This multi-faceted exploration provides evidence to inform MI- based BCI design and advocates the incorporation of different mediums into the design of MIBCI systems, experimental setups, and user studies. The influence of the medium used for cue presentation may be applied to develop more effective and inclusive MI applications in the realm of human-computer interaction and rehabilitation.},
}

@article {pmid39231466,
year = {2024},
author = {Yang, L and Sun, Q and Van Hulle, MM},
title = {Binocularly incongruent, multifrequency-coded SSVEP in VR: feasibility and characteristics.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad775f},
pmid = {39231466},
issn = {1741-2552},
abstract = {OBJECTIVE: Steady-state visual evoked potentials (SSVEPs) in response to flickering stimuli are popular in brain-computer interfacing (BCI) but their implementation in Virtual Reality (VR) offers new opportunities also for clinical applications. While traditional SSVEP target selection relies on single-frequency stimulation of both eyes simultaneously, further called congruent stimulation, recent studies attempted to improve the information transfer rate by using dual-frequency-coded SSVEP where each eye is presented with a stimulus flickering at a different frequency, further called incongruent stimulation. However, few studies have investigated incongruent multifrequency-coded SSVEP (MultiIncong-SSVEP).

APPROACH: This paper reports on a systematical investigation of incongruent dual-, triple-, and quadruple-frequency-coded SSVEP for use in VR, several of which are entirely novel, and compares their performance with that of congruent dual-frequency-coded SSVEP.

MAIN RESULTS: We were able to confirm the presence of a summation effect when comparing monocular- and binocular single-frequency congruent stimulation, and a suppression effect when comparing monocular- and binocular dual-frequency incongruent stimulation, as both tap into the binocular vision capabilities which, when hampered, could signal amblyopia.

SIGNIFICANCE: In sum, our findings not only evidence the potential of VR-based binocularly incongruent SSVEP but also underscore the importance of paradigm choice and decoder design to optimize system performance and user comfort.},
}

@article {pmid39231465,
year = {2024},
author = {Pulferer, HS and Kostoglou, K and Müller-Putz, G},
title = {Improving non-invasive trajectory decoding via neural correlates of continuous erroneous feedback processing.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad7762},
pmid = {39231465},
issn = {1741-2552},
abstract = {OBJECTIVE: Over the last decades, error-related potentials (ErrPs) have repeatedly proven especially useful as corrective mechanisms in invasive and non-invasive brain-computer interfaces (BCIs). However, research in this context exclusively investigated the distinction of discrete events into correct or erroneous to the present day. Due to this predominant formulation as a binary classification problem, classical ErrP-based BCIs fail to monitor tasks demanding quantitative information on error severity rather than mere qualitative decisions on error occurrence. As a result, fine-tuned and natural feedback control based on continuously perceived deviations from an intended target remains beyond the capabilities of previously used BCI setups.

APPROACH: To address this issue for future BCI designs, we investigated the feasibility of regressing rather than classifying error-related activity non-invasively from the brain.

MAIN RESULTS: Using pre-recorded data from ten able-bodied participants in three sessions each and a multi-output convolutional neural network, we demonstrated the above-chance regression of ongoing target-feedback discrepancies from brain signals in a pseudo-online fashion. In a second step, we used this inferred information about the target deviation to correct the initially displayed feedback accordingly, reporting significant improvements in correlations between corrected feedback and target trajectories across feedback conditions.

SIGNIFICANCE: Our results indicate that continuous information on target-feedback discrepancies can be successfully regressed from cortical activity, paving the way to increasingly naturalistic, fine-tuned correction mechanisms for future BCI applications.},
}

@article {pmid39231428,
year = {2024},
author = {Meng, J and Guger, C},
title = {Call for Papers for Special Issue on Brain-Computer Interfaces.},
journal = {Brain connectivity},
volume = {14},
number = {7},
pages = {352-353},
doi = {10.1089/brain.2024.14072.cfp},
pmid = {39231428},
issn = {2158-0022},
mesh = {*Brain-Computer Interfaces ; Humans ; Periodicals as Topic ; Brain/physiology ; },
}

*Brain-Computer Interfaces
Humans
Periodicals as Topic
Brain/physiology

@article {pmid39229920,
year = {2024},
author = {Ding, L and Guo, H and Zhang, J and Zheng, M and Zhang, W and Wang, L and Du, Q and Zhou, C and Xu, Y and Wu, H and He, Q and Yang, B},
title = {Zosuquidar Promotes Antitumor Immunity by Inducing Autophagic Degradation of PD-L1.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2400340},
doi = {10.1002/advs.202400340},
pmid = {39229920},
issn = {2198-3844},
support = {82330114//National Natural Science Foundation of China/ ; 82273949//National Natural Science Foundation of China/ ; },
abstract = {The intracellular distribution and transportation process are essential for maintaining PD-L1 (programmed death-ligand 1) expression, and intervening in this cellular process may provide promising therapeutic strategies. Here, through a cell-based high content screening, it is found that the ABCB1 (ATP binding cassette subfamily B member 1) modulator zosuquidar dramatically suppresses PD-L1 expression by triggering its autophagic degradation. Mechanistically, ABCB1 interacts with PD-L1 and impairs COP II-mediated PD-L1 transport from ER (endoplasmic reticulum) to Golgi apparatus. The treatment of zosuquidar enhances ABCB1-PD-L1 interaction and leads the ER retention of PD-L1, which is subsequently degraded in the SQSTM1-dependent selective autophagy pathway. In CT26 mouse model and a humanized xenograft mouse model, zosuquidar significantly suppresses tumor growth and accompanies by increased infiltration of cytotoxic T cells. In summary, this study indicates that ABCB1 serves as a negative regulator of PD-L1, and zosuquidar may act as a potential immunotherapy agent by triggering PD-L1 degradation in the early secretory pathway.},
}

@article {pmid39229190,
year = {2024},
author = {Vargas-Irwin, CE and Hosman, T and Gusman, JT and Pun, TK and Simeral, JD and Singer-Clark, T and Kapitonava, A and Nicolas, C and Shah, NP and Avansino, D and Kamdar, F and Williams, Z and Henderson, JM and Hochberg, LR},
title = {Gesture encoding in human left precentral gyrus neuronal ensembles.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.08.23.608325},
pmid = {39229190},
issn = {2692-8205},
abstract = {Understanding the cortical activity patterns driving dexterous upper limb motion has the potential to benefit a broad clinical population living with limited mobility through the development of novel brain-computer interface (BCI) technology. The present study examines the activity of ensembles of motor cortical neurons recorded using microelectrode arrays in the dominant hemisphere of two BrainGate clinical trial participants with cervical spinal cord injury as they attempted to perform a set of 48 different hand gestures. Although each participant displayed a unique organization of their respective neural latent spaces, it was possible to achieve classification accuracies of ~70% for all 48 gestures (and ~90% for sets of 10). Our results show that single unit ensemble activity recorded in a single hemisphere of human precentral gyrus has the potential to generate a wide range of gesture-related signals across both hands, providing an intuitive and diverse set of potential command signals for intracortical BCI use.},
}

@article {pmid39229047,
year = {2024},
author = {Wairagkar, M and Card, NS and Singer-Clark, T and Hou, X and Iacobacci, C and Hochberg, LR and Brandman, DM and Stavisky, SD},
title = {An instantaneous voice synthesis neuroprosthesis.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39229047},
issn = {2692-8205},
support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
abstract = {Brain computer interfaces (BCIs) have the potential to restore communication to people who have lost the ability to speak due to neurological disease or injury. BCIs have been used to translate the neural correlates of attempted speech into text [1-3] . However, text communication fails to capture the nuances of human speech such as prosody, intonation and immediately hearing one's own voice. Here, we demonstrate a "brain-to-voice" neuroprosthesis that instantaneously synthesizes voice with closed-loop audio feedback by decoding neural activity from 256 microelectrodes implanted into the ventral precentral gyrus of a man with amyotrophic lateral sclerosis and severe dysarthria. We overcame the challenge of lacking ground-truth speech for training the neural decoder and were able to accurately synthesize his voice. Along with phonemic content, we were also able to decode paralinguistic features from intracortical activity, enabling the participant to modulate his BCI-synthesized voice in real-time to change intonation, emphasize words, and sing short melodies. These results demonstrate the feasibility of enabling people with paralysis to speak intelligibly and expressively through a BCI.},
}

@article {pmid39227389,
year = {2024},
author = {Chang, H and Sun, Y and Lu, S and Lin, D},
title = {A multistrategy differential evolution algorithm combined with Latin hypercube sampling applied to a brain-computer interface to improve the effect of node displacement.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {20420},
pmid = {39227389},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; *Algorithms ; *Electroencephalography/methods ; Humans ; Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain-computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain-computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain-computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain-computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain-computer interface after node displacement optimization can be evaluated.},
}

*Brain-Computer Interfaces
*Algorithms
*Electroencephalography/methods
Humans
Brain/physiology
Signal Processing, Computer-Assisted

@article {pmid39226926,
year = {2024},
author = {Shen, J and Garrad, M and Zhang, Q and Wong, VCH and Pirrera, A and Groh, RMJ},
title = {A rapid-response soft end effector inspired by the hummingbird beak.},
journal = {Journal of the Royal Society, Interface},
volume = {21},
number = {218},
pages = {20240148},
doi = {10.1098/rsif.2024.0148},
pmid = {39226926},
issn = {1742-5662},
support = {//Royal Academy of Engineering/ ; //Leverhulme Trust/ ; },
mesh = {Animals ; *Beak/physiology/anatomy & histology ; *Birds/physiology ; Robotics ; Models, Biological ; Biomechanical Phenomena ; },
abstract = {Biology is a wellspring of inspiration in engineering design. This paper delves into the application of elastic instabilities-commonly used in biological systems to facilitate swift movement-as a power-amplification mechanism for soft robots. Specifically, inspired by the nonlinear mechanics of the hummingbird beak-and shedding further light on it-we design, build and test a novel, rapid-response, soft end effector. The hummingbird beak embodies the capacity for swift movement, achieving closure in less than [Formula: see text]. Previous work demonstrated that rapid movement is achieved through snap-through deformations, induced by muscular actuation of the beak's root. Using nonlinear finite element simulations coupled with continuation algorithms, we unveil a representative portion of the equilibrium manifold of the beak-inspired structure. The exploration involves the application of a sequence of rotations as exerted by the hummingbird muscles. Specific emphasis is placed on pinpointing and tailoring the position along the manifold of the saddle-node bifurcation at which the onset of elastic instability triggers dynamic snap-through. We show the critical importance of the intermediate rotation input in the sequence, as it results in the accumulation of elastic energy that is then explosively released as kinetic energy upon snap-through. Informed by our numerical studies, we conduct experimental testing on a prototype end effector fabricated using a compliant material (thermoplastic polyurethane). The experimental results support the trends observed in the numerical simulations and demonstrate the effectiveness of the bio-inspired design. Specifically, we measure the energy transferred by the soft end effector to a pendulum, varying the input levels in the sequence of prescribed rotations. Additionally, we demonstrate a potential robotic application in scenarios demanding explosive action. From a mechanics perspective, our work sheds light on how pre-stress fields can enable swift movement in soft robotic systems with the potential to facilitate high input-to-output energy efficiency.},
}

Animals
*Beak/physiology/anatomy & histology
*Birds/physiology
Robotics
Models, Biological
Biomechanical Phenomena

@article {pmid39226850,
year = {2024},
author = {Li, S and Daly, I and Guan, C and Cichocki, A and Jin, J},
title = {Inter-participant transfer learning with attention based domain adversarial training for P300 detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {180},
number = {},
pages = {106655},
doi = {10.1016/j.neunet.2024.106655},
pmid = {39226850},
issn = {1879-2782},
abstract = {A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which requires training. This training process is often time-consuming and inconvenient for new users. In recent years, deep learning models, especially participant-independent models, have garnered significant attention in the domain of ERP classification. However, individual differences in EEG signals hamper model generalization, as the ERP component and other aspects of the EEG signal vary across participants, even when they are exposed to the same stimuli. This paper proposes a novel One-source domain transfer learning method based Attention Domain Adversarial Neural Network (OADANN) to mitigate data distribution discrepancies for cross-participant classification tasks. We train and validate our proposed model on both a publicly available OpenBMI dataset and a Self-collected dataset, employing a leave one participant out cross validation scheme. Experimental results demonstrate that the proposed OADANN method achieves the highest and most robust classification performance and exhibits significant improvements when compared to baseline methods (CNN, EEGNet, ShallowNet, DeepCovNet) and domain generalization methods (ERM, Mixup, and Groupdro). These findings underscore the efficacy of our proposed method.},
}

@article {pmid39226701,
year = {2024},
author = {Ji, Y and Silva, RF and Adali, T and Wen, X and Zhu, Q and Jiang, R and Zhang, D and Qi, S and Calhoun, VD},
title = {Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders.},
journal = {NeuroImage. Clinical},
volume = {43},
number = {},
pages = {103663},
doi = {10.1016/j.nicl.2024.103663},
pmid = {39226701},
issn = {2213-1582},
abstract = {Identifying biomarkers for computer-aided diagnosis (CAD) is crucial for early intervention of psychiatric disorders. Multi-site data have been utilized to increase the sample size and improve statistical power, while multi-modality classification offers significant advantages over traditional single-modality based approaches for diagnosing psychiatric disorders. However, inter-site heterogeneity and intra-modality heterogeneity present challenges to multi-site and multi-modality based classification. In this paper, brain functional and structural networks (BFNs/BSNs) from multiple sites were constructed to establish a joint multi-site multi-modality framework for psychiatric diagnosis. To do this we developed a hypergraph based multi-source domain adaptation (HMSDA) which allowed us to transform source domain subjects into a target domain. A local ordinal structure based multi-task feature selection (LOSMFS) approach was developed by integrating the transformed functional and structural connections (FCs/SCs). The effectiveness of our method was validated by evaluating diagnosis of both schizophrenia (SZ) and autism spectrum disorder (ASD). The proposed method obtained accuracies of 92.2 %±2.22 % and 84.8 %±2.68 % for the diagnosis of SZ and ASD, respectively. We also compared with 6 DA, 10 multi-modality feature selection, and 8 multi-site and multi-modality methods. Results showed the proposed HMSDA+LOSMFS effectively integrated multi-site and multi-modality data to enhance psychiatric diagnosis and identify disorder-specific diagnostic brain connections.},
}

@article {pmid39226201,
year = {2024},
author = {Liu, J and Wang, R and Yang, Y and Zong, Y and Leng, Y and Zheng, W and Ge, S},
title = {Convolutional Transformer-based Cross Subject Model for SSVEP-based BCI Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3454158},
pmid = {39226201},
issn = {2168-2208},
abstract = {Steady-state visual evoked potential (SSVEP) is a commonly used brain-computer interface (BCI) paradigm. The performance of cross-subject SSVEP classification has a strong impact on SSVEP-BCI. This study designed a cross subject generalization SSVEP classification model based on an improved transformer structure that uses domain generalization (DG). The global receptive field of multi-head self-attention is used to learn the global generalized SSVEP temporal information across subjects. This is combined with a parallel local convolution module, designed to avoid oversmoothing the oscillation characteristics of temporal SSVEP data and better fit the feature. Moreover, to improve the cross-subject calibration-free SSVEP classification performance, an DG method named StableNet is combined with the proposed convolutional transformer structure to form the DG-Conformer method, which can eliminate spurious correlations between SSVEP discriminative information and background noise to improve cross-subject generalization. Experiments on two public datasets, Benchmark and BETA, demonstrated the outstanding performance of the proposed DG-Conformer compared with other calibration-free methods, FBCCA, tt-CCA, Compact-CNN, FB-tCNN, and SSVEPNet. Additionally, DG-Conformer outperforms the classic calibration-required algorithms eCCA, eTRCA and eSSCOR when calibration is used. An incomplete partial stimulus calibration scheme was also explored on the Benchmark dataset, and it was demonstrated to be a potential solution for further high-performance personalized SSVEP-BCI with quick calibration.},
}

@article {pmid39225378,
year = {2024},
author = {Zhang, H and Zheng, Z and Chen, X and Xu, L and Guo, C and Wang, J and Cui, Y and Yang, F},
title = {RADICAL: a rationally designed ion channel activated by ligand for chemogenetics.},
journal = {Protein & cell},
volume = {},
number = {},
pages = {},
doi = {10.1093/procel/pwae048},
pmid = {39225378},
issn = {1674-8018},
}

@article {pmid39225144,
year = {2024},
author = {Quan, P and Mao, T and Zhang, X and Wang, R and Lei, H and Wang, J and Liu, W and Dinges, DF and Jiang, C and Rao, H},
title = {Locus coeruleus microstructural integrity is associated with vigilance vulnerability to sleep deprivation.},
journal = {Human brain mapping},
volume = {45},
number = {13},
pages = {e70013},
pmid = {39225144},
issn = {1097-0193},
support = {2023WTSCX034//Guangdong Scientific Research Platform and Projects for the Higher-educational Institution/ ; 32200889//National Natural Science Foundation of China/ ; 71942003//National Natural Science Foundation of China/ ; 2021114002//Shanghai International Studies University Research Projects/ ; 2021KFKT012//Shanghai International Studies University Research Projects/ ; GD23XXL07//Guangdong Philosophy and Social Science Planning/ ; CTRC UL1RR024134/NH/NIH HHS/United States ; P30-NS045839/NH/NIH HHS/United States ; R01-HL102119/NH/NIH HHS/United States ; R01-MH107571/NH/NIH HHS/United States ; R21-AG051981/NH/NIH HHS/United States ; },
mesh = {Humans ; *Sleep Deprivation/diagnostic imaging/physiopathology/pathology ; *Locus Coeruleus/diagnostic imaging/pathology ; Male ; Female ; *Diffusion Tensor Imaging ; Adult ; Young Adult ; *Psychomotor Performance/physiology ; Arousal/physiology ; Anisotropy ; Neuropsychological Tests ; },
abstract = {Insufficient sleep compromises cognitive performance, diminishes vigilance, and disrupts daily functioning in hundreds of millions of people worldwide. Despite extensive research revealing significant variability in vigilance vulnerability to sleep deprivation, the underlying mechanisms of these individual differences remain elusive. Locus coeruleus (LC) plays a crucial role in the regulation of sleep-wake cycles and has emerged as a potential marker for vigilance vulnerability to sleep deprivation. In this study, we investigate whether LC microstructural integrity, assessed by fractional anisotropy (FA) through diffusion tensor imaging (DTI) at baseline before sleep deprivation, can predict impaired psychomotor vigilance test (PVT) performance during sleep deprivation in a cohort of 60 healthy individuals subjected to a rigorously controlled in-laboratory sleep study. The findings indicate that individuals with high LC FA experience less vigilance impairment from sleep deprivation compared with those with low LC FA. LC FA accounts for 10.8% of the variance in sleep-deprived PVT lapses. Importantly, the relationship between LC FA and impaired PVT performance during sleep deprivation is anatomically specific, suggesting that LC microstructural integrity may serve as a biomarker for vigilance vulnerability to sleep loss.},
}

Humans
*Sleep Deprivation/diagnostic imaging/physiopathology/pathology
*Locus Coeruleus/diagnostic imaging/pathology
Male
Female
*Diffusion Tensor Imaging
Adult
Young Adult
*Psychomotor Performance/physiology
Arousal/physiology
Anisotropy
Neuropsychological Tests

@article {pmid39222461,
year = {2024},
author = {Wang, Z and Liu, Y and Huang, S and Qiu, S and Zhang, Y and Huang, H and An, X and Ming, D},
title = {EEG Characteristic Comparison of Motor Imagery between Supernumerary and Inherent limb: Sixth-finger MI Enhances the ERD Pattern and Classification Performance.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3452701},
pmid = {39222461},
issn = {2168-2208},
abstract = {Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, it remains uncertain whether neural patterns different from the traditional inherent limbs motor imagery (MI) can be extracted, which is essential for high-dimensional control of external devices. In this work, we established a MI neo-framework consisting of novel supernumerary robotic sixth-finger MI (SRF-MI) and traditional right-hand MI (RH-MI) paradigms and validated the distinctness of EEG response patterns between two MI tasks for the first time. Twenty-four subjects were recruited for this experiment involving three mental tasks. Event-related spectral perturbation was adopted to supply details about event-related desynchronization (ERD). Activation region, intensity and response time (RT) of ERD were compared between SRF-MI and RH-MI tasks. Three classical classification algorithms were utilized to verify the separability between different mental tasks. And genetic algorithm aims to select optimal combination of channels for neo-framework. A bilateral sensorimotor and prefrontal modulation was found during the SRF-MI task, whereas in RH-MI only contralateral sensorimotor modulation was exhibited. The novel SRF-MI paradigm enhanced ERD intensity by a maximum of 117% in prefrontal area and 188% in the ipsilateral somatosensory-association cortex. And, a global decrease of RT was exhibited during SRF-MI tasks compared to RH-MI. Classification results indicate well separable performance among different mental tasks (88.1% maximum for 2-class and 88.2% maximum for 3-class). This work demonstrated the difference between the SRF-MI and RH-MI paradigms, widening the control bandwidth of the BCI system.},
}

@article {pmid39220036,
year = {2024},
author = {Kim, E and Kim, Y},
title = {Exploring the potential of spiking neural networks in biomedical applications: advantages, limitations, and future perspectives.},
journal = {Biomedical engineering letters},
volume = {14},
number = {5},
pages = {967-980},
pmid = {39220036},
issn = {2093-985X},
abstract = {In this paper, a comprehensive exploration is undertaken to elucidate the utilization of Spiking Neural Networks (SNNs) within the biomedical domain. The investigation delves into the experimentally validated advantages of SNNs in comparison to alternative models like LSTM, while also critically examining the inherent limitations of SNN classifiers or algorithms. SNNs exhibit distinctive advantages that render them particularly apt for targeted applications within the biomedical field. Over time, SNNs have undergone extensive scrutiny in realms such as neuromorphic processing, Brain-Computer Interfaces (BCIs), and Disease Diagnosis. Notably, SNNs demonstrate a remarkable affinity for the processing and analysis of biomedical signals, including but not limited to electroencephalogram (EEG), electromyography (EMG), and electrocardiogram (ECG) data. This paper initiates its exploration by introducing some of the biomedical applications of EMG, such as the classification of hand gestures and motion decoding. Subsequently, the focus extends to the applications of SNNs in the analysis of EEG and ECG signals. Moreover, the paper delves into the diverse applications of SNNs in specific anatomical regions, such as the eyes and noses. In the final sections, the paper culminates with a comprehensive analysis of the field, offering insights into the advantages, disadvantages, challenges, and opportunities introduced by various SNN models in the realm of healthcare and biomedical domains. This holistic examination provides a nuanced perspective on the potential transformative impact of SNN across a spectrum of applications within the biomedical landscape.},
}

@article {pmid39218593,
year = {2024},
author = {Guo, M and Yang, B and Geng, Y and Jie, R and Zhang, Y and Zheng, Y},
title = {[Visual object detection system based on augmented reality and steady-state visual evoked potential].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {4},
pages = {684-691},
pmid = {39218593},
issn = {1001-5515},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; *Augmented Reality ; *Electroencephalography ; Photic Stimulation ; User-Computer Interface ; Algorithms ; },
abstract = {This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects' brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.},
}

Humans
*Evoked Potentials, Visual/physiology
*Brain-Computer Interfaces
*Augmented Reality
*Electroencephalography
Photic Stimulation
User-Computer Interface
Algorithms

@article {pmid39218592,
year = {2024},
author = {Zhang, Y and Liu, D and Gao, F},
title = {[A deep transfer learning approach for cross-subject recognition of mental tasks based on functional near-infrared spectroscopy].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {4},
pages = {673-683},
pmid = {39218592},
issn = {1001-5515},
mesh = {*Spectroscopy, Near-Infrared/methods ; Humans ; *Brain-Computer Interfaces ; Deep Learning ; Algorithms ; Brain/physiology/diagnostic imaging ; Neural Networks, Computer ; },
abstract = {In the field of brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS), traditional subject-specific decoding methods suffer from the limitations of long calibration time and low cross-subject generalizability, which restricts the promotion and application of BCI systems in daily life and clinic. To address the above dilemma, this study proposes a novel deep transfer learning approach that combines the revised inception-residual network (rIRN) model and the model-based transfer learning (TL) strategy, referred to as TL-rIRN. This study performed cross-subject recognition experiments on mental arithmetic (MA) and mental singing (MS) tasks to validate the effectiveness and superiority of the TL-rIRN approach. The results show that the TL-rIRN significantly shortens the calibration time, reduces the training time of the target model and the consumption of computational resources, and dramatically enhances the cross-subject decoding performance compared to subject-specific decoding methods and other deep transfer learning methods. To sum up, this study provides a basis for the selection of cross-subject, cross-task, and real-time decoding algorithms for fNIRS-BCI systems, which has potential applications in constructing a convenient and universal BCI system.},
}

*Spectroscopy, Near-Infrared/methods
Humans
*Brain-Computer Interfaces
Deep Learning
Algorithms
Brain/physiology/diagnostic imaging
Neural Networks, Computer

@article {pmid39218591,
year = {2024},
author = {Xie, P and Men, Y and Zhen, J and Shao, X and Zhao, J and Chen, X},
title = {[The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {4},
pages = {664-672},
pmid = {39218591},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Humans ; *Electroencephalography ; *Robotics/instrumentation ; *Algorithms ; Discriminant Analysis ; },
abstract = {Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.},
}

*Brain-Computer Interfaces
*Evoked Potentials, Visual/physiology
Humans
*Electroencephalography
*Robotics/instrumentation
*Algorithms
Discriminant Analysis

@article {pmid39218590,
year = {2024},
author = {Shao, X and Zhang, Y and Zhang, D and Men, Y and Wang, Z and Chen, X and Xie, P},
title = {[Virtual reality-brain computer interface hand function enhancement rehabilitation system incorporating multi-sensory stimulation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {4},
pages = {656-663},
pmid = {39218590},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; *Hand/physiology ; *Virtual Reality ; *Stroke Rehabilitation/methods/instrumentation ; *Electroencephalography ; Feedback, Sensory ; User-Computer Interface ; Motor Cortex/physiology ; },
abstract = {Stroke is an acute cerebrovascular disease in which sudden interruption of blood supply to the brain or rupture of cerebral blood vessels cause damage to brain cells and consequently impair the patient's motor and cognitive abilities. A novel rehabilitation training model integrating brain-computer interface (BCI) and virtual reality (VR) not only promotes the functional activation of brain networks, but also provides immersive and interesting contextual feedback for patients. In this paper, we designed a hand rehabilitation training system integrating multi-sensory stimulation feedback, BCI and VR, which guides patients' motor imaginations through the tasks of the virtual scene, acquires patients' motor intentions, and then carries out human-computer interactions under the virtual scene. At the same time, haptic feedback is incorporated to further increase the patients' proprioceptive sensations, so as to realize the hand function rehabilitation training based on the multi-sensory stimulation feedback of vision, hearing, and haptic senses. In this study, we compared and analyzed the differences in power spectral density of different frequency bands within the EEG signal data before and after the incorporation of haptic feedback, and found that the motor brain area was significantly activated after the incorporation of haptic feedback, and the power spectral density of the motor brain area was significantly increased in the high gamma frequency band. The results of this study indicate that the rehabilitation training of patients with the VR-BCI hand function enhancement rehabilitation system incorporating multi-sensory stimulation can accelerate the two-way facilitation of sensory and motor conduction pathways, thus accelerating the rehabilitation process.},
}

Humans
*Brain-Computer Interfaces
*Hand/physiology
*Virtual Reality
*Stroke Rehabilitation/methods/instrumentation
*Electroencephalography
Feedback, Sensory
User-Computer Interface
Motor Cortex/physiology

@article {pmid39218589,
year = {2024},
author = {Wang, Y and Li, Y and Cui, H and Li, M and Chen, X},
title = {[A review of functional electrical stimulation based on brain-computer interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {4},
pages = {650-655},
pmid = {39218589},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; Electric Stimulation/methods ; Stroke Rehabilitation/methods ; Spinal Cord Injuries/rehabilitation ; Electric Stimulation Therapy/methods ; },
abstract = {Individuals with motor dysfunction caused by damage to the central nervous system are unable to transmit voluntary movement commands to their muscles, resulting in a reduced ability to control their limbs. However, traditional rehabilitation methods have problems such as long treatment cycles and high labor costs. Functional electrical stimulation (FES) based on brain-computer interface (BCI) connects the patient's intentions with muscle contraction, and helps to promote the reconstruction of nerve function by recognizing nerve signals and stimulating the moving muscle group with electrical impulses to produce muscle convulsions or limb movements. It is an effective treatment for sequelae of neurological diseases such as stroke and spinal cord injury. This article reviewed the current research status of BCI-based FES from three aspects: BCI paradigms, FES parameters and rehabilitation efficacy, and looked forward to the future development trend of this technology, in order to improve the understanding of BCI-based FES.},
}

*Brain-Computer Interfaces
Humans
Electric Stimulation/methods
Stroke Rehabilitation/methods
Spinal Cord Injuries/rehabilitation
Electric Stimulation Therapy/methods

@article {pmid39218588,
year = {2024},
author = {Chen, Y and Zhang, Z and Wang, F and Ding, P and Zhao, L and Fu, Y},
title = {[An emerging discipline: brain-computer interfaces medicine].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {4},
pages = {641-649},
pmid = {39218588},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; Electroencephalography ; Brain/physiology ; },
abstract = {With the development of brain-computer interface (BCI) technology and its translational application in clinical medicine, BCI medicine has emerged, ushering in profound changes to the practice of medicine, while also bringing forth a series of ethical issues related to BCI medicine. BCI medicine is progressively emerging as a new disciplinary focus, yet to date, there has been limited literature discussing it. Therefore, this paper focuses on BCI medicine, firstly providing an overview of the main potential medical applications of BCI technology. It then defines the discipline, outlines its objectives, methodologies, potential efficacy, and associated translational medical research. Additionally, it discusses the ethics associated with BCI medicine, and introduces the standardized operational procedures for BCI medical applications and the methods for evaluating the efficacy of BCI medical applications. Finally, it anticipates the challenges and future directions of BCI medicine. In the future, BCI medicine may become a new academic discipline or major in higher education. In summary, this article is hoped to provide thoughts and references for the development of the discipline of BCI medicine.},
}

*Brain-Computer Interfaces/trends
Humans
Electroencephalography
Brain/physiology

@article {pmid39218255,
year = {2024},
author = {Guellil, MS and Kies, F and Hussein, EK and Shabaz, M and Hampson, RE},
title = {Pushing the Boundaries of Brain-Computer Interfacing (BCI) and Neuron-Electronics.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110274},
doi = {10.1016/j.jneumeth.2024.110274},
pmid = {39218255},
issn = {1872-678X},
}

@article {pmid39218135,
year = {2024},
author = {Chu, T and Si, X and Xie, H and Ma, H and Shi, Y and Yao, W and Xing, D and Zhao, F and Dong, F and Gai, Q and Che, K and Guo, Y and Chen, D and Ming, D and Mao, N},
title = {Regional structural-functional connectivity coupling in major depressive disorder is associated with neurotransmitter and genetic profiles.},
journal = {Biological psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.biopsych.2024.08.022},
pmid = {39218135},
issn = {1873-2402},
abstract = {BACKGROUND: Abnormalities in structural-functional connectivity (SC-FC) coupling have been identified globally in patients with major depressive disorder (MDD). However, investigations have neglected the variability and hierarchical distribution of these abnormalities across different brain regions. Furthermore, the biological mechanisms underlying regional SC-FC coupling patterns are not well understood.

METHODS: We enrolled 182 patients with MDD and 157 healthy control (HC) subjects, quantifying the intergroup differences in regional SC-FC coupling. The extreme gradient boosting (XGBoost), support vector machines (SVM) and random forest (RF) models were constructed to assess the potential of SC-FC coupling as biomarkers for MDD diagnosis and symptom prediction. Then, we examined the link between changes in regional SC-FC coupling in patients with MDD, neurotransmitter distributions, and gene expression.

RESULTS: We observed increased regional SC-FC coupling in default mode network (T = 3.233) and decreased coupling in frontoparietal network (T = -3.471) in MDD relative to HC. XGBoost (AUC = 0.853), SVM (AUC = 0.832) and RF (p < 0.05) models exhibited good prediction performance. The alterations in regional SC-FC coupling in patients with MDD were correlated with the distributions of four neurotransmitters (p < 0.05) and expression maps of specific genes. These genes were strongly enriched in genes implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on two brain atlases.

CONCLUSIONS: This work enhances our understanding of MDD and pave the way for the development of additional targeted therapeutic interventions.},
}

@article {pmid39216077,
year = {2024},
author = {Sun, H and Cai, R and Li, R and Li, M and Gao, L and Li, X},
title = {Conjunctive processing of spatial border and locomotion in retrosplenial cortex during spatial navigation.},
journal = {The Journal of physiology},
volume = {},
number = {},
pages = {},
doi = {10.1113/JP286434},
pmid = {39216077},
issn = {1469-7793},
support = {32071097//MOST | National Natural Science Foundation of China (NSFC)/ ; 31871056//MOST | National Natural Science Foundation of China (NSFC)/ ; 61703365//MOST | National Natural Science Foundation of China (NSFC)/ ; 91732302//MOST | National Natural Science Foundation of China (NSFC)/ ; 81625006//MOST | National Natural Science Foundation of China (NSFC)/ ; 2018YFC1005003//National Key R&D Program of China/ ; 2019XZZX001-01-20//MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities)/ ; 2018QN81008//MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities)/ ; 2020YFB1313500//National Key Research and Development Program of the Ministry of Science and Technology of China/ ; //the MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University/ ; },
abstract = {Spatial information and dynamic locomotor behaviours are equally important for achieving locomotor goals during spatial navigation. However, it remains unclear how spatial and locomotor information is integrated during the processing of self-initiated spatial navigation. Anatomically, the retrosplenial cortex (RSC) has reciprocal connections with brain regions related to spatial processing, including the hippocampus and para-hippocampus, and also receives inputs from the secondary motor cortex. In addition, RSC is functionally associated with allocentric and egocentric spatial targets and head-turning. So, RSC may be a critical region for integrating spatial and locomotor information. In this study, we first examined the role of RSC in spatial navigation using the Morris water maze and found that mice with inactivated RSC took a longer time and distance to reach their destination. Then, by imaging neuronal activity in freely behaving mice within two open fields of different sizes, we identified a large proportion of border cells, head-turning cells and locomotor speed cells in the superficial layer of RSC. Interestingly, some RSC neurons exhibited conjunctive coding for both spatial and locomotor signals. Furthermore, these conjunctive neurons showed higher prediction accuracy compared with simple spatial or locomotor neurons in special navigator scenes using the border, turning and positive-speed conjunctive cells. Our study reveals that the RSC is an important conjunctive brain region that processes spatial and locomotor information during spatial navigation. KEY POINTS: Retrosplenial cortex (RSC) is indispensable during spatial navigation, which was displayed by the longer time and distance of mice to reach their destination after the inactivation of RSC in a water maze. The superficial layer of RSC has a larger population of spatial-related border cells, and locomotion-related head orientation and speed cells; however, it has few place cells in two-dimensional spatial arenas. Some RSC neurons exhibited conjunctive coding for both spatial and locomotor signals, and the conjunctive neurons showed higher prediction accuracy compared with simple spatial or locomotor neurons in special navigation scenes. Our study reveals that the RSC is an important conjunctive brain region that processes both spatial and locomotor information during spatial navigation.},
}

@article {pmid39215126,
year = {2024},
author = {Zhao, W and Jiang, X and Zhang, B and Xiao, S and Weng, S},
title = {CTNet: a convolutional transformer network for EEG-based motor imagery classification.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {20237},
pmid = {39215126},
issn = {2045-2322},
support = {3502Z202374054//Xiamen Natural Science Foundation of China/ ; CYKYPT02//Big data technology institute of Chengyi College, Jimei University of China/ ; 2023J01785//Natural Science Foundation of Fujian Province of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Neural Networks, Computer ; Brain/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling the translation of thought into actionable commands for interactive and assistive technologies. However, the constrained decoding performance of brain signals poses a limitation to the broader application and development of BCI systems. In this study, we introduce a convolutional Transformer network (CTNet) designed for EEG-based MI classification. Firstly, CTNet employs a convolutional module analogous to EEGNet, dedicated to extracting local and spatial features from EEG time series. Subsequently, it incorporates a Transformer encoder module, leveraging a multi-head attention mechanism to discern the global dependencies of EEG's high-level features. Finally, a straightforward classifier module comprising fully connected layers is followed to categorize EEG signals. In subject-specific evaluations, CTNet achieved remarkable decoding accuracies of 82.52% and 88.49% on the BCI IV-2a and IV-2b datasets, respectively. Furthermore, in the challenging cross-subject assessments, CTNet achieved recognition accuracies of 58.64% on the BCI IV-2a dataset and 76.27% on the BCI IV-2b dataset. In both subject-specific and cross-subject evaluations, CTNet holds a leading position when compared to some of the state-of-the-art methods. This underscores the exceptional efficacy of our approach and its potential to set a new benchmark in EEG decoding.},
}

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

@article {pmid39215011,
year = {2024},
author = {Egger, J and Kostoglou, K and Müller-Putz, GR},
title = {Chrono-EEG dynamics influencing hand gesture decoding: a 10-hour study.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {20247},
pmid = {39215011},
issn = {2045-2322},
support = {101070939//HORIZON EUROPE European Innovation Council/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Gestures ; *Hand/physiology ; Adult ; Young Adult ; Movement/physiology ; Motor Cortex/physiology ; Brain-Computer Interfaces ; },
abstract = {Long-term electroencephalography (EEG) recordings have primarily been used to study resting-state fluctuations. These recordings provide valuable insights into various phenomena such as sleep stages, cognitive processes, and neurological disorders. However, this study explores a new angle, focusing for the first time on the evolving nature of EEG dynamics over time within the context of movement. Twenty-two healthy individuals were measured six times from 2 p.m. to 12 a.m. with intervals of 2 h while performing four right-hand gestures. Analysis of movement-related cortical potentials (MRCPs) revealed a reduction in amplitude for the motor and post-motor potential during later hours of the day. Evaluation in source space displayed an increase in the activity of M1 of the contralateral hemisphere and the SMA of both hemispheres until 8 p.m. followed by a decline until midnight. Furthermore, we investigated how changes over time in MRCP dynamics affect the ability to decode motor information. This was achieved by developing classification schemes to assess performance across different scenarios. The observed variations in classification accuracies over time strongly indicate the need for adaptive decoders. Such adaptive decoders would be instrumental in delivering robust results, essential for the practical application of BCIs during day and nighttime usage.},
}

Humans
*Electroencephalography/methods
Male
Female
*Gestures
*Hand/physiology
Adult
Young Adult
Movement/physiology
Motor Cortex/physiology
Brain-Computer Interfaces

@article {pmid39214374,
year = {2024},
author = {Zheng, Y and Yu, X and Wei, L and Chen, Q and Xu, Y and Ni, P and Deng, W and Guo, W and Hu, X and Qi, X and Li, T},
title = {LT-102, an AMPA receptor potentiator, alleviates depression-like behavior and synaptic plasticity impairments in prefrontal cortex induced by sleep deprivation.},
journal = {Journal of affective disorders},
volume = {367},
number = {},
pages = {18-30},
doi = {10.1016/j.jad.2024.08.176},
pmid = {39214374},
issn = {1573-2517},
abstract = {BACKGROUND: Sleep loss is closely related to the onset and development of depression, and the mechanisms involved may include impaired synaptic plasticity. Considering the important role of glutamate α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate receptors (AMPARs) in synaptic plasticity as well as depression, we introduce LT-102, a novel AMPARs potentiator, to evaluate the potential of LT-102 in treating sleep deprivation-induced depression-like behaviors.

METHODS: We conducted a comprehensive behavioral assessment to evaluate the effects of LT-102 on depression-like symptoms in male C57BL/6J mice. This assessment included the open field test to measure general locomotor activity and anxiety-like behavior, the forced swimming test and tail suspension test to assess despair behaviors indicative of depressive states, and the sucrose preference test to quantify anhedonia, a core symptom of depression. Furthermore, to explore the impact of LT-102 on synaptic plasticity, we utilized a combination of Western blot analysis to detect protein expression levels, Golgi-Cox staining to visualize neuronal morphology, and immunofluorescence to examine the localization of synaptic proteins. Additionally, we utilized primary cortical neurons to delineate the signaling pathway modulated by LT-102.

RESULTS: Treatment with LT-102 significantly reduced depression-like behaviors associated with sleep deprivation. Quantitative Western blot (WB) analysis revealed a significant increase in GluA1 phosphorylation in the prefrontal cortex (PFC), triggering the Ca[2+]/calmodulin-dependent protein kinase II/cAMP response element-binding protein/brain-derived neurotrophic factor (CaMKII/CREB/BDNF) and forkhead box protein P2/postsynaptic density protein 95 (FoxP2/PSD95) signaling pathways. Immunofluorescence imaging confirmed that LT-102 treatment increased spine density and co-labeling of PSD95 and vesicular glutamate transporter 1 (VGLUT1) in the PFC, reversing the reductions typically observed following sleep deprivation. Golgi staining further validated these results, showing a substantial increase in neuronal dendritic spine density in sleep-deprived mice treated with LT-102. Mechanistically, application of LT-102 to primary cortical neurons, resulted in elevated levels of phosphorylated AKT (p-AKT) and phosphorylated glycogen synthase kinase-3 beta (p-GSK3β), key downstream molecules in the BDNF signaling pathway, which in turn upregulated FoxP2 and PSD95 expression.

LIMITATIONS: In our study, we chose to exclusively use male mice to eliminate potential influences of the estrous cycle on behavior and physiology. As there is no widely accepted positive drug control for sleep deprivation studies, we did not include one in our research.

CONCLUSION: Our results suggest that LT-102 is a promising therapeutic agent for counteracting depression-like behaviors and synaptic plasticity deficits induced by sleep deprivation, primarily through the activation of CaMKII/CREB/BDNF and AKT/GSK3β/FoxP2/PSD95 signaling pathways.},
}

@article {pmid39213709,
year = {2024},
author = {Si, X and Huang, D and Liang, Z and Sun, Y and Huang, H and Liu, Q and Yang, Z and Ming, D},
title = {Temporal aware Mixed Attention-based Convolution and Transformer Network for cross-subject EEG emotion recognition.},
journal = {Computers in biology and medicine},
volume = {181},
number = {},
pages = {108973},
doi = {10.1016/j.compbiomed.2024.108973},
pmid = {39213709},
issn = {1879-0534},
abstract = {Emotion recognition is crucial for human-computer interaction, and electroencephalography (EEG) stands out as a valuable tool for capturing and reflecting human emotions. In this study, we propose a hierarchical hybrid model called Mixed Attention-based Convolution and Transformer Network (MACTN). This model is designed to collectively capture both local and global temporal information and is inspired by insights from neuroscientific research on the temporal dynamics of emotions. First, we introduce depth-wise temporal convolution and separable convolution to extract local temporal features. Then, a self-attention-based transformer is used to integrate the sparse global emotional features. Besides, channel attention mechanism is designed to identify the most task-relevant channels, facilitating the capture of relationships between different channels and emotional states. Extensive experiments are conducted on three public datasets under both offline and online evaluation modes. In the multi-class cross-subject online evaluation using the THU-EP dataset, MACTN demonstrates an approximate 8% enhancement in 9-class emotion recognition accuracy in comparison to state-of-the-art methods. In the multi-class cross-subject offline evaluation using the DEAP and SEED datasets, a comparable performance is achieved solely based on the raw EEG signals, without the need for prior knowledge or transfer learning during the feature extraction and learning process. Furthermore, ablation studies have shown that integrating self-attention and channel-attention mechanisms improves classification performance. This method won the Emotional BCI Competition's final championship in the World Robot Contest. The source code is available at https://github.com/ThreePoundUniverse/MACTN.},
}

@article {pmid39213268,
year = {2024},
author = {Wang, T and Ke, Y and Huang, Y and He, F and Zhong, W and Liu, S and Ming, D},
title = {Using Semi-supervised Domain Adaptation to Enhance EEG-Based Cross-Task Mental Workload Classification Performance.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3452410},
pmid = {39213268},
issn = {2168-2208},
abstract = {Mental workload (MWL) assessment is critical for accident prevention and operator safety. However, achieving cross-task generalization of MWL classification models is a significant challenge for real-world applications. Classifiers trained on labeled samples from one task often experience a notable performance drop when directly applied to samples from other tasks, limiting its use cases. To address this issue, we propose a semi-supervised cross-task domain adaptation (SCDA) method using power spectral density (PSD) features for MWL recognition across tasks (MATB-II and n-back). Our results demonstrated that the SCDA method achieved the best cross-task classification performance on our data and COG-BCI public dataset, with accuracies of 90.98% ± 9.36% and 96.61% ± 4.35%, respectively. Furthermore, in the cross-task classification of cross-subject scenarios, SCDA showed the highest average accuracy (75.39% ± 9.56% on our data, 90.98% ± 9.36% on the COG-BCI public dataset). The findings indicate that the semi-supervised transfer learning approach using PSD features is feasible and effective for cross-task MWL assessment.},
}

@article {pmid39213025,
year = {2024},
author = {Yan, X and Li, Z and Cao, C and Huang, L and Li, Y and Meng, X and Zhang, B and Yu, M and Huang, T and Chen, J and Li, W and Hao, L and Huang, D and Yi, B and Zhang, M and Zha, S and Yang, H and Yao, J and Qian, P and Leung, CK and Fan, H and Jiang, P and Shui, T},
title = {Characteristics, Influence, Prevention, and Control Measures of the Mpox Infodemic: Scoping Review of Infodemiology Studies.},
journal = {Journal of medical Internet research},
volume = {26},
number = {},
pages = {e54874},
doi = {10.2196/54874},
pmid = {39213025},
issn = {1438-8871},
mesh = {Humans ; *Social Media/statistics & numerical data ; Pandemics/prevention & control ; Internet ; },
abstract = {BACKGROUND: The mpox pandemic has caused widespread public concern around the world. The spread of misinformation through the internet and social media could lead to an infodemic that poses challenges to mpox control.

OBJECTIVE: This review aims to summarize mpox-related infodemiology studies to determine the characteristics, influence, prevention, and control measures of the mpox infodemic and propose prospects for future research.

METHODS: The scoping review was conducted based on a structured 5-step methodological framework. A comprehensive search for mpox-related infodemiology studies was performed using PubMed, Web of Science, Embase, and Scopus, with searches completed by April 30, 2024. After study selection and data extraction, the main topics of the mpox infodemic were categorized and summarized in 4 aspects, including a trend analysis of online information search volume, content topics of mpox-related online posts and comments, emotional and sentiment characteristics of online content, and prevention and control measures for the mpox infodemic.

RESULTS: A total of 1607 articles were retrieved from the databases according to the keywords, and 61 studies were included in the final analysis. After the World Health Organization's declaration of an mpox public health emergency of international concern in July 2022, the number of related studies began growing rapidly. Google was the most widely used search engine platform (9/61, 15%), and Twitter was the most used social media app (32/61, 52%) for researchers. Researchers from 33 countries were concerned about mpox infodemic-related topics. Among them, the top 3 countries for article publication were the United States (27 studies), India (9 studies), and the United Kingdom (7 studies). Studies of online information search trends showed that mpox-related online search volume skyrocketed at the beginning of the mpox outbreak, especially when the World Health Organization provided important declarations. There was a large amount of misinformation with negative sentiment and discriminatory and hostile content against gay, bisexual, and other men who have sex with men. Given the characteristics of the mpox infodemic, the studies provided several positive prevention and control measures, including the timely and active publishing of professional, high-quality, and easy-to-understand information online; strengthening surveillance and early warning for the infodemic based on internet data; and taking measures to protect key populations from the harm of the mpox infodemic.

CONCLUSIONS: This comprehensive summary of evidence from previous mpox infodemiology studies is valuable for understanding the characteristics of the mpox infodemic and for formulating prevention and control measures. It is essential for researchers and policy makers to establish prediction and early warning approaches and targeted intervention methods for dealing with the mpox infodemic in the future.},
}

Humans
*Social Media/statistics & numerical data
Pandemics/prevention & control
Internet

@article {pmid39211094,
year = {2024},
author = {Mender, MJ and Ward, AL and Cubillos, LH and Kelberman, MM and Costello, JT and Temmar, H and Wallace, DM and Lin, ET and Lam, JLW and Willsey, MS and Ganesh Kumar, N and Kung, TA and Patil, PG and Chestek, CA},
title = {Functional Electrical Stimulation and Brain-Machine Interfaces for Simultaneous Control of Wrist and Finger Flexion.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.08.11.607263},
pmid = {39211094},
issn = {2692-8205},
abstract = {Brain-machine interface (BMI) controlled functional electrical stimulation (FES) is a promising treatment to restore hand movements to people with cervical spinal cord injury. Recent intracortical BMIs have shown unprecedented successes in decoding user intentions, however the hand movements restored by FES have largely been limited to predetermined grasps. Restoring dexterous hand movements will require continuous control of many biomechanically linked degrees-of-freedom in the hand, such as wrist and finger flexion, that would form the basis of those movements. Here we investigate the ability to restore simultaneous wrist and finger flexion, which would enable grasping with a controlled hand posture and assist in manipulating objects once grasped. We demonstrate that intramuscular FES can enable monkeys with temporarily paralyzed hands to move their fingers and wrist across a functional range of motion, spanning an average 88.6 degrees at the metacarpophalangeal joint flexion and 71.3 degrees of wrist flexion, and intramuscular FES can control both joints simultaneously in a real-time task. Additionally, we demonstrate a monkey using an intracortical BMI to control the wrist and finger flexion in a virtual hand, both before and after the hand is temporarily paralyzed, even achieving success rates and acquisition times equivalent to able-bodied control with BMI control after temporary paralysis in two sessions. Together, this outlines a method using an artificial brain-to-body interface that could restore continuous wrist and finger movements after spinal cord injury.},
}

@article {pmid38769157,
year = {2024},
author = {Silva, AB and Liu, JR and Metzger, SL and Bhaya-Grossman, I and Dougherty, ME and Seaton, MP and Littlejohn, KT and Tu-Chan, A and Ganguly, K and Moses, DA and Chang, EF},
title = {A bilingual speech neuroprosthesis driven by cortical articulatory representations shared between languages.},
journal = {Nature biomedical engineering},
volume = {8},
number = {8},
pages = {977-991},
pmid = {38769157},
issn = {2157-846X},
support = {DC018671-01A1//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; T32GM007618//U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)/ ; },
mesh = {Humans ; *Speech/physiology ; *Multilingualism ; *Electrocorticography/methods ; Motor Cortex/physiology ; Male ; Brain-Computer Interfaces ; Language ; Deep Learning ; Neural Prostheses ; },
abstract = {Advancements in decoding speech from brain activity have focused on decoding a single language. Hence, the extent to which bilingual speech production relies on unique or shared cortical activity across languages has remained unclear. Here, we leveraged electrocorticography, along with deep-learning and statistical natural-language models of English and Spanish, to record and decode activity from speech-motor cortex of a Spanish-English bilingual with vocal-tract and limb paralysis into sentences in either language. This was achieved without requiring the participant to manually specify the target language. Decoding models relied on shared vocal-tract articulatory representations across languages, which allowed us to build a syllable classifier that generalized across a shared set of English and Spanish syllables. Transfer learning expedited training of the bilingual decoder by enabling neural data recorded in one language to improve decoding in the other language. Overall, our findings suggest shared cortical articulatory representations that persist after paralysis and enable the decoding of multiple languages without the need to train separate language-specific decoders.},
}

Humans
*Speech/physiology
*Multilingualism
*Electrocorticography/methods
Motor Cortex/physiology
Male
Brain-Computer Interfaces
Language
Deep Learning
Neural Prostheses

@article {pmid39209118,
year = {2024},
author = {Byeon, H and Quraishi, A and Khalaf, MI and Mp, S and Khan, IR and Dutta, AK and Dasari, R and Yellu, RR and Reegu, FA and Bhatt, MW},
title = {Bio-inspired EEG Signal computing using Machine Learning and Fuzzy Theory for Decision making in future-oriented Brain-Controlled Vehicles.},
journal = {SLAS technology},
volume = {},
number = {},
pages = {100187},
doi = {10.1016/j.slast.2024.100187},
pmid = {39209118},
issn = {2472-6311},
abstract = {One kind of autonomous vehicle that can take instructions from the driver by reading their electroencephalogram (EEG) signals using a Brain-Computer Interface (BCI) is called a Brain-Controlled Vehicle (BCV). The operation of such a vehicle is greatly affected by how well the BCI works. At present, there are limitations on the accuracy of BCI recognition, the number of distinguishable command categories, and the execution duration of command recognition. Consequently, vehicles that are exclusively controlled by EEG signals demonstrate suboptimal control performance. To address the difficulty of improving the control capabilities of brain-controlled cars while maintaining BCI performance, a fuzzy logic-based technique called as Fuzzy Brain-Control Fusion Control is introduced. This approach uses Fuzzy Discrete Event System (FDES) supervisory theory to verify the accuracy of the driver's brain-controlled directives. Concurrently, a fuzzy logic-based automatic controller is developed to generate decisions automatically in accordance with the present state of the vehicle via fuzzy reasoning. The final decision is then reached through the application of secondary fuzzy reasoning to the accuracy of the driver's instructions and the automated decisions to make adjustments that are more consistent with human intent. A clever BCI gadget known as the Consistent State Visual Evoked Potential (SSVEP) is utilized to show the viability of the proposed technique. We recommend that additional research should be conducted at this time to confirm that our recommended system may further improve the control execution of BCI-fueled cars, regardless of whether BCIs have special limitations.},
}

@article {pmid39209064,
year = {2024},
author = {Zhang, E and Shotbolt, M and Chang, CY and Scott-Vandeusen, A and Chen, S and Liang, P and Radu, D and Khizroev, S},
title = {Controlling action potentials with magnetoelectric nanoparticles.},
journal = {Brain stimulation},
volume = {17},
number = {5},
pages = {1005-1017},
doi = {10.1016/j.brs.2024.08.008},
pmid = {39209064},
issn = {1876-4754},
abstract = {Non-invasive or minutely invasive and wireless brain stimulation that can target any region of the brain is an open problem in engineering and neuroscience with serious implications for the treatment of numerous neurological diseases. Despite significant recent progress in advancing new methods of neuromodulation, none has successfully replicated the efficacy of traditional wired stimulation and improved on its downsides without introducing new complications. Due to the capability to convert magnetic fields into local electric fields, MagnetoElectric NanoParticle (MENP) neuromodulation is a recently proposed framework based on new materials that can locally sensitize neurons to specific, low-strength alternating current (AC) magnetic fields (50Hz 1.7 kOe field). However, the current research into this neuromodulation concept is at a very early stage, and the theoretically feasible game-changing advantages remain to be proven experimentally. To break this stalemate phase, this study leveraged understanding of the non-linear properties of MENPs and the nanoparticles' field interaction with the cellular microenvironment. Particularly, the applied magnetic field's strength and frequency were tailored to the M - H hysteresis loop of the nanoparticles. Furthermore, rectangular prisms instead of the more traditional "spherical" nanoparticle shapes were used to: (i) maximize the magnetoelectric effect and (ii) improve the nanoparticle-cell-membrane surface interface. Neuromodulation performance was evaluated in a series of exploratory in vitro experiments on 2446 rat hippocampus neurons. Linear mixed effect models were used to ensure the independence of samples by accounting for fixed adjacency effects in synchronized firing. Neural activity was measured over repeated 4-min segments, containing 90 s of baseline measurements, 90 s of stimulation measurements, and 60 s of post stimulation measurements. 87.5 % of stimulation attempts produced statistically significant (P < 0.05) changes in neural activity, with 58.3 % producing large changes (P < 0.01). In negative controls using either zero or 1.7 kOe-strength field without nanoparticles, no experiments produced significant changes in neural activity (P > 0.05 and P > 0.15 respectively). Furthermore, an exploratory analysis of a direct current (DC) magnetic field indicated that the DC field could be used with MENPs to inhibit neuron activity (P < 0.01). These experiments demonstrated the potential for magnetoelectric neuromodulation to offer a near one-to-one functionality match with conventional electrode stimulation without requiring surgical intervention or genetic modification to achieve success, instead relying on physical properties of these nanoparticles as "On/Off" control mechanisms. ONE-SENTENCE SUMMARY: This in vitro neural cell culture study explores how to exploit the non-linear and anisotropic properties of magnetoelectric nanoparticles for wireless neuromodulation, the importance of magnetic field strength and frequency matching for optimization, and demonstrates, for the first time, that magnetoelectric neuromodulation can inhibit neural responses.},
}

@article {pmid39207622,
year = {2024},
author = {Feng, J and Wang, X and Pan, M and Li, CX and Zhang, Z and Sun, M and Liao, T and Wang, Z and Luo, J and Shi, L and Chen, YJ and Li, HF and Xu, J},
title = {The Medial Prefrontal Cortex-Basolateral Amygdala Circuit Mediates Anxiety in Shank3 InsG3680 Knock-in Mice.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {39207622},
issn = {1995-8218},
abstract = {Anxiety disorder is a major symptom of autism spectrum disorder (ASD) with a comorbidity rate of ~40%. However, the neural mechanisms of the emergence of anxiety in ASD remain unclear. In our study, we found that hyperactivity of basolateral amygdala (BLA) pyramidal neurons (PNs) in Shank3 InsG3680 knock-in (InsG3680[+/+]) mice is involved in the development of anxiety. Electrophysiological results also showed increased excitatory input and decreased inhibitory input in BLA PNs. Chemogenetic inhibition of the excitability of PNs in the BLA rescued the anxiety phenotype of InsG3680[+/+] mice. Further study found that the diminished control of the BLA by medial prefrontal cortex (mPFC) and optogenetic activation of the mPFC-BLA pathway also had a rescue effect, which increased the feedforward inhibition of the BLA. Taken together, our results suggest that hyperactivity of the BLA and alteration of the mPFC-BLA circuitry are involved in anxiety in InsG3680[+/+] mice.},
}

@article {pmid39207066,
year = {2024},
author = {Fan, C and Yang, B and Li, X and Gao, S and Zan, P},
title = {EEG-Based Feature Classification Combining 3D-Convolutional Neural Networks with Generative Adversarial Networks for Motor Imagery.},
journal = {Journal of integrative neuroscience},
volume = {23},
number = {8},
pages = {153},
doi = {10.31083/j.jin2308153},
pmid = {39207066},
issn = {0219-6352},
support = {2022YFC3602500//National Key Research and Development Program of China/ ; 2022YFC3602504//National Key Research and Development Program of China/ ; 62376149//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography ; *Imagination/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Adult ; Motor Activity/physiology ; Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: The adoption of convolutional neural networks (CNNs) for decoding electroencephalogram (EEG)-based motor imagery (MI) in brain-computer interfaces has significantly increased recently. The effective extraction of motor imagery features is vital due to the variability among individuals and temporal states.

METHODS: This study introduces a novel network architecture, 3D-convolutional neural network-generative adversarial network (3D-CNN-GAN), for decoding both within-session and cross-session motor imagery. Initially, EEG signals were extracted over various time intervals using a sliding window technique, capturing temporal, frequency, and phase features to construct a temporal-frequency-phase feature (TFPF) three-dimensional feature map. Generative adversarial networks (GANs) were then employed to synthesize artificial data, which, when combined with the original datasets, expanded the data capacity and enhanced functional connectivity. Moreover, GANs proved capable of learning and amplifying the brain connectivity patterns present in the existing data, generating more distinctive brain network features. A compact, two-layer 3D-CNN model was subsequently developed to efficiently decode these TFPF features.

RESULTS: Taking into account session and individual differences in EEG data, tests were conducted on both the public GigaDB dataset and the SHU laboratory dataset. On the GigaDB dataset, our 3D-CNN and 3D-CNN-GAN models achieved two-class within-session motor imagery accuracies of 76.49% and 77.03%, respectively, demonstrating the algorithm's effectiveness and the improvement provided by data augmentation. Furthermore, on the SHU dataset, the 3D-CNN and 3D-CNN-GAN models yielded two-class within-session motor imagery accuracies of 67.64% and 71.63%, and cross-session motor imagery accuracies of 58.06% and 63.04%, respectively.

CONCLUSIONS: The 3D-CNN-GAN algorithm significantly enhances the generalizability of EEG-based motor imagery brain-computer interfaces (BCIs). Additionally, this research offers valuable insights into the potential applications of motor imagery BCIs.},
}

Humans
*Electroencephalography
*Imagination/physiology
*Neural Networks, Computer
*Brain-Computer Interfaces
Adult
Motor Activity/physiology
Brain/physiology
Signal Processing, Computer-Assisted

@article {pmid39205122,
year = {2024},
author = {Moraes, CPA and Dos Santos, LH and Fantinato, DG and Neves, A and Adali, T},
title = {Independent Vector Analysis for Feature Extraction in Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {16},
pages = {},
pmid = {39205122},
issn = {1424-8220},
support = {2023/00640-1//São Paulo Research Foundation (FAPESP)/ ; 88887.595656/2020-00//Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)/ ; 2316420//US National Science Foundation (NSF)/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Support Vector Machine ; *Movement/physiology ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain-computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7%.},
}

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

@article {pmid39204948,
year = {2024},
author = {Dillen, A and Omidi, M and Ghaffari, F and Romain, O and Vanderborght, B and Roelands, B and Nowé, A and De Pauw, K},
title = {User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {16},
pages = {},
pmid = {39204948},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Robotics/methods/instrumentation ; *Electroencephalography/methods ; Male ; *Eye-Tracking Technology ; *Augmented Reality ; Female ; Adult ; *User-Computer Interface ; Middle Aged ; Young Adult ; Eye Movements/physiology ; },
abstract = {This study evaluates an innovative control approach to assistive robotics by integrating brain-computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times (p<0.001). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use (p=0.04). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements.},
}

Humans
*Brain-Computer Interfaces
*Robotics/methods/instrumentation
*Electroencephalography/methods
Male
*Eye-Tracking Technology
*Augmented Reality
Female
Adult
*User-Computer Interface
Middle Aged
Young Adult
Eye Movements/physiology

@article {pmid39204910,
year = {2024},
author = {Hu, W and Ji, B and Gao, K},
title = {A Method for the Spatial Interpolation of EEG Signals Based on the Bidirectional Long Short-Term Memory Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {16},
pages = {},
pmid = {39204910},
issn = {1424-8220},
support = {62106041, 62204204//the National Natural Science Foundation of China/ ; 223202100019//the Fundamental Research Funds for the Central Universities/ ; 2022ZD0208601//Science and Technology Innovation 2030-Major Project/ ; 21YF1451000//Shanghai Sailing Program/ ; },
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; Signal Processing, Computer-Assisted ; Algorithms ; Neural Networks, Computer ; Electrodes ; Deep Learning ; Memory, Short-Term/physiology ; },
abstract = {The precision of electroencephalograms (EEGs) significantly impacts the performance of brain-computer interfaces (BCI). Currently, the majority of research into BCI technology gives priority to lightweight design and a reduced electrode count to make it more suitable for application in wearable environments. This paper introduces a deep learning-based time series bidirectional (BiLSTM) network that is designed to capture the inherent characteristics of EEG channels obtained from neighboring electrodes. It aims to predict the EEG data time series and facilitate the conversion process from low-density EEG signals to high-density EEG signals. BiLSTM pays more attention to the dependencies in time series data rather than mathematical maps, and the root mean square error can be effectively restricted to below 0.4μV, which is less than half the error in traditional methods. After expanding the BCI Competition III 3a dataset from 18 channels to 60 channels, we conducted classification experiments on four types of motor imagery tasks. Compared to the original low-density EEG signals (18 channels), the classification accuracy was around 82%, an increase of about 20%. When juxtaposed with real high-density signals, the increment in the error rate remained below 5%. The expansion of the EEG channels showed a substantial and notable improvement compared with the original low-density signals.},
}

*Electroencephalography/methods
*Brain-Computer Interfaces
Humans
Signal Processing, Computer-Assisted
Algorithms
Neural Networks, Computer
Electrodes
Deep Learning
Memory, Short-Term/physiology

@article {pmid39204903,
year = {2024},
author = {Mattei, E and Lozzi, D and Di Matteo, A and Cipriani, A and Manes, C and Placidi, G},
title = {MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {16},
pages = {},
pmid = {39204903},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Hand/physiology ; *Movement/physiology ; Male ; Adult ; Biomechanical Phenomena/physiology ; Female ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) are pivotal in translating neural activities into control commands for external assistive devices. Non-invasive techniques like electroencephalography (EEG) offer a balance of sensitivity and spatial-temporal resolution for capturing brain signals associated with motor activities. This work introduces MOVING, a Multi-Modal dataset of EEG signals and Virtual Glove Hand Tracking. This dataset comprises neural EEG signals and kinematic data associated with three hand movements-open/close, finger tapping, and wrist rotation-along with a rest period. The dataset, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (∼1 min), although introducing more noise than the gold standard devices for data acquisition. The study investigates which frequency bands in EEG signals are the most informative for motor task classification and the impact of baseline reduction on gesture recognition. Deep learning techniques, particularly EEGnetV4, are applied to analyze and classify movements based on the EEG data. This dataset aims to facilitate advances in BCI research and in the development of assistive devices for people with impaired hand mobility. This study contributes to the repository of EEG datasets, which is continuously increasing with data from other subjects, which is hoped to serve as benchmarks for new BCI approaches and applications.},
}

Humans
*Electroencephalography/methods
*Brain-Computer Interfaces
*Hand/physiology
*Movement/physiology
Male
Adult
Biomechanical Phenomena/physiology
Female
Signal Processing, Computer-Assisted

@article {pmid39204507,
year = {2024},
author = {Boratto, MH and Graeff, CFO and Han, S},
title = {Highly Stable Flexible Organic Electrochemical Transistors with Natural Rubber Latex Additives.},
journal = {Polymers},
volume = {16},
number = {16},
pages = {},
pmid = {39204507},
issn = {2073-4360},
support = {2024-0029//Incheon National University/ ; },
abstract = {Organic electrochemical transistors (OECTs) have attracted considerable interest in the context of wearable and implantable biosensors due to their remarkable signal amplification combined with seamless integration into biological systems. These properties underlie OECTs' potential utility across a range of bioelectronic applications. One of the main challenges to their practical applications is the mechanical limitation of PEDOT:PSS, the most typical conductive polymer used as a channel layer, when the OECTs are applied to implantable and stretchable bioelectronics. In this work, we address this critical issue by employing natural rubber latex (NRL) as an additive in PEDOT:PSS to improve flexibility and stretchability of the OECT channels. Although the inclusion of NRL leads to a decrease in transconductance, mainly due to a reduced carrier mobility from 0.3 to 0.1 cm[2]/V·s, the OECTs maintain satisfactory transconductance, exceeding 5 mS. Furthermore, it is demonstrated that the OECTs exhibit excellent mechanical stability while maintaining their performance even after 100 repetitive bending cycles. This work, therefore, suggests that the NRL/PEDOT:PSS composite film can be deployed for wearable/implantable applications, where high mechanical stability is needed. This finding opens up new avenues for practical use of OECTs in more robust and versatile wearable and implantable biosensors.},
}

@article {pmid39202770,
year = {2024},
author = {Balčiauskas, L and Balčiauskienė, L},
title = {Extreme Body Condition Index Values in Small Mammals.},
journal = {Life (Basel, Switzerland)},
volume = {14},
number = {8},
pages = {},
pmid = {39202770},
issn = {2075-1729},
abstract = {The body condition index (BCI) values in small mammals are important in understanding their survival and reproduction. The upper values could be related to the Chitty effect (presence of very heavy individuals), while the minimum ones are little known. In this study, we analyzed extremes of BCI in 12 small mammal species, snap-trapped in Lithuania between 1980 and 2023, with respect to species, animal age, sex, and participation in reproduction. The proportion of small mammals with extreme body condition indices was negligible (1.33% with a BCI < 2 and 0.52% with a BCI > 5) when considering the total number of individuals processed (n = 27,073). When compared to the expected proportions, insectivores and herbivores were overrepresented, while granivores and omnivores were underrepresented among underfit animals. The proportions of granivores and insectivores were higher, while those of omnivores and herbivores were lower than expected in overfit animals. In several species, the proportions of age groups in underfit and overfit individuals differed from that expected. The male-female ratio was not expressed, with the exception of Sorex araneus. The highest proportion of overfit and absence of underfit individuals was found in Micromys minutus. The observation that individuals with the highest body mass are not among those with the highest BCI contributes to the interpretation of the Chitty effect. For the first time in mid-latitudes, we report individuals of very high body mass in three shrew species.},
}

@article {pmid39199779,
year = {2024},
author = {Tang, F and Yan, F and Zhong, Y and Li, J and Gong, H and Li, X},
title = {Optogenetic Brain-Computer Interfaces.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {8},
pages = {},
pmid = {39199779},
issn = {2306-5354},
support = {Grant No.821RC531//Hainan Provincial Natural Science Foundation of China for High-level Talents/ ; 2022ZD0205201//STI2030-Major Projects/ ; No. T2122015//National Nature Science Foundation of China/ ; No.2019-I2M-5-014//CAMS Innovation Fund for Medical Sciences/ ; },
abstract = {The brain-computer interface (BCI) is one of the most powerful tools in neuroscience and generally includes a recording system, a processor system, and a stimulation system. Optogenetics has the advantages of bidirectional regulation, high spatiotemporal resolution, and cell-specific regulation, which expands the application scenarios of BCIs. In recent years, optogenetic BCIs have become widely used in the lab with the development of materials and software. The systems were designed to be more integrated, lightweight, biocompatible, and power efficient, as were the wireless transmission and chip-level embedded BCIs. The software is also constantly improving, with better real-time performance and accuracy and lower power consumption. On the other hand, as a cutting-edge technology spanning multidisciplinary fields including molecular biology, neuroscience, material engineering, and information processing, optogenetic BCIs have great application potential in neural decoding, enhancing brain function, and treating neural diseases. Here, we review the development and application of optogenetic BCIs. In the future, combined with other functional imaging techniques such as near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI), optogenetic BCIs can modulate the function of specific circuits, facilitate neurological rehabilitation, assist perception, establish a brain-to-brain interface, and be applied in wider application scenarios.},
}

@article {pmid39199740,
year = {2024},
author = {Fernandes, JVMR and Alexandria, AR and Marques, JAL and Assis, DF and Motta, PC and Silva, BRDS},
title = {Emotion Detection from EEG Signals Using Machine Deep Learning Models.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {8},
pages = {},
pmid = {39199740},
issn = {2306-5354},
abstract = {Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain's electrical activity through electrodes placed on the scalp's surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain-computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was "subject-dependent". In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.},
}

@article {pmid39199739,
year = {2024},
author = {Chiou, N and Günal, M and Koyejo, S and Perpetuini, D and Chiarelli, AM and Low, KA and Fabiani, M and Gratton, G},
title = {Single-Trial Detection and Classification of Event-Related Optical Signals for a Brain-Computer Interface Application.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {8},
pages = {},
pmid = {39199739},
issn = {2306-5354},
abstract = {Event-related optical signals (EROS) measure fast modulations in the brain's optical properties related to neuronal activity. EROS offer a high spatial and temporal resolution and can be used for brain-computer interface (BCI) applications. However, the ability to classify single-trial EROS remains unexplored. This study evaluates the performance of neural network methods for single-trial classification of motor response-related EROS. EROS activity was obtained from a high-density recording montage covering the motor cortex during a two-choice reaction time task involving responses with the left or right hand. This study utilized a convolutional neural network (CNN) approach to extract spatiotemporal features from EROS data and perform classification of left and right motor responses. Subject-specific classifiers trained on EROS phase data outperformed those trained on intensity data, reaching an average single-trial classification accuracy of around 63%. Removing low-frequency noise from intensity data is critical for achieving discriminative classification results with this measure. Our results indicate that deep learning with high-spatial-resolution signals, such as EROS, can be successfully applied to single-trial classifications.},
}

@article {pmid39199537,
year = {2024},
author = {Fodor, MA and Herschel, H and Cantürk, A and Heisenberg, G and Volosyak, I},
title = {Evaluation of Different Visual Feedback Methods for Brain-Computer Interfaces (BCI) Based on Code-Modulated Visual Evoked Potentials (cVEP).},
journal = {Brain sciences},
volume = {14},
number = {8},
pages = {},
pmid = {39199537},
issn = {2076-3425},
support = {101118964//European Union/ ; },
abstract = {Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals. BCIs based on code-modulated visual evoked potentials (cVEPs) are based on visual stimuli, thus appropriate visual feedback on the interface is crucial for an effective BCI system. Many previous studies have demonstrated that implementing visual feedback can improve information transfer rate (ITR) and reduce fatigue. This research compares a dynamic interface, where target boxes change their sizes based on detection certainty, with a threshold bar interface in a three-step cVEP speller. In this study, we found that both interfaces perform well, with slight variations in accuracy, ITR, and output characters per minute (OCM). Notably, some participants showed significant performance improvements with the dynamic interface and found it less distracting compared to the threshold bars. These results suggest that while average performance metrics are similar, the dynamic interface can provide significant benefits for certain users. This study underscores the potential for personalized interface choices to enhance BCI user experience and performance. By improving user friendliness, performance, and reducing distraction, dynamic visual feedback could optimize BCI technology for a broader range of users.},
}

@article {pmid39199527,
year = {2024},
author = {Ail, BE and Ramele, R and Gambini, J and Santos, JM},
title = {An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks.},
journal = {Brain sciences},
volume = {14},
number = {8},
pages = {},
doi = {10.3390/brainsci14080836},
pmid = {39199527},
issn = {2076-3425},
support = {ITBACyT-2020//Instituto Tecnológico de Buenos Aires (ITBA)/ ; },
abstract = {This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision. The identification of this pattern is used to implement a P300-based speller device, which can serve as an alternative communication channel for persons affected by amyotrophic lateral sclerosis (ALS). This method is validated by identifying this signal by performing a brain-computer interface simulation on a public dataset from ALS patients. Letter identification rates from the speller on the dataset show that this method can identify the P300 signature on the set of 8 patients. The proposed approach achieves similar performance to other state-of-the-art proposals while providing clinically relevant explainability (XAI).},
}

@article {pmid39196743,
year = {2024},
author = {Ouahidi, YE and Gripon, V and Pasdeloup, B and Bouallegue, G and Farrugia, N and Lioi, G},
title = {A Strong and Simple Deep Learning Baseline for BCI Motor Imagery decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3451010},
pmid = {39196743},
issn = {1558-0210},
abstract = {We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline that achieves high classification accuracy, using only standard ingredients from the literature, to serve as a standard for comparison. The proposed architecture is composed of standard layers, including 1D convolutions, batch normalisations, ReLU activation functions and pooling functions. EEG-SimpleConv architecture is accompanied by a straightforward and tailored training routine, which is subjected to an extensive ablation study to quantify the influence of its components. We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time. We believe that using standard components and ingredients can significantly help the adoption of Deep Learning approaches for BCI. We make the code of the models and the experiments accessible.},
}

@article {pmid39196738,
year = {2024},
author = {Li, Z and Zhang, R and Li, W and Li, M and Chen, X and Cui, H},
title = {Enhancement of hybrid BCI system performance based on motor imagery and SSVEP by transcranial alternating current stimulation.},
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.2024.3451015},
pmid = {39196738},
issn = {1558-0210},
abstract = {The hybrid brain-computer interface (BCI) is verified to reduce disadvantages of conventional BCI systems. Transcranial electrical stimulation (tES) can also improve the performance and applicability of BCI. However, enhancement in BCI performance attained solely from the perspective of users or solely from the angle of BCI system design is limited. In this study, a hybrid BCI system combining MI and SSVEP was proposed. Furthermore, transcranial alternating current stimulation (tACS) was utilized to enhance the performance of the proposed hybrid BCI system. The stimulation interface presented a depiction of grabbing a ball with both of hands, with left-hand and right-hand flickering at frequencies of 34 Hz and 35 Hz. Subjects watched the interface and imagined grabbing a ball with either left hand or right hand to perform SSVEP and MI task. The MI and SSVEP signals were processed separately using filter bank common spatial patterns (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms, respectively. A fusion method was proposed to fuse the features extracted from MI and SSVEP. Twenty healthy subjects took part in the online experiment and underwent tACS sequentially. The fusion accuracy post-tACS reached 90.25% ± 11.40%, which was significantly different from pre-tACS. The fusion accuracy also surpassed MI accuracy and SSVEP accuracy respectively. These results indicated the superior performance of the hybrid BCI system and tACS would improve the performance of the hybrid BCI system.},
}

@article {pmid39196465,
year = {2024},
author = {Zou, Y},
title = {Genetic enhancement from the perspective of transhumanism: exploring a new paradigm of transhuman evolution.},
journal = {Medicine, health care, and philosophy},
volume = {},
number = {},
pages = {},
pmid = {39196465},
issn = {1572-8633},
support = {2023EZX004//Shanghai Office of Philosophy and Social Science/ ; 23FZXA012//National Social Science Fund of China/ ; },
abstract = {Transhumanism is a movement that advocates for the enhancement of human capabilities through the use of advanced technologies such as genetic enhancement. This article explores the definition, history, and development of transhumanism. Then, it compares the stance on genetic enhancement from the perspectives of bio-conservatism, bio-liberalism, and transhumanism. This article posits that transhuman evolution has twofold implications, allowing for the integration of transhumanist research and evolutionary biology. First, it offers a compelling scientific framework for understanding genetic enhancement, avoiding technological progressivism, and incorporating concepts of evolutionary biology. Second, it represents a new evolutionary paradigm distinct from traditional Lamarckism and Darwinism. It marks the third synthesis of evolutionary biology, offering fresh perspectives on established concepts such as artificial selection and gene-culture co-evolution. In recent decades, human enhancement has captivated not only evolutionary biologists, neurobiologists, psychologists, and philosophers, but also those in fields such as cybernetics and artificial intelligence. In addition to genetic enhancement, other human enhancement technologies, including brain-computer interfaces and brain uploading, are currently under development, which the paradigm of transhuman evolution can better integrate into its framework.},
}

@article {pmid39194652,
year = {2024},
author = {Jiang, Q and Liu, M},
title = {Recent Progress in Artificial Neurons for Neuromodulation.},
journal = {Journal of functional biomaterials},
volume = {15},
number = {8},
pages = {},
pmid = {39194652},
issn = {2079-4983},
abstract = {Driven by the rapid advancement and practical implementation of biomaterials, fabrication technologies, and artificial intelligence, artificial neuron devices and systems have emerged as a promising technology for interpreting and transmitting neurological signals. These systems are equipped with multi-modal bio-integrable sensing capabilities, and can facilitate the benefits of neurological monitoring and modulation through accurate physiological recognition. In this article, we provide an overview of recent progress in artificial neuron technology, with a particular focus on the high-tech applications made possible by innovations in material engineering, new designs and technologies, and potential application areas. As a rapidly expanding field, these advancements have a promising potential to revolutionize personalized healthcare, human enhancement, and a wide range of other applications, making artificial neuron devices the future of brain-machine interfaces.},
}

@article {pmid39194625,
year = {2024},
author = {Ullah, A and Zhang, F and Song, Z and Wang, Y and Zhao, S and Riaz, W and Li, G},
title = {Surface Electromyography-Based Recognition of Electronic Taste Sensations.},
journal = {Biosensors},
volume = {14},
number = {8},
pages = {},
pmid = {39194625},
issn = {2079-6374},
mesh = {Humans ; *Electromyography ; *Taste ; Brain-Computer Interfaces ; Adult ; },
abstract = {Taste sensation recognition is a core for taste-related queries. Most prior research has been devoted to recognizing the basic taste sensations using the Brain-Computer Interface (BCI), which includes EEG, MEG, EMG, and fMRI. This research aims to recognize electronic taste (E-Taste) sensations based on surface electromyography (sEMG). Silver electrodes with platinum plating of the E-Taste device were placed on the tongue's tip to stimulate various tastes and flavors. In contrast, the electrodes of the sEMG were placed on facial muscles to collect the data. The dataset was organized and preprocessed, and a random forest classifier was applied, giving a five-fold accuracy of 70.43%. The random forest classifier was used on each participant dataset individually and in groups, providing the highest accuracy of 84.79% for a single participant. Moreover, various feature combinations were extracted and acquired 72.56% accuracy after extracting eight features. For a future perspective, this research offers guidance for electronic taste recognition based on sEMG.},
}

Humans
*Electromyography
*Taste
Brain-Computer Interfaces
Adult

@article {pmid39194597,
year = {2024},
author = {Savić, AM and Novičić, M and Miler-Jerković, V and Djordjević, O and Konstantinović, L},
title = {Electrotactile BCI for Top-Down Somatosensory Training: Clinical Feasibility Trial of Online BCI Control in Subacute Stroke Patients.},
journal = {Biosensors},
volume = {14},
number = {8},
pages = {},
pmid = {39194597},
issn = {2079-6374},
support = {6066223//Science Fund of the Republic of Serbia/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Stroke/physiopathology ; *Electroencephalography ; Female ; Middle Aged ; Aged ; Feasibility Studies ; Evoked Potentials, Somatosensory/physiology ; Stroke Rehabilitation/methods ; Adult ; Touch ; },
abstract = {This study investigates the feasibility of a novel brain-computer interface (BCI) device designed for sensory training following stroke. The BCI system administers electrotactile stimuli to the user's forearm, mirroring classical sensory training interventions. Concurrently, selective attention tasks are employed to modulate electrophysiological brain responses (somatosensory event-related potentials-sERPs), reflecting cortical excitability in related sensorimotor areas. The BCI identifies attention-induced changes in the brain's reactions to stimulation in an online manner. The study protocol assesses the feasibility of online binary classification of selective attention focus in ten subacute stroke patients. Each experimental session includes a BCI training phase for data collection and classifier training, followed by a BCI test phase to evaluate online classification of selective tactile attention based on sERP. During online classification tests, patients complete 20 repetitions of selective attention tasks with feedback on attention focus recognition. Using a single electroencephalographic channel, attention classification accuracy ranges from 70% to 100% across all patients. The significance of this novel BCI paradigm lies in its ability to quantitatively measure selective tactile attention resources throughout the therapy session, introducing a top-down approach to classical sensory training interventions based on repeated neuromuscular electrical stimulation.},
}

Humans
*Brain-Computer Interfaces
Male
*Stroke/physiopathology
*Electroencephalography
Female
Middle Aged
Aged
Feasibility Studies
Evoked Potentials, Somatosensory/physiology
Stroke Rehabilitation/methods
Adult
Touch

@article {pmid39194438,
year = {2024},
author = {Omari, S and Omari, A and Abu-Dakka, F and Abderrahim, M},
title = {EEG Motor Imagery Classification: Tangent Space with Gate-Generated Weight Classifier.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {9},
number = {8},
pages = {},
pmid = {39194438},
issn = {2313-7673},
support = {2012/00605/003//Universidad Carlos III de Madrid/ ; S2018/NMT-4331//RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub/ ; KK-2022/00024 and HELDU KK-2023/00055//ELKARTEK, Basque Government (Spain)/ ; },
abstract = {Individuals grappling with severe central nervous system injuries often face significant challenges related to sensorimotor function and communication abilities. In response, brain-computer interface (BCI) technology has emerged as a promising solution by offering innovative interaction methods and intelligent rehabilitation training. By leveraging electroencephalographic (EEG) signals, BCIs unlock intriguing possibilities in patient care and neurological rehabilitation. Recent research has utilized covariance matrices as signal descriptors. In this study, we introduce two methodologies for covariance matrix analysis: multiple tangent space projections (M-TSPs) and Cholesky decomposition. Both approaches incorporate a classifier that integrates linear and nonlinear features, resulting in a significant enhancement in classification accuracy, as evidenced by meticulous experimental evaluations. The M-TSP method demonstrates superior performance with an average accuracy improvement of 6.79% over Cholesky decomposition. Additionally, a gender-based analysis reveals a preference for men in the obtained results, with an average improvement of 9.16% over women. These findings underscore the potential of our methodologies to improve BCI performance and highlight gender-specific performance differences to be examined further in our future studies.},
}

@article {pmid39194182,
year = {2024},
author = {Rabbani, Q and Shah, S and Milsap, G and Fifer, M and Hermansky, H and Crone, N},
title = {Iterative alignment discovery of speech-associated neural activity.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
pmid = {39194182},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Speech/physiology ; *Algorithms ; *Electrocorticography/methods ; Male ; Female ; Adult ; Neural Networks, Computer ; },
abstract = {Objective. Brain-computer interfaces (BCIs) have the potential to preserve or restore speech in patients with neurological disorders that weaken the muscles involved in speech production. However, successful training of low-latency speech synthesis and recognition models requires alignment of neural activity with intended phonetic or acoustic output with high temporal precision. This is particularly challenging in patients who cannot produce audible speech, as ground truth with which to pinpoint neural activity synchronized with speech is not available.Approach. In this study, we present a new iterative algorithm for neural voice activity detection (nVAD) called iterative alignment discovery dynamic time warping (IAD-DTW) that integrates DTW into the loss function of a deep neural network (DNN). The algorithm is designed to discover the alignment between a patient's electrocorticographic (ECoG) neural responses and their attempts to speak during collection of data for training BCI decoders for speech synthesis and recognition.Main results. To demonstrate the effectiveness of the algorithm, we tested its accuracy in predicting the onset and duration of acoustic signals produced by able-bodied patients with intact speech undergoing short-term diagnostic ECoG recordings for epilepsy surgery. We simulated a lack of ground truth by randomly perturbing the temporal correspondence between neural activity and an initial single estimate for all speech onsets and durations. We examined the model's ability to overcome these perturbations to estimate ground truth. IAD-DTW showed no notable degradation (<1% absolute decrease in accuracy) in performance in these simulations, even in the case of maximal misalignments between speech and silence.Significance. IAD-DTW is computationally inexpensive and can be easily integrated into existing DNN-based nVAD approaches, as it pertains only to the final loss computation. This approach makes it possible to train speech BCI algorithms using ECoG data from patients who are unable to produce audible speech, including those with Locked-In Syndrome.},
}

Humans
*Brain-Computer Interfaces
*Speech/physiology
*Algorithms
*Electrocorticography/methods
Male
Female
Adult
Neural Networks, Computer

@article {pmid39193830,
year = {2024},
author = {Xu, Q and Xi, Y and Wang, L and Du, Z and Xu, M and Ruan, T and Cao, J and Zheng, K and Wang, X and Yang, B and Liu, J},
title = {An Opto-electrophysiology Neural Probe with Photoelectric Artifact-Free for Advanced Single-Neuron Analysis.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.4c07379},
pmid = {39193830},
issn = {1936-086X},
abstract = {Opto-electrophysiology neural probes targeting single-cell levels offer an important avenue for elucidating the intrinsic mechanisms of the nervous system using different physical quantities, representing a significant future direction for brain-computer interface (BCI) devices. However, the highly integrated structure poses significant challenges to fabrication processes and the presence of photoelectric artifacts complicates the extraction and analysis of target signals. Here, we propose a highly miniaturized and integrated opto-electrophysiology neural probe for electrical recording and optical stimulation at the single-cell/subcellular level. The design of a total internal reflection layer addresses the photoelectric artifacts that are more pronounced in single-cell devices compared to conventional implantable BCI devices. Finite element simulations and electrical signal tests demonstrate that the opto-electrophysiology neural probe eliminates the photoelectric artifacts in the time domain, which represents a significant breakthrough for optoelectrical integrated BCI devices. Our proposed opto-electrophysiology neural probe holds substantial potential for promoting the development of in vivo BCI devices and developing advanced therapeutic strategies for neurological disorders.},
}

@article {pmid39191641,
year = {2024},
author = {Al-Khouja, F and Grigorian, A and Emigh, B and Schellenberg, M and Diaz, G and Duncan, TK and Tuli, R and Coimbra, R and Gilbert-Gard, K and Johnson, A and Marty, M and Jebbia, M and Obaid-Schmid, AK and Fierro, N and Ley, E and Bayat, D and Biffl, W and Ebrahimian, S and Tillou M, A and Tay-Lasso, E and Alvarez, C and Nahmias, J},
title = {24-hour Telemetry Monitoring May Not be Necessary for Patients With an Isolated Sternal Fracture and Minor ECG Abnormalities or Troponin Elevation: A Southern California Multicenter Study.},
journal = {The American surgeon},
volume = {},
number = {},
pages = {31348241278904},
doi = {10.1177/00031348241278904},
pmid = {39191641},
issn = {1555-9823},
abstract = {BACKGROUND: Current guidelines recommend 24-hour telemetry monitoring for isolated sternal fractures (ISFs) with electrocardiogram (ECG) abnormalities or troponin elevation. However, a single-center study suggested ISF patients with minor ECG abnormalities (sinus tachycardia/bradycardia, nonspecific arrhythmia/ST-changes, and bundle branch block) may not require 24-hour telemetry monitoring. This study sought to corroborate this, hypothesizing ISF patients would not develop blunt cardiac injury (BCI).

MATERIALS & METHODS: A retrospective study was performed at 8 trauma centers (1/2018-8/2020). Patients with ISF (abbreviated injury scale <2 for the head/neck/face/abdomen/extremities) and minor ECG abnormalities or troponin elevations were included. Patients with multiple rib fractures or hemothorax/pneumothorax were excluded. The primary outcome was an echocardiogram confirmed BCI. The secondary outcome was significant BCI defined as cardiogenic shock, dysrhythmia requiring treatment, post-traumatic cardiac structural defects, unexplained hypotension, or cardiac-related procedures. Descriptive statistics were performed.

RESULTS: Of 124 ISF patients with minor ECG abnormalities or troponin elevation, 90% were admitted with a mean stay of 35 hours. Echocardiogram was performed for 31.5% of patients, 10 (25.6%) of which had abnormalities. However, no patient had BCI diagnosed on echocardiography. In total, 2 patients (1.6%) had a significant BCI (atrial fibrillation and supraventricular tachycardia at 10 and 82 hours after injury). No patient died.

CONCLUSIONS: Following ISF with minor ECG changes or troponin elevation, <2% suffered significant BCI, and none had an echocardiogram diagnosed BCI, despite >30% receiving echocardiogram. These findings challenge the dogma of mandatory observation periods following ISF with associated ECG abnormalities and support the lack of utility for routine echocardiography in these patients.},
}

@article {pmid39190517,
year = {2024},
author = {Liu, K and Yang, T and Yu, Z and Yi, W and Yu, H and Wang, G and Wu, W},
title = {MSVTNet: Multi-Scale Vision Transformer Neural Network for EEG-Based Motor Imagery Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3450753},
pmid = {39190517},
issn = {2168-2208},
abstract = {OBJECT: Transformer-based neural networks have been applied to the electroencephalography (EEG) decoding for motor imagery (MI). However, most networks focus on applying the self-attention mechanism to extract global temporal information, while the cross-frequency coupling features between different frequencies have been neglected. Additionally, effectively integrating different neural networks poses challenges for the advanced design of decoding algorithms.

METHODS: This study proposes a novel end-to-end Multi-Scale Vision Transformer Neural Network (MSVTNet) for MI-EEG classification. MSVTNet first extracts local spatio-temporal features at different filtered scales through convolutional neural networks (CNNs). Then, these features are concatenated along the feature dimension to form local multi-scale spatio-temporal feature tokens. Finally, Transformers are utilized to capture cross-scale interaction information and global temporal correlations, providing more distinguishable feature embeddings for classification. Moreover, auxiliary branch loss is leveraged for intermediate supervision to ensure the effective integration of CNNs and Transformers.

RESULTS: The performance of MSVTNet was assessed through subject-dependent (session-dependent and session-independent) and subject-independent experiments on three MI datasets, i.e., the BCI competition IV 2a, 2b and OpenBMI datasets. The experimental results demonstrate that MSVTNet achieves state-of-the-art performance in all analyses.

CONCLUSION: MSVTNet shows superiority and robustness in enhancing MI decoding performance. The source code for MSVTNet is available at https://github.com/SheepTAO/MSVTNet.},
}

@article {pmid39190511,
year = {2024},
author = {Zhou, J and Duan, Y and Chang, YC and Wang, YK and Lin, CT},
title = {BELT: Bootstrapped EEG-to-language Training by Natural Language Supervision.},
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.2024.3450795},
pmid = {39190511},
issn = {1558-0210},
abstract = {Decoding natural language from noninvasive brain signals has been an exciting topic with the potential to expand the applications of brain-computer interface (BCI) systems. However, current methods face limitations in decoding sentences from electroencephalography (EEG) signals. Improving decoding performance requires the development of a more effective encoder for the EEG modality. Nonetheless, learning generalizable EEG representations remains a challenge due to the relatively small scale of existing EEG datasets. In this paper, we propose enhancing the EEG encoder to improve subsequent decoding performance. Specifically, we introduce the discrete Conformer encoder (D-Conformer) to transform EEG signals into discrete representations and bootstrap the learning process by imposing EEG-language alignment from the early training stage. The D-Conformer captures both local and global patterns from EEG signals and discretizes the EEG representation, making the representation more resilient to variations, while early-stage EEG-language alignment mitigates the limitations of small EEG datasets and facilitates the learning of the semantic representations from EEG signals. These enhancements result in improved EEG representations and decoding performance. We conducted extensive experiments and ablation studies to thoroughly evaluate the proposed method. Utilizing the D-Conformer encoder and bootstrapping training strategy, our approach demonstrates superior decoding performance across various tasks, including word-level, sentence-level, and sentiment-level decoding from EEG signals. Specifically, in word-level classification, we show that our encoding method produces more distinctive representations and higher classification performance compared to the EEG encoders from existing methods. At the sentence level, our model outperformed the baseline by 5.45%, achieving a BLEU-1 score of 42.31%. Furthermore, in sentiment classification, our model exceeded the baseline by 14%, achieving a sentiment classification accuracy of 69.3%.},
}

@article {pmid39188923,
year = {2024},
author = {Schwarck, S and Voelkle, MC and Becke, A and Busse, N and Glanz, W and Düzel, E and Ziegler, G},
title = {Interplay of physical and recognition performance using hierarchical continuous-time dynamic modeling and a dual-task training regime in Alzheimer's patients.},
journal = {Alzheimer's & dementia (Amsterdam, Netherlands)},
volume = {16},
number = {3},
pages = {e12629},
pmid = {39188923},
issn = {2352-8729},
abstract = {UNLABELLED: Training studies typically investigate the cumulative rather than the analytically challenging immediate effect of exercise on cognitive outcomes. We investigated the dynamic interplay between single-session exercise intensity and time-locked recognition speed-accuracy scores in older adults with Alzheimer's dementia (N = 17) undergoing a 24-week dual-task regime. We specified a state-of-the-art hierarchical Bayesian continuous-time dynamic model with fully connected state variables to analyze the bi-directional effects between physical and recognition scores over time. Higher physical performance was dynamically linked to improved recognition (-1.335, SD = 0.201, 95% Bayesian credible interval [BCI] [-1.725, -0.954]). The effect was short-term, lasting up to 5 days (-0.368, SD = 0.05, 95% BCI [-0.479, -0.266]). Clinical scores supported the validity of the model and observed temporal dynamics. Higher physical performance predicted improved recognition speed accuracy in a day-by-day manner, providing a proof-of-concept for the feasibility of linking exercise training and recognition in patients with Alzheimer's dementia.

HIGHLIGHTS: Hierarchical Bayesian continuous-time dynamic modeling approachA total of 72 repeated physical exercise (PP) and integrated recognition speed-accuracy (IRSA) measurementsPP is dynamically linked to session-to-session variability of IRSAHigher PP improved IRSA in subsequent sessions in subjects with Alzheimer's dementiaShort-term effect: lasting up to 4 days after training session.},
}

@article {pmid39187733,
year = {2024},
author = {Gu, J and Shao, W and Liu, L and Wang, Y and Yang, Y and Zhang, Z and Wu, Y and Xu, Q and Gu, L and Zhang, Y and Shen, Y and Zhao, H and Zeng, C and Zhang, H},
title = {Challenges and future directions of SUDEP models.},
journal = {Lab animal},
volume = {},
number = {},
pages = {},
pmid = {39187733},
issn = {1548-4475},
support = {81771403//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81974205//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82001379//National Natural Science Foundation of China (National Science Foundation of China)/ ; LZ20H090001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death among patients with epilepsy, causing a global public health burden. The underlying mechanisms of SUDEP remain elusive, and effective prevention or treatment strategies require further investigation. A major challenge in current SUDEP research is the lack of an ideal model that maximally mimics the human condition. Animal models are important for revealing the potential pathogenesis of SUDEP and preventing its occurrence; however, they have potential limitations due to species differences that prevent them from precisely replicating the intricate physiological and pathological processes of human disease. This Review provides a comprehensive overview of several available SUDEP animal models, highlighting their pros and cons. More importantly, we further propose the establishment of an ideal model based on brain-computer interfaces and artificial intelligence, hoping to offer new insights into potential advancements in SUDEP research. In doing so, we hope to provide valuable information for SUDEP researchers, offer new insights into the pathogenesis of SUDEP and open new avenues for the development of strategies to prevent SUDEP.},
}

@article {pmid39187218,
year = {2024},
author = {Ciaramidaro, A and Toppi, J and Vogel, P and Freitag, CM and Siniatchkin, M and Astolfi, L},
title = {Synergy of the Mirror Neuron System and the Mentalizing System in a single brain and between brains during Joint Actions.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {120783},
doi = {10.1016/j.neuroimage.2024.120783},
pmid = {39187218},
issn = {1095-9572},
abstract = {Cooperative action involves the simulation of actions and their co-representation by two or more people. This requires the involvement of two complex brain systems: the mirror neuron system (MNS) and the mentalizing system (MENT), both of critical importance for successful social interaction. However, their internal organization and the potential synergy of both systems during joint actions (JA) are yet to be determined. The aim of this study was to examine the role and interaction of these two fundamental systems-MENT and MNS-during continuous interaction. To this hand, we conducted a multiple-brain connectivity analysis in the source domain during a motor cooperation task using high-density EEG dual-recordings providing relevant insights into the roles of MNS and MENT at the intra- and interbrain levels. In particular, the intra-brain analysis demonstrated the essential function of both systems during JA, as well as the crucial role played by single brain regions of both neural mechanisms during cooperative activities. Specifically, our intra-brain analysis revealed that both neural mechanisms are essential during Joint Action (JA), showing a solid connection between MNS and MENT and a central role of the single brain regions of both mechanisms during cooperative actions. Additionally, our inter-brain study revealed increased inter-subject connections involving the motor system, MENT and MNS. Thus, our findings show a mutual influence between two interacting agents, based on synchronization of MNS and MENT systems. Our results actually encourage more research into the still-largely unknown realm of inter-brain dynamics and contribute to expand the body of knowledge in social neuroscience.},
}

@article {pmid39186838,
year = {2024},
author = {Qiu, L and Zhong, L and Li, J and Feng, W and Zhou, C and Pan, J},
title = {SFT-SGAT: A semi-supervised fine-tuning self-supervised graph attention network for emotion recognition and consciousness detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {180},
number = {},
pages = {106643},
doi = {10.1016/j.neunet.2024.106643},
pmid = {39186838},
issn = {1879-2782},
abstract = {Emotional recognition is highly important in the field of brain-computer interfaces (BCIs). However, due to the individual variability in electroencephalogram (EEG) signals and the challenges in obtaining accurate emotional labels, traditional methods have shown poor performance in cross-subject emotion recognition. In this study, we propose a cross-subject EEG emotion recognition method based on a semi-supervised fine-tuning self-supervised graph attention network (SFT-SGAT). First, we model multi-channel EEG signals by constructing a graph structure that dynamically captures the spatiotemporal topological features of EEG signals. Second, we employ a self-supervised graph attention neural network to facilitate model training, mitigating the impact of signal noise on the model. Finally, a semi-supervised approach is used to fine-tune the model, enhancing its generalization ability in cross-subject classification. By combining supervised and unsupervised learning techniques, the SFT-SGAT maximizes the utility of limited labeled data in EEG emotion recognition tasks, thereby enhancing the model's performance. Experiments based on leave-one-subject-out cross-validation demonstrate that SFT-SGAT achieves state-of-the-art cross-subject emotion recognition performance on the SEED and SEED-IV datasets, with accuracies of 92.04% and 82.76%, respectively. Furthermore, experiments conducted on a self-collected dataset comprising ten healthy subjects and eight patients with disorders of consciousness (DOCs) revealed that the SFT-SGAT attains high classification performance in healthy subjects (maximum accuracy of 95.84%) and was successfully applied to DOC patients, with four patients achieving emotion recognition accuracies exceeding 60%. The experiments demonstrate the effectiveness of the proposed SFT-SGAT model in cross-subject EEG emotion recognition and its potential for assessing levels of consciousness in patients with DOC.},
}

@article {pmid39186407,
year = {2024},
author = {Edelman, BJ and Zhang, S and Schalk, G and Brunner, P and Muller-Putz, G and Guan, C and He, B},
title = {Non-invasive Brain-Computer Interfaces: State of the Art and Trends.},
journal = {IEEE reviews in biomedical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/RBME.2024.3449790},
pmid = {39186407},
issn = {1941-1189},
abstract = {Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to widely influence research, clinical and recreational use. Non-invasive BCI approaches are particularly common as they can impact a large number of participants safely and at a relatively low cost. Where traditional non-invasive BCIs were used for simple computer cursor tasks, it is now increasingly common for these systems to control robotic devices for complex tasks that may be useful in daily life. In this review, we provide an overview of the general BCI framework as well as the various methods that can be used to record neural activity, extract signals of interest, and decode brain states. In this context, we summarize the current state-of-the-art of non-invasive BCI research, focusing on trends in both the application of BCIs for controlling external devices and algorithm development to optimize their use. We also discuss various open-source BCI toolboxes and software, and describe their impact on the field at large.},
}

@article {pmid39185373,
year = {2024},
author = {Yi, D and Yao, Y and Wang, Y and Chen, L},
title = {Design, Fabrication, and Implantation of Invasive Microelectrode Arrays as in vivo Brain Machine Interfaces: A Comprehensive Review.},
journal = {Journal of manufacturing processes},
volume = {126},
number = {},
pages = {185-207},
pmid = {39185373},
issn = {1526-6125},
support = {R15 NS133861/NS/NINDS NIH HHS/United States ; },
abstract = {Invasive Microelectrode Arrays (MEAs) have been a significant and useful tool for us to gain a fundamental understanding of how the brain works through high spatiotemporal resolution neuron-level recordings and/or stimulations. Through decades of research, various types of microwire, silicon, and flexible substrate-based MEAs have been developed using the evolving new materials, novel design concepts, and cutting-edge advanced manufacturing capabilities. Surgical implantation of the latest minimal damaging flexible MEAs through the hard-to-penetrate brain membranes introduces new challenges and thus the development of implantation strategies and instruments for the latest MEAs. In this paper, studies on the design considerations and enabling manufacturing processes of various invasive MEAs as in vivo brain-machine interfaces have been reviewed to facilitate the development as well as the state-of-art of such brain-machine interfaces from an engineering perspective. The challenges and solution strategies developed for surgically implanting such interfaces into the brain have also been evaluated and summarized. Finally, the research gaps have been identified in the design, manufacturing, and implantation perspectives, and future research prospects in invasive MEA development have been proposed.},
}

@article {pmid39185208,
year = {2024},
author = {Marino, PJ and Bahureksa, L and Fisac, CF and Oby, ER and Smoulder, AL and Motiwala, A and Degenhart, AD and Grigsby, EM and Joiner, WM and Chase, SM and Yu, BM and Batista, AP},
title = {A posture subspace in primary motor cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39185208},
issn = {2692-8205},
support = {T32 GM081760/GM/NIGMS NIH HHS/United States ; R01 EY035896/EY/NEI NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; RF1 NS127107/NS/NINDS NIH HHS/United States ; R01 NS129584/NS/NINDS NIH HHS/United States ; },
abstract = {To generate movements, the brain must combine information about movement goal and body posture. Motor cortex (M1) is a key node for the convergence of these information streams. How are posture and goal information organized within M1's activity to permit the flexible generation of movement commands? To answer this question, we recorded M1 activity while monkeys performed a variety of tasks with the forearm in a range of postures. We found that posture- and goal-related components of neural population activity were separable and resided in nearly orthogonal subspaces. The posture subspace was stable across tasks. Within each task, neural trajectories for each goal had similar shapes across postures. Our results reveal a simpler organization of posture information in M1 than previously recognized. The compartmentalization of posture and goal information might allow the two to be flexibly combined in the service of our broad repertoire of actions.},
}

@article {pmid39185198,
year = {2024},
author = {Ouchi, T and Scholl, LR and Rajeswaran, P and Canfield, RA and Smith, LI and Orsborn, AL},
title = {Mapping eye, arm, and reward information in frontal motor cortices using electrocorticography in non-human primates.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39185198},
issn = {2692-8205},
abstract = {Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms, and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (µECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over a 1.37 cm [2] area of frontal motor cortices (primary motor cortex, premotor cortex, frontal eye field, and dorsolateral pre-frontal cortex). Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. We then used phase-based analyses to reveal potential overlaps of eye and arm information. We found that arm movement decoding performance was impacted by task-irrelevant eye movements, consistent with the presence of intermixed eye and arm information across much of motor cortices. Phase-based analyses also identified reward-related activity primarily around the principal sulcus in the pre-frontal cortex as well as near the arcuate sulcus in the premotor cortex. Our results demonstrate µECoG's strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces. Significance statement Picking up your coffee mug requires coordinating movements of your eyes and hand and processing the outcomes of those movements. Mapping how neural activity relates to different functions helps us understand how the brain performs these computations. Many mapping techniques have limited spatial or temporal resolution, restricting our ability to dissect computations that overlap closely in space and time. We used micro-electrocorticography recordings to map neural activity across multiple cortical areas while monkeys made goal-directed reaches. These measurements revealed high spatial and temporal resolution maps of neural activity related to eye, arm, and reward information processing. These maps reveal overlapping neural computations underlying movement and open opportunities to use eye and reward information to improve therapies to restore motor function.},
}

@article {pmid39184953,
year = {2024},
author = {Raghuram, V and Datye, AD and Fried, SI and Timko, BP},
title = {Transparent and Conformal Microcoil Arrays for Spatially Selective Neuronal Activation.},
journal = {Device},
volume = {2},
number = {4},
pages = {},
pmid = {39184953},
issn = {2666-9986},
support = {R01 NS110575/NS/NINDS NIH HHS/United States ; R21 EB034527/EB/NIBIB NIH HHS/United States ; },
abstract = {Micromagnetic stimulation (μMS) using small, implantable microcoils is a promising method for achieving neuronal activation with high spatial resolution and low toxicity. Herein, we report a microcoil array for localized activation of cortical neurons and retinal ganglion cells. We developed a computational model to relate the electric field gradient (activating function) to the geometry and arrangement of microcoils, and selected a design that produced an anisotropic region of activation <50 μm wide. The device was comprised of an SU-8/Cu/SU-8 tri-layer structure, which was flexible, transparent and conformal and featured four individually-addressable microcoils. Interfaced with cortex or retina explants from GCaMP6-expressing mice, we observed that individual neurons localized within 40 μm of a microcoil tip could be activated repeatedly and in a dose- (power-) dependent fashion. These results demonstrate the potential of μMS devices for brain-machine interfaces and could enable routes toward bioelectronic therapies including prosthetic vision devices.},
}

@article {pmid39184542,
year = {2024},
author = {Jeon, H and Park, IM},
title = {Quantifying Signal-to-Noise Ratio in Neural Latent Trajectories via Fisher Information.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
pmid = {39184542},
issn = {2331-8422},
abstract = {Spike train signals recorded from a large population of neurons often exhibit low-dimensional spatio-temporal structure and modeled as conditional Poisson observations. The low-dimensional signals that capture internal brain states are useful for building brain machine interfaces and understanding the neural computation underlying meaningful behavior. We derive a practical upper bound to the signal-to-noise ratio (SNR) of inferred neural latent trajectories using Fisher information. We show that the SNR bound is proportional to the overdispersion factor and the Fisher information per neuron. Further numerical experiments show that inference methods that exploit the temporal regularities can achieve higher SNRs that are proportional to the bound. Our results provide insights for fitting models to data, simulating neural responses, and design of experiments.},
}

@article {pmid39181508,
year = {2024},
author = {Ho, L and Ramanujan, S and Pramod, N and Tang, S and Bena, JF and De, S},
title = {Clinical Outcomes in Patients with Hypocontractile Bladders Undergoing Holmium Laser Enucleation of the Prostate.},
journal = {Urology},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.urology.2024.08.023},
pmid = {39181508},
issn = {1527-9995},
abstract = {OBJECTIVE: To compare post-operative outcomes in patients who underwent holmium laser enucleation of the prostate (HoLEP) for benign prostatic hyperplasia (BPH) and had urodynamic evidence of bladder hypocontractility versus those with normocontractile bladders.

METHODS: We retrospectively reviewed HoLEP patients with pre-operative urodynamic studies at a single institution, categorizing them into normocontractile and hypocontractile groups based on the bladder contractility index (BCI) (hypocontractile defined as BCI < 100). Post-void residual (PVR) volume was measured at 6 weeks and 6 months. Secondary outcomes included maximum flow rate (Qmax) and catheterization status.

RESULTS: Among 114 HoLEP patients with pre-operative urodynamic data, 49 had hypocontractile bladders. The median pre-operative PVR was 305 (202-446) ml in the hypocontractile group, higher than the median PVR of 190 (60-361) ml in the normocontractile group (p=.013). At 6 weeks post-op, the median PVR was higher in the hypocontractile compared to normocontractile group [38 (3-61) vs. 5 (0-44) ml, p=.016], but at 6 months post-op there was no significant difference [18 (0-39) vs. 12 (0-70) ml, p=.97]. Among men who were catheter-dependent pre-operatively, 98% of hypocontractile and 100% of normocontractile patients were catheter-free postoperatively. Qmax and symptom scores were similar at both follow-up time points.

CONCLUSIONS: HoLEP can be an effective surgical option for BPH patients with hypocontractile bladders, including those who are catheter-dependent, with minimal differences in post-operative voiding parameters compared to those with normal bladder function.},
}

@article {pmid39180976,
year = {2024},
author = {Wang, K and Wei, W and Yi, W and Qiu, S and He, H and Xu, M and Ming, D},
title = {Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {179},
number = {},
pages = {106617},
doi = {10.1016/j.neunet.2024.106617},
pmid = {39180976},
issn = {1879-2782},
abstract = {Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on a large amount of labeled data to train a satisfactory model for the specific subject, which limits the practical application of the methods. This study aimed to build a reliable vigilance estimation method using a small amount of unlabeled calibration data. We conducted a vigilance experiment in the designed BCI-based cursor-control task. Electroencephalogram (EEG) signals of eighteen participants were recorded in two sessions on two different days. And, we proposed a contrastive fine-grained domain adaptation network (CFGDAN) for vigilance estimation. Here, an adaptive graph convolution network (GCN) was built to project the EEG data of different domains into a common space. The fine-grained feature alignment mechanism was designed to weight and align the feature distributions across domains at the EEG channel level, and the contrastive information preservation module was developed to preserve the useful target-specific information during the feature alignment. The experimental results show that the proposed CFGDAN outperforms the compared methods in our BCI vigilance dataset and SEED-VIG dataset. Moreover, the visualization results demonstrate the efficacy of the designed feature alignment mechanisms. These results indicate the effectiveness of our method for vigilance estimation. Our study is helpful for reducing calibration efforts and promoting the practical application potential of vigilance estimation methods.},
}

@article {pmid39178905,
year = {2024},
author = {Kothe, CA and Hanada, G and Mullen, S and Mullen, TR},
title = {Decoding working-memory load during n-Back task performance from high channel fNIRS data.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad731b},
pmid = {39178905},
issn = {1741-2552},
abstract = {OBJECTIVE: Functional near-infrared spectroscopy (fNIRS) can measure neural activity through blood oxygenation changes in the brain in a wearable form factor, enabling unique applications for research in and outside the lab and in practical occupational settings. fNIRS has proven capable of measuring cognitive states such as mental workload, often using machine learning (ML) based brain-computer interfaces (BCIs). To date, this research has largely relied on probes with channel counts from under ten to several hundred, although recently a new class of wearable NIRS devices featuring thousands of channels has emerged. This poses unique challenges for ML classification, as fNIRS is typically limited by few training trials, which results in severely under-determined estimation problems. So far, it is not well understood how such high-resolution data is best leveraged in practical BCIs and whether state-of-the-art or better performance can be achieved.

APPROACH: To address these questions, we propose an ML strategy to classify working-memory load that relies on spatio-temporal regularization and transfer learning from other subjects in a combination that, to our knowledge, has not been used in previous fNIRS BCIs. The approach can be interpreted as an end-to-end generalized linear model and allows for a high degree of interpretability using channel-level or cortical imaging approaches.

MAIN RESULTS: We show that using the proposed methodology, it is possible to achieve state-of-the-art decoding performance with high-resolution fNIRS data. We also replicated several state-of-the-art approaches on our dataset of 43 participants wearing a 3198 dual-channel NIRS device while performing the n-Back task and show that these existing methodologies struggle in the high-channel regime and are largely outperformed by the proposed pipeline.

SIGNIFICANCE: Our approach helps establish high-channel NIRS devices as a viable platform for state-of-the-art BCI and opens new applications using this class of headset while also enabling high-resolution model imaging and interpretation.},
}

@article {pmid39178898,
year = {2024},
author = {Kim, M and Kim, SP},
title = {Distraction impact of concurrent conversation on event-related potential based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad731e},
pmid = {39178898},
issn = {1741-2552},
abstract = {OBJECTIVE: This study investigates the impact of conversation on the performance of visual event-related potential (ERP)-based brain-computer interfaces (BCIs), considering distractions in real life environment. The research aims to understand how cognitive distractions from speaking and listening activities affect ERP-BCI performance.

APPROACH: The experiment employs a dual-task paradigm where participants control a smart light using visual ERP-BCIs while simultaneously conducting speaking or listening tasks.

MAIN RESULTS: The findings reveal that speaking notably degrades BCI accuracy and the amplitude of ERP components, while increases the latency variability of ERP components and occipital alpha power. In contrast, listening and simple syllable repetition tasks have a lesser impact on these variables. The results suggest that speaking activity significantly distracts visual attentional processes critical for BCI operation Significance. This study highlights the need to take distractions by daily conversation into account of the design and implementation of ERP-BCIs.},
}

@article {pmid39178637,
year = {2024},
author = {Zheng, M and Hong, T and Zhou, H and Garland, EL and Hu, Y},
title = {The acute effect of mindfulness-based regulation on neural indices of cue-induced craving in smokers.},
journal = {Addictive behaviors},
volume = {159},
number = {},
pages = {108134},
doi = {10.1016/j.addbeh.2024.108134},
pmid = {39178637},
issn = {1873-6327},
abstract = {Mindfulness has garnered attention for its potential in alleviating cigarette cravings; however, the neural mechanisms underlying its efficacy remain inadequately understood. This study (N=46, all men) aims to examine the impact of a mindfulness strategy on regulating cue-induced craving and associated brain activity. Twenty-three smokers, consuming over 10 cigarettes daily for at least 2 years， were compared to twenty-three non-smokers. During a regulation of craving task, participants were asked to practice mindfulness during smoking cue-exposure or passively view smoking cues while fMRI scans were completed. A 2 (condition: mindfulness-cigarette and look-cigarette) × 2 (phase: early, late of whole smoking cue-exposure period) repeated measures ANOVA showed a significant interaction of the craving scores between condition and phase, indicating that the mindfulness strategy dampened late-phase craving. Additionally, within the smoker group, the fMRI analyses revealed a significant main effect of mindfulness condition and its interaction with time in several brain networks involving reward, emotion, and interoception. Specifically, the bilateral insula, ventral striatum, and amygdala showed lower activation in the mindfulness condition, whereas the activation of right orbitofrontal cortex mirrored the strategy-time interaction effect of the craving change. This study illuminates the dynamic interplay between mindfulness, smoking cue-induced craving, and neural activity, offering insights into how mindfulness may effectively regulate cigarette cravings.},
}

@article {pmid39178112,
year = {2024},
author = {Tu, WY and Xu, W and Bai, L and Liu, J and Han, Y and Luo, B and Wang, B and Zhang, K and Shen, C},
title = {Local protein synthesis at neuromuscular synapses is required for motor functions.},
journal = {Cell reports},
volume = {43},
number = {9},
pages = {114661},
doi = {10.1016/j.celrep.2024.114661},
pmid = {39178112},
issn = {2211-1247},
abstract = {Motor neurons are highly polarized, and their axons extend over great distances to form connections with myofibers via neuromuscular junctions (NMJs). Local translation at the NMJs in vivo has not been identified. Here, we utilized motor neuron-labeled RiboTag mice and the TRAP (translating ribosome affinity purification) technique to spatiotemporally profile the translatome at NMJs. We found that mRNAs associated with glucose catabolism, synaptic connection, and protein homeostasis are enriched at presynapses. Local translation at the synapse shifts from the assembly of cytoskeletal components during early developmental stages to energy production in adulthood. The mRNA of neuronal Agrin (Agrn), the key molecule for NMJ assembly, is present at motor axon terminals and locally translated. Disrupting the axonal location of Agrn mRNA causes impairment of synaptic transmission and motor functions in adult mice. Our findings indicate that spatiotemporal regulation of mRNA local translation at NMJs plays critical roles in synaptic transmission and motor functions in vivo.},
}

@article {pmid39175558,
year = {2024},
author = {Azadi Moghadam, M and Maleki, A},
title = {Comparative Study of Frequency Recognition Techniques for Steady-State Visual Evoked Potentials According to the Frequency Harmonics and Stimulus Number.},
journal = {Journal of biomedical physics & engineering},
volume = {14},
number = {4},
pages = {365-378},
pmid = {39175558},
issn = {2251-7200},
abstract = {BACKGROUND: A key challenge in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems is to effectively recognize frequencies within a short time window. To address this challenge, the specific characteristics of the data are needed to select the frequency recognition method. These characteristics include factors, such as the number of stimulation targets and the presence of harmonic frequencies, resulting in optimizing the performance and accuracy of SSVEP-based BCI systems.

OBJECTIVE: The current study aimed to examine the effect of data characteristics on frequency recognition accuracy.

MATERIAL AND METHODS: In this analytical study, five commonly used frequency recognition methods were examined, used to various datasets containing different numbers of frequencies, including sub-data with and without frequency harmonics.

RESULTS: The increase in the number of frequencies in the Multivariate Linear Regression (MLR) method has led to a decrease in frequency recognition accuracy by 9%. Additionally, the presence of harmonic frequencies resulted in an 8% decrease in accuracy for the MLR method.

CONCLUSION: Frequency recognition using the MLR method reduces the effect of the number of different frequencies and harmonics of the stimulation frequencies on the frequency recognition accuracy.},
}

@article {pmid39175425,
year = {2024},
author = {Yao, J and Li, Z and Zhou, Z and Bao, A and Wang, Z and Wei, H and He, H},
title = {Distinct regional vulnerability to Aβ and iron accumulation in post mortem AD brains.},
journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association},
volume = {},
number = {},
pages = {},
doi = {10.1002/alz.14188},
pmid = {39175425},
issn = {1552-5279},
support = {2020AAA0109502//National Key R&D Program of China/ ; 82372036//National Natural Science Foundation of China/ ; 226-2023-00095//Fundamental Research Funds for the Central Universities/ ; 82151303//National Natural Science Foundation/ ; 2021ZD0204000//STI2030-Major Projects/ ; },
abstract = {INTRODUCTION: The paramagnetic iron, diamagnetic amyloid beta (Aβ) plaques and their interaction are crucial in Alzheimer's disease (AD) pathogenesis, complicating non-invasive magnetic resonance imaging for prodromal AD detection.

METHODS: We used a state-of-the-art sub-voxel quantitative susceptibility mapping method to simultaneously measure Aβ and iron levels in post mortem human brains, validated by histology. Further transcriptomic analysis using Allen Human Brain Atlas elucidated the underlying biological processes.

RESULTS: Regional increased paramagnetic and diamagnetic susceptibility were observed in medial prefrontal, medial parietal, and para-hippocampal cortices associated with iron deposition (R = 0.836, p = 0.003) and Aβ accumulation (R = 0.853, p = 0.002) in AD brains. Higher levels of gene expression relating to cell cycle, post-translational protein modifications, and cellular response to stress were observed.

DISCUSSION: These findings provide quantitative insights into the variable vulnerability of cortical regions to higher levels of Aβ aggregation, iron overload, and subsequent neurodegeneration, indicating changes preceding clinical symptoms.

HIGHLIGHTS: The vulnerability of distinct brain regions to amyloid beta (Aβ) and iron accumulation varies. Histological validation was performed on stained sections of ex-vivo human brains. Regional variations in susceptibility were linked to gene expression profiles. Iron and Aβ levels in ex-vivo brains were simultaneously quantified.},
}

@article {pmid39173928,
year = {2024},
author = {Cao, HL and Yu, H and Xue, R and Yang, X and Ma, X and Wang, Q and Deng, W and Guo, WJ and Li, ML and Li, T},
title = {Convergence and divergence in neurostructural signatures of unipolar and bipolar depressions: Insights from surface-based morphometry and prospective follow-up.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jad.2024.08.101},
pmid = {39173928},
issn = {1573-2517},
abstract = {BACKGROUND: Bipolar disorder (BD) is often misidentified as unipolar depression (UD) during its early stages, typically until the onset of the first manic episode. This study aimed to explore both shared and unique neurostructural changes in patients who transitioned from UD to BD during follow-up, as compared to those with UD.

METHODS: This study utilized high-resolution structural magnetic resonance imaging (MRI) to collect brain data from individuals initially diagnosed with UD. During the average 3-year follow-up, 24 of the UD patients converted to BD (cBD). For comparison, the study included 48 demographically matched UD patients who did not convert and 48 healthy controls. The MRI data underwent preprocessing using FreeSurfer, followed by surface-based morphometry (SBM) analysis to identify cortical thickness (CT), surface area (SA), and cortical volume (CV) among groups.

RESULTS: The SBM analysis identified shared neurostructural characteristics between the cBD and UD groups, specifically thinner CT in the right precentral cortex compared to controls. Unique to the cBD group, there was a greater SA in the right inferior parietal cortex compared to the UD group. Furthermore, no significant correlations were observed between cortical morphological measures and cognitive performance and clinical features in the cBD and UD groups.

LIMITATIONS: The sample size is relatively small.

CONCLUSIONS: Our findings suggest that while cBD and UD exhibit some common alterations in cortical macrostructure, numerous distinct differences are also present. These differences offer valuable insights into the neuropathological underpinnings that distinguish these two conditions.},
}

@article {pmid39171098,
year = {2024},
author = {Freudenburg, Z and Berezutskaya, J and Herbert, C},
title = {Editorial: The ethics of speech ownership in the context of neural control of augmented assistive communication.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1468938},
pmid = {39171098},
issn = {1662-5161},
}

@article {pmid39170600,
year = {2024},
author = {Pan, H and Fu, Y and Zhang, Q and Zhang, J and Qin, X},
title = {The decoder design and performance comparative analysis for closed-loop brain-machine interface system.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {1},
pages = {147-164},
pmid = {39170600},
issn = {1871-4080},
abstract = {Brain-machine interface (BMI) can convert electroencephalography signals (EEGs) into the control instructions of external devices, and the key of control performance is the accuracy and efficiency of decoder. However, the performance of different decoders obtaining control instructions from complex and variable EEG signals is very different and irregular in the different neural information transfer model. Aiming at this problem, the off-line and on-line performance of eight decoders based on the improved single-joint information transmission (SJIT) model is compared and analyzed in this paper, which can provide a theoretical guidance for decoder design. Firstly, in order to avoid the different types of neural activities in the decoding process on the decoder performance, eight decoders based on the improved SJIT model are designed. And then the off-line decoding performance of these decoders is tested and compared. Secondly, a closed-loop BMI system which combining by the designed decoder and the random forest encoder based on the improved SJIT model is constructed. Finally, based on the constructed closed-loop BMI system, the on-line decoding performance of decoders is compared and analyzed. The results show that the LSTM-based decoder has better on-line decoding performance than others in the improved SJIT model.},
}

@article {pmid39167669,
year = {2024},
author = {Matsiko, A},
title = {Bilingual speech neuroprosthesis.},
journal = {Science robotics},
volume = {9},
number = {93},
pages = {eads4122},
doi = {10.1126/scirobotics.ads4122},
pmid = {39167669},
issn = {2470-9476},
mesh = {Humans ; *Multilingualism ; *Speech ; *Brain-Computer Interfaces ; Neural Prostheses ; Brain/physiology ; Prosthesis Design ; Male ; },
abstract = {A neuroprosthesis could decode two languages from the brain activity of a bilingual participant who was unable to articulate speech.},
}

Humans
*Multilingualism
*Speech
*Brain-Computer Interfaces
Neural Prostheses
Brain/physiology
Prosthesis Design
Male

@article {pmid39169955,
year = {2022},
author = {Ukhovskyi, V and Pyskun, A and Korniienko, L and Aliekseieva, H and Moroz, O and Pyskun, O and Kyivska, G and Mezhenskyi, A},
title = {Serological prevalence of Leptospira serovars among pigs in Ukraine during the period of 2001-2019.},
journal = {Veterinarni medicina},
volume = {67},
number = {1},
pages = {13-27},
pmid = {39169955},
issn = {0375-8427},
abstract = {Leptospirosis is a widespread infection among pigs throughout the world. In most cases in Ukraine, only the microscopic agglutination test (MAT) is used for the diagnosis of leptospirosis in animals. In general, during the period of 2001-2019, 2 381 163 samples of blood sera from swine were tested in our country and 85 338 positive reactions were received, which is 3.58% [binomial confidence intervals (BCI), 3.56-3.61%]. It was established that the serovars copenhageni - 33.91% (BCI, 33.59-34.23%), bratislava - 14.14% (BCI, 13.90-14.37%), pomona - 8.58% (BCI, 8.39-8.77%), and tarassovi - 7.12% (BCI, 6.95-7.30%) play a leading role in the aetiological structure of swine leptospirosis. A large number of positive reactions to several serovars was observed - 29.78% (BCI, 29.47-30.09%) of the total number of positive cases. In addition, the article presents data according to a retrospective analysis of the eight serovars circulating among pigs in Ukraine. Thus, during the nineteen year period, there was a decrease in the number of positive reactions to bratislava, pomona, and tarassovi and an increase in the number of positive reactions to copenhageni, polonica, and kabura. Mapping Ukraine's territory for leptospirosis among pigs was carried out. This allows one to identify zones with a risk of leptospirosis infections among swine. The maps show that the highest incidence rates were identified in the eastern and central parts of Ukraine.},
}