@article {pmid39697780,
year = {2024},
author = {Wan, X and Xing, S and Zhang, Y and Duan, D and Liu, T and Li, D and Yu, H and Wen, D},
title = {Combining motion performance with EEG for diagnosis of mild cognitive impairment: a new perspective.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1476730},
pmid = {39697780},
issn = {1662-4548},
}

@article {pmid39697779,
year = {2024},
author = {Cui, S and Lee, D and Wen, D},
title = {Toward brain-inspired foundation model for EEG signal processing: our opinion.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1507654},
pmid = {39697779},
issn = {1662-4548},
}

@article {pmid39696695,
year = {2024},
author = {Xue, YY and Zhang, ZS and Lin, RR and Huang, HF and Zhu, KQ and Chen, DF and Wu, ZY and Tao, QQ},
title = {CD2AP deficiency aggravates Alzheimer's disease phenotypes and pathology through p38 MAPK activation.},
journal = {Translational neurodegeneration},
volume = {13},
number = {1},
pages = {64},
pmid = {39696695},
issn = {2047-9158},
support = {81970998//National Natural Science Foundation of China/ ; 2024SSYS0018//Key Research and Development Program of Zhejiang Province/ ; 2021ZD0201103//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; 2021ZD0201803//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; },
mesh = {Animals ; *Alzheimer Disease/genetics/pathology/metabolism ; *p38 Mitogen-Activated Protein Kinases/genetics/metabolism ; Mice ; *Adaptor Proteins, Signal Transducing/genetics/deficiency ; Mice, Transgenic ; Phenotype ; Disease Models, Animal ; Humans ; Mice, Knockout ; Neurons/pathology/metabolism ; Cytoskeletal Proteins ; },
abstract = {BACKGROUND: Alzheimer's disease (AD) is the most common form of neurodegenerative disorder, which is characterized by a decline in cognitive abilities. Genome-wide association and clinicopathological studies have demonstrated that the CD2-associated protein (CD2AP) gene is one of the most important genetic risk factors for AD. However, the precise mechanisms by which CD2AP is linked to AD pathogenesis remain unclear.

METHODS: The spatiotemporal expression pattern of CD2AP was determined. Then, we generated and characterized an APP/PS1 mouse model with neuron-specific Cd2ap deletion, using immunoblotting, immunofluorescence, enzyme-linked immunosorbent assay, electrophysiology and behavioral tests. Additionally, we established a stable CD2AP-knockdown SH-SY5Y cell line to further elucidate the specific molecular mechanisms by which CD2AP contributes to AD pathogenesis. Finally, the APP/PS1 mice with neuron-specific Cd2ap deletion were treated with an inhibitor targeting the pathway identified above to further validate our findings.

RESULTS: CD2AP is widely expressed in various regions of the mouse brain, with predominant expression in neurons and vascular endothelial cells. In APP/PS1 mice, neuronal knockout of Cd2ap significantly aggravated tau pathology, synaptic impairments and cognitive deficits. Mechanistically, the knockout of Cd2ap activated p38 mitogen-activated protein kinase (MAPK) signaling, which contributed to increased tau phosphorylation, synaptic injury, neuronal apoptosis and cognitive impairment. Furthermore, the phenotypes of neuronal Cd2ap knockout were ameliorated by a p38 MAPK inhibitor.

CONCLUSION: Our study presents the first in vivo evidence that CD2AP deficiency exacerbates the phenotypes and pathology of AD through the p38 MAPK pathway, identifying CD2AP/p38 MAPK as promising therapeutic targets for AD.},
}

Animals
*Alzheimer Disease/genetics/pathology/metabolism
*p38 Mitogen-Activated Protein Kinases/genetics/metabolism
Mice
*Adaptor Proteins, Signal Transducing/genetics/deficiency
Mice, Transgenic
Phenotype
Disease Models, Animal
Humans
Mice, Knockout
Neurons/pathology/metabolism
Cytoskeletal Proteins

@article {pmid39695740,
year = {2024},
author = {Lowers, V and Kirby, R and Young, B and Harris, RV},
title = {Scoping review of fidelity strategies used in behaviour change trials delivered in primary dental care settings.},
journal = {Trials},
volume = {25},
number = {1},
pages = {824},
pmid = {39695740},
issn = {1745-6215},
mesh = {Humans ; *Randomized Controlled Trials as Topic/standards/methods ; *Primary Health Care/standards ; *Dental Care/standards ; Health Behavior ; Research Design/standards ; Behavior Therapy/methods/standards ; Oral Health/standards ; Health Knowledge, Attitudes, Practice ; },
abstract = {BACKGROUND: Primary dental care settings are strategically important locations where randomised controlled trials (RCTs) of behaviour change interventions (BCIs) can be tested to tackle oral diseases. Findings have so far produced equivocal results. Improving treatment fidelity is posed as a mechanism to improve scientific rigour, consistency and implementation of BCIs. The National Institutes of Health Behaviour Change Consortium (NIH BCC) developed a tool to assess and evaluate treatment fidelity in health behaviour change interventions, which has yet to be applied to the primary dental care BCI literature.

METHOD: We conducted a scoping review of RCTs delivered in primary dental care by dental team members (in real-world settings) between 1980 and 2023. Eligible studies were coded using the NIH BCC checklist to determine the presence of reported fidelity strategies across domains: design, training, delivery, receipt and enactment.

RESULTS: We included 34 eligible articles, reporting 21 RCTs. Fidelity reporting variations were found both between and within NIH BCC domains: strategy reporting ranged from 9.5 to 85.7% in design, 9.5 to 57.1% in training, 0 to 66.7% in delivery, 14.3 to 36.8% in receipt and 13.3 to 33.3% in enactment. The most reported domain was design (M = 0.45), and the least reported domain was delivery (M = 0.21). Only one study reported over 50% of the recommended strategies in every domain.

CONCLUSIONS: This review revealed inconsistencies in fidelity reporting with no evidence that fidelity guidelines or frameworks were being used within primary dental care trials. This has highlighted issues with interpretability, reliability and reproducibility of research findings. Recommendations are proposed to assist primary dental care trialists with embedding fidelity strategies into future research.},
}

Humans
*Randomized Controlled Trials as Topic/standards/methods
*Primary Health Care/standards
*Dental Care/standards
Health Behavior
Research Design/standards
Behavior Therapy/methods/standards
Oral Health/standards
Health Knowledge, Attitudes, Practice

@article {pmid39694240,
year = {2024},
author = {Fonseca, M and Kurban, D and Roy, JP and Santschi, DE and Molgat, E and Yang, DA and Dufour, S},
title = {Usefulness of differential somatic cell count for udder health monitoring: Identifying referential values for differential somatic cell count in healthy quarters and quarters with subclinical mastitis.},
journal = {Journal of dairy science},
volume = {},
number = {},
pages = {},
doi = {10.3168/jds.2024-25403},
pmid = {39694240},
issn = {1525-3198},
abstract = {Mastitis, an inflammation of the udder primarily caused by an intramammary infection, is one of the most common diseases in dairy cattle. Somatic cell count (SCC) has been widely used as an indicator of udder inflammation, assisting in the detection of subclinical mastitis. More recently, differential somatic cell count (DSCC), which represents the combined proportion of lymphocytes and polymorphonuclear leukocytes, has become available for routine dairy milk screening, though it was not yet widely studied. Therefore, the objective of this study was to assess and compare the usefulness of quarter-level somatic cell score (SCS) or DSCC to predict the probability of subclinical mastitis. Additionally, our goals included estimating the sensitivity (Se) and specificity (Sp) of SCS and DSCC across all potential cut-off values. The current study was an observational study conducted on commercial dairy farms. Five dairy herds were selected using a convenience sampling. A Gaussian finite mixture model (GFMM) was applied to investigate the latent quarter subclinical mastitis status with either measurement, i.e., SCS or DSCC. Posterior values for SCS and DSCC obtained from the GFMM were used for predictive estimation of the parameters. The estimated SCS distribution for healthy quarters had a mean (standard deviation) of 1.4 (1.3), while, for quarters with subclinical mastitis, it was 4.5 (2.4). For DSCC, the estimated mean was 55.6% (15.2) for healthy quarters, whereas it was 80.4% (6.4) for quarters with subclinical mastitis. The most discriminant cut-off for SCS, as indicated by the Youden index, was 3.0, corresponding to exactly 100,000 cells/mL. At this threshold, the Se and Sp of SCS were 0.73 (95% Bayesian Credible Interval [BCI]: 0.70-0.77) and 0.90 (95% BCI: 0.89-0.91), respectively. The most discriminant cut-off point for DSCC was 70.0%, with corresponding the Se and Sp values of 0.95 (0.93, 0.96) and 0.83 (0.81, 0.85), respectively. For the SCS analysis, we obtained predictive probabilities of subclinical mastitis approaching 0 and 100%, with only a narrow range of SCS results yielding intermediate probabilities. On the other hand, predictive probabilities ranging from 0 to 90% were obtained for DSCC analysis, with a large range of DSCC results presenting intermediate probabilities. Thus, SCS seemed to surpass DSCC for predicting subclinical mastitis. These findings provided a foundation for future studies to further explore and validate the efficacy of GFMM for diagnostic tests yielding quantitative results.},
}

@article {pmid39693762,
year = {2024},
author = {Oxley, T and Deo, DR and Cernera, S and Sawyer, A and Putrino, D and Ramsey, NF and Fry, A},
title = {The 'Brussels 4': essential requirements for implantable brain-computer interface user autonomy.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ada0e6},
pmid = {39693762},
issn = {1741-2552},
abstract = {Implantable brain-computer interfaces (iBCIs) hold great promise for individuals with severe paralysis and are advancing toward commercialization. The features required to achieve autonomous use of an iBCI may be under recognized in traditional academic measures of iBCI function and deserve further consideration to achieve successful clinical translation and patient adoption. Here, we present four key considerations to achieve autonomous use, reflecting the authors' perspectives based on discussions during various sessions and workshops across the 10th International BCI Society Meeting, Brussels, 2023: (1) immediate use, (2) easy to use, (3) continuous use, and (4) stable system use.},
}

@article {pmid39693735,
year = {2024},
author = {Kim, J and Cho, YS and Kim, SP},
title = {Task-relevant stimulus design improves P300-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ada0e3},
pmid = {39693735},
issn = {1741-2552},
abstract = {OBJECTIVE: In the pursuit of refining P300-based brain-computer interfaces (BCIs), our research aims to propose a novel stimulus design focused on selective attention and task relevance to address the challenges of P300-based BCIs, including the necessity of repetitive stimulus presentations, accuracy improvement, user variability, and calibration demands.

APPROACH: In the oddball task for P300-based BCIs, we develop a stimulus design involving task-relevant dynamic stimuli implemented as finger-tapping to enhance the elicitation and consistency of event-related potentials (ERPs). We further improve the performance of P300-based BCIs by optimizing ERP feature extraction and classification in offline analyses.

MAIN RESULTS: With the proposed stimulus design, online P300-based BCIs in 37 healthy participants achieve an accuracy of 91.2% and an information transfer rate (ITR) of 28.37 bits/min with two stimulus repetitions. With optimized computational modeling in BCIs, our offline analyses reveal the possibility of single-trial execution, showcasing an accuracy of 91.7% and an ITR of 59.92 bits/min. Furthermore, our exploration into the feasibility of across-subject zero-calibration BCIs through offline analyses, where a BCI built on a dataset of 36 participants is directly applied to a left-out participant with no calibration, yields an accuracy of 94.23% and the ITR of 31.56 bits/min with two stimulus repetitions and the accuracy of 87.75% and the ITR of 52.61 bits/min with single-trial execution. When using the finger-tapping stimulus, the variability in performance among participants is the lowest, and a greater increase in performance is observed especially for those showing lower performance using the conventional color-changing stimulus. Signficance. Using a novel task-relevant dynamic stimulus design, this study achieves one of the highest levels of P300-based BCI performance to date. This underscores the importance of coupling stimulus paradigms with computational methods for improving P300-based BCIs.},
}

@article {pmid39693734,
year = {2024},
author = {Srisrisawang, N and Müller-Putz, G},
title = {Simultaneous encoding of speed, distance, and direction in discrete reaching: an EEG study.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ada0ea},
pmid = {39693734},
issn = {1741-2552},
abstract = {OBJECTIVE: The complicated processes of carrying out a hand reach are still far from fully understood. In order to further the understanding of the kinematics of hand movement, the simultaneous representation of speed, distance, and direction in the brain is explored.

APPROACH: We utilized electroencephalography (EEG) signals and hand position recorded during a 4-direction center-out reaching task with either quick or slow speed, near and far distance. Linear models were employed in two modes: decoding and encoding. First, to test the discriminability of speed, distance, and direction. Second, to find the contribution of the cortical sources via the source localization. Additionally, we compared the decoding accuracy when using features obtained from EEG signals and source-localized EEG signals based on the results from the encoding model.

RESULTS: Speed, distance, and direction can be classified better than chance. The accuracy of the speed was also higher than the distance, indicating a stronger representation of the speed than the distance. The speed and distance showed similar significant sources in the central regions related to the movement initiation, while the direction indicated significant sources in the parieto-occipital regions related to the movement preparation. The combination of the features from EEG and source localized signals improved the classification.

SIGNIFICANCE: Directional and non-directional information are represented in two separate networks. The quick movement resulted in improvement in the direction classification. Our results enhance our understanding of hand movement in the brain and help us make informed decisions when designing an improved paradigm in the future. .},
}

@article {pmid39693678,
year = {2024},
author = {Shen, J and Wang, K and Gao, W and Liu, JK and Xu, Q and Pan, G and Chen, X and Tang, H},
title = {Temporal spiking generative adversarial networks for heading direction decoding.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {184},
number = {},
pages = {106975},
doi = {10.1016/j.neunet.2024.106975},
pmid = {39693678},
issn = {1879-2782},
abstract = {The spike-based neuronal responses within the ventral intraparietal area (VIP) exhibit intricate spatial and temporal dynamics in the posterior parietal cortex, presenting decoding challenges such as limited data availability at the biological population level. The practical difficulty in collecting VIP neuronal response data hinders the application of sophisticated decoding models. To address this challenge, we propose a unified spike-based decoding framework leveraging spiking neural networks (SNNs) for both generative and decoding purposes, for their energy efficiency and suitability for neural decoding tasks. We propose the Temporal Spiking Generative Adversarial Networks (T-SGAN), a model based on a spiking transformer, to generate synthetic time-series data reflecting the neuronal response of VIP neurons. T-SGAN incorporates temporal segmentation to reduce the temporal dimension length, while spatial self-attention facilitates the extraction of associated information among VIP neurons. This is followed by recurrent SNNs decoder equipped with an attention mechanism, designed to capture the intricate spatial and temporal dynamics for heading direction decoding. Experimental evaluations conducted on biological datasets from monkeys showcase the effectiveness of the proposed framework. Results indicate that T-SGAN successfully generates realistic synthetic data, leading to a significant improvement of up to 1.75% in decoding accuracy for recurrent SNNs. Furthermore, the SNN-based decoding framework capitalizes on the low power consumption advantages, offering substantial benefits for neuronal response decoding applications.},
}

@article {pmid39692757,
year = {2024},
author = {van Oosterhout, K and Chilundo, A and Branco, MP and Aarnoutse, EJ and Timmermans, M and Fattori, M and Ramsey, NF and Cantatore, E},
title = {Brain-Computer Interfaces Using Flexible Electronics: An a-IGZO Front-End for Active ECoG Electrodes.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2408576},
doi = {10.1002/advs.202408576},
pmid = {39692757},
issn = {2198-3844},
support = {//Dutch Research Council (NWO)/ ; 17608//Stichting voor de Technische Wetenschappen/ ; 19072//Stichting voor de Technische Wetenschappen/ ; UEBIT//EWUU Alliance/ ; },
abstract = {Brain-computer interfaces (BCIs) are evolving toward higher electrode count and fully implantable solutions, which require extremely low power densities (<15mW cm[-2]). To achieve this target, and allow for a large and scalable number of channels, flexible electronics can be used as a multiplexing interface. This work introduces an active analog front-end fabricated with amorphous Indium-Gallium-Zinx-Oxide (a-IGZO) Thin-Film Transistors (TFTs) on foil capable of active matrix multiplexing. The circuit achieves only 70nV per sqrt(Hz) input referred noise, consuming 46µW, or 3.5mW cm[-2]. It demonstrates for the first time in literature a flexible front-end with a noise efficiency factor comparable with Silicon solutions (NEF = 9.8), which is more than 10X lower compared to previously reported flexible front-ends. These results have been achieved using a modified bootstrap-load amplifier. The front end is tested by playing through it recordings obtained from a conventional BCI system. A gesture classification based on the flexible front-end outputs achieves 94% accuracy. Using a flexible active front end can improve the state-of-the-art in high channel count BCI systems by lowering the multiplexer noise and enabling larger areas of the brain to be monitored while reducing power density. Therefore, this work enables a new generation of high channel-count active BCI electrode grids.},
}

@article {pmid39692430,
year = {2024},
author = {Nie, J and Huang, T and Sun, Y and Peng, Z and Dong, W and Chen, J and Zheng, D and Guo, F and Shi, W and Ling, Y and Zhao, W and Yang, H and Shui, T and Yan, X},
title = {Influence of the Enterovirus 71 Vaccine and the COVID-19 Pandemic on Hand, Foot, and Mouth Disease in China Based on Counterfactual Models: Observational Study.},
journal = {JMIR public health and surveillance},
volume = {10},
number = {},
pages = {e63146},
doi = {10.2196/63146},
pmid = {39692430},
issn = {2369-2960},
mesh = {*Hand, Foot and Mouth Disease/epidemiology/prevention & control ; Humans ; China/epidemiology ; *COVID-19/epidemiology/prevention & control ; Infant ; Child, Preschool ; Incidence ; *Enterovirus A, Human ; *Viral Vaccines/administration & dosage ; Male ; Child ; Female ; Adolescent ; Pandemics/prevention & control ; },
abstract = {BACKGROUND: Hand, foot, and mouth disease (HFMD) is a highly contagious viral illness. Understanding the long-term trends of HFMD incidence and its epidemic characteristics under the circumstances of the enterovirus 71 (EV71) vaccination program and the outbreak of COVID-19 is crucial for effective disease surveillance and control.

OBJECTIVE: We aim to give an overview of the trends of HFMD over the past decades and evaluate the impact of the EV71 vaccination program and the COVID-19 pandemic on the epidemic trends of HFMD.

METHODS: Using official surveillance data from the Yunnan Province, China, we described long-term incidence trends and severity rates of HFMD as well as the variation of enterovirus proportions among cases. We conducted the autoregressive integrated moving average (ARIMA) of time series analyses to predict monthly incidences based on given subsets. The difference between the actual incidences and their counterfactual predictions was compared using absolute percentage errors (APEs) for periods after the EV71 vaccination program and the COVID-19 pandemic, respectively.

RESULTS: The annual incidence of HFMD fluctuated between 25.62 cases per 100,000 people in 2008 and 221.52 cases per 100,000 people in 2018. The incidence for men ranged from 30 to 250 cases per 100,000 people from 2008 to 2021, which was constantly higher than that for women. The annual incidence for children aged 1 to 2 years old ranged from 54.54 to 630.06 cases per 100,000 people, which was persistently higher than that for other age groups. For monthly incidences, semiannual peaks were observed for each year. All actual monthly incidences of 2014 to 2015 fell within the predicted 95% CI by the ARIMA(1,0,1)(1,1,0)[12] model. The average APE was 19% for a 2-year prediction. After the EV71 vaccination program, the actual monthly incidence of HFMD was consistently lower than the counterfactual predictions by ARIMA(1,0,1)(1,1,0)[12], with negative APEs ranging from -11% to -229% from January 2017 to April 2018. In the meantime, the proportion of EV71 among the enteroviruses causing HFMD decreased significantly, and the proportion was highly correlated (r=0.73, P=.004) with the severity rate. After the onset of the COVID-19 pandemic in 2020, the actual monthly incidence of HFMD consistently maintained a lower magnitude compared to the counterfactual predictions-ARIMA(1,0,1)(0,1,0)[12]-from February to September 2020, with considerable negative APEs (ranging from -31% to -2248%).

CONCLUSIONS: EV71 vaccination alleviated severe HFMD cases and altered epidemiological trends. The HFMD may also benefit from nonpharmaceutical interventions during outbreaks such as the COVID-19 pandemic. Further development of a multivalent virus vaccine is crucial for effectively controlling HFMD outbreaks. Policymakers should implement nonpharmaceutical interventions and emphasize personal hygiene for routine prevention when appropriate.},
}

*Hand, Foot and Mouth Disease/epidemiology/prevention & control
Humans
China/epidemiology
*COVID-19/epidemiology/prevention & control
Infant
Child, Preschool
Incidence
*Enterovirus A, Human
*Viral Vaccines/administration & dosage
Male
Child
Female
Adolescent
Pandemics/prevention & control

@article {pmid39691819,
year = {2024},
author = {Vieira, R and Moreno, P and Vourvopoulos, A},
title = {EEG-based action anticipation in human-robot interaction: a comparative pilot study.},
journal = {Frontiers in neurorobotics},
volume = {18},
number = {},
pages = {1491721},
doi = {10.3389/fnbot.2024.1491721},
pmid = {39691819},
issn = {1662-5218},
abstract = {As robots become integral to various sectors, improving human-robot collaboration is crucial, particularly in anticipating human actions to enhance safety and efficiency. Electroencephalographic (EEG) signals offer a promising solution, as they can detect brain activity preceding movement by over a second, enabling predictive capabilities in robots. This study explores how EEG can be used for action anticipation in human-robot interaction (HRI), leveraging its high temporal resolution and modern deep learning techniques. We evaluated multiple Deep Learning classification models on a motor imagery (MI) dataset, achieving up to 80.90% accuracy. These results were further validated in a pilot experiment, where actions were accurately predicted several hundred milliseconds before execution. This research demonstrates the potential of combining EEG with deep learning to enhance real-time collaborative tasks, paving the way for safer and more efficient human-robot interactions.},
}

@article {pmid39691581,
year = {2024},
author = {Eken, A and Yüce, M and Yükselen, G and Erdoğan, SB},
title = {Explainable fNIRS-based pain decoding under pharmacological conditions via deep transfer learning approach.},
journal = {Neurophotonics},
volume = {11},
number = {4},
pages = {045015},
pmid = {39691581},
issn = {2329-423X},
abstract = {SIGNIFICANCE: Assessment of pain and its clinical diagnosis rely on subjective methods which become even more complicated under analgesic drug administrations.

AIM: We aim to propose a deep learning (DL)-based transfer learning (TL) methodology for objective classification of functional near-infrared spectroscopy (fNIRS)-derived cortical oxygenated hemoglobin responses to painful and non-painful stimuli presented under different timings post-analgesic and placebo drug administration.

APPROACH: A publicly available fNIRS dataset obtained during painful/non-painful stimuli was used. Separate fNIRS scans were taken under the same protocol before drug (morphine and placebo) administration and at three different timings (30, 60, and 90 min) post-administration. Data from pre-drug fNIRS scans were utilized for constructing a base DL model. Knowledge generated from the pre-drug model was transferred to six distinct post-drug conditions by following a TL approach. The DeepSHAP method was utilized to unveil the contribution weights of nine regions of interest for each of the pre-drug and post-drug decoding models.

RESULTS: Accuracy, sensitivity, specificity, and area under curve (AUC) metrics of the pre-drug model were above 90%, whereas each of the post-drug models demonstrated a performance above 90% for the same metrics. Post-placebo models had higher decoding accuracy than post-morphine models. Knowledge obtained from a pre-drug base model could be successfully utilized to build pain decoding models for six distinct brain states that were scanned at three different timings after either analgesic or placebo drug administration. The contribution of different cortical regions to classification performance varied across the post-drug models.

CONCLUSIONS: The proposed DL-based TL methodology may remove the necessity to build DL models for data collected at clinical or daily life conditions for which obtaining training data is not practical or building a new decoding model will have a computational cost. Unveiling the explanation power of different cortical regions may aid the design of more computationally efficient fNIRS-based brain-computer interface (BCI) system designs that target other application areas.},
}

@article {pmid39688131,
year = {2024},
author = {Wang, S and Song, X and Xu, J and Wang, J and Yu, L},
title = {Flexible silicon for high-performance photovoltaics, photodetectors and bio-interfaced electronics.},
journal = {Materials horizons},
volume = {},
number = {},
pages = {},
doi = {10.1039/d4mh01466a},
pmid = {39688131},
issn = {2051-6355},
abstract = {Silicon (Si) is currently the most mature and reliable semiconductor material in the industry, playing a pivotal role in the development of modern microelectronics, renewable energy, and bio-electronic technologies. In recent years, widespread research attention has been devoted to the development of advanced flexible electronics, photovoltaics, and bio-interfaced sensors/detectors, boosting their emerging applications in distributed energy sources, healthcare, environmental monitoring, and brain-computer interfaces (BCIs). Despite the rigid and brittle nature of Si, a series of new fabrication technologies and integration strategies have been developed to enable a wide range of c-Si-based high-performance flexible photovoltaics and electronics, which were previously only achievable with intrinsically soft organic and polymer semiconductors. More interestingly, programmable geometric engineering of crystalline silicon (c-Si) units and logic circuits has been explored to enable the fabrication of various highly flexible nanoprobes for intracellular sensing and the deployment of soft BCI matrices to record and understand brain neural activities for the development of advanced neuroprosthetics. This review will systematically examine the latest progress in the fabrication of Si-based flexible solar cells, photodetectors, and biological probing interfaces over the past decade, identifying key design principles, mechanisms, and technological milestones achieved through novel geometry, morphology, and composition control. These advancements, when combined, will not only promote the practical applications of sustainable energy and wearable electronics but also spur new breakthroughs in emerging human-machine interfaces (HMIs) and artificial intelligence applications, which hold significant implications for understanding neural activities, implementing more efficient artificial Intelligence (AI) algorithms, and developing new therapies or treatments. Finally, we will summarize and provide an outlook on the current challenges and future opportunities of Si-based electronics, flexible optoelectronics, and bio-sensing.},
}

@article {pmid39687714,
year = {2024},
author = {Hofmann, MJ and Chang, YN and Brouwer, H and Zock, M},
title = {Editorial: Neurocomputational models of language processing.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1524366},
doi = {10.3389/fnhum.2024.1524366},
pmid = {39687714},
issn = {1662-5161},
}

@article {pmid39687306,
year = {2024},
author = {Kancaoğlu, M and Kuntalp, M},
title = {Low-cost, mobile EEG hardware for SSVEP applications.},
journal = {HardwareX},
volume = {19},
number = {},
pages = {e00567},
doi = {10.1016/j.ohx.2024.e00567},
pmid = {39687306},
issn = {2468-0672},
abstract = {The global shortage of integrated circuits due to the COVID-19 pandemic has made it challenging to build biopotential acquisition devices like electroencephalography (EEG) hardware. To address this issue, a new hardware system using common ICs has been designed, which is cost-effective, precise, and easily accessible from global distributors. The hardware system comprises 8-channel inputs EEG hardware with a mobile headset capable of acquiring 5-30Hz EEG signals. First two channels of the design is enabled for steady-state visual evoked potential (SSVEP) operations, and the remaining channels can be powered up as needed. A small 3D-printable enclosure is also designed for the hardware board, which is attached to protective glasses to be used as a head-mounted device. The board includes an additional green LED, 4 pulse width modulation (PWM) outputs for general-purpose input/output (GPIO), 2 buttons for input, and exposed programming pins and digital-to-analog converter (DAC) output from the microcontroller unit (MCU). The proposed hardware system is expected to enable students and young researchers to experiment with EEG signals, especially SSVEP, before investing in professional equipment with the availability of programming codes.},
}

@article {pmid39687114,
year = {2024},
author = {Ma, J and Rui, Z and Zou, Y and Qin, Z and Zhao, Z and Zhang, Y and Mao, Z and Bai, H and Zhang, J},
title = {Neurosurgical and BCI approaches to visual rehabilitation in occipital lobe tumor patients.},
journal = {Heliyon},
volume = {10},
number = {23},
pages = {e39072},
doi = {10.1016/j.heliyon.2024.e39072},
pmid = {39687114},
issn = {2405-8440},
abstract = {This study investigates the effects of occipital lobe tumors on visual processing and the role of brain-computer interface (BCI) technologies in post-surgical visual rehabilitation. Through a combination of pre-surgical functional magnetic resonance imaging (fMRI) and Diffusion Tensor Imaging (DTI), intra-operative direct cortical stimulation (DCS) and Electrocorticography (ECoG), and post-surgical BCI interventions, we provide insight into the complex dynamics between occipital lobe tumors and visual function. Our results highlight a discrepancy between clinical assessments of visual field damage and the patient's reported visual experiences, suggesting a residual functional capacity within the damaged occipital regions. Additionally, the absence of expected visual phenomena during surgery and the promising outcomes from BCI-driven rehabilitation underscore the complexity of visual processing and the potential of technology-enhanced rehabilitation strategies. This work emphasizes the need for an interdisciplinary approach in developing effective treatments for visual impairments related to brain tumors, illustrating the significant implications for neurosurgical practices and the advancement of rehabilitation sciences.},
}

@article {pmid39686818,
year = {2024},
author = {Lv, Q and Li, Q and Cao, P and Wei, C and Li, Y and Wang, Z and Wang, L},
title = {Designing Silk Biomaterials toward Better Future Healthcare: The Development and Application of Silk-Based Implantable Electronic Devices in Clinical Diagnosis and Therapy.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e2411946},
doi = {10.1002/adma.202411946},
pmid = {39686818},
issn = {1521-4095},
support = {2022YFC2408100//National Key Research and Development Program of China/ ; 82372511//National Natural Science Foundation of China/ ; 82302585//National Natural Science Foundation of China/ ; 82072167//National Natural Science Foundation of China/ ; 82272277//National Natural Science Foundation of China/ ; 82173315//National Natural Science Foundation of China/ ; 81974382//National Natural Science Foundation of China/ ; [2022] No.11//Hubei Province Science and Technology Innovation Team Project/ ; 2022BCA013//the Major Scientific and Technological Innovation Projects of Hubei Province/ ; 2021CFB416//Natural Science Foundation of Hubei Province/ ; 2024AFB652//Natural Science Foundation of Hubei Province/ ; 2022CFB736//Natural Science Foundation of Hubei Province/ ; },
abstract = {Implantable medical electronic devices (IMEDs) have attracted great attention and shown versatility for solving clinical problems ranging from real-time monitoring of physiological/ pathological states to electrical stimulation therapy and from monitoring brain cell activity to deep brain stimulation. The ongoing challenge is to select appropriate materials in target device configuration for biomedical applications. Currently, silk-based biomaterials have been developed for the design of diagnostic and therapeutic electronic devices due to their excellent properties and abundant active sites in the structure. Herein, the aim is to summarize the structural characteristics, physicochemical properties, and bioactivities of natural silk biomaterials as well as their derived materials, with a particular focus on the silk-based implantable biomedical electronic devices, such as implantable devices for invasive brain-computer interfaces, neural recording, and in vivo electrostimulation. In addition, future opportunities and challenges are also envisioned, hoping to spark the interests of researchers in interdisciplinary fields such as biomaterials, clinical medicine, and electronics.},
}

@article {pmid39686393,
year = {2024},
author = {Zhuang, W and Zhang, Y and Wang, Y and He, K},
title = {3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {23},
pages = {},
doi = {10.3390/s24237856},
pmid = {39686393},
issn = {1424-8220},
mesh = {Humans ; *Neural Networks, Computer ; Learning/physiology ; Students ; Emotions/physiology ; Deep Learning ; Machine Learning ; Memory, Short-Term/physiology ; },
abstract = {Evaluating students' learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing learning outcomes, provides a novel perspective for identifying cognitive states and emotional experiences. However, traditional evaluation methods suffer from one sidedness in feature extraction and high complexity in model construction, often making it difficult to fully explore the deep value of emotional data. To address this challenge, we have innovatively proposed a lightweight neurodynamic model: 3D-BCLAM. This model cleverly integrates Bidirectional Convolutional Long Short-Term Memory (BCL) and dynamic attention mechanism, in order to efficiently capture emotional dynamic changes in time series with extremely low computational cost. 3D-BCLAM can achieve a comprehensive evaluation of students' learning outcomes, covering not only the cognitive level but also delving into the emotional dimension for detailed analysis. Under testing on public datasets, 3D-BCLAM has demonstrated outstanding performance, significantly outperforming traditional machine learning and deep learning models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This achievement not only validates the effectiveness of the 3D-BCLAM model, but also provides strong support for promoting the innovation of student learning effectiveness assessment.},
}

Humans
*Neural Networks, Computer
Learning/physiology
Students
Emotions/physiology
Deep Learning
Machine Learning
Memory, Short-Term/physiology

@article {pmid39686227,
year = {2024},
author = {Megalingam, RK and Sankardas, KS and Manoharan, SK},
title = {An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {23},
pages = {},
doi = {10.3390/s24237690},
pmid = {39686227},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; *Algorithms ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Motion ; Signal Processing, Computer-Assisted ; Wheelchairs ; Imagination/physiology ; },
abstract = {Electroencephalography (EEG) is a non-invasive technique with high temporal resolution and cost-effective, portable, and easy-to-use features. Motor imagery EEG (MI-EEG) data classification is one of the key applications within brain-computer interface (BCI) systems, utilizing EEG signals from motor imagery tasks. BCI is very useful for people with severe mobility issues like quadriplegics, spinal cord injury patients, stroke patients, etc., giving them the freedom to a certain extent to perform activities without the need for a caretaker, like driving a wheelchair. However, motion artifacts can significantly affect the quality of EEG recordings. The conventional EEG enhancement algorithms are effective in removing ocular and muscle artifacts for a stationary subject but not as effective when the subject is in motion, e.g., a wheelchair user. In this research study, we propose an empirical error model-based artifact removal approach for the cross-subject classification of motor imagery (MI) EEG data using a modified CNN-based deep learning algorithm, designed to assist wheelchair users with severe mobility issues. The classification method applies to real tasks with measured EEG data, focusing on accurately interpreting motor imagery signals for practical application. The empirical error model evolved from the inertial sensor-based acceleration data of the subject in motion, the weight of the wheelchair, the weight of the subject, and the surface friction of the terrain under the wheelchair. Three different wheelchairs and five different terrains, including road, brick, concrete, carpet, and marble, are used for artifact data recording. After evaluating and benchmarking the proposed CNN and empirical model, the classification accuracy achieved is 94.04% for distinguishing between four specific classes: left, right, front, and back. This accuracy demonstrates the model's effectiveness compared to other state-of-the-art techniques. The comparative results show that the proposed approach is a potentially effective way to raise the decoding efficiency of motor imagery BCI.},
}

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

@article {pmid39686148,
year = {2024},
author = {Li, LL and Cao, GZ and Zhang, YP and Li, WC and Cui, F},
title = {MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {23},
pages = {},
doi = {10.3390/s24237611},
pmid = {39686148},
issn = {1424-8220},
support = {U1813212//the State Key Program of National Natural Science Foundation of China/ ; 52277061//National Natural Science Foundation of China/ ; JCYJ20220818095804009 and JSGG20200701095406010//Shenzhen Science and Technology Program/ ; 20220809200041001//Shenzhen Science and Technology Program/ ; },
mesh = {Humans ; *Neural Networks, Computer ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Lower Extremity/physiology ; Imagination/physiology ; Attention/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {Decoding lower-limb motor imagery (MI) is highly important in brain-computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (LLMs) including MI are excessively close to physiological representations in the human brain and generate low-quality EEG signals. To address this challenge, this paper proposes a multidimensional attention-based convolutional neural network (CNN), termed MACNet, which is specifically designed for lower-limb MI classification. MACNet integrates a temporal refining module and an attention-enhanced convolutional module by leveraging the local and global feature representation abilities of CNNs and attention mechanisms. The temporal refining module adaptively investigates critical information from each electrode channel to refine EEG signals along the temporal dimension. The attention-enhanced convolutional module extracts temporal and spatial features while refining the feature maps across the channel and spatial dimensions. Owing to the scarcity of public datasets available for lower-limb MI, a specified lower-limb MI dataset involving four routine LLMs is built, consisting of 10 subjects over 20 sessions. Comparison experiments and ablation studies are conducted on this dataset and a public BCI Competition IV 2a EEG dataset. The experimental results show that MACNet achieves state-of-the-art performance and outperforms alternative models for the subject-specific mode. Visualization analysis reveals the excellent feature learning capabilities of MACNet and the potential relationship between lower-limb MI and brain activity. The effectiveness and generalizability of MACNet are verified.},
}

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

@article {pmid39685825,
year = {2024},
author = {Villiger, AS and Hoehn, D and Ruggeri, G and Vaineau, C and Nirgianakis, K and Imboden, S and Kuhn, A and Mueller, MD},
title = {Lower Urinary Tract Dysfunction Among Patients Undergoing Surgery for Deep Infiltrating Endometriosis: A Prospective Cohort Study.},
journal = {Journal of clinical medicine},
volume = {13},
number = {23},
pages = {},
doi = {10.3390/jcm13237367},
pmid = {39685825},
issn = {2077-0383},
abstract = {Background/Objectives: Postsurgical lower urinary tract dysfunction (LUTD) is a common problem following deep infiltrating endometriosis (DIE) resection. The condition may be caused either by surgically induced damage to the bladder innervation or by pre-existing endometriosis-associated nerve damage. The aim of this study is to evaluate the efficacy of preoperative and postoperative multichannel urodynamic testing (UD) in identifying pre-existing or surgically induced LUTD among patients with DIE. Methods: Women with suspected DIE and planned surgical resection of DIE at the Department of Obstetrics and Gynecology at the University Hospital of Bern from September 2015 to October 2022 were invited to participate in this prospective cohort study. UD was performed before and 6 weeks after surgery. The primary outcome was the maximum flow rate (uroflow), an indicator of LUTD. Secondary outcomes were further urodynamic observations of cystometry and pressure flow studies, lower urinary tract symptoms (LUTS) as assessed by the International Prostate Symptom Score (IPSS), and pain as assessed by the visual analog scale (VAS). Results: A total of 51 patients requiring surgery for DIE were enrolled in this study. All patients underwent surgical excision of the DIE. The cohort demonstrated a uroflow of 22.1 mL/s prior to surgery, which decreased postoperatively to 21.5 mL/s (p = 0.56, 95%CI -1.5-2.71). The mean bladder contractility index (BCI) exhibited a notable decline from 130.4 preoperatively to 116.6 postoperatively (p = 0.046, 95%CI 0.23-27.27). Significant improvements were observed in the prevalence of dysmenorrhea, abdominal pain, dyspareunia, and dyschezia following surgical intervention (p = <0.001). The IPSS score was within the lower moderate range both pre- and postoperatively (mean 8.37 vs. 8.51, p = 0.893, 95%CI -2.35-2.05). Subgroup analysis identified previous endometriosis surgery as a significant preoperative risk factor for elevated post-void residual (43.6 mL, p = 0.026, 95%CI 13.89-73.37). The postoperative post-void residual increased among participants with DIE on the rectum to 54.39 mL (p = 0.078, 95%CI 24.06-84.71). Participants who underwent hysterectomy exhibited a significantly decreased uroflow (16.4 mL/s, p = 0.014, 95%CI 12-20) and BCI (75.1, p = 0.036, 95%CI 34.9-115.38). Conclusions: Nerve-respecting laparoscopy for DIE may alter bladder function. UD is not advisable before surgery, but the measurement may detect patients with LUTD.},
}

@article {pmid39681925,
year = {2024},
author = {Garrott, K and Ogilvie, D and Panter, J and Petticrew, M and Sowden, A and Jones, CP and Foubister, C and Lawlor, ER and Ikeda, E and Patterson, R and van Tulleken, D and Armstrong-Moore, R and Vethanayakam, G and Bo, L and White, M and Adams, J},
title = {Development and application of the Demands for Population Health Interventions (Depth) framework for categorising the agentic demands of population health interventions.},
journal = {BMC global and public health},
volume = {2},
number = {1},
pages = {13},
pmid = {39681925},
issn = {2731-913X},
support = {Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; },
abstract = {BACKGROUND: The 'agentic demand' of population health interventions (PHIs) refers to the capacity, resources and freedom to act that interventions demand of their recipients to benefit, which have a socio-economical pattern. Highly agentic interventions, e.g. information campaigns, rely on recipients noticing and responding to the intervention and thus might affect intervention effectiveness and equity. The absence of an adequate framework to classify agentic demands limits the fields' ability to systematically explore these associations.

METHODS: We systematically developed the Demands for Population Health Interventions (Depth) framework using an iterative approach: (1) developing the Depth framework by systematically identifying examples of PHIs aiming to promote healthier diets and physical activity, coding of intervention actors and actions and synthesising the data to develop the framework; (2) testing the Depth framework in online workshops with academic and policy experts and a quantitative reliability assessment. We applied the final framework in a proof-of-concept review, extracting studies from three existing equity-focused systematic reviews on framework category, overall effectiveness and differential socioeconomic effects and visualised the findings in harvest plots.

RESULTS: The Depth framework identifies three constructs influencing agentic demand: exposure - initial contact with intervention (two levels), mechanism of action - how the intervention enables or discourages behaviour (five levels) and engagement - recipient response (two levels). When combined, these constructs form a matrix of 20 possible classifications. In the proof-of-concept review, we classified all components of 31 interventions according to the Depth framework. Intervention components were concentrated in a small number of Depth classifications; Depth classification appeared to be related to intervention equity but not effectiveness.

CONCLUSIONS: This framework holds potential for future research, policy and practice, facilitating the design, selection and evaluation of interventions and evidence synthesis.},
}

@article {pmid39679398,
year = {2024},
author = {Wang, S and Zhang, W and Fu, P and Zhong, Y and Piatkevich, KD and Zhang, D and Lee, HJ},
title = {Structural diversity of Alzheimer-related protein aggregations revealed using photothermal ratio-metric micro-spectroscopy.},
journal = {Biomedical optics express},
volume = {15},
number = {12},
pages = {6768-6782},
pmid = {39679398},
issn = {2156-7085},
abstract = {The crucial link between pathological protein aggregations and lipids in Alzheimer's disease pathogenesis is increasingly recognized, yet its spatial dynamics remain challenging for labeling-based microscopy. Here, we demonstrate photothermal ratio-metric infrared spectro-microscopy (PRISM) to investigate the in situ structural and molecular compositions of pathological features in brain tissues at submicron resolution. By identifying the vibrational spectroscopic signatures of protein secondary structures and lipids, PRISM tracks the structural dynamics of pathological proteins, including amyloid and hyperphosphorylated Tau (pTau). Amyloid-associated lipid features in major brain regions were observed, notably the enrichment of lipid-dissociated plaques in the hippocampus. Spectroscopic profiling of pTau revealed significant heterogeneity in phosphorylation levels and a distinct lipid-pTau relationship that contrasts with the anticipated lipid-plaque correlation. Beyond in vitro studies, our findings provide direct visualization evidence of aggregate-lipid interactions across the brain, offering new insights into mechanistic and therapeutic research of neurodegenerative diseases.},
}

@article {pmid39678727,
year = {2024},
author = {Pan, H and Song, W and Li, L and Qin, X},
title = {The design and implementation of multi-character classification scheme based on EEG signals of visual imagery.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2299-2309},
pmid = {39678727},
issn = {1871-4080},
abstract = {In visual-imagery-based brain-computer interface (VI-BCI), there are problems of singleness of imagination task and insufficient description of feature information, which seriously hinder the development and application of VI-BCI technology in the field of restoring communication. In this paper, we design and optimize a multi-character classification scheme based on electroencephalogram (EEG) signals of visual imagery (VI), which is used to classify 29 characters including 26 lowercase English letters and three punctuation marks. Firstly, a new paradigm of randomly presenting characters and including preparation stage is designed to acquire EEG signals and construct a multi-character dataset, which can eliminate the influence between VI tasks. Secondly, tensor data is obtained by the Morlet wavelet transform, and a feature extraction algorithm based on tensor-uncorrelated multilinear principal component analysis is used to extract high-quality features. Finally, three classifiers, namely support vector machine, K-nearest neighbor, and extreme learning machine, are employed for classifying multi-character, and the results are compared. The experimental results demonstrate that, the proposed scheme effectively extracts character features with minimal redundancy, weak correlation, and strong representation capability, and successfully achieves an average classification accuracy 97.59% for 29 characters, surpassing existing research in terms of both accuracy and quantity of classification. The present study designs a new paradigm for acquiring EEG signals of VI, and combines the Morlet wavelet transform and UMPCA algorithm to extract the character features, enabling multi-character classification in various classifiers. This research paves a novel pathway for establishing direct brain-to-world communication.},
}

@article {pmid39678535,
year = {2024},
author = {Wang, Y and Gong, L and Zhao, Y and Yu, Y and Liu, H and Yang, X},
title = {Dynamic graph attention network based on multi-scale frequency domain features for motion imagery decoding in hemiplegic patients.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1493264},
pmid = {39678535},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) establish a direct communication pathway between the brain and external devices and have been widely applied in upper limb rehabilitation for hemiplegic patients. However, significant individual variability in motor imagery electroencephalogram (MI-EEG) signals leads to poor generalization performance of MI-based BCI decoding methods to new patients. This paper proposes a Multi-scale Frequency domain Feature-based Dynamic graph Attention Network (MFF-DANet) for upper limb MI decoding in hemiplegic patients. MFF-DANet employs convolutional kernels of various scales to extract feature information across multiple frequency bands, followed by a channel attention-based average pooling operation to retain the most critical frequency domain features. Additionally, MFF-DANet integrates a graph attention convolutional network to capture spatial topological features across different electrode channels, utilizing electrode positions as prior knowledge to construct and update the graph adjacency matrix. We validated the performance of MFF-DANet on the public PhysioNet dataset, achieving optimal decoding accuracies of 61.6% for within-subject case and 52.7% for cross-subject case. t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the features demonstrates the effectiveness of each designed module, and visualization of the adjacency matrix indicates that the extracted spatial topological features have physiological interpretability.},
}

@article {pmid39678531,
year = {2024},
author = {Wang, N and Tu, WJ},
title = {Editorial: Brain-computer interfaces in neurological disorders: expanding horizons for diagnosis, treatment, and rehabilitation.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1526723},
pmid = {39678531},
issn = {1662-4548},
}

@article {pmid39677402,
year = {2024},
author = {Ninenko, I and Medvedeva, A and Efimova, VL and Kleeva, DF and Morozova, M and Lebedev, MA},
title = {Olfactory neurofeedback: current state and possibilities for further development.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1419552},
pmid = {39677402},
issn = {1662-5161},
abstract = {This perspective considers the novel concept of olfactory neurofeedback (O-NFB) within the framework of brain-computer interfaces (BCIs), where olfactory stimuli are integrated in various BCI control loops. In particular, electroencephalography (EEG)-based O-NFB systems are capable of incorporating different components of complex olfactory processing - from simple discrimination tasks to using olfactory stimuli for rehabilitation of neurological disorders. In our own work, EEG theta and alpha rhythms were probed as control variables for O-NFB. Additionaly, we developed an olfactory-based instructed-delay task. We suggest that the unique functions of olfaction offer numerous medical and consumer applications where O-NFB is combined with sensory inputs of other modalities within a BCI framework to engage brain plasticity. We discuss the ways O-NFB could be implemented, including the integration of different types of olfactory displays in the experiment set-up and EEG features to be utilized. We emphasize the importance of synchronizing O-NFB with respiratory rhythms, which are known to influence EEG patterns and cognitive processing. Overall, we expect that O-NFB systems will contribute to both practical applications in the clinical world and the basic neuroscience of olfaction.},
}

@article {pmid39674592,
year = {2024},
author = {Barker, N and Parker, H},
title = {Hybrid performances in sport: Cybathlon spectatorship for critically imagining technologies for disability futures.},
journal = {Medical humanities},
volume = {},
number = {},
pages = {},
doi = {10.1136/medhum-2024-013031},
pmid = {39674592},
issn = {1473-4265},
abstract = {Disabled bodies have been historically marginalised in sporting arenas and spectacles. Assistive technologies have been increasingly featuring in, and changing, sporting landscapes. In some ways recent shifts have made disability more present and visible across many (para) sporting cultures, and yet sport continues to operate on a tiered system that assumes a normative able body. This paper responds to this moment by offering imaginaries of future hybrid performances that critically engage with the politics and possibilities of novel technologies in sporting arenas and their wider impact on disability futures. These were generated from a collaborative ethnography that centred on becoming spectators of the Cybathlon Games. The Cybathlon Games began in 2016 as a global event where people with disabilities compete with technologies such as Brain-Computer Interfaces or robotic Prosthesis. Our imaginings are presented as three speculative fragments in the form of pages ripped from a comic book series, The In/Visibles These fragments and critical reflections are grounded on themes generated through watching the Games together. The purpose of this paper is not to offer predictions or even visions of desirable futures. Rather we present future technologised sporting bodies and spectacles with a view to extend critical posthuman discussions to these arenas. Through this we highlight: (1) The arbitrariness of where to draw the between un/natural performances; (2) The absurdities of unrestricted and open use of performance technologies when hybrid forms and functions are judged through current sporting-humanist values; and (3) The need to stay alert to socioeconomic and political drivers of sporting and disability futures. We offer these three zones of friction to guide further research when navigating the complex and shifting relations between sport, technology and the (dis)abled body now and into the future.},
}

@article {pmid39672531,
year = {2024},
author = {Li, K and Qian, L and Zhang, C and Zhang, J and Xue, C and Zhang, Y and Deng, W},
title = {The entorhinal cortex and cognitive impairment in schizophrenia: A comprehensive review.},
journal = {Progress in neuro-psychopharmacology & biological psychiatry},
volume = {},
number = {},
pages = {111218},
doi = {10.1016/j.pnpbp.2024.111218},
pmid = {39672531},
issn = {1878-4216},
abstract = {Schizophrenia, a severe mental illness characterized by cognitive impairment and olfactory dysfunction, remains an enigma with its pathological mechanism yet to be fully elucidated. The entorhinal cortex, a pivotal structure involved in numerous neural loop circuits related to olfaction, cognition, and emotion, has garnered significant attention due to its structural and functional abnormalities, which have been implicated in the pathogenesis of schizophrenia. This review focuses on the abnormal structural and functional changes in the entorhinal cortex in schizophrenia patients, as evidenced by neuroimaging, cellular biology, and genetic studies. These changes are posited to play a crucial role in the pathogenesis of cognitive impairment in schizophrenia. Furthermore, this review explores the various intervention strategies targeting the entorhinal cortex in current treatment modalities and proposes potential directions for future research endeavors, thereby providing a novel perspective on unraveling the complexity of neural mechanisms underlying schizophrenia and developing innovative therapeutic approaches for schizophrenia.},
}

@article {pmid39672015,
year = {2024},
author = {Hameed, I and Khan, DM and Ahmed, SM and Aftab, SS and Fazal, H},
title = {Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress.},
journal = {Computers in biology and medicine},
volume = {185},
number = {},
pages = {109534},
doi = {10.1016/j.compbiomed.2024.109534},
pmid = {39672015},
issn = {1879-0534},
abstract = {This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to enhance the quality of life for individuals with motor disabilities. Currently, the most used non-invasive method for measuring brain activity is the EEG, due to its high temporal resolution, user-friendliness, and safety. A Brain Computer Interface (BCI) framework can be made using these signals which can provide a new communication channel to people that are suffering from motor disabilities or other neurological disorders. However, implementing EEG-based BCI systems in real-world scenarios for motor imagery recognition presents challenges, primarily due to the inherent variability among individuals and low signal-to-noise ratio (SNR) of EEG signals. To assist researchers in navigating this complex problem, a comprehensive review article is presented, summarizing the key findings from relevant studies since 2017. This review primarily focuses on the datasets, preprocessing methods, feature extraction techniques, and deep learning models employed by various researchers. This review aims to contribute valuable insights and serve as a resource for researchers, practitioners, and enthusiasts interested in the combination of neuroscience and deep learning, ultimately hoping to contribute to advancements that bridge the gap between the human mind and machine interfaces.},
}

@article {pmid39656892,
year = {2024},
author = {Guo, D and Yao, B and Shao, WW and Zuo, JC and Chang, ZH and Shi, JX and Hu, N and Bao, SQ and Chen, MM and Fan, X and Li, XH},
title = {The Critical Role of YAP/BMP/ID1 Axis on Simulated Microgravity-Induced Neural Tube Defects in Human Brain Organoids.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2410188},
doi = {10.1002/advs.202410188},
pmid = {39656892},
issn = {2198-3844},
support = {2021YFF1200800//National Key Research and Development Program of China/ ; 82171861//National Natural Science Foundation of China/ ; 81971782//National Natural Science Foundation of China/ ; 82101448//National Natural Science Foundation of China/ ; },
abstract = {Integrated biochemical and biophysical signals regulate embryonic development. Correct neural tube formation is critical for the development of central nervous system. However, the role of microgravity in neurodevelopment and its underlying molecular mechanisms remain unclear. In this study, the effects of stimulated microgravity (SMG) on the development of human brain organoids are investigated. SMG impairs N-cadherin-based adherens junction formation, leading to neural tube defects associated with dysregulated self-renewal capacity and neuroepithelial disorganization in human brain organoids. Bulk gene expression analyses reveal that SMG alters Hippo and BMP signaling in brain organoids. The neuropathological deficits in SMG-treated organoids can be rescued by regulating YAP/BMP/ID1 axis. Furthermore, sing-cell RNA sequencing data show that SMG results in perturbations in the number and function of neural stem and progenitor cell subpopulations. One of these subpopulations senses SMG cues and transmits BMP signals to the subpopulation responsible for tube morphogenesis, ultimately affecting the proliferating cell population. Finally, SMG intervention leads to persistent neurologic damage even after returning to normal gravity conditions. Collectively, this study reveals molecular and cellular abnormalities associated with SMG during human brain development, providing opportunities for countermeasures to maintain normal neurodevelopment in space.},
}

@article {pmid39671976,
year = {2024},
author = {Brooks, KA and Kolousek, A and Holman, EK and Evans, SS and Govil, N and Alfonso, KP},
title = {MED-EL Bonebridge implantation in pediatric patients age 11 Years and younger: Is it safe and effective?.},
journal = {International journal of pediatric otorhinolaryngology},
volume = {188},
number = {},
pages = {112198},
doi = {10.1016/j.ijporl.2024.112198},
pmid = {39671976},
issn = {1872-8464},
abstract = {OBJECTIVE: To present our experience with off-label MED-EL Bonebridge implantation in pediatric patients younger than 12 years of age and compare outcomes to pediatric patients 12 years and older.

METHODS: Pediatric patients who underwent Bonebridge implantation were included in a retrospective cohort study and were categorized by off-label use (<12 years) and ≥12 years at time of bone conduction implantation (BCI). Hearing outcomes were collected after implant activation, which was typically 4-8 weeks post-implantation. Mann-Whitney U tests were performed to assess for differences between audiometric outcomes. Significance was set at p < 0.05.

RESULTS: Twenty patients (25 implants) < 12 years of age and 17 patients (23 implants) ≥12 years of age underwent BCI. Pre-BCI speech recognition threshold (SRT) was better for the older patient group (median 50 dB) than the younger patient group (median 60 dB). Post-BCI SRT, however, was significantly lower in the younger patient group (median 22.5 dB) as compared to the older patient group (median 35 dB), (p < 0.001, Z = 3.1). The two groups performed similarly on age-appropriate wordlists presented at 50 dB HL in aided conditions (p > 0.05, -1
CONCLUSION: Pediatric patients younger than 12 years showed similar or better audiometric benefit from off-label Bonebridge implantation when compared to older patients. Pediatric patients younger than 12 years can be considered Bonebridge implant candidates if clinically indicated; Bonebridge implantation in this age group appears safe and technically feasible.},
}

@article {pmid39671787,
year = {2024},
author = {Liu, DH and Hsieh, JC and Alawieh, H and Kumar, S and Iwane, F and Pyatnitskiy, I and Ahmad, Z and Wang, H and Millan, JDR},
title = {Novel AIRTrode-based wearable electrode supports long-term, online brain-computer interface operations.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9edf},
pmid = {39671787},
issn = {1741-2552},
abstract = {OBJECTIVE: Non-invasive electroencephalograms (EEG)-based brain-computer interfaces (BCIs) play a crucial role in a diverse range of applications, including motor rehabilitation, assistive and communication technologies, benefiting users across various clinical spectrums. Effective integration of these applications into daily life requires systems that provide stable and reliable BCI control for extended periods. Our prior research introduced the AIRTrode, a self-adhesive (A), injectable (I), and room-temperature (RT) spontaneously-crosslinked hydrogel electrode (AIRTrode). The AIRTrode has shown lower skin-contact impedance and greater stability than dry electrodes and, unlike wet gel electrodes, does not dry out after just a few hours, enhancing its suitability for long-term application. This study aims to demonstrate the efficacy of AIRTrodes in facilitating reliable, stable and long-term online EEG-based BCI operations.

APPROACH: In this study, four healthy participants utilized AIRTrodes in two BCI control tasks - continuous and discrete - across two sessions separated by six hours. Throughout this duration, the AIRTrodes remained attached to the participants' heads. In the continuous task, participants controlled the BCI through decoding of upper-limb motor imagery (MI). In the discrete task, the control was based on decoding of error-related potentials (ErrPs).

MAIN RESULTS: Using AIRTrodes, participants demonstrated consistently reliable online BCI performance across both sessions and tasks. The physiological signals captured during MI and ErrPs tasks were valid and remained stable over sessions. Lastly, both the BCI performances and physiological signals captured were comparable with those from freshly applied, research-grade wet gel electrodes, the latter requiring inconvenient re-application at the start of the second session.

SIGNIFICANCE: AIRTrodes show great potential promise for integrating non-invasive BCIs into everyday settings due to their ability to support consistent BCI performances over extended periods. This technology could significantly enhance the usability of BCIs in real-world applications, facilitating continuous, all-day functionality that was previously challenging with existing electrode technologies.},
}

@article {pmid39671281,
year = {2024},
author = {Ghodrati, MT and Aghababaei, S and Mirfathollahi, A and Shalchyan, V and Zarrindast, MR and Daliri, MR},
title = {Protocol for state-based decoding of hand movement parameters using neural signals.},
journal = {STAR protocols},
volume = {5},
number = {4},
pages = {103503},
doi = {10.1016/j.xpro.2024.103503},
pmid = {39671281},
issn = {2666-1667},
abstract = {We present a protocol for decoding kinematic and kinetic parameters from the primary somatosensory cortex during active and passive hand movements in a center-out reaching task using state-based and conventional decoders. We describe steps for preparing data and using the state-based model to classify movement directions into states via feature extraction and predict parameters with regression models (partial least squares and multilinear regression) trained per state. This state-based approach outperforms conventional methods, enhancing accuracy for brain-computer interface applications. For complete details on the use and execution of this protocol, please refer to Mirfathollahi et al.[1].},
}

@article {pmid39670475,
year = {2024},
author = {Xie, S and He, C},
title = {An empirical study on native Mandarin-speaking children's metonymy comprehension development.},
journal = {Journal of child language},
volume = {},
number = {},
pages = {1-28},
doi = {10.1017/S0305000924000539},
pmid = {39670475},
issn = {1469-7602},
abstract = {This study investigates Mandarin-speaking children's (age 3-7) comprehension development of novel and conventional metonymy, combining online and offline methods. Both online and offline data show significantly better performances from the oldest group (6-to-7-year-old) and a delayed acquisition of conventional metonymy compared with novel metonymy. However, part of offline data shows no significant difference between adjacent age groups, while the eye-tracking data show a chronological development from age 3-7. Furthermore, in offline tasks, the three-year-old group features a high choice randomness and the four-to-five-year-olds show the longest reaction time. Therefore, we argue that, not only age but also metonymy type can influence metonymy acquisition, and that a lack of socio-cultural experience can be a source of acquisition difficulty for children under six. Methodologically speaking, we believe that online methods should not be considered superior to offline ones as they investigate different aspects of implicit and explicit language comprehension.},
}

@article {pmid39669979,
year = {2024},
author = {Nakamura, D and Kaji, S and Kanai, R and Hayashi, R},
title = {Unsupervised method for representation transfer from one brain to another.},
journal = {Frontiers in neuroinformatics},
volume = {18},
number = {},
pages = {1470845},
pmid = {39669979},
issn = {1662-5196},
abstract = {Although the anatomical arrangement of brain regions and the functional structures within them are similar across individuals, the representation of neural information, such as recorded brain activity, varies among individuals owing to various factors. Therefore, appropriate conversion and translation of brain information is essential when decoding neural information using a model trained using another person's data or to achieving brain-to-brain communication. We propose a brain representation transfer method that involves transforming a data representation obtained from one person's brain into that obtained from another person's brain, without relying on corresponding label information between the transferred datasets. We defined the requirements to enable such brain representation transfer and developed an algorithm that distills the assumption of common similarity structure across the brain datasets into a rotational and reflectional transformation across low-dimensional hyperspheres using encoders for non-linear dimensional reduction. We first validated our proposed method using data from artificial neural networks as substitute neural activity and examining various experimental factors. We then evaluated the applicability of our method to real brain activity using functional magnetic resonance imaging response data acquired from human participants. The results of these validation experiments showed that our method successfully performed representation transfer and achieved transformations in some cases that were similar to those obtained when using corresponding label information. Additionally, we reconstructed images from individuals' data without training personalized decoders by performing brain representation transfer. The results suggest that our unsupervised transfer method is useful for the reapplication of existing models personalized to specific participants and datasets to decode brain information from other individuals. Our findings also serve as a proof of concept for the methodology, enabling the exchange of the latent properties of neural information representing individuals' sensations.},
}

@article {pmid39669288,
year = {2024},
author = {Nejatbakhsh, A and Fumarola, F and Esteki, S and Toyoizumi, T and Kiani, R and Mazzucato, L},
title = {Predicting the effect of micro-stimulation on macaque prefrontal activity based on spontaneous circuit dynamics.},
journal = {Physical review research},
volume = {5},
number = {4},
pages = {},
pmid = {39669288},
issn = {2643-1564},
abstract = {A crucial challenge in targeted manipulation of neural activity is to identify perturbation sites whose stimulation exerts significant effects downstream with high efficacy, a procedure currently achieved by labor-intensive and potentially harmful trial and error. Can one predict the effects of electrical stimulation on neural activity based on the circuit dynamics during spontaneous periods? Here we show that the effects of single-site micro-stimulation on ensemble activity in an alert monkey's prefrontal cortex can be predicted solely based on the ensemble's spontaneous activity. We first inferred the ensemble's causal flow based on the directed functional interactions inferred during spontaneous periods using convergent cross-mapping and showed that it uncovers a causal hierarchy between the recording electrodes. We find that causal flow inferred at rest successfully predicts the spatiotemporal effects of micro-stimulation. We validate the computational features underlying causal flow using ground truth data from recurrent neural network models, showing that it is robust to noise and common inputs. A detailed comparison between convergent-cross mapping and alternative methods based on information theory reveals the advantages of the former method in predicting perturbation effects. Our results elucidate the causal interactions within neural ensembles and will facilitate the design of intervention protocols and targeted circuit manipulations suitable for brain-machine interfaces.},
}

@article {pmid39667504,
year = {2024},
author = {Cao, HL and Wei, W and Meng, YJ and Tao, YJ and Yang, X and Li, T and Guo, WJ},
title = {Association of altered cortical gyrification and working memory in male early abstinent alcohol-dependent individuals.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111166},
doi = {10.1016/j.brainresbull.2024.111166},
pmid = {39667504},
issn = {1873-2747},
abstract = {BACKGROUND: Alcohol dependence (AD) is an addictive disorder with multifaceted neurobiological features. Recent research on the pathophysiological mechanisms of AD has emphasized the important role of dysconnectivity. Cortical gyrification is known to be a reliable marker of neural connectivity. This study aimed to explore cortical gyrification using the local gyrification index (LGI) between alcohol-dependent patients and controls.

METHODS: Magnetic resonance images were collected from 60 early abstinent patients with AD (5-12 days after stopping alcohol consumption) and 59 controls and preprocessed using FreeSurfer, followed by surface-based morphometry (SBM) analysis to compare the LGI between the two groups. Cognitive performance was assessed using the Spatial Working Memory (SWM) test in the Cambridge Neuropsychological Test Automated Battery (CANTAB). The relationship between LGI, cognitive performance, and clinical variables was also explored in the patient group.

RESULTS: Compared with controls, patients with AD exhibited significantly decreased LGI in several regions, including the postcentral gyrus, precentral gyrus, middle frontal, superior temporal, middle temporal, insula, superior parietal, and inferior parietal cortex. AD patients did worse than controls in several SWM measures. Furthermore, decreased LGI in the left postcentral was negatively correlated with working memory performance after multiple comparison corrections in the patient group.

CONCLUSION: Alcohol-dependent individuals exhibit abnormal patterns of cortical gyrification, which may be underlying neurobiological markers of AD. Our findings further indicate that working memory deficits may be related to abnormalities in cortical gyrification in alcohol addiction.},
}

@article {pmid39667216,
year = {2024},
author = {Sun, P and De Winne, J and Zhang, M and Devos, P and Botteldooren, D},
title = {Delayed knowledge transfer: Cross-modal knowledge transfer from delayed stimulus to EEG for continuous attention detection based on spike-represented EEG signals.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {183},
number = {},
pages = {107003},
doi = {10.1016/j.neunet.2024.107003},
pmid = {39667216},
issn = {1879-2782},
abstract = {Decoding visual and auditory stimuli from brain activities, such as electroencephalography (EEG), offers promising advancements for enhancing machine-to-human interaction. However, effectively representing EEG signals remains a significant challenge. In this paper, we introduce a novel Delayed Knowledge Transfer (DKT) framework that employs spiking neurons for attention detection, using our experimental EEG dataset. This framework extracts patterns from audiovisual stimuli to model brain responses in EEG signals, while accounting for inherent response delays. By aligning audiovisual features with EEG signals through a shared embedding space, our approach improves the performance of brain-computer interface (BCI) systems. We also present WithMeAttention, a multimodal dataset designed to facilitate research in continuously distinguishing between target and distractor responses. Our methodology demonstrates a 3% improvement in accuracy on the WithMeAttention dataset compared to a baseline model that decodes EEG signals from scratch. This significant performance increase highlights the effectiveness of our approach Comprehensive analysis across four distinct conditions shows that rhythmic enhancement of visual information can optimize multi-sensory information processing. Notably, the two conditions featuring rhythmic target presentation - with and without accompanying beeps - achieved significantly superior performance compared to other scenarios. Furthermore, the delay distribution observed under different conditions indicates that our delay layer effectively emulates the neural processing delays in response to stimuli.},
}

@article {pmid39667215,
year = {2024},
author = {Lan, Y and Wang, Y and Zhang, Y and Zhu, H},
title = {Low-power and lightweight spiking transformer for EEG-based auditory attention detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {183},
number = {},
pages = {106977},
doi = {10.1016/j.neunet.2024.106977},
pmid = {39667215},
issn = {1879-2782},
abstract = {EEG signal analysis can be used to study brain activity and the function and structure of neural networks, helping to understand neural mechanisms such as cognition, emotion, and behavior. EEG-based auditory attention detection is using EEG signals to determine an individual's level of attention to specific auditory stimuli. In this technique, researchers record and analyze a subject's electrical activity to infer whether an individual is paying attention to a specific auditory stimulus. The model deployed in edge devices will be greatly convenient for subjects to use. However, most of the existing EEG-based auditory attention detection models use traditional neural network models, and their high computing load makes deployment on edge devices challenging. We present a pioneering approach in the form of a binarized spiking Transformer for EEG-based auditory attention detection, which is characterized by high accuracy, low power consumption, and lightweight design, making it highly suitable for deployment on edge devices. In terms of low power consumption, the network is constructed using spiking neurons, which emit sparse and binary spike sequences, which can effectively reduce computing power consumption. In terms of lightweight, we use a post-training quantization strategy to quantize the full-precision network weights into binary weights, which greatly reduces the model size. In addition, the structure of the Transformer ensures that the model can learn effective information and ensure its high performance. We verify the model through mainstream datasets, and experimental results show that our model performance can exceed the existing state-of-the-art models, and the model size can be reduced by more than 21 times compared with the original full-precision network counterpart.},
}

@article {pmid39665789,
year = {2024},
author = {Tang, C and Wang, P and Li, Z and Zhong, S and Yang, L and Li, G},
title = {Neural functional rehabilitation: exploring neuromuscular reconstruction technology advancements and challenges.},
journal = {Neural regeneration research},
volume = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-24-00613},
pmid = {39665789},
issn = {1673-5374},
abstract = {Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders, traumatic injuries, and neurological diseases. Neural machine interface technology establishes direct connections with the brain or peripheral nervous system to restore impaired motor, sensory, and cognitive functions, significantly improving patients' quality of life. This review analyzes the chronological development and integration of various neural machine interface technologies, including regenerative peripheral nerve interfaces, targeted muscle and sensory reinnervation, agonist-antagonist myoneural interfaces, and brain-machine interfaces. Recent advancements in flexible electronics and bioengineering have led to the development of more biocompatible and high-resolution electrodes, which enhance the performance and longevity of neural machine interface technology. However, significant challenges remain, such as signal interference, fibrous tissue encapsulation, and the need for precise anatomical localization and reconstruction. The integration of advanced signal processing algorithms, particularly those utilizing artificial intelligence and machine learning, has the potential to improve the accuracy and reliability of neural signal interpretation, which will make neural machine interface technologies more intuitive and effective. These technologies have broad, impactful clinical applications, ranging from motor restoration and sensory feedback in prosthetics to neurological disorder treatment and neurorehabilitation. This review suggests that multidisciplinary collaboration will play a critical role in advancing neural machine interface technologies by combining insights from biomedical engineering, clinical surgery, and neuroengineering to develop more sophisticated and reliable interfaces. By addressing existing limitations and exploring new technological frontiers, neural machine interface technologies have the potential to revolutionize neuroprosthetics and neurorehabilitation, promising enhanced mobility, independence, and quality of life for individuals with neurological impairments. By leveraging detailed anatomical knowledge and integrating cutting-edge neuroengineering principles, researchers and clinicians can push the boundaries of what is possible and create increasingly sophisticated and long-lasting prosthetic devices that provide sustained benefits for users.},
}

@article {pmid39664295,
year = {2024},
author = {Chen, LX and Zhang, MD and Xu, HF and Ye, HQ and Chen, DF and Wang, PS and Bao, ZW and Zou, SM and Lv, YT and Wu, ZY and Li, HF},
title = {Single-Nucleus RNA Sequencing Reveals the Spatiotemporal Dynamics of Disease-Associated Microglia in Amyotrophic Lateral Sclerosis.},
journal = {Research (Washington, D.C.)},
volume = {7},
number = {},
pages = {0548},
pmid = {39664295},
issn = {2639-5274},
abstract = {Disease-associated microglia (DAM) are observed in neurodegenerative diseases, demyelinating disorders, and aging. However, the spatiotemporal dynamics and evolutionary trajectory of DAM during the progression of amyotrophic lateral sclerosis (ALS) remain unclear. Using a mouse model of ALS that expresses a human SOD1 gene mutation, we found that the microglia subtype DAM begins to appear following motor neuron degeneration, primarily in the brain stem and spinal cord. Using reverse transcription quantitative polymerase chain reaction, RNAscope in situ hybridization, and flow cytometry, we found that DAM increased in number as the disease progressed, reaching their peak in the late disease stage. DAM responded to disease progression in both SOD1[G93A] mice and sporadic ALS and C9orf72-mutated patients. Motor neuron loss in SOD1[G93A] mice exhibited 2 accelerated phases: P90 to P110 (early stage) and P130 to P150 (late stage). Some markers were synchronized with the accelerated phase of motor neuron loss, suggesting that these proteins may be particularly responsive to disease progression. Through pseudotime trajectory analysis, we tracked the dynamic transition of homeostatic microglia into DAM and cluster 6 microglia. Interestingly, we used the colony-stimulating factor 1 receptor (CSF1R) inhibitor PLX5622 to deplete microglia in SOD1[G93A] mice and observed that DAM survival is independent of CSF1R. An in vitro phagocytosis assay directly confirmed that DAM could phagocytose more beads than other microglia subtypes. These findings reveal that the induction of the DAM phenotype is a shared cross-species and cross-subtype characteristic in ALS. Inducing the DAM phenotype and enhancing its function during the early phase of disease progression, or the time window between P130 and P150 where motor neuron loss slows, could serve as a neuroprotective strategy for ALS.},
}

@article {pmid39663729,
year = {2024},
author = {Qin, Y and Zhao, H and Chang, Q and Liu, Y and Jing, Z and Yu, D and Mugo, SM and Wang, H and Zhang, Q},
title = {Amylopectin-based Hydrogel Probes for Brain-machine Interfaces.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e2416926},
doi = {10.1002/adma.202416926},
pmid = {39663729},
issn = {1521-4095},
support = {22377122//National Natural Science Foundation of China/ ; U22A20183//National Natural Science Foundation of China/ ; SKL202402018//Jilin Province Science and Technology Development Plan/ ; 029GJHZ2024038FN//International Partnership Program of the Chinese Academy of Sciences/ ; },
abstract = {Implantable neural probes hold promise for acquiring brain data, modulating neural circuits, and treating various brain disorders. However, traditional implantable probes face significant challenges in practical applications, such as balancing sensitivity with biocompatibility and the difficulties of in situ neural information monitoring and neuromodulation. To address these challenges, this study developed an implantable hydrogel probe capable of recording neural signals, modulating neural circuits, and treating stroke. Amylopectin is integrated into the hydrogels, which can induce reorientation of the poly(3,4-ethylenedioxythiophene) (PEDOT) chain and create compliant interfaces with brain tissues, enhancing both sensitivity and biocompatibility. The hydrogel probe shows the capability of continuously recording deep brain signals for 8 weeks. The hydrogel probe is effectively utilized to study deep brain signals associated with various physiological activities. Neuromodulation and neural signal monitoring are performed directly in the primary motor cortex of rats, enabling control over their limb behaviors through evoked signals. When applied to the primary motor cortex of stroke-affected rats, neuromodulation significantly reduced the brain infarct area, promoted synaptic reorganization, and restored motor functions and balance. This research represents a significant scientific breakthrough in the design of neural probes for brain monitoring, neural circuit modulation, and the development of brain disease therapies.},
}

@article {pmid39662472,
year = {2024},
author = {Xin, Q and Zheng, D and Zhou, T and Xu, J and Ni, Z and Hu, H},
title = {Deconstructing the neural circuit underlying social hierarchy in mice.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2024.11.007},
pmid = {39662472},
issn = {1097-4199},
abstract = {Social competition determines hierarchical social status, which profoundly influences animals' behavior and health. The dorsomedial prefrontal cortex (dmPFC) plays a fundamental role in regulating social competitions, but it was unclear how the dmPFC orchestrates win- and lose-related behaviors through its downstream neural circuits. Here, through whole-brain c-Fos mapping, fiber photometry, and optogenetics- or chemogenetics-based manipulations, we identified anatomically segregated win- and lose-related neural pathways downstream of the dmPFC in mice. Specifically, layer 5 neurons projecting to the dorsal raphe nucleus (DRN) and periaqueductal gray (PAG) promote social competition, whereas layer 2/3 neurons projecting to the anterior basolateral amygdala (aBLA) suppress competition. These two neuronal populations show opposite changes in activity during effortful pushes in competition. In vivo and in vitro electrophysiology recordings revealed inhibition from the lose-related pathway to the win-related pathway. Such antagonistic interplay may represent a central principle in how the mPFC orchestrates complex behaviors through top-down control.},
}

@article {pmid39661668,
year = {2024},
author = {Fu, P and Zhang, Y and Wang, S and Ye, X and Wu, Y and Yu, M and Zhu, S and Lee, HJ and Zhang, D},
title = {INSPIRE: Single-beam probed complementary vibrational bioimaging.},
journal = {Science advances},
volume = {10},
number = {50},
pages = {eadm7687},
pmid = {39661668},
issn = {2375-2548},
mesh = {*Spectrum Analysis, Raman/methods ; *Vibration ; Humans ; *Molecular Imaging/methods ; Animals ; Mice ; Spectrophotometry, Infrared/methods ; },
abstract = {Molecular spectroscopy provides intrinsic contrast for in situ chemical imaging, linking the physiochemical properties of biomolecules to the functions of living systems. While stimulated Raman imaging has found successes in deciphering biological machinery, many vibrational modes are Raman inactive or weak, limiting the broader impact of the technique. It can potentially be mitigated by the spectral complementarity from infrared (IR) spectroscopy. However, the vastly different optical windows make it challenging to develop such a platform. Here, we introduce in situ pump-probe IR and Raman excitation (INSPIRE) microscopy, a nascent cross-modality spectroscopic imaging approach by encoding the ultrafast Raman and the IR photothermal relaxation into a single probe beam for simultaneous detection. INSPIRE inherits the merits of complementary modalities and demonstrates high-content molecular imaging of chemicals, cells, tissues, and organisms. Furthermore, INSPIRE applies to label-free and molecular tag imaging, offering possibilities for optical sensing and imaging in biomedicine and materials science.},
}

*Spectrum Analysis, Raman/methods
*Vibration
Humans
*Molecular Imaging/methods
Animals
Mice
Spectrophotometry, Infrared/methods

@article {pmid39661515,
year = {2024},
author = {Chen, Z and Tang, S and Xiao, X and Hong, Y and Fu, B and Li, X and Shao, Y and Chen, L and Yuan, D and Long, Y and Wang, H and Hong, H},
title = {Adiponectin receptor 1-mediated basolateral amygdala-prelimbic cortex circuit regulates methamphetamine-associated memory.},
journal = {Cell reports},
volume = {43},
number = {12},
pages = {115074},
doi = {10.1016/j.celrep.2024.115074},
pmid = {39661515},
issn = {2211-1247},
abstract = {The association between drug-induced rewards and environmental cues represents a promising strategy to address addiction. However, the neural networks and molecular mechanisms orchestrating methamphetamine (MA)-associated memories remain incompletely characterized. In this study, we demonstrated that AdipoRon (AR), a specific adiponectin receptor (AdipoR) agonist, inhibits the formation of MA-induced conditioned place preference (CPP) in MA-conditioned mice, accompanied by suppression of basolateral amygdala (BLA) CaMKIIα neuron activity. Furthermore, we identified an association between the excitatory circuit from the BLA to the prelimbic cortex (PrL) and the integration of MA-induced rewards with environmental cues. We also determined that the phosphorylated AMPK (p-AMPK)/Cav1.3 signaling pathway mediates the modulatory effects of AdipoR1 in PrL-projecting BLA CaMKIIα neurons on the formation of MA reward memories, a process influenced by physical exercise. These findings highlight the critical function of AdipoR1 in the BLA[CaMKIIα]→PrL[CaMKIIα] circuit in regulating MA-related memory formation, suggesting a potential target for managing MA use disorders.},
}

@article {pmid39660042,
year = {2024},
author = {Kojima, S and Kanoh, S},
title = {Four-class ASME BCI: investigation of the feasibility and comparison of two strategies for multiclassing.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1461960},
pmid = {39660042},
issn = {1662-5161},
abstract = {INTRODUCTION: The ASME (stands for Auditory Stream segregation Multiclass ERP) paradigm is proposed and used for an auditory brain-computer interface (BCI). In this paradigm, a sequence of sounds that are perceived as multiple auditory streams are presented simultaneously, and each stream is an oddball sequence. The users are requested to focus selectively on deviant stimuli in one of the streams, and the target of the user attention is detected by decoding event-related potentials (ERPs). To achieve multiclass ASME BCI, the number of streams must be increased. However, increasing the number of streams is not easy because of a person's limited audible frequency range. One method to achieve multiclass ASME with a limited number of streams is to increase the target stimuli in a single stream.

METHODS: Two approaches for the ASME paradigm, ASME-4stream (four streams with a single target stimulus in each stream) and ASME-2stream (two streams with two target stimuli in each stream) were investigated. Fifteen healthy subjects with no neurological disorders participated in this study. An electroencephalogram was acquired, and ERPs were analyzed. The binary classification and BCI simulation (detecting the target class of the trial out of four) were conducted with the help of linear discriminant analysis, and its performance was evaluated offline. Its usability and workload were also evaluated using a questionnaire.

RESULTS: Discriminative ERPs were elicited in both paradigms. The average accuracies of the BCI simulations were 0.83 (ASME-4stream) and 0.86 (ASME-2stream). In the ASME-2stream paradigm, the latency and the amplitude of P300 were shorter and larger, the average binary classification accuracy was higher, and the average weighted workload was smaller.

DISCUSSION: Both four-class ASME paradigms achieved a sufficiently high accuracy (over 80%). The shorter latency and larger amplitude of P300 and the smaller workload indicated that subjects could perform the task confidently and had high usability in ASME-2stream compared to ASME-4stream paradigm. A paradigm with multiple target stimuli in a single stream could create a multiclass ASME BCI with limited streams while maintaining task difficulty. These findings expand the potential for an ASME BCI multiclass extension, offering practical auditory BCI choices for users.},
}

@article {pmid39658529,
year = {2024},
author = {Wang, WW and Ji, SY and Xu, P and Zhang, Y and Zhang, Y},
title = {The future of G protein-coupled receptor therapeutics: Apelin receptor acts as a prototype for the advancement of precision drug design.},
journal = {Clinical and translational medicine},
volume = {14},
number = {12},
pages = {e70120},
doi = {10.1002/ctm2.70120},
pmid = {39658529},
issn = {2001-1326},
support = {//National Natural Science Foundation of China/ ; 2024C03147//Yan Zhang [ZJU]); the ''Pioneer'' and ''Leading Goose'' R&D Program of Zhejiang/ ; //Ministry of Science and Technology/ ; 2021C03039//the Key R&D Projects of Zhejiang Province/ ; 2020R01006//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 82325004//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 92168114//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 32400575//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 2024T170783//the China Postdoctoral Science Foundation/ ; GZC20232326//Postdoctoral Fellowship Program of CPSF/ ; },
}

@article {pmid39657314,
year = {2024},
author = {Li, S and Tian, M and Xu, R and Cichocki, A and Jin, J},
title = {Decoding continuous motion trajectories of upper limb from EEG signals based on feature selection and nonlinear methods.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9cc1},
pmid = {39657314},
issn = {1741-2552},
abstract = {OBJECTIVE: Brain-computer interface (BCI) system has emerged as a promising technology that provides direct communication and control between the human brain and external devices. Among the various applications of BCI, limb motion decoding has gained significant attention due to its potential for patients with motor impairment to regain independence and improve their quality of life. However, the reconstruction of continuous motion trajectories in BCI systems based on electroencephalography (EEG) signals remains a challenge in practical life.

APPROACH: This study investigates the feasibility of applying feature selection and nonlinear regression for decoding motion trajectory from EEG. We propose to fix the time window, select the optimal feature set, and reconstruct the motion trajectory of motor execution tasks using polynomial regression. The proposed approach is validated on a public dataset consisting of EEG and hand position data recorded from 15 subjects. Several methods including ridge regression and multiple linear regression are employed as comparisons.

MAIN RESULTS: The cross-validation results show that the proposed reconstructed method has the highest correlation with actual motion trajectories, with an average value of 0.511±0.019 (p<0.05).

SIGNIFICANCE: This finding demonstrates the great potential of our approach for real-world motor kinematics BCI applications.},
}

@article {pmid39657294,
year = {2024},
author = {Du, L and Zeng, J and Yu, H and Chen, B and Deng, W and Li, T},
title = {Efficacy of bright light therapy improves outcomes of perinatal depression: A systematic review and meta-analysis of randomized controlled trials.},
journal = {Psychiatry research},
volume = {344},
number = {},
pages = {116303},
doi = {10.1016/j.psychres.2024.116303},
pmid = {39657294},
issn = {1872-7123},
abstract = {The efficacy of bright light therapy (BLT) in the context of perinatal depression remains underexplored. This meta-analysis aimed to systematically assess the effectiveness of BLT among perinatal depression. A comprehensive literature search was performed across several databases, including the Cochrane Central Register of Controlled Trials, PubMed, Embase, CNKI and the clinical trials registry platform, covering the period from the inception of each database up to January 2024. The Cochrane Collaboration's bias assessment tool was employed to evaluate the quality of the included studies. Review Manager 5.3 Software was utilized to conduct the meta-analysis. Six trials, encompassed a total of 167 participants diagnosed with perinatal depression were incorporated quantitative analysis, all of those have been published in English, with no restriction on publication year, and used BLT and dim light therapy (DLT) as intervention. The relative risk (RR) of BLT compared to DLT for perinatal depression is 1.46 (fixed effects model, p = 0.04, 95 % CI = [1.02, 2.10]), indicating a significant improvement in depression outcomes compared to DLT groups. The heterogeneity test yielded an I[2] value of 41 % (p = 0.13), indicated a low degree of heterogeneity. Considering the small sample size, we conducted a sensitivity analysis, found RR increased to 2.33 (fixed effects model, p = 0.001, CI = 1.39-3.92). Cochrane Risk of Bias Tool showed only a single study was deemed high quality. This study indicates a beneficial impact of BLT on perinatal depression, subgroup analysis finds no significant mediation effects of different parameters after sensitivity analyses. It is recommended that future studies with larger samples be conducted to explore the effects of BLT on perinatal depression.},
}

@article {pmid39576681,
year = {2024},
author = {Liu, D and Wei, Y},
title = {CVR-BBI: an open-source VR platform for multi-user collaborative brain to brain interfaces.},
journal = {Bioinformatics (Oxford, England)},
volume = {40},
number = {12},
pages = {},
pmid = {39576681},
issn = {1367-4811},
support = {12101570//National Natural Science Foundation of China/ ; 2024C01142//Key Research and Development Program of Zhejiang Province/ ; },
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Brain/physiology ; Virtual Reality ; Software ; Male ; Adult ; User-Computer Interface ; Female ; },
abstract = {SUMMARY: As brain imaging and neurofeedback technologies advance, the brain-to-brain interface (BBI) has emerged as an innovative field, enabling in-depth exploration of cross-brain information exchange and enhancing our understanding of collaborative intelligence. However, no open-source virtual reality (VR) platform currently supports the rapid and efficient configuration of multi-user, collaborative BBIs. To address this gap, we introduce the Collaborative Virtual Reality Brain-to-Brain Interface (CVR-BBI), an open-source platform consisting of a client and server. The CVR-BBI client enables users to participate in collaborative experiments, collect electroencephalogram (EEG) data, and manage interactive multisensory stimuli within the VR environment. Meanwhile, the CVR-BBI server manages multi-user collaboration paradigms, and performs real-time analysis of the EEG data. We evaluated the CVR-BBI platform using the SSVEP paradigm and observed that collaborative decoding outperformed individual decoding, validating the platform's effectiveness in collaborative settings. The CVR-BBI offers a pioneering platform that facilitates the development of innovative BBI applications within collaborative VR environments, thereby enhancing the understanding of brain collaboration and cognition.

CVR-BBI is released as an open-source platform, with its source code being available at https://github.com/DILIU1/CVR-BBI.},
}

*Electroencephalography/methods
*Brain-Computer Interfaces
Humans
*Brain/physiology
Virtual Reality
Software
Male
Adult
User-Computer Interface
Female

@article {pmid39652971,
year = {2024},
author = {Hinss, MF and Jahanpour, ES and Brock, AM and Roy, RN},
title = {A passive Brain-Computer Interface for operatormental fatigue estimation in monotonoussurveillance operations: time-on-task andperformance labeling issues.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9bed},
pmid = {39652971},
issn = {1741-2552},
abstract = {A central component of search and rescue missions is the visual search of survivors. In large parts, this depends on human operators and is, therefore, subject to the constraints of human cognition, such as mental fatigue. This makes detecting mental fatigue a critical step to be implemented in future systems. However, to the best of our knowledge, it has seldom been evaluated using a realistic visual search task. In addition, an accuracy discrepancy exists between studies that use time-on-task (TOT) - the popular method- and performance metrics for labels. Yet, to our knowledge, they have never been directly compared. This study was designed to address both issues: the use of a realistic task to elicit mental fatigue during a monotonous visual search task and the labelling type used for intra-participant fatigue estimation. Over four blocks of 15 minutes, participants had to identify targets on a computer while their cardiac, cerebral (EEG), and eye-movement activities were recorded. The recorded data were then fed into several physiological computing pipelines. The results show that the capability of a machine learning algorithm to detect mental fatigue depends less on the input data but rather on how mental fatigue is defined. Using TOT, very high classification accuracies are obtained (e.g. 99.3%). On the other hand, if mental fatigue is estimated based on behavioural performance, a metric with a much greater operational value, classification accuracies return to chance level (i.e. 52.2%). TOTbased mental fatigue estimation is popular, and strong classification accuracies can be achieved with a multitude of sensors. These factors contribute to the popularity of this method, but both usability and the relation to the concept of mental fatigue are neglected.},
}

@article {pmid39652893,
year = {2024},
author = {Noorbasha, SK and Kumar, A},
title = {VME-EFD : A novel framework to eliminate the Electrooculogram artifact from single-channel EEGs.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ad9bb6},
pmid = {39652893},
issn = {2057-1976},
abstract = {The diagnosis of neurological disorders often involves analyzing EEG data, which can be contaminated by artifacts from eye movements or blinking (EOG). To improve the accuracy of EEG-based analysis, we propose a novel framework, VME-EFD, which combines Variational Mode Extraction (VME) and Empirical Fourier Decomposition (EFD) for effective EOG artifact removal. In this approach, the EEG signal is first decomposed by VME into two segments: the desired EEG signal and the EOG artifact. The EOG component is further processed by EFD, where decomposition levels are analyzed based on energy and skewness. The level with the highest energy and skewness, corresponding to the artifact, is discarded, while the remaining levels are reintegrated with the desired EEG. Simulations on both synthetic and real EEG datasets demonstrate that VME-EFD outperforms existing methods, with lower RRMSE (0.1358 vs. 0.1557, 0.1823, 0.2079, 0.2748), lower ΔPSD in the α band (0.10±0.01 and 0.17±0.04 vs. 0.89±0.91 and 0.22±0.19, 1.32±0.23 and 1.10±0.07, 2.86±1.30 and 1.19±0.07, 3.96±0.56 and 2.42±2.48), and higher correlation coefficient (CC: 0.9732 vs. 0.9695, 0.9514, 0.8994, 0.8730). The framework effectively removes EOG artifacts and preserves critical EEG features, particularly in the α band, making it highly suitable for brain-computer interface (BCI) applications. .},
}

@article {pmid39651250,
year = {2024},
author = {Liang, KF and Kao, JC},
title = {A reinforcement learning based software simulator for motor brain-computer interfaces.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.11.25.625180},
pmid = {39651250},
issn = {2692-8205},
abstract = {Intracortical motor brain-computer interfaces (BCIs) are expensive and time-consuming to design because accurate evaluation traditionally requires real-time experiments. In a BCI system, a user interacts with an imperfect decoder and continuously changes motor commands in response to unexpected decoded movements. This "closed-loop" nature of BCI leads to emergent interactions between the user and decoder that are challenging to model. The gold standard for BCI evaluation is therefore real-time experiments, which significantly limits the speed and community of BCI research. We present a new BCI simulator that enables researchers to accurately and quickly design BCIs for cursor control entirely in software. Our simulator replaces the BCI user with a deep reinforcement learning (RL) agent that interacts with a simulated BCI system and learns to optimally control it. We demonstrate that our simulator is accurate and versatile, reproducing the published results of three distinct types of BCI decoders: (1) a state-of-the-art linear decoder (FIT-KF), (2) a "two-stage" BCI decoder requiring closed-loop decoder adaptation (ReFIT-KF), and (3) a nonlinear recurrent neural network decoder (FORCE).},
}

@article {pmid39651231,
year = {2024},
author = {Sorrell, E and Wilson, DE and Rule, ME and Yang, H and Forni, F and Harvey, CD and O'Leary, T},
title = {An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.11.29.626034},
pmid = {39651231},
issn = {2692-8205},
abstract = {Cortical circuits contain diverse sensory, motor, and cognitive signals, and form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We developed a calcium imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we discovered that mice could immediately navigate toward goal locations when control was switched to BMI. No learning or adaptation was observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decoupled from the mouse's physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.},
}

@article {pmid39648165,
year = {2024},
author = {Zhang, J and Dong, E and Zhang, Y and Zhang, Y},
title = {Harnessing the power of structure-based design: A new lease on life for cardiovascular drug development with apelin receptor modulators.},
journal = {Clinical and translational medicine},
volume = {14},
number = {12},
pages = {e70116},
doi = {10.1002/ctm2.70116},
pmid = {39648165},
issn = {2001-1326},
support = {2021YFF0501401//from the National Key R&D Program of China/ ; 2018YFA0800501//from the National Key R&D Program of China/ ; 82325004//National Natural Science Foundation of China/ ; 92168114//National Natural Science Foundation of China/ ; 92353303//National Natural Science Foundation of China/ ; 32141004//National Natural Science Foundation of China/ ; 82300286//National Natural Science Foundation of China/ ; 92168113//National Natural Science Foundation of China/ ; 32430051//National Natural Science Foundation of China/ ; 22HHXBSS00007//Haihe Laboratory of Cell Ecosystem Innovation Fund/ ; BX20220023//China Postdoctoral Science Foundation/ ; 2022M720288//China Postdoctoral Science Foundation/ ; },
}

@article {pmid39645364,
year = {2024},
author = {Abuduaini, Y and Pu, Y and Chen, W and Kong, XZ},
title = {Imaging the Unseen: Charting Amygdalar Tau's Link to Affective Symptoms in Preclinical Alzheimer's Disease.},
journal = {Biological psychiatry. Cognitive neuroscience and neuroimaging},
volume = {9},
number = {12},
pages = {1236-1238},
doi = {10.1016/j.bpsc.2024.10.003},
pmid = {39645364},
issn = {2451-9030},
}

@article {pmid39645086,
year = {2024},
author = {Li, S and Zhou, Y and Kong, D and Miao, Y and Guan, N and Gao, G and Jin, J and Ye, H},
title = {A visually-induced optogenetically-engineered system enables autonomous glucose homeostasis in mice.},
journal = {Journal of controlled release : official journal of the Controlled Release Society},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jconrel.2024.12.006},
pmid = {39645086},
issn = {1873-4995},
abstract = {With the global population increasing and the demographic shifting toward an aging society, the number of patients diagnosed with conditions such as peripheral neuropathies resulting from diabetes is expected to rise significantly. This growing health burden has emphasized the need for innovative solutions, such as brain-computer interfaces. brain-computer interfaces, a multidisciplinary field that integrates neuroscience, engineering, and computer science, enable direct communication between the human brain and external devices. In this study, we developed an autonomous diabetes therapeutic system that employs visually-induced electroencephalography devices to capture and decode event-related potentials using machine learning techniques. We present the visually-induced optogenetically-engineered system for therapeutic expression regulation (VISITER), which generates diverse output commands to control illumination durations. This system regulates insulin expression through optogenetically-engineered cells, achieving blood glucose homeostasis in mice. Our results demonstrate that VISITER effectively and precisely modulates therapeutic protein expression in mammalian cells, facilitating the rapid restoration of blood glucose homeostasis in diabetic mice. These findings underscore the potential for diabetic patients to manage insulin levels autonomously by focusing on target images, paving the way for a more self-directed approach to blood glucose control.},
}

@article {pmid39644999,
year = {2024},
author = {Wang, Y and Wang, X and Wang, L and Zheng, L and An, X and Zheng, C},
title = {Attenuated task-responsive representations of hippocampal place cells induced by amyloid-beta accumulation.},
journal = {Behavioural brain research},
volume = {},
number = {},
pages = {115384},
doi = {10.1016/j.bbr.2024.115384},
pmid = {39644999},
issn = {1872-7549},
abstract = {Alzheimer's disease (AD) is a typical neurodegenerative disease featuring deficits in spatial memory, which relies on spatial representations by hippocampal place cells. Place cells exhibit task-responsive representation to support memory encoding and retrieval processes. Yet, it remains unclear how this task-responsive spatial representation was interrupted under AD pathologies. Here, we employed a delayed match-to-place spatial memory task with associative and predictive memory processes, during which we electrophysiologically recorded hippocampal place cells with multi-tetrode hyperdrives in rats with i.c.v. amyloid/saline injection. We found that the directional selectivity of place cells coding was maintained in the Amyloid group. The firing stability was higher during predictive memory than during associative memory in both groups. However, the spatial specificity was decreased in the Amyloid group during both associative and predictive memory. Importantly, the place cells in the Amyloid group exhibited attenuated task-responsive representations, i.e. lack of spatial over-representations towards the goal zone and a higher representation of the rest zone, especially during the predictive memory stage. These results raise a hypothesis that the disrupted task-responsive representations of place cells could be an underlying mechanism of spatial memory deficits induced by amyloid proteins.},
}

@article {pmid39643773,
year = {2024},
author = {Liu, X and Zhu, J and Zheng, J and Xu, H},
title = {Role of the Thalamic Reticular Nucleus in Social Memory.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {39643773},
issn = {1995-8218},
}

@article {pmid39643728,
year = {2024},
author = {Haghi, B and Aflalo, T and Kellis, S and Guan, C and Gamez de Leon, JA and Huang, AY and Pouratian, N and Andersen, RA and Emami, A},
title = {Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {39643728},
issn = {2157-846X},
abstract = {To infer intent, brain-computer interfaces must extract features that accurately estimate neural activity. However, the degradation of signal quality over time hinders the use of feature-engineering techniques to recover functional information. By using neural data recorded from electrode arrays implanted in the cortices of three human participants, here we show that a convolutional neural network can be used to map electrical signals to neural features by jointly optimizing feature extraction and decoding under the constraint that all the electrodes must use the same neural-network parameters. In all three participants, the neural network led to offline and online performance improvements in a cursor-control task across all metrics, outperforming the rate of threshold crossings and wavelet decomposition of the broadband neural data (among other feature-extraction techniques). We also show that the trained neural network can be used without modification for new datasets, brain areas and participants.},
}

@article {pmid39642366,
year = {2024},
author = {Ikegaya, N and Mallela, AN and Warnke, PC and Kunigk, NG and Liu, F and Schone, HR and Verbaarschot, C and Hatsopoulos, NG and Downey, JE and Boninger, ML and Gaunt, R and Collinger, JL and Gonzalez-Martinez, JA},
title = {A novel robot-assisted method for implanting intracortical sensorimotor devices for brain-computer interface studies: principles, surgical techniques, and challenges.},
journal = {Journal of neurosurgery},
volume = {},
number = {},
pages = {1-9},
doi = {10.3171/2024.7.JNS241296},
pmid = {39642366},
issn = {1933-0693},
abstract = {Precise anatomical implantation of a microelectrode array is fundamental for successful brain-computer interface (BCI) surgery, ensuring high-quality, robust signal communication between the brain and the computer interface. Robotic neurosurgery can contribute to this goal, but its application in BCI surgery has been underexplored. Here, the authors present a novel robot-assisted surgical technique to implant rigid intracortical microelectrode arrays for the BCI. Using this technique, the authors performed surgery in a 31-year-old male with tetraplegia due to a traumatic C4 spinal cord injury that occurred a decade earlier. Each of the arrays was embedded into the parenchyma with a single insertion without complication. Postoperative imaging verified that the devices were placed as intended. With the motor cortex arrays, the participant successfully accomplished 2D control of a virtual arm and hand, with a success rate of 20 of 20 attempts, and recording quality was maintained at 100 and 200 days postimplantation. Intracortical microstimulation of the somatosensory cortex arrays elicited sensations in the fingers and palm. A robotic neurosurgery technique was successfully translated into BCI device implantation as part of an early feasibility trial with the long-term goal of restoring upper-limb function. The technique was demonstrated to be accurate and subsequently contributed to high-quality signal communication.},
}

@article {pmid39642216,
year = {2024},
author = {Li, C and Zhang, S and Jiang, J and Wang, S and He, S and Song, J},
title = {Laser-induced adhesives with excellent adhesion enhancement and reduction capabilities for transfer printing of microchips.},
journal = {Science advances},
volume = {10},
number = {49},
pages = {eads9226},
pmid = {39642216},
issn = {2375-2548},
abstract = {Transfer printing based on tunable and reversible adhesive that enables the heterogeneous integration of materials is essential for developing envisioned electronic systems. An adhesive with both adhesion enhancement and reduction capabilities in a rapid and selective manner is challenging. Here, we report a laser-induced adhesive, featuring a geometrically simple shape memory polymer layer on a glass backing, with excellent adhesion modulation capability for programmable pickup and noncontact printing of microchips. Selective and rapid laser heating substantially enhances the adhesive's adhesion strength from kilopascal to megapascal within 10 ms due to the shape fixing effect, allowing for precise and programmable pickup. Conversely, the enhanced adhesion can be quickly reduced and eliminated within 3 ms through the shape recovery effect, enabling noncontact printing. Demonstrations of transfer printing microlight-emitting diodes (LEDs) and mini-LEDs onto various low-adhesive flat, rough, and curved surfaces highlight the unusual capabilities of this adhesive for deterministic assembly.},
}

@article {pmid39640342,
year = {2024},
author = {Deruelle, F},
title = {Microwave radiofrequencies, 5G, 6G, graphene nanomaterials: Technologies used in neurological warfare.},
journal = {Surgical neurology international},
volume = {15},
number = {},
pages = {439},
pmid = {39640342},
issn = {2229-5097},
abstract = {BACKGROUND: Scientific literature, with no conflicts of interest, shows that even below the limits defined by the International Commission on Non-Ionizing Radiation Protection, microwaves from telecommunication technologies cause numerous health effects: neurological, oxidative stress, carcinogenicity, deoxyribonucleic acid and immune system damage, electro-hypersensitivity. The majority of these biological effects of non-thermal microwave radiation have been known since the 1970s.

METHODS: Detailed scientific, political, and military documents were analyzed. Most of the scientific literature comes from PubMed. The other articles (except for a few) come from impacted journals . The rare scientific documents that were not peer reviewed were produced by recognized scientists in their fields. The rest of the documentation comes from official sources: political (e.g., European Union and World Health Organization), military (e.g., US Air Force and NATO), patents, and national newspapers.

RESULTS: (1) Since their emergence, the authorities have deployed and encouraged the use of wireless technologies (2G, 3G, 4G, WiFi, WiMAX, DECT, Bluetooth, cell phone towers/masts/base stations, small cells, etc.) in full awareness of their harmful effects on health. (2) Consequences of microwave radiation from communication networks are comparable to the effects of low-power directed-energy microwave weapons, whose objectives include behavioral modification through neurological (brain) targeting. Above 20 gigahertz, 5G behaves like an unconventional chemical weapon. (3) Biomedical engineering (via graphene-based nanomaterials) will enable brain-computer connections, linked wirelessly to the Internet of Everything through 5G and 6G networks (2030) and artificial intelligence, gradually leading to human-machine fusion (cyborg) before the 2050s.

CONCLUSION: Despite reports and statements from the authorities presenting the constant deployment of new wireless communication technologies, as well as medical research into nanomaterials, as society's ideal future, in-depth research into these scientific fields shows, above all, an objective linked to the current cognitive war. It could be hypothesized that, in the future, this aim will correspond to the control of humanity by machines.},
}

@article {pmid39639181,
year = {2024},
author = {Qian, M and Wang, J and Gao, Y and Chen, M and Liu, Y and Zhou, D and Lu, HD and Zhang, X and Hu, JM and Roe, AW},
title = {Multiple loci for foveolar vision in macaque monkey visual cortex.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {39639181},
issn = {1546-1726},
support = {31627802//National Natural Science Foundation of China (National Science Foundation of China)/ ; 52277232//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81701774//National Natural Science Foundation of China (National Science Foundation of China)/ ; 61771423//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {In humans and nonhuman primates, the central 1° of vision is processed by the foveola, a retinal structure that comprises a high density of photoreceptors and is crucial for primate-specific high-acuity vision, color vision and gaze-directed visual attention. Here, we developed high-spatial-resolution ultrahigh-field 7T functional magnetic resonance imaging methods for functional mapping of the foveolar visual cortex in awake monkeys. In the ventral pathway (visual areas V1-V4 and the posterior inferior temporal cortex), viewing of a small foveolar spot elicits a ring of multiple (eight) foveolar representations per hemisphere. This ring surrounds an area called the 'foveolar core', which is populated by millimeter-scale functional domains sensitive to fine stimuli and high spatial frequencies, consistent with foveolar visual acuity, color and achromatic information and motion. Thus, this elaborate rerepresentation of central vision coupled with a previously unknown foveolar core area signifies a cortical specialization for primate foveation behaviors.},
}

@article {pmid39638842,
year = {2024},
author = {Zhang, X and Wei, W and Qian, L and Yao, L and Jin, X and Xing, L and Qian, Z},
title = {Real-time monitoring of bioelectrical impedance for minimizing tissue carbonization in microwave ablation of porcine liver.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {30404},
pmid = {39638842},
issn = {2045-2322},
support = {xcxjh20230333//Nanjing University of Aeronautics and Astronautics Research and Practice Innovation Program/ ; 81727804//National Major Scientifc Instruments and Equipment Development Project Funded by National Natural Science Foundation of China/ ; 82151311//National Natural Science Foundation of China/ ; 12372306//National Natural Science Foundation of China/ ; NP2024102//Fundamental Research Funds for the Central Universities/ ; NJ2024016//Fundamental Research Funds for the Central Universities/ ; NJ2024029//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Animals ; *Microwaves ; *Electric Impedance ; *Liver/surgery/metabolism ; Swine ; Ablation Techniques/methods ; Finite Element Analysis ; },
abstract = {The charring tissue generated by the high temperature during microwave ablation can affect the therapeutic effect, such as limiting the volume of the coagulation zone and causing rejection. This paper aimed to prevent tissue carbonization while delivering an appropriate thermal dose for effective ablations by employing a treatment protocol with real-time bioelectrical impedance monitoring. Firstly, the current field response under different microwave ablation statuses is analyzed based on finite element simulation. Next, the change of impedance measured by the electrodes is correlated with the physical state of the ablated tissue, and a microwave ablation carbonization control protocol based on real-time electrical impedance monitoring was established. The finite element simulation results show that the dielectric properties of biological tissues changed dynamically during the ablation process. Finally, the relative change rule of the electrical impedance magnitude of the ex vivo porcine liver throughout the entire MWA process and the reduction of the central zone carbonization were obtained by the MWA experiment. Charring tissue was eliminated without water cooling at 40 W and significantly reduced at 50 W and 60 W. The carbonization during MWA can be reduced according to the changes in tissue electrical impedance to optimize microwave thermal ablation efficacy.},
}

Animals
*Microwaves
*Electric Impedance
*Liver/surgery/metabolism
Swine
Ablation Techniques/methods
Finite Element Analysis

@article {pmid39638804,
year = {2024},
author = {Ma, X and Chen, LN and Liao, M and Zhang, L and Xi, K and Guo, J and Shen, C and Shen, DD and Cai, P and Shen, Q and Qi, J and Zhang, H and Zang, SK and Dong, YJ and Miao, L and Qin, J and Ji, SY and Li, Y and Liu, J and Mao, C and Zhang, Y and Chai, R},
title = {Molecular insights into the activation mechanism of GPR156 in maintaining auditory function.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {10601},
pmid = {39638804},
issn = {2041-1723},
mesh = {Humans ; *Receptors, G-Protein-Coupled/metabolism/genetics/chemistry ; *Cryoelectron Microscopy ; Animals ; HEK293 Cells ; Protein Binding ; Protein Multimerization ; Mice ; GTP-Binding Protein alpha Subunits, Gi-Go/metabolism/chemistry/genetics ; Models, Molecular ; },
abstract = {The class C orphan G-protein-coupled receptor (GPCR) GPR156, which lacks the large extracellular region, plays a pivotal role in auditory function through Gi2/3. Here, we firstly demonstrate that GPR156 with high constitutive activity is essential for maintaining auditory function, and further reveal the structural basis of the sustained role of GPR156. We present the cryo-EM structures of human apo GPR156 and the GPR156-Gi3 complex, unveiling a small extracellular region formed by extracellular loop 2 (ECL2) and the N-terminus. The GPR156 dimer in both apo state and Gi3 protein-coupled state adopt a transmembrane (TM)5/6-TM5/6 interface, indicating the high constitutive activity of GPR156 in the apo state. Furthermore, C-terminus in G-bound subunit of GPR156 plays a dual role in promoting G protein binding within G-bound subunit while preventing the G-free subunit from binding to additional G protein. Together, these results explain how GPR156 constitutive activity is maintained through dimerization and provide a mechanistic insight into the sustained role of GPR156 in maintaining auditory function.},
}

Humans
*Receptors, G-Protein-Coupled/metabolism/genetics/chemistry
*Cryoelectron Microscopy
Animals
HEK293 Cells
Protein Binding
Protein Multimerization
Mice
GTP-Binding Protein alpha Subunits, Gi-Go/metabolism/chemistry/genetics
Models, Molecular

@article {pmid39637463,
year = {2024},
author = {Wimmer, M and Pepicelli, A and Volmer, B and ElSayed, N and Cunningham, A and Thomas, BH and Müller-Putz, GR and Veas, EE},
title = {Counting on AR: EEG responses to incongruent information with real-world context.},
journal = {Computers in biology and medicine},
volume = {185},
number = {},
pages = {109483},
doi = {10.1016/j.compbiomed.2024.109483},
pmid = {39637463},
issn = {1879-0534},
abstract = {Augmented Reality (AR) technologies enhance the real world by integrating contextual digital information about physical entities. However, inconsistencies between physical reality and digital augmentations, which may arise from errors in the visualized information or the user's mental context, can considerably impact user experience. This work characterizes the brain dynamics associated with processing incongruent information within an AR environment. To study these effects, we designed an interactive paradigm featuring the manipulation of a Rubik's cube serving as a physical referent. Congruent and incongruent information regarding the cube's current status was presented via symbolic (digits) and non-symbolic (graphs) stimuli, thus examining the impact of different means of data representation. The analysis of electroencephalographic signals from 19 participants revealed the presence of centro-parietal N400 and P600 components following the processing of incongruent information, with significantly increased latencies for non-symbolic stimuli. Additionally, we explored the feasibility of exploiting incongruency effects for brain-computer interfaces. Hence, we implemented decoders using linear discriminant analysis, support vector machines, and EEGNet, achieving comparable performances with all methods. Therefore, this work contributes to the design of adaptive AR systems by demonstrating that above-chance detection of incongruent information based on physiological signals is feasible. The successful decoding of incongruency-induced modulations can inform systems about the current mental state of users without making it explicit, aiming for more coherent and contextually appropriate AR interactions.},
}

@article {pmid39634507,
year = {2024},
author = {Wei, M and Lin, X and Xu, K and Wu, Y and Wang, C and Wang, Z and Lei, K and Bao, K and Li, J and Li, L and Li, E and Lin, H},
title = {Inverse design of compact nonvolatile reconfigurable silicon photonic devices with phase-change materials.},
journal = {Nanophotonics (Berlin, Germany)},
volume = {13},
number = {12},
pages = {2183-2192},
pmid = {39634507},
issn = {2192-8614},
abstract = {In the development of silicon photonics, the continued downsizing of photonic integrated circuits will further increase the integration density, which augments the functionality of photonic chips. Compared with the traditional design method, inverse design presents a novel approach for achieving compact photonic devices. However, achieving compact, reconfigurable photonic devices with the inverse design that employs the traditional modulation method exemplified by the thermo-optic effect poses a significant challenge due to the weak modulation capability. Low-loss phase change materials (PCMs) exemplified by Sb2Se3 are a promising candidate for solving this problem benefiting from their high refractive index contrast. In this work, we first developed a robust inverse design method to realize reconfigurable silicon and phase-change materials hybrid photonic devices including mode converter and optical switch. The mode converter exhibits a broadband operation of >100 nm. The optical switch shows an extinction ratio of >25 dB and a multilevel switching of 41 (>5 bits) by simply changing the crystallinity of Sb2Se3. Here, we experimentally demonstrated a Sb2Se3/Si hybrid integrated optical switch for the first time, wherein routing can be switched by the phase transition of the whole Sb2Se3. Our work provides an effective solution for the design of photonic devices that is insensitive to fabrication errors, thereby paving the way for high integration density in future photonic chips.},
}

@article {pmid39632923,
year = {2024},
author = {Karthiga, M and Suganya, E and Sountharrajan, S and Balusamy, B and Selvarajan, S},
title = {Eeg based smart emotion recognition using meta heuristic optimization and hybrid deep learning techniques.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {30251},
pmid = {39632923},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; *Emotions/physiology ; *Brain-Computer Interfaces ; Heuristics ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Neural Networks, Computer ; },
abstract = {In the domain of passive brain-computer interface applications, the identification of emotions is both essential and formidable. Significant research has recently been undertaken on emotion identification with electroencephalogram (EEG) data. The aim of this project is to develop a system that can analyse an individual's EEG and differentiate among positive, neutral, and negative emotional states. The suggested methodology use Independent Component Analysis (ICA) to remove artefacts from Electromyogram (EMG) and Electrooculogram (EOG) in EEG channel recordings. Filtering techniques are employed to improve the quality of EEG data by segmenting it into alpha, beta, gamma, and theta frequency bands. Feature extraction is performed with a hybrid meta-heuristic optimisation technique, such as ABC-GWO. The Hybrid Artificial Bee Colony and Grey Wolf Optimiser are employed to extract optimised features from the selected dataset. Finally, comprehensive evaluations are conducted utilising DEAP and SEED, two publically accessible datasets. The CNN model attains an accuracy of approximately 97% on the SEED dataset and 98% on the DEAP dataset. The hybrid CNN-ABC-GWO model achieves an accuracy of approximately 99% on both datasets, with ABC-GWO employed for hyperparameter tuning and classification. The proposed model demonstrates an accuracy of around 99% on the SEED dataset and 100% on the DEAP dataset. The experimental findings are contrasted utilising a singular technique, a widely employed hybrid learning method, or the cutting-edge method; the proposed method enhances recognition performance.},
}

*Electroencephalography/methods
*Deep Learning
Humans
*Emotions/physiology
*Brain-Computer Interfaces
Heuristics
Algorithms
Signal Processing, Computer-Assisted
Male
Neural Networks, Computer

@article {pmid39632897,
year = {2024},
author = {Chen, Z and Tang, Y and Liu, X and Li, W and Hu, Y and Hu, B and Xu, T and Zhang, R and Xia, L and Zhang, JX and Xiao, Z and Chen, J and Feng, Z and Zhou, Y and He, Q and Qiu, J and Lei, X and Chen, H and Qin, S and Feng, T},
title = {Edge-centric connectome-genetic markers of bridging factor to comorbidity between depression and anxiety.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {10560},
pmid = {39632897},
issn = {2041-1723},
support = {32300907//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Connectome ; Male ; Female ; *Comorbidity ; Adult ; *Depression/genetics/epidemiology ; *Anxiety/genetics/epidemiology ; Young Adult ; Genetic Markers ; Adolescent ; Child ; Anxiety Disorders/genetics/epidemiology ; Brain/diagnostic imaging/metabolism ; Magnetic Resonance Imaging ; },
abstract = {Depression-anxiety comorbidity is commonly attributed to the occurrence of specific symptoms bridging the two disorders. However, the significant heterogeneity of most bridging symptoms presents challenges for psychopathological interpretation and clinical applicability. Here, we conceptually established a common bridging factor (cb factor) to characterize a general structure of these bridging symptoms, analogous to the general psychopathological p factor. We identified a cb factor from 12 bridging symptoms in depression-anxiety comorbidity network. Moreover, this cb factor could be predicted using edge-centric connectomes with robust generalizability, and was characterized by connectome patterns in attention and frontoparietal networks. In an independent twin cohort, we found that these patterns were moderately heritable, and identified their genetic connectome-transcriptional markers that were associated with the neurobiological enrichment of vasculature and cerebellar development, particularly during late-childhood-to-young-adulthood periods. Our findings revealed a general factor of bridging symptoms and its neurobiological architectures, which enriched neurogenetic understanding of depression-anxiety comorbidity.},
}

Humans
*Connectome
Male
Female
*Comorbidity
Adult
*Depression/genetics/epidemiology
*Anxiety/genetics/epidemiology
Young Adult
Genetic Markers
Adolescent
Child
Anxiety Disorders/genetics/epidemiology
Brain/diagnostic imaging/metabolism
Magnetic Resonance Imaging

@article {pmid39630811,
year = {2024},
author = {Grogan, M and Blum, KP and Wu, Y and Harston, JA and Miller, LE and Faisal, AA},
title = {Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders.},
journal = {PLoS computational biology},
volume = {20},
number = {12},
pages = {e1012614},
doi = {10.1371/journal.pcbi.1012614},
pmid = {39630811},
issn = {1553-7358},
abstract = {Proprioception is one of the least understood senses, yet fundamental for the control of movement. Even basic questions of how limb pose is represented in the somatosensory cortex are unclear. We developed a topographic variational autoencoder with lateral connectivity (topo-VAE) to compute a putative cortical map from a large set of natural movement data. Although not fitted to neural data, our model reproduces two sets of observations from monkey centre-out reaching: 1. The shape and velocity dependence of proprioceptive receptive fields in hand-centered coordinates despite the model having no knowledge of arm kinematics or hand coordinate systems. 2. The distribution of neuronal preferred directions (PDs) recorded from multi-electrode arrays. The model makes several testable predictions: 1. Encoding across the cortex has a blob-and-pinwheel-type geometry of PDs. 2. Few neurons will encode just a single joint. Our model provides a principled basis for understanding of sensorimotor representations, and the theoretical basis of neural manifolds, with applications to the restoration of sensory feedback in brain-computer interfaces and the control of humanoid robots.},
}

@article {pmid39630448,
year = {2024},
author = {Kelly, BC and Cova, TJ and Debbink, MP and Onega, T and Brewer, SC},
title = {Racial and Ethnic Disparities in Regulatory Air Quality Monitor Locations in the US.},
journal = {JAMA network open},
volume = {7},
number = {12},
pages = {e2449005},
doi = {10.1001/jamanetworkopen.2024.49005},
pmid = {39630448},
issn = {2574-3805},
mesh = {Humans ; United States ; *Air Pollution/analysis/statistics & numerical data ; Cross-Sectional Studies ; *Environmental Monitoring/methods ; *Air Pollutants/analysis ; Particulate Matter/analysis ; Ozone/analysis ; Ethnicity/statistics & numerical data ; United States Environmental Protection Agency ; Racial Groups/statistics & numerical data ; Nitrogen Dioxide/analysis ; Sulfur Dioxide/analysis ; Environmental Exposure/analysis/statistics & numerical data ; },
abstract = {IMPORTANCE: Understanding exposure to air pollution is important to public health, and disparities in the spatial distribution of regulatory air quality monitors could lead to exposure misclassification bias.

OBJECTIVE: To determine whether racial and ethnic disparities exist in Environmental Protection Agency (EPA) regulatory air quality monitor locations in the US.

This national cross-sectional study included air quality monitors in the EPA Air Quality System regulatory monitoring repository, as well as 2022 American Community Survey Census block group estimates for racial and ethnic composition and population size. Bayesian mixed-effects models of the count of criteria pollutant monitors measuring an area were used, adjusting for population size and accounting for spatial autocorrelation. Data were analyzed from March to June 2024.

EXPOSURE: Census block group-level racial and ethnic composition.

MAIN OUTCOME AND MEASURES: Number of regulatory monitors measuring a census block group by criteria pollutant (particulate matter [PM], ozone [O3], nitrogen dioxide [NO2], sulfur dioxide [SO2], lead [Pb], and carbon monoxide [CO]).

RESULTS: This analysis included 329 725 481 individuals living in 237 631 block groups in the US (1 936 842 [0.6%] American Indian and Alaska Native, 18 554 697 [5.6%] Asian, 40 196 302 [12.2%] Black, 60 806 969 [18.4%] Hispanic, 555 712 [0.2%] Native Hawaiian and Other Pacific Islander, 196 010 370 [59.4%] White, 1 208 267 [0.3%] some other race, and 10 456 322 [0.4%] 2 or more races). Adjusting for population size, monitoring disparities were identified for each criteria pollutant. Relative to the White non-Latino population, all groups were associated with fewer NO2, O3, Pb, and PM monitors. Disparities were consistently largest for Native Hawaiian and Other Pacific Islander populations, followed by American Indian and Alaska Native populations and those of 2 or more races. An increase in percentage of Native Hawaiian and Other Pacific Islander race was associated with fewer monitors for SO2 (adjusted odds ratio [aOR], 0.91; 95% BCI, 0.90-0.91), CO (aOR, 0.95; 95% BCI, 0.94-0.95), O3 (aOR, 0.95; 95% BCI, 0.94-0.95), NO2 (aOR, 0.97; 95% BCI, 0.91-0.94), and PM (aOR, 0.96; 95% BCI, 0.95-0.96). An increase in the percentage of those of Asian race was associated with slightly more SO2 (aOR, 1.04; 95% BCI, 1.03-1.04) monitors.

CONCLUSIONS AND RELEVANCE: This cross-sectional study of racial and ethnic disparities in the location of EPA regulatory monitors determined that data may not be equitably representative of air quality, particularly for areas with predominantly Native Hawaiian and Other Pacific Islander or American Indian or Alaska Native populations. Integration of multiple data sources may aid in filling monitoring gaps across race and ethnicity. Where possible, researchers should quantify uncertainty in exposure estimates.},
}

Humans
United States
*Air Pollution/analysis/statistics & numerical data
Cross-Sectional Studies
*Environmental Monitoring/methods
*Air Pollutants/analysis
Particulate Matter/analysis
Ozone/analysis
Ethnicity/statistics & numerical data
United States Environmental Protection Agency
Racial Groups/statistics & numerical data
Nitrogen Dioxide/analysis
Sulfur Dioxide/analysis
Environmental Exposure/analysis/statistics & numerical data

@article {pmid39628656,
year = {2024},
author = {Pryss, R and Vom Brocke, J and Reichert, M and Rukzio, E and Schlee, W and Weber, B},
title = {Editorial: Application of neuroscience in information systems and software engineering.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1402603},
pmid = {39628656},
issn = {1662-4548},
}

@article {pmid39628388,
year = {2024},
author = {Wang, X and Xu, M and Yang, H and Jiang, W and Jiang, J and Zou, D and Zhu, Z and Tao, C and Ni, S and Zhou, Z and Sun, L and Li, M and Nie, Y and Zhao, Y and He, F and Tao, TH and Wei, X},
title = {Ultraflexible Neural Electrodes Enabled Synchronized Long-Term Dopamine Detection and Wideband Chronic Recording Deep in Brain.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.4c12429},
pmid = {39628388},
issn = {1936-086X},
abstract = {Ultraflexible neural electrodes have shown superior stability compared with rigid electrodes in long-term in vivo recordings, owing to their low mechanical mismatch with brain tissue. It is desirable to detect neurotransmitters as well as electrophysiological signals for months in brain science. This work proposes a stable electronic interface that can simultaneously detect neural electrical activity and dopamine concentration deep in the brain. This ultraflexible electrode is modified by a nanocomposite of reduced graphene oxide (rGO) and poly(3,4-ethylenedioxythiophene):poly(sodium 4-styrenesulfonate) (rGO/PEDOT:PSS), enhancing the electrical stability of the coating and increasing its specific surface area, thereby improving the sensitivity to dopamine response with 15 pA/μM. This electrode can detect dopamine fluctuations and can conduct long-term, stable recordings of local field potentials (LFPs), spiking activities, and amplitudes with high spatial and temporal resolution across multiple regions, especially in deep brain areas. The electrodes were implanted into the brains of rodent models to monitor the changes in neural and electrochemical signals across different brain regions during the administration of nomifensine. Ten minutes after drug injection, enhanced neuronal firing activity and increased LFP power were detected in the motor cortex and deeper cortical layers, accompanied by a gradual rise in dopamine levels with 192 ± 29 nM. The in vivo recording consistently demonstrates chronic high-quality neural signal monitoring with electrochemical signal stability for up to 6 weeks. These findings highlight the high quality and stability of our electrophysiological/electrochemical codetection neural electrodes, underscoring their tremendous potential for applications in neuroscience research and brain-machine interfaces.},
}

@article {pmid39627937,
year = {2024},
author = {Gielas, AM},
title = {Wounds and Vulnerabilities. The Participation of Special Operations Forces in Experimental Brain-Computer Interface Research.},
journal = {Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees},
volume = {},
number = {},
pages = {1-22},
doi = {10.1017/S096318012400063X},
pmid = {39627937},
issn = {1469-2147},
abstract = {Brain-computer interfaces (BCIs) exemplify a dual-use neurotechnology with significant potential in both civilian and military contexts. While BCIs hold promise for treating neurological conditions such as spinal cord injuries and amyotrophic lateral sclerosis in the future, military decisionmakers in countries such as the United States and China also see their potential to enhance combat capabilities. Some predict that U.S. Special Operations Forces (SOF) will be early adopters of BCI enhancements. This article argues for a shift in focus: the U.S. Special Operations Command (SOCOM) should pursue translational research of medical BCIs for treating severely injured or ill SOF personnel. After two decades of continuous military engagement and on-going high-risk operations, SOF personnel face unique injury patterns, both physical and psychological, which BCI technology could help address. The article identifies six key medical applications of BCIs that could benefit wounded SOF members and discusses the ethical implications of involving SOF personnel in translational research related to these applications. Ultimately, the article challenges the traditional civilian-military divide in neurotechnology, arguing that by collaborating more closely with military stakeholders, scientists can not only help individuals with medical needs, including servicemembers, but also play a role in shaping the future military applications of BCI technology.},
}

@article {pmid39627867,
year = {2024},
author = {Patarini, F and Tamburella, F and Pichiorri, F and Mohebban, S and Bigioni, A and Ranieri, A and Di Tommaso, F and Tagliamonte, NL and Serratore, G and Lorusso, M and Ciaramidaro, A and Cincotti, F and Scivoletto, G and Mattia, D and Toppi, J},
title = {On the role of visual feedback and physiotherapist-patient interaction in robot-assisted gait training: an eye-tracking and HD-EEG study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {21},
number = {1},
pages = {211},
pmid = {39627867},
issn = {1743-0003},
support = {GR2019-12369207//Ministero della Salute/ ; RM123188F229EC72//Sapienza Università di Roma/ ; },
mesh = {Humans ; *Robotics/instrumentation/methods ; Male ; Female ; *Feedback, Sensory/physiology ; Adult ; *Electroencephalography/methods ; Middle Aged ; *Eye-Tracking Technology ; Spinal Cord Injuries/rehabilitation ; Physical Therapists ; Exercise Therapy/methods/instrumentation ; Gait/physiology ; Gait Disorders, Neurologic/rehabilitation ; },
abstract = {BACKGROUND: Treadmill based Robotic-Assisted Gait Training (t-RAGT) provides for automated locomotor training to help the patient achieve a physiological gait pattern, reducing the physical effort required by therapist. By introducing the robot as a third agent to the traditional one-to-one physiotherapist-patient (Pht-Pt) relationship, the therapist might not be fully aware of the patient's motor performance. This gap has been bridged by the integration in rehabilitation robots of a visual FeedBack (FB) that informs about patient's performance. Despite the recognized importance of FB in t-RAGT, the optimal role of the therapist in the complex patient-robot interaction is still unclear. This study aimed to describe whether the type of FB combined with different modalities of Pht's interaction toward Pt would affect the patients' visual attention and emotional engagement during t-RAGT.

METHODS: Ten individuals with incomplete Spinal Cord Injury (C or D ASIA Impairment Scale level) were assessed using eye-tracking (ET) and high-density EEG during seven t-RAGT sessions with Lokomat where (i) three types of visual FB (chart, emoticon and game) and (ii) three levels of Pht-Pt interaction (low, medium and high) were randomly combined. ET metrics (fixations and saccades) were extracted for each of the three defined areas of interest (AoI) (monitor, Pht and surrounding) and compared among the different experimental conditions (FB, Pht-Pt interaction level). The EEG spectral activations in theta and alpha bands were reconstructed for each FB type applying Welch periodogram to data localised in the whole grey matter volume using sLORETA.

RESULTS: We found an effect of FB type factor on all the ET metrics computed in the three AoIs while the factor Pht-Pt interaction level also combined with FB type showed an effect only on the ET metrics calculated in Pht and surrounding AoIs. Neural activation in brain regions crucial for social cognition resulted for high Pht-Pt interaction level, while activation of the insula was found during low interaction, independently on the FB used.

CONCLUSIONS: The type of FB and the way in which Pht supports the patients both have a strong impact on patients' engagement and should be considered in the design of a t-RAGT-based rehabilitation session.},
}

Humans
*Robotics/instrumentation/methods
Male
Female
*Feedback, Sensory/physiology
Adult
*Electroencephalography/methods
Middle Aged
*Eye-Tracking Technology
Spinal Cord Injuries/rehabilitation
Physical Therapists
Exercise Therapy/methods/instrumentation
Gait/physiology
Gait Disorders, Neurologic/rehabilitation

@article {pmid39622415,
year = {2024},
author = {Zhu, M and Yang, Y and Niu, X and Peng, Y and Liu, R and Zhang, M and Han, Y and Wang, Z},
title = {Different responses of MVL neurons when pigeons attend to local versus global information during object classification.},
journal = {Behavioural brain research},
volume = {},
number = {},
pages = {115363},
doi = {10.1016/j.bbr.2024.115363},
pmid = {39622415},
issn = {1872-7549},
abstract = {Most prior studies have indicated that pigeons have a tendency to rely on local information for target categorization, yet there is a lack of electrophysiological evidence to support this claim. The mesopallium ventrolaterale (MVL) is believed to play a role in processing both local and global information during visual cognition. The difference between responses of MVL neurons when pigeons are focusing on local versus global information during visual object categorization remain unknown. In this study, pigeons were trained to categorize hierarchical stimuli that maintained consistency in local and global information. Subsequently, stimuli with different local and global components were presented to examine the pigeons' behavioral preferences. Not surprisingly, the behavioral findings revealed that pigeons predominantly attended to the local elements when performing categorization tasks. Moreover, MVL neurons exhibited significantly distinct responses when pigeons prioritized local versus global information. Specifically, most recording sites showed heightened gamma band power and increased nonlinear entropy values, indicating strong neural responses and rich information when pigeons concentrated on the local components of an object. Furthermore, neural population functional connectivity was weaker when the pigeons focused on local elements, suggesting that individual neurons operated more independently and effectively when focusing on local features. These findings offer electrophysiological evidence supporting the notion of pigeons displaying a behavioral preference for local information. The study provides valuable insight into the understanding of cognitive processes of pigeons when presented with complex objects, and further sheds light on the neural mechanisms underlying pigeons' behavioral preference for attending to local information.},
}

@article {pmid39622169,
year = {2024},
author = {Wei, X and Narayan, J and Faisal, A},
title = {The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9957},
pmid = {39622169},
issn = {1741-2552},
abstract = {Machine learning has enhanced the performance of decoding signals indicating human behaviour. EEG decoding, as an exemplar indicating neural activity and human thoughts non-invasively, has aided patients via brain-computer interfaces in neural activity analysis. However, training machine learning algorithms on EEG encounters two primary challenges: variability across data sets and privacy concerns using data from individuals and data centres. Our objective is to address these challenges by integrating transfer learning for data variability and federated learning for data privacy into a unified approach. We introduce the Sandwich as a novel deep privacy-preserving meta-framework combining transfer learning and federated learning. The Sandwich framework comprises three components: federated networks (first layers) that handle data set differences at the input level, a shared network (middle layer) learning common rules and applying transfer learning, and individual classifiers (final layers) for specific tasks of each data set. It enables the central network (central server) to benefit from multiple data sets, while local branches (local servers) maintain data and label privacy. We evaluated the `Sandwich' meta-architecture in various configurations using the BEETL motor imagery challenge, a benchmark for heterogeneous EEG data sets. Compared with baseline models, our `Sandwich' implementations showed superior performance. The best-performing model, the Inception Sandwich with deep set alignment (Inception-SD-Deepset), exceeded baseline methods by 9%. The `Sandwich' framework demonstrates significant advancements in federated deep transfer learning for diverse tasks and data sets. It outperforms conventional deep learning methods, showcasing the potential for effective use of larger, heterogeneous data sets with enhanced privacy as a model-agnostic meta-framework.},
}

@article {pmid39622162,
year = {2024},
author = {Chen, C and Fang, H and Yang, Y and Zhou, Y},
title = {Model-agnostic meta-learning for EEG-based inter-subject emotion recognition.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad9956},
pmid = {39622162},
issn = {1741-2552},
abstract = {Developing an efficient and generalizable method for inter-subject emotion recognition from neural signals is an emerging and challenging problem in affective computing. In particular, human subjects usually have heterogeneous neural signal characteristics and variable emotional activities that challenge the existing recognition algorithms from achieving high inter-subject emotion recognition accuracy. Approach. In this work, we propose a model-agnostic meta-learning algorithm to learn an adaptable and generalizable Electroencephalogram (EEG)-based emotion decoder at the subject's population level. Different from many prior end-to-end emotion recognition algorithms, our learning algorithms include a pre-training step and an adaptation step. Specifically, our meta-decoder first learns on diverse known subjects and then further adapts it to unknown subjects with one-shot adaptation. More importantly, our algorithm is compatible with a variety of mainstream machine learning decoders for emotion recognition. Main results. We evaluate the adapted decoders obtained by our proposed algorithm on three Emotion-EEG datasets: SEED, DEAP, and DREAMER. Our comprehensive experimental results show that the adapted meta-emotion decoder achieves state-of-the-art inter-subject emotion recognition accuracy and outperforms the classical supervised learning baseline across different decoder architectures. Significance. Our results hold promise to incorporate the proposed meta-learning emotion recognition algorithm to effectively improve the inter-subject generalizability in designing future affective brain-computer interfaces (BCIs).},
}

@article {pmid39621862,
year = {2024},
author = {Xia, Y and He, M and Basang, S and Sha, L and Huang, Z and Jin, L and Duan, Y and Tang, Y and Li, H and Lai, W and Chen, L},
title = {Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.},
journal = {JMIR medical informatics},
volume = {12},
number = {},
pages = {e57727},
doi = {10.2196/57727},
pmid = {39621862},
issn = {2291-9694},
mesh = {Humans ; *Electronic Health Records ; *Epilepsy/diagnosis/classification ; *Machine Learning ; Retrospective Studies ; *Natural Language Processing ; Female ; Male ; Adult ; Middle Aged ; China ; Adolescent ; Child ; Young Adult ; },
abstract = {BACKGROUND: Obtaining and describing semiology efficiently and classifying seizure types correctly are crucial for the diagnosis and treatment of epilepsy. Nevertheless, there exists an inadequacy in related informatics resources and decision support tools.

OBJECTIVE: We developed a symptom entity extraction tool and an epilepsy semiology ontology (ESO) and used machine learning to achieve an automated binary classification of epilepsy in this study.

METHODS: Using present history data of electronic health records from the Southwest Epilepsy Center in China, we constructed an ESO and a symptom-entity extraction tool to extract seizure duration, seizure symptoms, and seizure frequency from the unstructured text by combining manual annotation with natural language processing techniques. In addition, we achieved automatic classification of patients in the study cohort with high accuracy based on the extracted seizure feature data using multiple machine learning methods.

RESULTS: Data included present history from 10,925 cases between 2010 and 2020. Six annotators labeled a total of 2500 texts to obtain 5844 words of semiology and construct an ESO with 702 terms. Based on the ontology, the extraction tool achieved an accuracy rate of 85% in symptom extraction. Furthermore, we trained a stacking ensemble learning model combining XGBoost and random forest with an F1-score of 75.03%. The random forest model had the highest area under the curve (0.985).

CONCLUSIONS: This work demonstrated the feasibility of natural language processing-assisted structural extraction of epilepsy medical record texts and downstream tasks, providing open ontology resources for subsequent related work.},
}

Humans
*Electronic Health Records
*Epilepsy/diagnosis/classification
*Machine Learning
Retrospective Studies
*Natural Language Processing
Female
Male
Adult
Middle Aged
China
Adolescent
Child
Young Adult

@article {pmid39621615,
year = {2024},
author = {Yuan, X and Li, H and Guo, F},
title = {Temperature cues are integrated in a flexible circadian neuropeptidergic feedback circuit to remodel sleep-wake patterns in flies.},
journal = {PLoS biology},
volume = {22},
number = {12},
pages = {e3002918},
doi = {10.1371/journal.pbio.3002918},
pmid = {39621615},
issn = {1545-7885},
mesh = {Animals ; *Sleep/physiology ; *Circadian Rhythm/physiology ; *Drosophila Proteins/metabolism/genetics ; *Connectome ; *Drosophila melanogaster/physiology ; *Neuropeptides/metabolism/genetics ; Neurons/physiology/metabolism ; Temperature ; Wakefulness/physiology ; Feedback, Physiological ; Brain/physiology/metabolism ; Drosophila/physiology ; Cues ; Signal Transduction ; },
abstract = {Organisms detect temperature signals through peripheral neurons, which relay them to central circadian networks to drive adaptive behaviors. Despite recent advances in Drosophila research, how circadian circuits integrate temperature cues with circadian signals to regulate sleep/wake patterns remains unclear. In this study, we used the FlyWire brain electron microscopy connectome to map neuronal connections, identifying lateral posterior neurons LPNs as key nodes for integrating temperature information into the circadian network. LPNs receive input from both circadian and temperature-sensing neurons, promoting sleep behavior. Through connectome analysis, genetic manipulation, and behavioral assays, we demonstrated that LPNs, downstream of thermo-sensitive anterior cells (ACs), suppress activity-promoting lateral dorsal neurons LNds via the AstC pathway, inducing sleep Disrupting LPN-LNd communication through either AstCR1 RNAi in LNds or in an AstCR1 mutant significantly impairs the heat-induced reduction in the evening activity peak. Conversely, optogenetic calcium imaging and behavioral assays revealed that cold-activated LNds subsequently stimulate LPNs through NPF-NPFR signaling, establishing a negative feedback loop. This feedback mechanism limits LNd activation to appropriate levels, thereby fine-tuning the evening peak increase at lower temperatures. In conclusion, our study constructed a comprehensive connectome centered on LPNs and identified a novel peptidergic circadian feedback circuit that coordinates temperature and circadian signals, offering new insights into the regulation of sleep patterns in Drosophila.},
}

Animals
*Sleep/physiology
*Circadian Rhythm/physiology
*Drosophila Proteins/metabolism/genetics
*Connectome
*Drosophila melanogaster/physiology
*Neuropeptides/metabolism/genetics
Neurons/physiology/metabolism
Temperature
Wakefulness/physiology
Feedback, Physiological
Brain/physiology/metabolism
Drosophila/physiology
Cues
Signal Transduction

@article {pmid39619679,
year = {2024},
author = {Zhang, W and Tang, X and Wang, M},
title = {Attention model of EEG signals based on reinforcement learning.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1442398},
pmid = {39619679},
issn = {1662-5161},
abstract = {BACKGROUND: Applying convolutional neural networks to a large number of EEG signal samples is computationally expensive because the computational complexity is linearly proportional to the number of dimensions of the EEG signal. We propose a new Gated Recurrent Unit (GRU) network model based on reinforcement learning, which considers the implementation of attention mechanisms in Electroencephalogram (EEG) signal processing scenarios as a reinforcement learning problem.

METHODS: The model can adaptively select target regions or position sequences from inputs and effectively extract information from EEG signals of different resolutions at multiple scales. Just as convolutional neural networks benefit from translation invariance, our proposed network also has a certain degree of translation invariance, making its computational complexity independent of the EEG signal dimension, thus maintaining a lower learning cost. Although the introduction of reinforcement learning makes the model non differentiable, we use policy gradient methods to achieve end-to-end learning of the model.

RESULTS: We evaluated our proposed model on publicly available EEG dataset (BCI Competition IV-2a). The proposed model outperforms the current state-of-the-art techniques in the BCI Competition IV- 2a dataset with an accuracy of 86.78 and 71.54% for the subject-dependent and subject-independent modes, respectively.

CONCLUSION: In the field of EEG signal processing, attention models that combine reinforcement learning principles can focus on key features, automatically filter out noise and redundant data, and improve the accuracy of signal decoding.},
}

@article {pmid39619531,
year = {2024},
author = {Patel, M and Gosai, J and Patel, P and Roy, M and Solanki, A},
title = {Insights of BDAPbI4-Based Flexible Memristor for Artificial Synapses and In-Memory Computing.},
journal = {ACS omega},
volume = {9},
number = {47},
pages = {46841-46850},
pmid = {39619531},
issn = {2470-1343},
abstract = {Inspired by brain-like spiking computational frameworks, neuromorphic computing-brain-inspired computing for machine intelligence promises to realize artificial intelligence (AI) while reducing the energy requirements of computing platforms. In this work, we show the potential of advanced learnings of butane-1,4-diammonium based low-dimensional Dion-Jacobson hybrid perovskite (BDAPbI4) memristor devices in the realm of artificial synapses and neuromorphic computing. Memristors validate Hebbian learning rules with various spike-dependent plasticity within a 10 ± 2 ms time frame, reminiscent of the human brains under flat and bending conditions (∼5 mm radium). A high recognition accuracy of ∼94% of handwritten images from the MNIST database via an artificial neural network (ANN) is achieved with only 50 epochs. An efficient demonstration of second-order memristors and the Pavlovian dog experiment exhibit significant promise in expediting learning and memory consolidation. To showcase the in-memory computing potential, a flexible 4 × 4 crossbar array is designed with measured data retention up to ∼10[3] s along with 26 multilevel resistance states. The crossbar array is successfully programmed for the facile configurability of image "Z". In conclusion, the integration of supervised, unsupervised, and associative learning holds great promise across a spectrum of future technologies, ranging from the realm of spiking neural networks to neuromorphic computing, brain-machine interfaces, and adaptive control systems.},
}

@article {pmid39615858,
year = {2024},
author = {Xue, Z and Zhong, W and Cao, Y and Liu, S and An, X},
title = {Impact of different auditory environments on task performance and EEG activity.},
journal = {Brain research bulletin},
volume = {220},
number = {},
pages = {111142},
doi = {10.1016/j.brainresbull.2024.111142},
pmid = {39615858},
issn = {1873-2747},
abstract = {Mental workload could affect human performance. An inappropriate workload level, whether too high or too low, leads to discomfort and decreased task performance. Auditory stimuli have been shown to act as an emotional medium to influence the workload. For example, the 'Mozart effect' has been shown to enhance performance in spatial reasoning tasks. However, the impact of auditory stimuli on task performance and brain activity remains unclear. This study examined the effects of three different environments-quiet, music, and white noise-on task performance and EEG activities. The N-back task was employed to induce mental workload, and the Psychomotor Vigilance Task assessed participants' alertness. We proposed a novel, statistically-based method to construct the brain functional network, avoiding issues associated with subjective threshold selection. This method systematically analyzed the connectivity patterns under different environments. Our analysis revealed that white noise negatively affected participants, primarily impacting brain activity in high-frequency ranges. This study provided deeper insights into the relationship between auditory stimuli and mental workload, offering a robust framework for future research on mental workload regulation.},
}

@article {pmid39615554,
year = {2024},
author = {Cai, Z and Gao, Y and Fang, F and Zhang, Y and Du, S},
title = {Multi-layer transfer learning algorithm based on improved common spatial pattern for brain-computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110332},
doi = {10.1016/j.jneumeth.2024.110332},
pmid = {39615554},
issn = {1872-678X},
abstract = {In the application of brain-computer interface, the differences in imaging methods and brain structure between subjects hinder the effectiveness of decoding algorithms when applied on different subjects. Transfer learning has been designed to solve this problem. There have been many applications of transfer learning in motor imagery (MI), however the effectiveness is still limited due to the inconsistent domain alignment, lack of prominent data features and allocation of weights in trails. In this paper, a Multi-layer transfer learning algorithm based on improved Common Spatial Patterns (MTICSP) was proposed to solve these problems. Firstly, the source domain data and target domain data were aligned by Target Alignment (TA)method to reduce distribution differences between subjects. Secondly, the mean covariance matrix of the two classes was re-weighted by calculating the distance between the covariance matrix of each trial in the source domain and the target domain. Thirdly, the improved Common Spatial Patterns (CSP) by introducing regularization coefficient was proposed to further reduce the difference between source domain and target domain to extract features. Finally, the feature blocks of the source domain and target domain were aligned again by Joint Distribution Adaptation (JDA) method. Experiments on two public datasets in two transfer paradigms multi-source to single-target (MTS) and single-source to single-target (STS) verified the effectiveness of our proposed method. The MTS and STS in the 5-person dataset were 80.21% and 77.58%, respectively, and 80.10% and 73.91%, respectively, in the 9-person dataset. Experimental results also showed that the proposed algorithm was superior to other state-of-the-art algorithms. In addition, the generalization ability of our algorithm MTICSP was validated on the fatigue EEG dataset collected by ourselves, and obtained 94.83% and 87.41% accuracy in MTS and STS experiments respectively. The proposed method combines improved CSP with transfer learning to extract the features of source and target domains effectively, providing a new method for combining transfer learning with motor imagination.},
}

@article {pmid39612132,
year = {2024},
author = {Yang, Y and Li, M and Wang, L},
title = {An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {39612132},
issn = {1741-0444},
abstract = {Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and self-updating capability. Broad learning system (BLS) can be remodeled in an efficient incremental learning way. However, its architecture is intractable to change automatically to adapt to new incoming MI-EEG with time-varying and complex temporal-spatial characteristics. In this paper, an adaptive session-incremental BLS (ASiBLS) is proposed based on mutual information theory and BLS. For the initial session data, a compact temporal-spatial feature extractor (CTS) is designed to acquire the temporal-spatial features, which are input to a baseline BLS (bBLS). Furthermore, for new session data, a mutual information maximization constraint (MIMC) is introduced into the loss function of CTS to make the features' probability distribution sufficiently similar to that of the previous session, a new incremental BLS sequence (iBLS) is obtained by adding a small number of nodes to the previous model, and so on. Experiments are conducted based on the BCI Competition IV-2a dataset with two sessions and IV-2b dataset with five sessions, ASiBLS achieves average decoding accuracies of 79.89% and 87.04%, respectively. The kappa coefficient and forgetting rate are also used to evaluate the model performance. The results show that ASiBLS can adaptively generate an optimized and reduced model for each session successively, which has better plasticity in learning new knowledge and stability in retaining old knowledge as well.},
}

@article {pmid39609786,
year = {2024},
author = {Sun, J and Chen, S and Wang, S and Guo, H and Wang, X},
title = {The relationship between work-family conflict, stress and depression among Chinese correctional officers: a mediation and network analysis study.},
journal = {BMC public health},
volume = {24},
number = {1},
pages = {3317},
pmid = {39609786},
issn = {1471-2458},
support = {2021ZD0200700//the 2030 Plan Technology and Innovation of China/ ; },
mesh = {Humans ; *Depression/epidemiology/psychology ; Male ; Adult ; Cross-Sectional Studies ; China/epidemiology ; Female ; Middle Aged ; Mediation Analysis ; Stress, Psychological/epidemiology/psychology ; Prisons ; Family/psychology ; Occupational Stress/psychology/epidemiology ; Police/psychology/statistics & numerical data ; Social Network Analysis ; Correctional Facilities Personnel ; East Asian People ; },
abstract = {BACKGROUND: Numerous studies have found that depression is prevalent among correctional officers (COs), which may be related to the work-family conflict (WFC) faced by this cohort. Role conflict theory posits that WFC emerges from the incompatibility between the demands of work and family roles, which induces stress and, in turn, results in emotional problems. Thus, this study seeks to investigate the association between WFC and depression, along with examining the mediating role of stress. Further network analysis is applied to identify the core and bridge symptoms within the network of WFC, stress, and depression, providing a basis for targeted interventions.

OBJECTIVE: This study aims to investigate the relationship between work-family conflict (WFC) and depressive symptoms among a larger sample of Chinese correctional officers (COs), exploring the potential mechanisms of stress in this population through network analysis.

METHODS: A cross-sectional study of 472 Chinese COs was conducted from October 2021 to January 2022. WFC, stress, and depressive symptoms were evaluated using the Work-Family Conflict Scale (WFCS) and the Depression Anxiety Stress Scale (DASS). Subsequently, correlation and regression analyses were conducted using SPSS 26.0, while mediation analysis was performed using Model 4 in PROCESS. By using the EBICglasso model, network analyses were utilized to estimate the network structure of WFC, stress and depression. Visualization and centrality measures were performed using the R package.

RESULTS: The results showed that (1) there was a significant positive correlation between WFC and stress and depression, as well as between stress and depression, (2) WFC and stress had a significant positive predictive effect on depression, (3) stress mediated the relationship between WFC and depression, with a total mediating effect of 0.262 (BootSE = 0.031, BCI 95% = 0.278, 0.325), which accounted for 81.62% of the total effect, and (4) in the WFC, stress, and depression network model, strain-based work interference with family (SWF, (betweenness = 2.24, closeness = -0.19, strength = 1.40), difficult to relax (DR, betweenness = 1.20, closeness = 1.85, strength = 1.06), and had nothing (HN, betweenness = -0.43, closeness = 0.62, strength = 0.73) were the core symptoms, and SWF, IT, and DH were the bridge symptoms, and (5) first-line COs had significantly higher levels of WFC, stress, and depression than non-first-line correctional officers.

CONCLUSION: Our findings elucidate the interrelationships between WFC, stress, and depression among COs. The study also enhances the understanding of the factors influencing WFC in this population and provides valuable guidance for the development of future interventions, offering practical clinical significance.},
}

Humans
*Depression/epidemiology/psychology
Male
Adult
Cross-Sectional Studies
China/epidemiology
Female
Middle Aged
Mediation Analysis
Stress, Psychological/epidemiology/psychology
Prisons
Family/psychology
Occupational Stress/psychology/epidemiology
Police/psychology/statistics & numerical data
Social Network Analysis
Correctional Facilities Personnel
East Asian People

@article {pmid39609406,
year = {2024},
author = {Huang, W and Jin, N and Guo, J and Shen, C and Xu, C and Xi, K and Bonhomme, L and Quast, RB and Shen, DD and Qin, J and Liu, YR and Song, Y and Gao, Y and Margeat, E and Rondard, P and Pin, JP and Zhang, Y and Liu, J},
title = {Structural basis of orientated asymmetry in a mGlu heterodimer.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {10345},
pmid = {39609406},
issn = {2041-1723},
support = {ANR 18-CE11-0004-01//Agence Nationale de la Recherche (French National Research Agency)/ ; ANR 18-CE11-0004-01//Agence Nationale de la Recherche (French National Research Agency)/ ; ANR-10-INBS-04//Agence Nationale de la Recherche (French National Research Agency)/ ; },
mesh = {*Receptors, Metabotropic Glutamate/metabolism/chemistry/ultrastructure ; *Cryoelectron Microscopy ; Humans ; *Protein Multimerization ; HEK293 Cells ; Allosteric Regulation ; Glutamic Acid/metabolism/chemistry ; Models, Molecular ; Fluorescence Resonance Energy Transfer ; Animals ; Protein Binding ; Binding Sites ; },
abstract = {The structural basis for the allosteric interactions within G protein-coupled receptors (GPCRs) heterodimers remains largely unknown. The metabotropic glutamate (mGlu) receptors are complex dimeric GPCRs important for the fine tuning of many synapses. Heterodimeric mGlu receptors with specific allosteric properties have been identified in the brain. Here we report four cryo-electron microscopy structures of mGlu2-4 heterodimer in different states: an inactive state bound to antagonists, two intermediate states bound to either mGlu2 or mGlu4 agonist only and an active state bound to both glutamate and a mGlu4 positive allosteric modulator (PAM) in complex with Gi protein. In addition to revealing a unique PAM binding pocket among mGlu receptors, our data bring important information for the asymmetric activation of mGlu heterodimers. First, we show that agonist binding to a single subunit in the extracellular domain is not sufficient to stabilize an active dimer conformation. Single-molecule FRET data show that the monoliganded mGlu2-4 can be found in both intermediate states and an active one. Second, we provide a detailed view of the asymmetric interface in seven-transmembrane (7TM) domains and identified key residues within the mGlu2 7TM that limits its activation leaving mGlu4 as the only subunit activating G proteins.},
}

*Receptors, Metabotropic Glutamate/metabolism/chemistry/ultrastructure
*Cryoelectron Microscopy
Humans
*Protein Multimerization
HEK293 Cells
Allosteric Regulation
Glutamic Acid/metabolism/chemistry
Models, Molecular
Fluorescence Resonance Energy Transfer
Animals
Protein Binding
Binding Sites

@article {pmid39606007,
year = {2024},
author = {Chang, C and Piao, Y and Zhang, M and Liu, Y and Du, M and Yang, M and Mei, T and Wu, C and Wang, Y and Chen, X and Zeng, GQ and Zhang, X},
title = {Evaluation of tolerability and safety of transcranial electrical stimulation with gel particle electrodes in healthy subjects.},
journal = {Frontiers in psychiatry},
volume = {15},
number = {},
pages = {1441533},
pmid = {39606007},
issn = {1664-0640},
abstract = {BACKGROUND: With the advancement of transcranial electrical stimulation (tES) technology, an increasing number of stimulation devices and treatment protocols have emerged. However, safety and tolerability remain critical concerns before new strategies can be implemented. Particularly, the use of gel particle electrodes brings new challenges to the safety and tolerability of tES, which hinders its widespread adoption and further research.

OBJECTIVE: Our study utilized a specially designed and validated transcranial electrical stimulation stimulator along with preconfigured gel particle electrodes placed at F3 and F4 in the prefrontal lobes. We aimed to assess the tolerance and safety of these electrodes in healthy subjects by administering different durations and types of tES.

METHODS: Each participant underwent ten sessions of either transcranial direct current stimulation (tDCS) or transcranial alternating current stimulation (tACS), with session durations varying. In the experiment, we collected various measurement data from participants, including self-report questionnaire data and behavioral keystroke data. Tolerability was evaluated through adverse events (AEs), the relationship of adverse events with tES (AEs-rela), the Self-Rating Anxiety Scale (SAS), and the Visual Analog Mood Scale-Revised (VAMS-R). Safety was assessed using the Visual Analog Scale (VAS), the Skin Sensation Rating (SSR), Montreal Cognitive Assessment (MoCA), and Stroop task. These data were analyzed to determine the impact of different parameters on the tolerability and safety of tES.

RESULTS: There were no significant changes in the results of the MoCA and SAS scales before and after the experiment. However, significant differences were observed in VAS, SSR, AEs, and AEs-rela between tDCS and tACS. Additionally, fatigue increased, and energy levels decreased on VAMS-R with longer durations. No significant differences were found in other neuropsychological tests.

CONCLUSION: Our study revealed significant differences in tolerability and safety between tDCS and tACS, underscoring the importance of considering the stimulation type when evaluating these factors. Although tolerance and safety did not vary significantly across different stimulation durations in this study, future research may benefit from exploring shorter durations to further assess tolerability and safety efficiently.},
}

@article {pmid39605556,
year = {2024},
author = {Singer-Clark, T and Hou, X and Card, NS and Wairagkar, M and Iacobacci, C and Peracha, H and Hochberg, LR and Stavisky, SD and Brandman, DM},
title = {Speech motor cortex enables BCI cursor control and click.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.11.12.623096},
pmid = {39605556},
issn = {2692-8205},
abstract = {Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also enable computer control via neural cursor and click, as is typically associated with dorsal (arm and hand) motor cortex. We recruited a clinical trial participant with ALS and implanted intracortical microelectrode arrays in ventral precentral gyrus (vPCG), which the participant used to operate a speech BCI in a prior study. We developed a cursor BCI driven by the participant's vPCG neural activity, and evaluated performance on a series of target selection tasks. The reported vPCG cursor BCI enabled rapidly-calibrating (40 seconds), accurate (2.90 bits per second) cursor control and click. The participant also used the BCI to control his own personal computer independently. These results suggest that placing electrodes in vPCG to optimize for speech decoding may also be a viable strategy for building a multi-modal BCI which enables both speech-based communication and computer control via cursor and click.},
}

@article {pmid39605372,
year = {2024},
author = {Kumaresan, V and Hung, CY and Hermann, BP and Seshu, J},
title = {Role of Dual Specificity Phosphatase 1 (DUSP1) in influencing inflammatory pathways in macrophages modulated by Borrelia burgdorferi lipoproteins.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.11.20.624562},
pmid = {39605372},
issn = {2692-8205},
abstract = {UNLABELLED: Borrelia burgdorferi (Bb) , the spirochetal agent of Lyme disease, has a large array of lipoproteins that play a significant role in mediating host-pathogen interactions within ticks and vertebrates. Although there is substantial information on the effects of B. burgdorferi lipoproteins (Bb LP) on immune modulatory pathways, the application of multi-omics methodologies to decode the transcriptional and proteomic patterns associated with host cell responses induced by lipoproteins in murine bone marrow-derived macrophages (BMDMs) has identified additional effectors and pathways. S ingle- c ell RNA-Seq (scRNA-Seq) performed on BMDMs treated with various concentrations of borrelial lipoproteins revealed macrophage subsets within the BMDMs. Differential expression analysis showed that genes encoding various receptors, type I IFN-stimulated genes, signaling chemokines, and mitochondrial genes are altered in BMDMs in response to lipoproteins. Unbiased proteomics analysis of lysates of BMDMs treated with lipoproteins corroborated several of these findings. Notably, du al s pecificity p hosphatase 1 (Dusp1) gene was upregulated during the early stages of BMDM exposure to Bb LP. Pre-treatment with benzylidene-3-cyclohexylamino-1-indanone hydrochloride (BCI), an inhibitor of both DUSP1 and 6 prior to exposure to Bb LP, demonstrated that DUSP1 negatively regulates NLRP3-mediated pro-inflammatory signaling and positively regulates the expression of interferon-stimulated genes and those encoding Ccl5 , Il1b , and Cd274 . Moreover, DUSP1, IkB kinase complex and MyD88 also modulate mitochondrial changes in BMDMs treated with borrelial lipoproteins. These findings advance the potential for exploiting DUSP1 as a therapeutic target to regulate host responses in reservoir hosts to limit survival of B. burgdorferi during its infectious cycle between ticks and mammalian hosts.

IMPORTANCE: Borrelia burgdorferi , the agent of Lyme disease, encodes numerous lipoproteins that play a crucial role as a pathogen associated molecular pattern affecting interactions with tick- and vertebrate-host cells. Single cell transcriptomics validated using unbiased proteomics and conventional molecular biology approaches have demonstrated significant differences in gene expression patterns in a dose- and time-dependent manner following treatment of murine bone marrow derived macrophages with borrelial lipoproteins. Distinct populations of macrophages, alterations in immune signaling pathways, cellular energy production and mitochondrial responses were identified and validated using primary murine macrophages and human reporter cell lines. Notably, the role of Dual Specificity Phosphatase 1 (DUSP1) in influencing several inflammatory, metabolic and mitochondrial responses of macrophages were observed in these studies using known pharmacological inhibitors. Significant outcomes include novel strategies to interfere with immunomodulatory and survival capabilities of B. burgdorferi in reservoir hosts affecting its natural infectious life cycle between ticks and vertebrate hosts.},
}

@article {pmid39603445,
year = {2024},
author = {Kerezoudis, P and Jensen, MA and Huang, H and Ojemann, JG and Klassen, BT and Ince, NF and Hermes, D and Miller, KJ},
title = {Spatial and spectral changes in cortical surface Potentials during pinching versusThumb and index finger flexion.},
journal = {Neuroscience letters},
volume = {},
number = {},
pages = {138062},
doi = {10.1016/j.neulet.2024.138062},
pmid = {39603445},
issn = {1872-7972},
abstract = {Electrocorticographic (ECoG) signals provide high-fidelity representations of sensorimotor cortex activation during contralateral hand movements. Understanding the relationship between independent and coordinated finger movements along with their corresponding ECoG signals is crucial for precise brain mapping and neural prosthetic development. We analyzed subdural ECoG signals from three adult epilepsy patients with subdural electrode arrays implanted for seizure foci identification. Patients performed a cue-based task consisting of thumb flexion, index finger flexion or a pinching movement of both fingers together. Broadband power changes were estimated using principal component analysis of the power spectrum. All patients showed significant increases in broadband power during each movement compared to rest. We created topological maps for each movement type on brain renderings and quantified spatial overlap between movement types using a resampling metric. Pinching exhibited the highest spatial overlap with index flexion, followed by superimposed index and thumb flexion, with the least overlap observed for thumb flexion alone. This analysis provides practical insights into the complex overlap of finger representations in the motor cortex during various movement types and may help guide more nuanced approaches to brain-computer interfaces and neural prosthetics.},
}

@article {pmid39600948,
year = {2024},
author = {Adams, M and Cottrell, J},
title = {Development and characterization of an in vitro fluorescently tagged 3D bone-cartilage interface model.},
journal = {Frontiers in endocrinology},
volume = {15},
number = {},
pages = {1484912},
pmid = {39600948},
issn = {1664-2392},
mesh = {Animals ; Mice ; *Cell Differentiation ; *Osteoblasts/metabolism/cytology ; *Chondrocytes/metabolism/cytology ; *Osteoclasts/metabolism/cytology ; Bone and Bones/metabolism/cytology ; Cartilage/metabolism/cytology ; RAW 264.7 Cells ; Osteocytes/metabolism/cytology ; Cell Culture Techniques, Three Dimensional/methods ; Osteogenesis/physiology ; Cell Line ; },
abstract = {Three-dimensional cultures are widely used to study bone and cartilage. These models often focus on the interaction between osteoblasts and osteoclasts or osteoblasts and chondrocytes. A culture of osteoblasts, osteoclasts and chondrocytes would represent the cells that interact in the joint and a model with these cells could be used to study many diseases that affect the joints. The goal of this study was to develop 3D bone-cartilage interface (3D-BCI) that included osteoblasts, osteocytes, osteoclasts, and cartilage. Fluorescently tagged cell lines were developed to assess the interactions as cells differentiate to form bone and cartilage. Mouse cell line, MC3T3, was labeled with a nuclear GFP tag and differentiated into osteoblasts and osteocytes in Matrigel. Raw264.7 cells transfected with a red cytoplasmic tag were added to the system and differentiated with the MC3T3 cells to form osteoclasts. A new method was developed to differentiate chondrocyte cell line ATDC5 in a cartilage spheroid, and the ATDC5 spheroid was added to the MC3T3 and Raw264.7 cell model. We used an Incucyte and functional analysis to assess the cells throughout the differentiation process. The 3D-BCI model was found to be positive for TRAP, ALP, Alizarin red and Alcian blue staining to confirm osteoblastogenesis, osteoclastogenesis, and cartilage formation. Gene expression confirmed differentiation of cells based on increased expression of osteoblast markers: Alpl, Bglap, Col1A2, and Runx2, cartilage markers: Acan, Col2A1, Plod2, and osteoclast markers: Acp5, Rank and Ctsk. Based on staining, protein expression and gene expression results, we conclude that we successfully developed a mouse model with a 3D bone-cartilage interface.},
}

Animals
Mice
*Cell Differentiation
*Osteoblasts/metabolism/cytology
*Chondrocytes/metabolism/cytology
*Osteoclasts/metabolism/cytology
Bone and Bones/metabolism/cytology
Cartilage/metabolism/cytology
RAW 264.7 Cells
Osteocytes/metabolism/cytology
Cell Culture Techniques, Three Dimensional/methods
Osteogenesis/physiology
Cell Line

@article {pmid39600168,
year = {2024},
author = {Amandusson, Å and Nilsson, J and Pequito, S},
title = {[The role of EEG in tomorrow's medicine].},
journal = {Lakartidningen},
volume = {121},
number = {},
pages = {},
pmid = {39600168},
issn = {1652-7518},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Wearable Electronic Devices ; Artificial Intelligence ; Brain/physiology ; },
abstract = {There is a breathtakingly rapid development in various areas that take advantage of the ever-improving possibilities to record and analyze the electrical activity generated in the brain. In this article, we attempt to briefly describe some of these areas, including AI-assisted EEG interpretation, the use of BCI (brain-computer interface) in a medical setting, and the possible new applications connected to the development of very small wearable EEG devices. Furthermore, we discuss the concerns and challenges presented by these advancements in neurotechnology.},
}

Humans
*Electroencephalography
*Brain-Computer Interfaces
Wearable Electronic Devices
Artificial Intelligence
Brain/physiology

@article {pmid39598903,
year = {2024},
author = {Angulo Medina, AS and Aguilar Bonilla, MI and Rodríguez Giraldo, ID and Montenegro Palacios, JF and Cáceres Gutiérrez, DA and Liscano, Y},
title = {Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013-2023).},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {22},
pages = {},
doi = {10.3390/s24227125},
pmid = {39598903},
issn = {1424-8220},
support = {Convocatoria Interna No. 01-2024//Universidad Santiago de Cali/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Bibliometrics ; Artificial Intelligence ; Rehabilitation/methods ; Brain/physiology ; },
abstract = {EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability to capture brain activity and restore neurological functions in patients with conditions such as stroke and spinal cord injuries. This study offers a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023. It focuses on primary research and review articles addressing technological innovations, effectiveness, and system advancements in clinical rehabilitation. Data were sourced from databases like Web of Science, and bibliometric tools (bibliometrix R) were used to analyze publication trends, geographic distribution, keyword co-occurrences, and collaboration networks. The results reveal a rapid increase in EEG-BCI research, peaking in 2022, with a primary focus on motor and sensory rehabilitation. EEG remains the most commonly used method, with significant contributions from Asia, Europe, and North America. Additionally, there is growing interest in applying BCIs to mental health, as well as integrating artificial intelligence (AI), particularly machine learning, to enhance system accuracy and adaptability. However, challenges remain, such as system inefficiencies and slow learning curves. These could be addressed by incorporating multi-modal approaches and advanced neuroimaging technologies. Further research is needed to validate the applicability of EEG-BCI advancements in both cognitive and motor rehabilitation, especially considering the high global prevalence of cerebrovascular diseases. To advance the field, expanding global participation, particularly in underrepresented regions like Latin America, is essential. Improving system efficiency through multi-modal approaches and AI integration is also critical. Ethical considerations, including data privacy, transparency, and equitable access to BCI technologies, must be prioritized to ensure the inclusive development and use of these technologies across diverse socioeconomic groups.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
*Bibliometrics
Artificial Intelligence
Rehabilitation/methods
Brain/physiology

@article {pmid39597167,
year = {2024},
author = {Li, W and Zhou, J and Sheng, W and Jia, Y and Xu, W and Zhang, T},
title = {Highly Flexible and Compressible 3D Interconnected Graphene Foam for Sensitive Pressure Detection.},
journal = {Micromachines},
volume = {15},
number = {11},
pages = {},
doi = {10.3390/mi15111355},
pmid = {39597167},
issn = {2072-666X},
support = {12072151 and 12472153//National Natural Science Foundation of China/ ; 2019YFA0705400//National Key Research and Development Program of China/ ; MCAS-E-0124Y03//Research Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures/ ; },
abstract = {A flexible pressure sensor, capable of effectively detecting forces exerted on soft or deformable surfaces, has demonstrated broad application in diverse fields, including human motion tracking, health monitoring, electronic skin, and artificial intelligence systems. However, the design of convenient sensors with high sensitivity and excellent stability is still a great challenge. Herein, we present a multi-scale 3D graphene pressure sensor composed of two types of 3D graphene foam. The sensor exhibits a high sensitivity of 0.42 kPa[-1] within the low-pressure range of 0-390 Pa and 0.012 kPa[-1] within the higher-pressure range of 0.4 to 42 kPa, a rapid response time of 62 ms, and exceptional repeatability and stability exceeding 10,000 cycles. These characteristics empower the sensor to realize the sensation of a drop of water, the speed of airflow, and human movements.},
}

@article {pmid39597097,
year = {2024},
author = {Ji, W and Su, H and Jin, S and Tian, Y and Li, G and Yang, Y and Li, J and Zhou, Z and Wei, X and Tao, TH and Qin, L and Ye, Y and Sun, L},
title = {A Wireless Bi-Directional Brain-Computer Interface Supporting Both Bluetooth and Wi-Fi Transmission.},
journal = {Micromachines},
volume = {15},
number = {11},
pages = {},
doi = {10.3390/mi15111283},
pmid = {39597097},
issn = {2072-666X},
support = {2022YFF0706500//National Key R & D Program of China/ ; 2022ZD0209300//National Key R & D Program of China/ ; 2021ZD0201600//National Key R & D Program of China/ ; 2019YFA0905200//National Key R & D Program of China/ ; 2021YFC2501500//National Key R & D Program of China/ ; 2021YFF1200700//National Key R & D Program of China/ ; 2022ZD0212300//National Key R & D Program of China/ ; 61974154//National Natural Science Foundation of China/ ; ZDBS-LY-JSC024//Key Research Program of Frontier Sciences, CAS/ ; JCYJ-SHFY-2022-01//Shanghai Pilot Program for Basic Research-Chinese Academy of Science, Shanghai Branch/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; 21PJ1415100//Shanghai Pujiang Program/ ; 19PJ1410900//Shanghai Pujiang Program/ ; 21JM0010200//Science and Technology Commission Foundation of Shanghai/ ; 21142200300//Science and Technology Commission Foundation of Shanghai/ ; 22QA1410900//Shanghai Rising-Star Program/ ; 22YF1454700//Shanghai Sailing Program/ ; 20212ABC03W07//Jiangxi Province 03 Special Project and 5G Project/ ; 20201ZDE04013//Fund for Central Government in Guidance of Local Science and Technology Development/ ; 2021B0909060002//Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province/ ; 2021B0909050004//Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province/ ; },
abstract = {Wireless neural signal transmission is essential for both neuroscience research and neural disorder therapies. However, conventional wireless systems are often constrained by low sampling rates, limited channel counts, and their support of only a single transmission mode. Here, we developed a wireless bi-directional brain-computer interface system featuring dual transmission modes. This system supports both low-power Bluetooth transmission and high-sampling-rate Wi-Fi transmission, providing flexibility for various application scenarios. The Bluetooth mode, with a maximum sampling rate of 14.4 kS/s, is well suited for detecting low-frequency signals, as demonstrated by both in vitro recordings of signals from 10 to 50 Hz and in vivo recordings of 16-channel local field potentials in mice. More importantly, the Wi-Fi mode, offering a maximum sampling rate of 56.8 kS/s, is optimized for recording high-frequency signals. This capability was validated through in vitro recordings of signals from 500 to 2000 Hz and in vivo recordings of single-neuron spike firings with amplitudes reaching hundreds of microvolts and high signal-to-noise ratios. Additionally, the system incorporates a wireless stimulation function capable of delivering current pulses up to 2.55 mA, with adjustable pulse width and polarity. Overall, this dual-mode system provides an efficient and flexible solution for both neural recording and stimulation applications.},
}

@article {pmid39595855,
year = {2024},
author = {Tubbs, A and Vazquez, EA},
title = {Engineering and Technological Advancements in Repetitive Transcranial Magnetic Stimulation (rTMS): A Five-Year Review.},
journal = {Brain sciences},
volume = {14},
number = {11},
pages = {},
doi = {10.3390/brainsci14111092},
pmid = {39595855},
issn = {2076-3425},
abstract = {In the past five years, repetitive transcranial magnetic stimulation (rTMS) has evolved significantly, driven by advancements in device design, treatment protocols, software integration, and brain-computer interfaces (BCIs). This review evaluates how these innovations enhance the safety, efficacy, and accessibility of rTMS while identifying key challenges such as protocol standardization and ethical considerations. A structured review of peer-reviewed studies from 2019 to 2024 focused on technological and clinical advancements in rTMS, including AI-driven personalized treatments, portable devices, and integrated BCIs. AI algorithms have optimized patient-specific protocols, while portable devices have expanded access. Enhanced coil designs and BCI integration offer more precise and adaptive neuromodulation. However, challenges remain in standardizing protocols, addressing device complexity, and ensuring equitable access. While recent innovations improve rTMS's clinical utility, gaps in long-term efficacy and ethical concerns persist. Future research must prioritize standardization, accessibility, and robust ethical frameworks to ensure rTMS's sustainable impact.},
}