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A canonical polyadic tensor basis for fast Bayesian estimation of multi-subject brain activation patterns
A canonical polyadic tensor basis for fast Bayesian estimation of multi-subject brain activation patterns
Task-evoked functional magnetic resonance imaging studies, such as the Human Connectome Project (HCP), are a powerful tool...
Investigating cortical complexity and connectivity in rats with schizophrenia
Investigating cortical complexity and connectivity in rats with schizophrenia
BackgroundThe above studies indicate that the SCZ animal model has abnormal gamma oscillations and abnormal functional cou...
Predicting the clinical prognosis of acute ischemic stroke using machine learning: an application of radiomic biomarkers on non-contrast CT after intravascular interventional treatment
Predicting the clinical prognosis of acute ischemic stroke using machine learning: an application of radiomic biomarkers on non-contrast CT after intravascular interventional treatment
PurposeThis study aimed to develop a radiomic model based on non-contrast computed tomography (NCCT) after interventional ...
Interpretable machine learning comprehensive human gait deterioration analysis
Interpretable machine learning comprehensive human gait deterioration analysis
IntroductionGait analysis, an expanding research area, employs non-invasive sensors and machine learning techniques for a ...
SEEG4D: a tool for 4D visualization of stereoelectroencephalography data
SEEG4D: a tool for 4D visualization of stereoelectroencephalography data
Epilepsy is a prevalent and serious neurological condition which impacts millions of people worldwide. Stereoelectroenceph...
Optimizing neuroscience data management by combining REDCap, BIDS and SQLite: a case study in Deep Brain Stimulation
Optimizing neuroscience data management by combining REDCap, BIDS and SQLite: a case study in Deep Brain Stimulation
Neuroscience studies entail the generation of massive collections of heterogeneous data (e.g. demographics, clinical recor...
Efficient federated learning for distributed neuroimaging data
Efficient federated learning for distributed neuroimaging data
Recent advancements in neuroimaging have led to greater data sharing among the scientific community. However, institutions...
Light-weight neural network for intra-voxel structure analysis
Light-weight neural network for intra-voxel structure analysis
We present a novel neural network-based method for analyzing intra-voxel structures, addressing critical challenges in dif...
Reproducible supervised learning-assisted classification of spontaneous synaptic waveforms with Eventer
Reproducible supervised learning-assisted classification of spontaneous synaptic waveforms with Eventer
Detection and analysis of spontaneous synaptic events is an extremely common task in many neuroscience research labs. Vari...
The ROSMAP project: aging and neurodegenerative diseases through omic sciences
The ROSMAP project: aging and neurodegenerative diseases through omic sciences
The Religious Order Study and Memory and Aging Project (ROSMAP) is an initiative that integrates two longitudinal cohort s...
Cooperation objective evaluation in aviation: validation and comparison of two novel approaches in simulated environment
Cooperation objective evaluation in aviation: validation and comparison of two novel approaches in simulated environment
IntroductionIn operational environments, human interaction and cooperation between individuals are critical to efficiency ...
Artificial intelligence role in advancement of human brain connectome studies
Artificial intelligence role in advancement of human brain connectome studies
Neurons are interactive cells that connect via ions to develop electromagnetic fields in the brain. This structure functio...
Reproducible brain PET data analysis: easier said than done
Reproducible brain PET data analysis: easier said than done
While a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural m...
Can micro-expressions be used as a biomarker for autism spectrum disorder?
Can micro-expressions be used as a biomarker for autism spectrum disorder?
IntroductionEarly and accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective intervention, yet it r...
Research on ECG signal reconstruction based on improved weighted nuclear norm minimization and approximate message passing algorithm
Research on ECG signal reconstruction based on improved weighted nuclear norm minimization and approximate message passing algorithm
In order to improve the energy efficiency of wearable devices, it is necessary to compress and reconstruct the collected e...
Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism
Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism
Memory formation is usually associated with Hebbian learning and synaptic plasticity, which changes the synaptic strengths...
Brain MRI sequence and view plane identification using deep learning
Brain MRI sequence and view plane identification using deep learning
Brain magnetic resonance imaging (MRI) scans are available in a wide variety of sequences, view planes, and magnet strengt...
An optimized framework for processing multicentric polysomnographic data incorporating expert human oversight
An optimized framework for processing multicentric polysomnographic data incorporating expert human oversight
IntroductionPolysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis pre...
EPAT: a user-friendly MATLAB toolbox for EEG/ERP data processing and analysis
EPAT: a user-friendly MATLAB toolbox for EEG/ERP data processing and analysis
BackgroundAt the intersection of neural monitoring and decoding, event-related potential (ERP) based on electroencephalogr...
Gershgorin circle theorem-based feature extraction for biomedical signal analysis
Gershgorin circle theorem-based feature extraction for biomedical signal analysis
Recently, graph theory has become a promising tool for biomedical signal analysis, wherein the signals are transformed int...
Harmonizing data on correlates of sleep in children within and across neurodevelopmental disorders: lessons learned from an Ontario Brain Institute cross-program collaboration
Harmonizing data on correlates of sleep in children within and across neurodevelopmental disorders: lessons learned from an Ontario Brain Institute cross-program collaboration
There is an increasing desire to study neurodevelopmental disorders (NDDs) together to understand commonalities to develop...
Events in context—The HED framework for the study of brain, experience and behavior
Events in context—The HED framework for the study of brain, experience and behavior
The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and antic...
Finding the limits of deep learning clinical sensitivity with fractional anisotropy (FA) microstructure maps
Finding the limits of deep learning clinical sensitivity with fractional anisotropy (FA) microstructure maps
BackgroundQuantitative maps obtained with diffusion weighted (DW) imaging, such as fractional anisotropy (FA) –calculated ...
Enhancing brain tumor detection in MRI with a rotation invariant Vision Transformer
Enhancing brain tumor detection in MRI with a rotation invariant Vision Transformer
BackgroundThe Rotation Invariant Vision Transformer (RViT) is a novel deep learning model tailored for brain tumor classif...
Identifying discriminative features of brain network for prediction of Alzheimer’s disease using graph theory and machine learning
Identifying discriminative features of brain network for prediction of Alzheimer’s disease using graph theory and machine learning
Alzheimer’s disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. Thi...
Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage
Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage
Decoding of cognitive states aims to identify individuals' brain states and brain fingerprints to predict behavior. D...
Editorial: Innovative methods for sleep staging using neuroinformatics
1 Introduction Sleep staging and analysis play a critical role in understanding sleep mechanisms and diagnosing sleep di...
Dynamic topological data analysis: a novel fractal dimension-based testing framework with application to brain signals
Dynamic topological data analysis: a novel fractal dimension-based testing framework with application to brain signals
Topological data analysis (TDA) is increasingly recognized as a promising tool in the field of neuroscience, unveiling the...
Neuroimaging article reexecution and reproduction assessment system
Neuroimaging article reexecution and reproduction assessment system
The value of research articles is increasingly contingent on complex data analysis results which substantiate their claims...
LYNSU: automated 3D neuropil segmentation of fluorescent images for Drosophila brains
LYNSU: automated 3D neuropil segmentation of fluorescent images for Drosophila brains
The brain atlas, which provides information about the distribution of genes, proteins, neurons, or anatomical regions, pla...
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