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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...
Customizable automated cleaning of multichannel sleep EEG in SleepTrip
Customizable automated cleaning of multichannel sleep EEG in SleepTrip
While standard polysomnography has revealed the importance of the sleeping brain in health and disease, more specific insi...
Corrigendum: Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism
Corrigendum: Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism
In the published article, there was an error in Figure 7 as published. The wrong image was included. Figures 7 and 8 wer...
Editorial: Neuromodulation using spatiotemporally complex patterns
Standard high-frequency deep brain stimulation (DBS) is an established therapy for the treatment of Parkinson's disease ...
Exploring white matter dynamics and morphology through interactive numerical phantoms: the White Matter Generator
Exploring white matter dynamics and morphology through interactive numerical phantoms: the White Matter Generator
Brain white matter is a dynamic environment that continuously adapts and reorganizes in response to stimuli and pathologic...
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...
M3: using mask-attention and multi-scale for multi-modal brain MRI classification
M3: using mask-attention and multi-scale for multi-modal brain MRI classification
IntroductionBrain diseases, particularly the classification of gliomas and brain metastases and the prediction of HT in st...
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...
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...
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...
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...
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...
Enhancing brain tumor detection in MRI with a rotation invariant Vision Transformer
Enhancing brain tumor detection in MRI with a rotation invariant Vision Transformer
1 Introduction The prevalence of brain tumors varies globally, with primary brain tumors representing 17% of all cancers ...
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 ...
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...
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...
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...
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...
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...
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...
Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke
Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke
BackgroundHemorrhagic transformation (HT) following reperfusion therapies is a serious complication for patients with acut...
Erratum: NeuroDecodeR: a package for neural decoding in R
Erratum: NeuroDecodeR: a package for neural decoding in R
Meyers EM (2024) NeuroDecodeR: a package for neural decoding in R. Front. Neuroinform. 17:1275903. doi: 10.3389/fninf.2023...
ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations
ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations
IntroductionIn-silico simulations are a powerful tool in modern neuroscience for enhancing our understanding of complex br...
Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury
Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury
Traumatic Brain Injury (TBI) is a prevalent disorder mostly characterized by persistent impairments in cognitive function ...
suMRak: a multi-tool solution for preclinical brain MRI data analysis
suMRak: a multi-tool solution for preclinical brain MRI data analysis
IntroductionMagnetic resonance imaging (MRI) is invaluable for understanding brain disorders, but data complexity poses a ...
A computational model of Alzheimer's disease at the nano, micro, and macroscales
A computational model of Alzheimer's disease at the nano, micro, and macroscales
IntroductionMathematical models play a crucial role in investigating complex biological systems, enabling a comprehensive ...
Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN
Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN
Epileptic seizures are characterized by their sudden and unpredictable nature, posing significant risks to a patient’s dai...
A scoping review of mathematical models covering Alzheimer's disease progression
A scoping review of mathematical models covering Alzheimer's disease progression
Alzheimer's disease is a complex, multi-factorial, and multi-parametric neurodegenerative etiology. Mathematical model...
Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence
Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence
IntroductionAutomated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assi...
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