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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...
Intra-V1 functional networks and classification of observed stimuli
Intra-V1 functional networks and classification of observed stimuli
IntroductionPrevious studies suggest that co-fluctuations in neural activity within V1 (measured with fMRI) carry informat...
Editorial: Navigating the landscape of FAIR data sharing and reuse: repositories, standards, and resources
Editorial: Navigating the landscape of FAIR data sharing and reuse: repositories, standards, and resources
In response to the expanding landscape of neuroscience data and the diverse array of formats emerging from various researc...
Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet
Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet
IntroductionIn recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a foc...
Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch
Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch
Spiking neural network simulations are a central tool in Computational Neuroscience, Artificial Intelligence, and Neuromor...
Long-range temporal correlations in resting state alpha oscillations in major depressive disorder and obsessive-compulsive disorder
Long-range temporal correlations in resting state alpha oscillations in major depressive disorder and obsessive-compulsive disorder
IntroductionMental disorders are a significant concern in contemporary society, with a pressing need to identify biologica...
Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation
Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation
IntroductionPharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a...
Empirical comparison of deep learning models for fNIRS pain decoding
Empirical comparison of deep learning models for fNIRS pain decoding
IntroductionPain assessment is extremely important in patients unable to communicate and it is often done by clinical judg...
Multiscale co-simulation design pattern for neuroscience applications
Multiscale co-simulation design pattern for neuroscience applications
Integration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools an...
In silico analyses of the involvement of GPR55, CB1R and TRPV1: response to THC, contribution to temporal lobe epilepsy, structural modeling and updated evolution
In silico analyses of the involvement of GPR55, CB1R and TRPV1: response to THC, contribution to temporal lobe epilepsy, structural modeling and updated evolution
IntroductionThe endocannabinoid (eCB) system is named after the discovery that endogenous cannabinoids bind to the same re...
The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases
The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases
Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function....
Enabling uncertainty estimation in neural networks through weight perturbation for improved Alzheimer's disease classification
Enabling uncertainty estimation in neural networks through weight perturbation for improved Alzheimer's disease classification
BackgroundThe willingness to trust predictions formulated by automatic algorithms is key in a wide range of domains. Howev...
In silico analyzes of the involvement of GPR55, CB1R and TRPV1: response to THC, contribution to temporal lobe epilepsy, structural modeling and updated evolution
In silico analyzes of the involvement of GPR55, CB1R and TRPV1: response to THC, contribution to temporal lobe epilepsy, structural modeling and updated evolution
IntroductionThe endocannabinoid (eCB) system is named after the discovery that endogenous cannabinoids bind to the same re...
Retraction: NeuroSuites: an online platform for running neuroscience, statistical, and machine learning tools
Retraction: NeuroSuites: an online platform for running neuroscience, statistical, and machine learning tools
The journal retracts the 17th February 2023 article above. In light of complaints received following publication of the o...
Improving the detection of sleep slow oscillations in electroencephalographic data
Improving the detection of sleep slow oscillations in electroencephalographic data
Study objectivesWe aimed to build a tool which facilitates manual labeling of sleep slow oscillations (SOs) and evaluate t...
Discovering optimal features for neuron-type identification from extracellular recordings
Discovering optimal features for neuron-type identification from extracellular recordings
Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel fea...
Domain adaptation for EEG-based, cross-subject epileptic seizure prediction
Domain adaptation for EEG-based, cross-subject epileptic seizure prediction
The ability to predict the occurrence of an epileptic seizure is a safeguard against patient injury and health complicatio...
SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation
SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation
The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detect...
Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models
Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models
IntroductionA challenge when applying an artificial intelligence (AI) deep learning (DL) approach to novel electroencephal...
Transdiagnostic clustering of self-schema from self-referential judgements identifies subtypes of healthy personality and depression
Transdiagnostic clustering of self-schema from self-referential judgements identifies subtypes of healthy personality and depression
IntroductionThe heterogeneity of depressive and anxiety disorders complicates clinical management as it may account for di...
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