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Simulation Study of Envelope Wave Electrical Nerve Stimulation Based on a Real Head Model
Simulation Study of Envelope Wave Electrical Nerve Stimulation Based on a Real Head Model
In recent years, the modulation of brain neural activity by applied electromagnetic fields has become a hot spot in neuros...
NeuroCarto: A Toolkit for Building Custom Read-out Channel Maps for High Electrode-count Neural Probes
NeuroCarto: A Toolkit for Building Custom Read-out Channel Maps for High Electrode-count Neural Probes
Neuropixels probes contain thousands of electrodes across one or more shanks and are sufficiently small to allow chronic r...
Stimulation Effects Mapping for Optimizing Coil Placement for Transcranial Magnetic Stimulation
Stimulation Effects Mapping for Optimizing Coil Placement for Transcranial Magnetic Stimulation
The position and orientation of transcranial magnetic stimulation (TMS) coil, which we collectively refer to as coil place...
Automated Lesion and Feature Extraction Pipeline for Brain MRIs with Interpretability
Automated Lesion and Feature Extraction Pipeline for Brain MRIs with Interpretability
This paper introduces the Automated Lesion and Feature Extraction (ALFE) pipeline, an open-source, Python-based pipeline t...
Computational Generation of Long-range Axonal Morphologies
Computational Generation of Long-range Axonal Morphologies
Long-range axons are fundamental to brain connectivity and functional organization, enabling communication between differe...
“The Brain is…”: A Survey of the Brain’s Many Definitions
“The Brain is…”: A Survey of the Brain’s Many Definitions
A reader of the peer-reviewed neuroscience literature will often encounter expressions like the following: ‘the brai...
Classification Prediction of Hydrocephalus After Intercerebral Haemorrhage Based on Machine Learning Approach
Classification Prediction of Hydrocephalus After Intercerebral Haemorrhage Based on Machine Learning Approach
In order to construct a clinical classification prediction model for hydrocephalus after intercerebral haemorrhage(ICH) to...
Twenty Years of Neuroinformatics: A Bibliometric Analysis
Twenty Years of Neuroinformatics: A Bibliometric Analysis
This study presents a thorough bibliometric analysis of Neuroinformatics over the past 20 years, offering insights into th...
Patch-Wise Deep Learning Method for Intracranial Stenosis and Aneurysm Detection-the Tromsø Study
Patch-Wise Deep Learning Method for Intracranial Stenosis and Aneurysm Detection-the Tromsø Study
Intracranial atherosclerotic stenosis (ICAS) and intracranial aneurysms are prevalent conditions in the cerebrovascular sy...
Predicting Paediatric Brain Disorders from MRI Images Using Advanced Deep Learning Techniques
Predicting Paediatric Brain Disorders from MRI Images Using Advanced Deep Learning Techniques
The problem at hand is the significant global health challenge posed by children’s diseases, where timely and accura...
Complementary Strategies to Identify Differentially Expressed Genes in the Choroid Plexus of Patients with Progressive Multiple Sclerosis
Complementary Strategies to Identify Differentially Expressed Genes in the Choroid Plexus of Patients with Progressive Multiple Sclerosis
Multiple sclerosis (MS) is a neurological disease causing myelin and axon damage through inflammatory and autoimmune proce...
Blood Flow Velocity Analysis in Cerebral Perforating Arteries on 7T 2D Phase Contrast MRI with an Open-Source Software Tool (SELMA)
Blood Flow Velocity Analysis in Cerebral Perforating Arteries on 7T 2D Phase Contrast MRI with an Open-Source Software Tool (SELMA)
Blood flow velocity in the cerebral perforating arteries can be quantified in a two-dimensional plane with phase contrast ...
CDCG-UNet: Chaotic Optimization Assisted Brain Tumor Segmentation Based on Dilated Channel Gate Attention U-Net Model
CDCG-UNet: Chaotic Optimization Assisted Brain Tumor Segmentation Based on Dilated Channel Gate Attention U-Net Model
Brain tumours are one of the most deadly and noticeable types of cancer, affecting both children and adults. One of the ma...
Determination of the Time-frequency Features for Impulse Components in EEG Signals
Determination of the Time-frequency Features for Impulse Components in EEG Signals
Accurately identifying the timing and frequency characteristics of impulse components in EEG signals is essential but limi...
Cardiac Heterogeneity Prediction by Cardio-Neural Network Simulation
Cardiac Heterogeneity Prediction by Cardio-Neural Network Simulation
The bidirectional interactions between brain and heart through autonomic nervous system is the prime focus of neuro-cardio...
Generalized Coupled Matrix Tensor Factorization Method Based on Normalized Mutual Information for Simultaneous EEG-fMRI Data Analysis
Generalized Coupled Matrix Tensor Factorization Method Based on Normalized Mutual Information for Simultaneous EEG-fMRI Data Analysis
The complementary properties of both modalities can be exploited through the fusion of electroencephalography (EEG) and fu...
FrAMBI: A Software Framework for Auditory Modeling Based on Bayesian Inference
FrAMBI: A Software Framework for Auditory Modeling Based on Bayesian Inference
Research in hearing science often relies on auditory models to describe listener’s behaviour and its neural underpin...
Unraveling Integration-Segregation Imbalances in Schizophrenia Through Topological High-Order Functional Connectivity
Unraveling Integration-Segregation Imbalances in Schizophrenia Through Topological High-Order Functional Connectivity
Background: The occurrence of brain disorders correlates with detectable dysfunctions in the specialization of brain conne...
AnNoBrainer, An Automated Annotation of Mouse Brain Images using Deep Learning
AnNoBrainer, An Automated Annotation of Mouse Brain Images using Deep Learning
Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evalu...
AnNoBrainer, An Automated Annotation of Mouse Brain Images using Deep Learning
AnNoBrainer, An Automated Annotation of Mouse Brain Images using Deep Learning
Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evalu...
Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models
Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models
Theta oscillations, ranging from 4-8 Hz, play a significant role in spatial learning and memory functions during navigatio...
Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models
Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models
Theta oscillations, ranging from 4-8 Hz, play a significant role in spatial learning and memory functions during navigatio...
Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA
Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA
This study concentrates on the segmentation of intracranial aneurysms, a pivotal aspect of diagnosis and treatment plannin...
Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA
Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA
This study concentrates on the segmentation of intracranial aneurysms, a pivotal aspect of diagnosis and treatment plannin...
MBV-Pipe: A One-Stop Toolbox for Assessing Mouse Brain Morphological Changes for Cross-Scale Studies
MBV-Pipe: A One-Stop Toolbox for Assessing Mouse Brain Morphological Changes for Cross-Scale Studies
Mouse models are crucial for neuroscience research, yet discrepancies arise between macro- and meso-scales due to sample p...
MBV-Pipe: A One-Stop Toolbox for Assessing Mouse Brain Morphological Changes for Cross-Scale Studies
MBV-Pipe: A One-Stop Toolbox for Assessing Mouse Brain Morphological Changes for Cross-Scale Studies
Mouse models are crucial for neuroscience research, yet discrepancies arise between macro- and meso-scales due to sample p...
Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning
Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning
Schizophrenia is a mental disorder characterized by neurophysiological dysfunctions that result in disturbances in thinkin...
Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning
Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning
Schizophrenia is a mental disorder characterized by neurophysiological dysfunctions that result in disturbances in thinkin...
Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging – A Symposium Review
Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging – A Symposium Review
Advances in the spatiotemporal resolution and field-of-view of neuroimaging tools are driving mesoscale studies for transl...
Effect of Electrode Distance and Size on Electrocorticographic Recordings in Human Sensorimotor Cortex
Effect of Electrode Distance and Size on Electrocorticographic Recordings in Human Sensorimotor Cortex
Subdural electrocorticography (ECoG) is a valuable technique for neuroscientific research and for emerging neurotechnologi...
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