AIMC Topic: Brain Mapping
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BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets.
Communications biology
Mar 5, 2020
Understanding how cognitive functions emerge from brain structure depends on quantifying how discrete regions are integrated within the broader cortical landscape. Recent work established that macroscale brain organization and function can be describ...
Predicting individual clinical trajectories of depression with generative embedding.
NeuroImage. Clinical
Feb 17, 2020
Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual cl...
Identifying the pulsed neuron networks' structures by a nonlinear Granger causality method.
BMC neuroscience
Feb 12, 2020
BACKGROUND: It is a crucial task of brain science researches to explore functional connective maps of Biological Neural Networks (BNN). The maps help to deeply study the dominant relationship between the structures of the BNNs and their network funct...
Logarithmic Fuzzy Entropy Function for Similarity Measurement in Multimodal Medical Images Registration.
Computational and mathematical methods in medicine
Feb 12, 2020
Multimodal medical images are useful for observing tissue structure clearly in clinical practice. To integrate multimodal information, multimodal registration is significant. The entropy-based registration applies a structure descriptor set to replac...
Decoding dynamic affective responses to naturalistic videos with shared neural patterns.
NeuroImage
Feb 7, 2020
This study explored the feasibility of using shared neural patterns from brief affective episodes (viewing affective pictures) to decode extended, dynamic affective sequences in a naturalistic experience (watching movie-trailers). Twenty-eight partic...
Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks.
Medical image analysis
Jan 11, 2020
The parcellation of the human cortex into meaningful anatomical units is a common step of various neuroimaging studies. There have been multiple successful efforts to process magnetic resonance (MR) brain images automatically and identify specific an...
Object parsing in the left lateral occipitotemporal cortex: Whole shape, part shape, and graspability.
Neuropsychologia
Jan 11, 2020
Small and manipulable objects (tools) preferentially evoke a network of brain regions relative to other objects, including the lateral occipitotemporal cortex (LOTC), which is assumed to process tool shape information. Given the correlation between v...
A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification.
Computational intelligence and neuroscience
Dec 31, 2019
Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their ...
Increased gait variability during robot-assisted walking is accompanied by increased sensorimotor brain activity in healthy people.
Journal of neuroengineering and rehabilitation
Dec 27, 2019
BACKGROUND: Gait disorders are major symptoms of neurological diseases affecting the quality of life. Interventions that restore walking and allow patients to maintain safe and independent mobility are essential. Robot-assisted gait training (RAGT) p...
Hypergraph based multi-task feature selection for multimodal classification of Alzheimer's disease.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Dec 19, 2019
Multi-modality based classification methods are superior to the single modality based approaches for the automatic diagnosis of the Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most of the multi-modality based methods usuall...
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