AIMC Topic: Connectome
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Convolutional Neural Network With Graphical Lasso to Extract Sparse Topological Features for Brain Disease Classification.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2021
The functional connectivity provides new insights into the mechanisms of the human brain at network-level, which has been proved to be an effective biomarker for brain disease classification. Recently, machine learning methods have played an importan...
A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD.
NeuroImage
Nov 30, 2021
The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for s...
Using deep learning to classify pediatric posttraumatic stress disorder at the individual level.
BMC psychiatry
Oct 28, 2021
BACKGROUND: Children exposed to natural disasters are vulnerable to developing posttraumatic stress disorder (PTSD). Previous studies using resting-state functional neuroimaging have revealed alterations in graph-based brain topological network metri...
Echo state network models for nonlinear Granger causality.
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Oct 25, 2021
While Granger causality (GC) has been often employed in network neuroscience, most GC applications are based on linear multivariate autoregressive (MVAR) models. However, real-life systems like biological networks exhibit notable nonlinear behaviour,...
Brain functional and effective connectivity based on electroencephalography recordings: A review.
Human brain mapping
Oct 20, 2021
Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional...
Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?
NeuroImage
Oct 19, 2021
Cognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological proces...
Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics.
Human brain mapping
Oct 16, 2021
Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional ...
Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity.
Brain structure & function
Oct 11, 2021
Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the unde...
A deep learning based approach identifies regions more relevant than resting-state networks to the prediction of general intelligence from resting-state fMRI.
Human brain mapping
Sep 29, 2021
Prediction of cognitive ability latent factors such as general intelligence from neuroimaging has elucidated questions pertaining to their neural origins. However, predicting general intelligence from functional connectivity limit hypotheses to that ...
Identifying resting state differences salient for resilience to chronic pain based on machine learning multivariate pattern analysis.
Psychophysiology
Aug 12, 2021
Studies have documented behavior differences between more versus less resilient adults with chronic pain (CP), but the presence and nature of underlying neurophysiological differences have received scant attention. In this study, we attempted to iden...
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