WebSep 9, 2024 · Using a resting-state fMRI graph theory approach, we identified larger global efficiency, specifically in the left habenula, the left pulvinar (located in the thalamus), the left dlPFC, and the right temporal pole, as well as a trend for lower clustering coefficient, specifically in DMN nodes (including the left dorso-medial PFC and left ... Webimaging (fMRI) of the brain provides the features for the graph nodes, and brain fiber connectivity is utilized as the structural representation of the graph edges. Self-attention graph pooling (SAGPOOL)-based GNN is then applied to jointly study the function and structure of the brain and identify the biomarkers. The construction of brain network
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WebDec 1, 2024 · Abstract. Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimer’s disease or depression have adapted tools from graph theory to characterize differences between healthy and patient … As a novel, non-invasive way to measure spontaneous neural activity in the human brain, resting-state functional magnetic resonance imaging … See more Spontaneous neural activity can be recorded by multiple imaging techniques, such as EEG, MEG, and R-fMRI, each with different … See more Through the combination of R-fMRI and graph theory-based network analysis techniques, intrinsic functional networks of the human brain … See more In this review, we summarized the recent advances in the application of modern graph theory-based network analysis techniques to study the intrinsic or spontaneous human brain functional networks derived … See more increase achievement
Module 19: Network Analysis I – Graph theory - Coursera
WebJun 7, 2010 · Graph theory provides a method for quantitatively describing the topological organization of brain networks [38]. Graph theory measures in our analyses included global and regional efficiency ... WebGraph Theory GLM (GTG) This Matlab toolbox calculates & runs a GLM on graph theory properties (i.e., invariants) derived from brain networks. The GLM accepts continuous & categorical between-participant predictors & categorical within-participant predictors. Significance is determined via non-parametric permutation tests. WebBrainGNN: Interpretable Brain Graph NeuralNetwork for fMRI Analysis: bioRxiv: Xiaoxiao Li: Biopoint HCP __ __ bioRxiv 2024: Machine Learning and other types of algorithm for Network Neuroscience Single/Multi-view prediction. Title Paper Author Dataset Code Youtube Video Proceeding/Journal/Year; increase access to mental health services