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Found 6 papers, 3 papers with code
Latent Similarity Identifies Important Functional Connections for Phenotype Prediction
Significance: We propose a novel algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data.
Distance Correlation Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies
Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity patterns have been extensively utilized to delineate global functional organization of the human brain in health, development, and neuropsychiatric disorders.
A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence
Our network analysis revealed the development of emotion-related intra- and inter- modular connectivity and pinpointed several emotion-related hubs.
Causal inference of brain connectivity from fMRI with $ψ$-Learning Incorporated Linear non-Gaussian Acyclic Model ($ψ$-LiNGAM)
A popular definition of FC is by statistical associations between measured brain regions.
Interpretable multimodal fusion networks reveal mechanisms of brain cognition
Moreover, the estimated activation maps are class-specific, and the captured cross-data associations are interest/label related, which further facilitates class-specific analysis and biological mechanism analysis.
Causality based Feature Fusion for Brain Neuro-Developmental Analysis
Second, we used causality values as the weight for the directional connectivity between brain regions.
