[HTML][HTML] A comprehensive survey of complex brain network representation
Recent years have shown great merits in utilizing neuroimaging data to understand brain
structural and functional changes, as well as its relationship to different neurodegenerative …
structural and functional changes, as well as its relationship to different neurodegenerative …
Federated multi-task learning for joint diagnosis of multiple mental disorders on MRI scans
Objective: Deep learning (DL) techniques have been introduced to assist doctors in the
interpretation of medical images by detecting image-derived phenotype abnormality. Yet the …
interpretation of medical images by detecting image-derived phenotype abnormality. Yet the …
Multi-band brain network analysis for functional neuroimaging biomarker identification
The functional connectomic profile is one of the non-invasive imaging biomarkers in the
computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic …
computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic …
Brain network analysis of schizophrenia patients based on hypergraph signal processing
X Song, K Wu, L Chai - IEEE Transactions on Image …, 2023 - ieeexplore.ieee.org
Since high-order relationships among multiple brain regions-of-interests (ROIs) are helpful
to explore the pathogenesis of neurological diseases more deeply, hypergraph-based brain …
to explore the pathogenesis of neurological diseases more deeply, hypergraph-based brain …
Modularity-Constrained Dynamic Representation Learning for Interpretable Brain Disorder Analysis with Functional MRI
Resting-state functional MRI (rs-fMRI) is increasingly used to detect altered functional
connectivity patterns caused by brain disorders, thereby facilitating objective quantification …
connectivity patterns caused by brain disorders, thereby facilitating objective quantification …
Constructing hierarchical attentive functional brain networks for early AD diagnosis
Analyzing functional brain networks (FBN) with deep learning has demonstrated great
potential for brain disorder diagnosis. The conventional construction of FBN is typically …
potential for brain disorder diagnosis. The conventional construction of FBN is typically …
Ordinal Pattern Tree: A New Representation Method for Brain Network Analysis
Brain networks, describing the functional or structural interactions of brain with graph theory,
have been widely used for brain imaging analysis. Currently, several network representation …
have been widely used for brain imaging analysis. Currently, several network representation …
OCD diagnosis via smooth sparse network and fused sparse auto-encoder learning
Obsessive-compulsive disorder (OCD) brings many problems to patients. Redundant
information in the OCD data can be removed to preserve valuable biological functions …
information in the OCD data can be removed to preserve valuable biological functions …
[HTML][HTML] A joint subspace mapping between structural and functional brain connectomes
Understanding the connection between the brain's structural connectivity and its functional
connectivity is of immense interest in computational neuroscience. Although some studies …
connectivity is of immense interest in computational neuroscience. Although some studies …
Learning robust hierarchical patterns of human brain across many fmri studies
D Sahoo, C Davatzikos - Advances in Neural Information …, 2021 - proceedings.neurips.cc
Multi-site fMRI studies face the challenge that the pooling introduces systematic non-
biological site-specific variance due to hardware, software, and environment. In this paper …
biological site-specific variance due to hardware, software, and environment. In this paper …