154 citations · 203 across the 16 of their papers we have counts for
16 papers
Biomarker Investigation using Multiple Brain Measures from MRI through XAI in Alzheimer's Disease Classification
Davide Coluzzi, Valentina Bordin, Massimo Walter Rivolta +4
Alzheimer's Disease (AD) is the world leading cause of dementia, a progressively impairing condition leading to high hospitalization rates and mortality. To optimize the diagnostic…
Normative Modeling via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease
Xuetong Wang, Kanhao Zhao, Rong Zhou +4
Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modelin…
Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational Auto-Encoder
Carlo Amodeo, Igor Fortel, Olusola Ajilore +3
Graph theoretical analyses have become standard tools in modeling functional and anatomical connectivity in the brain. With the advent of connectomics, the primary graphs or networ…
Functional2Structural: Cross-Modality Brain Networks Representation Learning
Haoteng Tang, Xiyao Fu, Lei Guo +7
MRI-based modeling of brain networks has been widely used to understand functional and structural interactions and connections among brain regions, and factors that affect them, su…
Structure-Preserving Graph Kernel for Brain Network Classification
Jun Yu, Zhaoming Kong, Aditya Kendre +5
This paper presents a novel graph-based kernel learning approach for connectome analysis. Specifically, we demonstrate how to leverage the naturally available structure within the…
TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy
Ran Xu, Manu Mathew Thomas, Alex Leow +2
We introduce TempoCave, a novel visualization application for analyzing dynamic brain networks, or connectomes. TempoCave provides a range of functionality to explore metrics relat…