Learning Connectivity Patterns via Graph Kernels for fMRI-Based Depression Diagnostics

It has long been known that patients with depression exhibit abnormal brain functional connectivity patterns, that are often studied from a graph-theoretic perspective. However, while certain simpler graph features have been examined, little has been done in the direction of advanced feature learnin...

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Bibliographic Details
Main Authors: Sharaev, Maksim, Artemov, Alexey, Kondrateva, Ekaterina, Ivanov, Sergei, Sushchinskaya, Svetlana, Bernstein, Alexander, Cichocki, Andrzej, Burnaev, Evgeny
Format: Conference Proceeding
Language:English
Subjects:
anonymous walk
depression
Epilepsy
Feature extraction
Functional magnetic resonance imaging
functional MRI
graph kernels
Kernel
Machine learning
neuroimaging
random walk
Task analysis
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Summary:It has long been known that patients with depression exhibit abnormal brain functional connectivity patterns, that are often studied from a graph-theoretic perspective. However, while certain simpler graph features have been examined, little has been done in the direction of advanced feature learning methodologies such as network embeddings. Our work aims to extend the understanding of importance of graph-based features for medical applications by evaluating the recently proposed anonymous walk embeddings (AWE) in difficult depression classification problems. For two challenging datasets, we obtain performance gains and investigate the learned vector representations. Our results indicate that using AWE-based features is a promising new direction for medical applications.
ISSN:2375-9259
DOI:10.1109/ICDMW.2018.00051