Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis

Qingyu Zhao, Nicolas Honnorat, Ehsan Adeli, Adolf Pfefferbaum, Edith V. Sullivan, Kilian M. Pohl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Resting-state functional connectivity states are often identified as clusters of dynamic connectivity patterns. However, existing clustering approaches do not distinguish major states from rarely occurring minor states and hence are sensitive to noise. To address this issue, we propose to model major states using a non-linear generative process guided by a Gaussian-mixture distribution in a low-dimensional latent space, while separately modeling the connectivity patterns of minor states by a non-informative uniform distribution. We embed this truncated Gaussian-Mixture model in a Variational AutoEncoder framework to obtain a general joint clustering and outlier detection approach, called tGM-VAE. When applied to synthetic data with known ground-truth, tGM-VAE is more accurate in clustering dynamic connectivity patterns than existing approaches. On the rs-fMRI data of 593 healthy adolescents from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study, tGM-VAE identified meaningful major connectivity states. The dwell time of these states significantly correlated with age.

Original languageEnglish (US)
Title of host publicationInformation Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings
EditorsAlbert C.S. Chung, Siqi Bao, James C. Gee, Paul A. Yushkevich
PublisherSpringer Verlag
Pages867-879
Number of pages13
ISBN (Print)9783030203504
DOIs
StatePublished - 2019
Externally publishedYes
Event26th International Conference on Information Processing in Medical Imaging, IPMI 2019 - Hong Kong, China
Duration: Jun 2 2019Jun 7 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11492 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Information Processing in Medical Imaging, IPMI 2019
Country/TerritoryChina
CityHong Kong
Period6/2/196/7/19

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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