Deep Parametric Mixtures for Modeling the Functional Connectome

Nicolas Honnorat, Adolf Pfefferbaum, Edith V. Sullivan, Kilian M. Pohl

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

Abstract

Functional connectivity between brain regions is often estimated by correlating brain activity measured by resting-state fMRI in those regions. The impact of factors (e.g., disorder or substance use) are then modeled by their effects on these correlation matrices in individuals. A crucial step in better understanding their effects on brain function could lie in estimating connectomes, which encode the correlation matrices across subjects. Connectomes are mostly estimated by creating a single average for a specific cohort, which works well for binary factors (such as sex) but is unsuited for continuous ones, such as alcohol consumption. Alternative approaches based on regression methods usually model each pair of regions separately, which generally produces incoherent connectomes as correlations across multiple regions contradict each other. In this work, we address these issues by introducing a deep learning model that predicts connectomes based on factor values. The predictions are defined on a simplex spanned across correlation matrices, whose convex combination guarantees that the deep learning model generates well-formed connectomes. We present an efficient method for creating these simplexes and improve the accuracy of the entire analysis by defining loss functions based on robust norms. We show that our deep learning approach is able to produce accurate models on challenging synthetic data. Furthermore, we apply the approach to the resting-state fMRI scans of 281 subjects to study the effect of sex, alcohol, and HIV on brain function.

Original languageEnglish (US)
Title of host publicationPredictive Intelligence in Medicine - 3rd International Workshop, PRIME 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsIslem Rekik, Ehsan Adeli, Sang Hyun Park, Maria del C. Valdés Hernández
PublisherSpringer Science and Business Media Deutschland GmbH
Pages133-143
Number of pages11
ISBN (Print)9783030593537
DOIs
StatePublished - 2020
Externally publishedYes
Event3rd International Workshop on Predictive Intelligence in Medicine, PRIME 2020, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: Oct 8 2020Oct 8 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12329 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Predictive Intelligence in Medicine, PRIME 2020, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period10/8/2010/8/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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