Bayesian structure learning for dynamic brain connectivity

Michael Riis Andersen, Ole Winther, Lars Kai Hansen, Russell Poldrack, Oluwasanmi Koyejo

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

11 Citations (Scopus)
24 Downloads (Pure)

Abstract

Human brain activity as measured by fMRI exhibits strong correlations between brain regions which are believed to vary over time. Importantly, dynamic connectivity has been linked to individual differences in physiology, psychology and behavior, and has shown promise as a biomarker for disease. The state of the art in computational neuroimaging is to estimate the brain networks as relatively short sliding window covariance matrices, which leads to high variance estimates, thereby resulting in high overall error. This manuscript proposes a novel Bayesian model for dynamic brain connectivity. Motivated by the underlying neuroscience, the model estimates covariances which vary smoothly over time, with an instantaneous decomposition into a collection of spatially sparse components – resulting in parsimonious and highly interpretable estimates of dynamic brain connectivity. Simulated results are presented to illustrate the performance of the model even when it is mis-specified. For real brain imaging data with unknown ground truth, in addition to qualitative evaluation, we devise a simple classification task which suggests that the estimated brain networks better capture the underlying structure.

Original languageEnglish
Title of host publicationProceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018, Lanzarote, Spain
PublisherJMLR
Pages1436-1446
Number of pages11
Publication statusPublished - 1 Jan 2018
MoE publication typeA4 Conference publication
EventInternational Conference on Artificial Intelligence and Statistics - Playa Blanca, Spain
Duration: 9 Apr 201811 Apr 2018
Conference number: 21

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Volume84
ISSN (Electronic)2640-3498

Conference

ConferenceInternational Conference on Artificial Intelligence and Statistics
Abbreviated titleAISTATS
Country/TerritorySpain
CityPlaya Blanca
Period09/04/201811/04/2018

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