Evaluating Models of Dynamic Functional Connectivity Using Predictive Classification Accuracy

Research output: Research - peer-reviewArticle in proceedings – Annual report year: 2018

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Dynamic functional connectivity has become a prominent approach for tracking the changes of macroscale statistical dependencies between regions in the brain. Effective parametrization of these statistical dependencies, referred to as brain states, is however still an open problem. We investigate different emission models in the hidden Markov model framework, each representing certain assumptions about dynamic changes in the brain. We evaluate each model by how well they can discriminate between schizophrenic patients and healthy controls based on a group independent component analysis of resting-state functional magnetic resonance imaging data. We find that simple emission models without full covariance matrices can achieve similar classification results as the models with more parameters. This raises questions about the predictability of dynamic functional connectivity in comparison to simpler dynamic features when used as biomarkers. However, we must stress that there is a distinction between characterization and classification, which has to be investigated further.
Original languageEnglish
Title of host publicationProceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
Publication date2018
Pages2566-2570
ISBN (Print)978-1-5386-4658-8
DOIs
StatePublished - 2018
Event2018 IEEE International Conference on Acoustics, Speech and Signal Processing - Calgary Telus Convention Center, Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Conference

Conference2018 IEEE International Conference on Acoustics, Speech and Signal Processing
LocationCalgary Telus Convention Center
CountryCanada
CityCalgary
Period15/04/201820/04/2018
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Hidden Markov models, Data models, Brain modeling, Functional magnetic resonance imaging, Covariance matrices, Training, Predictive models, Dynamic functional connectivity, Classification, Schizophrenia
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