Evaluating Models of Dynamic Functional Connectivity Using Predictive Classification Accuracy

Soren Fons Vind Nielsen, Yuri Levin-Schwartz, Diego Vidaurre, Tulay Adali, Vince Daniel Calhoun, Kristoffer H. Madsen, Lars Kai Hansen, Morten Morup

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

Abstract

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 (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2566-2570
Number of pages5
Volume2018-April
ISBN (Print)9781538646588
DOIs
StatePublished - Sep 10 2018
Externally publishedYes
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: Apr 15 2018Apr 20 2018

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period4/15/184/20/18

Keywords

  • Classification
  • Dynamic functional connectivity
  • Hidden Markov models
  • Schizophrenia

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Nielsen, S. F. V., Levin-Schwartz, Y., Vidaurre, D., Adali, T., Calhoun, V. D., Madsen, K. H., Hansen, L. K., & Morup, M. (2018). Evaluating Models of Dynamic Functional Connectivity Using Predictive Classification Accuracy. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (Vol. 2018-April, pp. 2566-2570). [8462310] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8462310