Nonparametric modeling of dynamic functional connectivity in fmri data

Søren Føns Vind Nielsen, Kristoffer H. Madsen, Rasmus Røge, Mikkel Nørgaard Schmidt, Morten Mørup

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

369 Downloads (Pure)

Abstract

Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), and the results point towards the conclusion that FC exhibits dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a nonparametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted in Bayesian statistical modeling we use the predictive likelihood to investigate if the model can discriminate between a motor task and rest both within and across subjects. We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest. We find that the number of states extracted are driven by subject variability and preprocessing differences while the individual states are almost purely defined by either task or rest. This questions how we in general interpret dynamic FC and points to the need for more research on what drives dynamic FC.
Original languageEnglish
Title of host publicationProceedings of the 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2015)
Number of pages8
Publication date2015
Publication statusPublished - 2015
Event5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2015) - Montreal, Quebec, Canada
Duration: 11 Dec 201512 Dec 2015
Conference number: 5
https://sites.google.com/site/mliniworkshop2015/

Workshop

Workshop5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2015)
Number5
CountryCanada
CityMontreal, Quebec
Period11/12/201512/12/2015
Internet address

Keywords

  • Dynamic functional connectivity
  • Bayesian nonparametric modeling
  • Hidden Markov modeling
  • Wishart mixture modeling
  • Predictive likelihood

Cite this