Multivariate Heteroscedasticity Models for Functional Brain Connectivity

Christof Seiler, Susan Holmes

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Functional brain connectivity is the co-occurrence of brain activity in different areas during resting and while doing tasks. The data of interest are multivariate timeseries measured simultaneously across brain parcels using resting-state fMRI (rfMRI). We analyze functional connectivity using two heteroscedasticity models. Our first model is low-dimensional and scales linearly in the number of brain parcels. Our second model scales quadratically. We apply both models to data from the Human Connectome Project (HCP) comparing connectivity between short and conventional sleepers. We find stronger functional connectivity in short than conventional sleepers in brain areas consistent with previous findings. This might be due to subjects falling asleep in the scanner. Consequently, we recommend the inclusion of average sleep duration as a covariate to remove unwanted variation in rfMRI studies. A power analysis using the HCP data shows that a sample size of 40 detects 50% of the connectivity at a false discovery rate of 20%. We provide implementations using R and the probabilistic programming language Stan.

Original languageEnglish
Article number696
Pages (from-to)696
Number of pages11
JournalFrontiers in Neuroscience
Volume11
DOIs
Publication statusPublished - 12 Dec 2017
Externally publishedYes

Keywords

  • Bayesian analysis
  • functional connectivity
  • heteroscedasticity
  • covariance regression
  • sleep duration
  • INDEPENDENT COMPONENT ANALYSIS
  • HUMAN CONNECTOME PROJECT
  • SPARSE PARTIAL CORRELATION
  • SMALL-WORLD
  • COVARIANCE ESTIMATION
  • STATISTICAL-ANALYSIS
  • FMRI
  • NETWORK
  • MATRICES
  • SLEEP

Cite this