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Cross-Modal Multivariate Pattern Analysis
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Multivariate Heteroscedasticity Models for Functional Brain Connectivity.

Christof Seiler1, Susan Holmes1

  • 1Department of Statistics, Stanford University, Stanford, CA, United States.

Frontiers in Neuroscience
|January 10, 2018
PubMed
Summary
This summary is machine-generated.

Short sleepers exhibit stronger functional brain connectivity (rfMRI) than conventional sleepers, potentially due to in-scanner sleep. Average sleep duration should be a covariate in rfMRI studies.

Keywords:
Bayesian analysiscovariance regressionfunctional connectivityheteroscedasticitysleep duration

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Data Science

Background:

  • Functional brain connectivity (FBC) measures synchronized activity across brain regions during rest or tasks.
  • Resting-state fMRI (rfMRI) provides multivariate time-series data across brain parcels.

Purpose of the Study:

  • To analyze FBC using two novel heteroscedasticity models.
  • To compare FBC between short and conventional sleepers using Human Connectome Project (HCP) data.

Main Methods:

  • Developed and applied two models: a linear-scaling low-dimensional model and a quadratic-scaling model.
  • Utilized rfMRI data from the HCP dataset.
  • Performed statistical analysis comparing connectivity patterns.

Main Results:

  • Identified stronger FBC in short sleepers compared to conventional sleepers.
  • Observed connectivity differences in brain areas consistent with prior research.
  • Recommended including average sleep duration as a covariate to mitigate confounds.

Conclusions:

  • Sleep duration is a significant factor influencing rfMRI-based FBC.
  • The developed models offer scalable approaches for FBC analysis.
  • A sample size of 40 is sufficient to detect 50% of connectivity effects at a 20% FDR.