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• W2949277850 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." @default.
• W2949277850 created "2019-06-27" @default.
• W2949277850 creator A5003674456 @default.
• W2949277850 creator A5065938629 @default.
• W2949277850 date "2017-12-12" @default.
• W2949277850 modified "2023-10-11" @default.
• W2949277850 title "Multivariate Heteroscedasticity Models for Functional Brain Connectivity" @default.
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• W2949277850 doi "https://doi.org/10.3389/fnins.2017.00696" @default.
• W2949277850 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5733000" @default.
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• W2949277850 hasPublicationYear "2017" @default.
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