ArticleFrontiers in neuroscience2017
Multivariate Heteroscedasticity Models for Functional Brain Connectivity.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Mediation analysis with graph mediator.Biostatistics (Oxford, England) · 2025Article
- Connectivity Regression.Biostatistics (Oxford, England) · 2025Article
- Bayesian estimation of covariate assisted principal regression for brain functional connectivity.Biostatistics (Oxford, England) · 2025Article
- Covariance-on-covariance regression.Biometrics · 2025Article
- Bayesian scalar-on-network regression with applications to brain functional connectivity.Biometrics · 2025Article
- Characterizing patterns of diffusion tensor imaging variance in aging brains.medRxiv : the preprint server for health sciences · 2024Article
- Characterizing patterns of diffusion tensor imaging variance in aging brains.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Covariance regression with random forests.BMC bioinformatics · 2023Article
- Covariate Assisted Principal regression for covariance matrix outcomes.Biostatistics (Oxford, England) · 2021Article
- Principal regression for high dimensional covariance matrices.Electronic journal of statistics · 2021Article
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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.
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