B1045
Title: On the impact of autocorrelation and heterogeneity on functional connectivity
Authors: Mark Fiecas - University of Minnesota (United States) [presenting]
Ivor Cribben - Alberta School of Business (Canada)
Abstract: We discuss functional connectivity analyses of the human brain. The conventional estimate of functional connectivity does not account for temporal autocorrelation or heterogeneity across the subjects of the experiment; the former leads to inflated Type I errors in a single-subject analysis and the latter leads to low power in a multi-subject analysis. To address this, we propose a flexible general linear model framework for estimating functional connectivity that accounts for i) temporal autocorrelation in a nonparametric manner and ii) heterogeneity across subjects by allowing for subject-specific estimates of the variance. We use simulated data to assess the performance of our proposed method with respect to Type I and II errors, and we illustrate the utility of our proposed method on a depression study.
