fMRI functional connectivity estimators robust to region size bias

Analysis of interactions in the brain in terms of functional resting-state networks has yielded fundamental results in neuroscience. The first step in such analyses of functional connectivity typically involves computing correlations between brain regions. In this paper, we show theoretical results explaining why brain region sizes bias correlation estimators, and propose three new estimators to correct for region size influence. We show experimental results on both synthetic and real fMRI data and discuss the influence of noise and intra-regional correlation on the robustness of the estimators. The bootstrap-based estimator of correlations emerges as the preferred choice.