Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches

摘要: A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable exists connectivity patterns between different areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising total 188 human participants. Both were decomposed into networks (RSNs) using probabilistic spatial independent component analysis (ICA). We estimated voxel-wise with these dual-regression analysis, which characterizes participant-level spatiotemporal dynamics each network while controlling (via multiple regression) influence other and sources variability. found that males females exhibit distinct RSNs, including both visual auditory right frontal–parietal network. These results replicated across not explained by head motion, data quality, volume, cortisol levels, or testosterone levels. Importantly, also demonstrate is better at detecting than traditional seed-based approaches. Our findings characterize robust—yet frequently ignored—neural females, pointing necessity studies individual Moreover, our highlight importance employing network-based models study connectivity.