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

Smith, David V. and Utevsky, Amanda V. and Bland, Amy R. and Clement, Nathan and Clithero, John A. and Harsch, Anne E. W. and Carter, R. McKell and Huettel, Scott A. (2014) Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches. NeuroImage, 95 . pp. 1-12. ISSN 1053-8119. PMCID PMC4074548. doi:10.1016/j.neuroimage.2014.03.042.

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

Item Type:Article
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Additional Information:© 2014 Elsevier Inc. Accepted 14 March 2014, Available online 21 March 2014. This study was funded by a grant from the National Institutes of Health (NIMH RC1-88680), an Incubator Award from the Duke Institute for Brain Sciences (SAH), and by a NIMH National Research Service Award F31-086248 (DVS). We thank Steve Stanton for hormone analyses and Edward McLaurin for assistance with data collection. We also thank Timothy Strauman and Jacob Young for feedback on previous drafts of the manuscript. DVS is now at Rutgers University.
Funding AgencyGrant Number
National Institutes of Health (NIH)NIMH RC1-88680
Duke Institute for Brain Sciences Incubator AwardUNSPECIFIED
NIMH National Research Service AwardF31-086248
Subject Keywords:Individual differences; Functional connectivity; Seed-based analysis; Dual-regression analysis; Split-sample validation; Independent component analysis
PubMed Central ID:PMC4074548
Record Number:CaltechAUTHORS:20140714-100021496
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Official Citation:David V. Smith, Amanda V. Utevsky, Amy R. Bland, Nathan Clement, John A. Clithero, Anne E.W. Harsch, R. McKell Carter, Scott A. Huettel, Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches, NeuroImage, Volume 95, 15 July 2014, Pages 1-12, ISSN 1053-8119, (
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:47164
Deposited By: Ruth Sustaita
Deposited On:14 Jul 2014 17:26
Last Modified:10 Nov 2021 17:35

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