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

Publication ,  Journal Article
Smith, DV; Utevsky, AV; Bland, AR; Clement, N; Clithero, JA; Harsch, AEW; McKell Carter, R; Huettel, SA
Published in: NeuroImage
July 2014

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.

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Published In

NeuroImage

DOI

EISSN

1095-9572

ISSN

1053-8119

Publication Date

July 2014

Volume

95

Start / End Page

1 / 12

Related Subject Headings

  • Young Adult
  • Sex Characteristics
  • Regression Analysis
  • Neurology & Neurosurgery
  • Neural Pathways
  • Male
  • Magnetic Resonance Imaging
  • Individuality
  • Image Processing, Computer-Assisted
  • Humans
 

Citation

APA
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ICMJE
MLA
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Smith, D. V., Utevsky, A. V., Bland, A. R., Clement, N., Clithero, J. A., Harsch, A. E. W., … Huettel, S. A. (2014). Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches. NeuroImage, 95, 1–12. https://doi.org/10.1016/j.neuroimage.2014.03.042
Smith, David V., Amanda V. Utevsky, Amy R. Bland, Nathan Clement, John A. Clithero, Anne E. W. Harsch, R. McKell Carter, and Scott A. Huettel. “Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches.NeuroImage 95 (July 2014): 1–12. https://doi.org/10.1016/j.neuroimage.2014.03.042.
Smith DV, Utevsky AV, Bland AR, Clement N, Clithero JA, Harsch AEW, et al. Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches. NeuroImage. 2014 Jul;95:1–12.
Smith, David V., et al. “Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches.NeuroImage, vol. 95, July 2014, pp. 1–12. Epmc, doi:10.1016/j.neuroimage.2014.03.042.
Smith DV, Utevsky AV, Bland AR, Clement N, Clithero JA, Harsch AEW, McKell Carter R, Huettel SA. Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches. NeuroImage. 2014 Jul;95:1–12.
Journal cover image

Published In

NeuroImage

DOI

EISSN

1095-9572

ISSN

1053-8119

Publication Date

July 2014

Volume

95

Start / End Page

1 / 12

Related Subject Headings

  • Young Adult
  • Sex Characteristics
  • Regression Analysis
  • Neurology & Neurosurgery
  • Neural Pathways
  • Male
  • Magnetic Resonance Imaging
  • Individuality
  • Image Processing, Computer-Assisted
  • Humans