Sex differences in the functional topography of association networks in youth

Sheila Shanmugan, Jakob Seidlitz, Zaixu Cui, Azeez Adebimpe, Danielle S. Bassett, Maxwell A. Bertolero, Christos Davatzikos, Damien A. Fair, Raquel E. Gur, Ruben C. Gur, Bart Larsen, Hongming Li, Adam Pines, Armin Raznahan, David R. Roalf, Russell T. Shinohara, Jacob Vogel, Daniel H. Wolf, Yong Fan, Aaron Alexander-BlochTheodore D. Satterthwaite

Research output: Contribution to journalArticlepeer-review


Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy (P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.

Original languageEnglish (US)
Article numbere2110416119
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number33
StatePublished - Aug 16 2022
Externally publishedYes

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS. This study was supported by grants from the National Institute of Health: R01MH120482, R37MH125829, R01MH113550, R01EB022573, R01MH107703, RF1MH116920, R01MH112847, P50MH096891, R01MH11186, K01MH102609, R01MH107235, R01MH112070, R01MH123550, K99MH127293, R01NS085211, RC2MH08998, RC2MH089924, R25MH119043, K08MH120564, T32MH014654, T32MH019112, and T32NS091008. This study was also supported by the Penn/CHOP Lifespan Brain Institute.

Publisher Copyright:
Copyright © 2022 the Author(s).


  • association networks
  • functional topography
  • personalized functional networks
  • sex differences

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't


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