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Intrinsic activity development unfolds along a sensorimotor–association cortical axis in youth

Nature Neuroscience volume 26, pages 638–649 (2023)Cite this article

Subjects

Abstract

Animal studies of neurodevelopment have shown that recordings of intrinsic cortical activity evolve from synchronized and high amplitude to sparse and low amplitude as plasticity declines and the cortex matures. Leveraging resting-state functional MRI (fMRI) data from 1,033 youths (ages 8–23 years), we find that this stereotyped refinement of intrinsic activity occurs during human development and provides evidence for a cortical gradient of neurodevelopmental change. Declines in the amplitude of intrinsic fMRI activity were initiated heterochronously across regions and were coupled to the maturation of intracortical myelin, a developmental plasticity regulator. Spatiotemporal variability in regional developmental trajectories was organized along a hierarchical, sensorimotor–association cortical axis from ages 8 to 18. The sensorimotor–association axis furthermore captured variation in associations between youths’ neighborhood environments and intrinsic fMRI activity; associations suggest that the effects of environmental disadvantage on the maturing brain diverge most across this axis during midadolescence. These results uncover a hierarchical neurodevelopmental axis and offer insight into the progression of cortical plasticity in humans.

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Fig. 1: Developmental refinement of fluctuation amplitude varies across the cortex.
Fig. 2: Development of fluctuation amplitude spatially and temporally parallels cortical T1w/T2w ratio development.
Fig. 3: The principal axis of fluctuation amplitude development exhibits convergent spatial embedding with the S–A axis.
Fig. 4: Neurodevelopment unfolds along the S–A axis until late adolescence.
Fig. 5: Region-specific and cortex-wide developmental patterns are robust to methodological variation.
Fig. 6: Associations between fluctuation amplitude and the developmental environment vary along the S–A axis in adolescence.

Data availability

The current study analyzes an existing, publicly available dataset from the Philadelphia Neurodevelopmental Cohort, available in the Database of Genotypes and Phenotypes (phs000607.v3.p2). Study analyses additionally made use of publicly available cortical atlases, including the HCP-MMP atlas (downloaded from https://github.com/PennLINC/xcp_d/blob/main/xcp_d/data/ciftiatlas/glasser_space-fsLR_den-32k_desc-atlas.dlabel.nii), the Schaefer-400 atlas (downloaded from https://github.com/PennLINC/xcp_d/blob/main/xcp_d/data/ciftiatlas/Schaefer2018_400Parcels_17Networks_order.dlabel.nii) and the S–A axis (downloaded from https://pennlinc.github.io/S-A_ArchetypalAxis/). Data derivatives from the current study, including development effect and environment effect maps, are available for download from https://doi.org/10.5281/zenodo.7606653.

Code availability

Neuroimaging data were processed with containerized software packages available on dockerhub. Resting-state fMRI data were processed with fMRIPrep 20.2.3 (https://hub.docker.com/r/nipreps/fmriprep/tags) and xcp_d 0.0.4 (https://hub.docker.com/r/pennlinc/xcp_abcd/tags). Following image processing, all subsequent analyses and statistics were conducted in R 4.0.2 (https://www.r-project.org) using original analysis code and Connectome Workbench 1.5.0 tools (https://www.humanconnectome.org/software/get-connectome-workbench). Original code, including code used to calculate regional fluctuation amplitude, fit regional GAMs, contextualize developmental and environmental effects and perform sensitivity analyses, has been deposited at Zenodo and is available at https://doi.org/10.5281/zenodo.7606653. A detailed description of the code and guide to code implementation is additionally provided at https://pennlinc.github.io/spatiotemp_dev_plasticity/.

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Acknowledgements

This study was supported by grants from the National Institute of Health: R01MH113550 to T.D.S. and D.S.B., R01MH120482 to T.D.S., R01MH112847 to R.T.S. and T.D.S., R01MH119219 to R.C.G. and R.E.G., R01MH123563 to R.T.S., R01MH119185 to D.R.R., R01MH120174 to D.R.R., R01NS060910 to R.T.S., R01EB022573 to T.D.S., RF1MH116920 to T.D.S. and D.S.B., RF1MH121867 to T.D.S., R37MH125829 to T.D.S., R34DA050297 to A.P.M., K08MH120564 to A.F.A.-B., K99MH127293 to B.L. and T32MH014654 to J.S. V.J.S. was supported by a National Science Foundation Graduate Research Fellowship (DGE-1845298). The Philadelphia Neurodevelopmental Cohort was supported by RC2 grants MH089983 and MH089924. Additional support was provided by the Penn-CHOP Lifespan Brain Institute and the Penn Center for Biomedical Image Computing and Analytics.

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Authors and Affiliations

  1. Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Valerie J. Sydnor, Bart Larsen, Azeez Adebimpe, Maxwell A. Bertolero, Matthew Cieslak, Sydney Covitz & Theodore D. Satterthwaite

  2. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Valerie J. Sydnor, Bart Larsen, Jakob Seidlitz, Azeez Adebimpe, Aaron F. Alexander-Bloch, Dani S. Bassett, Maxwell A. Bertolero, Matthew Cieslak, Sydney Covitz, Raquel E. Gur, Ruben C. Gur, Tyler M. Moore, David R. Roalf & Theodore D. Satterthwaite

  3. Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Jakob Seidlitz, Aaron F. Alexander-Bloch & Raquel E. Gur

  4. Penn–CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Jakob Seidlitz, Aaron F. Alexander-Bloch, Raquel E. Gur, Ruben C. Gur, Tyler M. Moore & Theodore D. Satterthwaite

  5. Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Dani S. Bassett

  6. Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Dani S. Bassett

  7. Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA

    Dani S. Bassett

  8. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Dani S. Bassett

  9. Santa Fe Institute, Santa Fe, NM, USA

    Dani S. Bassett

  10. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Yong Fan

  11. Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA

    Yong Fan, Russell T. Shinohara & Theodore D. Satterthwaite

  12. Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA

    Allyson P. Mackey

  13. Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Russell T. Shinohara & Theodore D. Satterthwaite

  14. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Russell T. Shinohara

Authors
  1. Valerie J. Sydnor
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  2. Bart Larsen
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  5. Aaron F. Alexander-Bloch
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  17. Theodore D. Satterthwaite
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V.J.S. conceived the neurodevelopmental framework and designed the study with B.L. and T.D.S. R.E.G., R.C.G. and T.D.S. provided resources and supervised collection of the neuroimaging data. D.R.R. and T.D.S. curated and quality checked the neuroimaging data. V.J.S., A.A., M.A.B., M.C. and S.C. processed the neuroimaging data with software tools developed by A.A., M.C. and S.C. V.J.S. implemented all statistical analyses with R code written by V.J.S. and B.L. A.F.A.-B. and R.T.S. provided input on statistical approaches. D.S.B. and Y.F. provided input on image analytic approaches. B.L. conducted an internal code review and technical replication of all study findings. J.S. provided data used to derive the S–A axis. T.M.M. generated the neighborhood environment factor scores, and A.P.M. provided expert guidance on environmental analyses. V.J.S. generated all figures. V.J.S. wrote the original draft and all authors (V.J.S., B.L., J.S., A.A., A.F.A.-B., D.S.B., M.A.B., M.C., S.C., Y.F., R.E.G., R.C.G., A.P.M., T.M.M., D.R.R., R.T.S. and T.D.S.) reviewed and revised the final draft.

Corresponding author

Correspondence to Theodore D. Satterthwaite.

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The authors declare the following competing interest: R.T.S. receives consulting income from Octave Bioscience for work wholly unrelated to the present research. All other authors declare no competing interests.

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Nature Neuroscience thanks Natalie Brito, Lucina Uddin, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Sydnor, V.J., Larsen, B., Seidlitz, J. et al. Intrinsic activity development unfolds along a sensorimotor–association cortical axis in youth. Nat Neurosci 26, 638–649 (2023). https://doi.org/10.1038/s41593-023-01282-y

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