Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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/.

References

  1. Sydnor, V. J. & Satterthwaite, T. D. Neuroimaging of plasticity mechanisms in the human brain: from critical periods to psychiatric conditions. Neuropsychopharmacology 48, 219–220 (2023).

    Article  PubMed  Google Scholar 

  2. Cooper, E. A. & Mackey, A. P. Sensory and cognitive plasticity: implications for academic interventions. Curr. Opin. Behav. Sci. 10, 21–27 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Sisk, L. M. & Gee, D. G. Stress and adolescence: vulnerability and opportunity during a sensitive window of development. Curr. Opin. Psychol. 44, 286–292 (2022).

    Article  PubMed  Google Scholar 

  4. Sydnor, V. J. et al. Neurodevelopment of the association cortices: patterns, mechanisms, and implications for psychopathology. Neuron 109, 2820–2846 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bethlehem, R. A. I. et al. Brain charts for the human lifespan. Nature 604, 525–533 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Gogtay, N. et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc. Natl Acad. Sci. USA 101, 8174–8179 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Dong, H.-M., Margulies, D. S., Zuo, X.-N. & Holmes, A. J. Shifting gradients of macroscale cortical organization mark the transition from childhood to adolescence. Proc. Natl Acad. Sci. USA 118, e2024448118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Grydeland, H. et al. Waves of maturation and senescence in micro-structural mri markers of human cortical myelination over the lifespan. Cereb. Cortex 29, 1369–1381 (2019).

    Article  PubMed  Google Scholar 

  9. Paquola, C. et al. Shifts in myeloarchitecture characterise adolescent development of cortical gradients. eLife 8, e50482 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Huttenlocher, P. R. & Dabholkar, A. S. Regional differences in synaptogenesis in human cerebral cortex. J. Comp. Neurol. 387, 167–178 (1997).

    Article  CAS  PubMed  Google Scholar 

  11. Larsen, B. & Luna, B. Adolescence as a neurobiological critical period for the development of higher-order cognition. Neurosci. Biobehav. Rev. 94, 179–195 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  12. García-Cabezas, M. Á., Zikopoulos, B. & Barbas, H. The structural model: a theory linking connections, plasticity, pathology, development and evolution of the cerebral cortex. Brain Struct. Funct. 224, 985–1008 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Huntenburg, J. M., Bazin, P.-L. & Margulies, D. S. Large-scale gradients in human cortical organization. Trends Cogn. Sci. 22, 21–31 (2018).

    Article  PubMed  Google Scholar 

  14. Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl Acad. Sci. USA 113, 12574–12579 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hilgetag, C. C., Goulas, A. & Changeux, J.-P. A natural cortical axis connecting the outside and inside of the human brain. Netw. Neurosci. 6, 950–959 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Reh, R. K. et al. Critical period regulation across multiple timescales. Proc. Natl Acad. Sci. USA 117, 23242–23251 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lensjø, K. K., Lepperød, M. E., Dick, G., Hafting, T. & Fyhn, M. Removal of perineuronal nets unlocks juvenile plasticity through network mechanisms of decreased inhibition and increased gamma activity. J. Neurosci. 37, 1269–1283 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Martini, F. J., Guillamón-Vivancos, T., Moreno-Juan, V., Valdeolmillos, M. & López-Bendito, G. Spontaneous activity in developing thalamic and cortical sensory networks. Neuron 109, 2519–2534 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Frye, C. G. & MacLean, J. N. Spontaneous activations follow a common developmental course across primary sensory areas in mouse neocortex. J. Neurophysiol. 116, 431–437 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Golshani, P. et al. Internally mediated developmental desynchronization of neocortical network activity. J. Neurosci. 29, 10890–10899 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Nakazawa, S., Yoshimura, Y., Takagi, M., Mizuno, H. & Iwasato, T. Developmental phase transitions in spatial organization of spontaneous activity in postnatal barrel cortex layer 4. J. Neurosci. 40, 7637–7650 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Toyoizumi, T. et al. A theory of the transition to critical period plasticity: inhibition selectively suppresses spontaneous activity. Neuron 80, 51–63 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chini, M., Pfeffer, T. & Hanganu-Opatz, I. An increase of inhibition drives the developmental decorrelation of neural activity. eLife 11, e78811 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Fagiolini, M. & Hensch, T. K. Inhibitory threshold for critical-period activation in primary visual cortex. Nature 404, 183–186 (2000).

    Article  CAS  PubMed  Google Scholar 

  25. McGee, A. W., Yang, Y., Fischer, Q. S., Daw, N. W. & Strittmatter, S. M. Experience-driven plasticity of visual cortex limited by myelin and Nogo receptor. Science 309, 2222–2226 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Laumann, T. O. & Snyder, A. Z. Brain activity is not only for thinking. Curr. Opin. Behav. Sci. 40, 130–136 (2021).

    Article  Google Scholar 

  27. Luhmann, H. J. et al. Spontaneous neuronal activity in developing neocortical networks: from single cells to large-scale interactions. Front. Neural Circuits 10, 40 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Lake, E. M. R. et al. Simultaneous cortex-wide fluorescence Ca2+ imaging and whole-brain fMRI. Nat. Methods 17, 1262–1271 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ma, Z., Zhang, Q., Tu, W. & Zhang, N. Gaining insight into the neural basis of resting-state fMRI signal. NeuroImage 250, 118960 (2022).

    Article  PubMed  Google Scholar 

  30. Magri, C., Schridde, U., Murayama, Y., Panzeri, S. & Logothetis, N. K. The amplitude and timing of the BOLD signal reflects the relationship between local field potential power at different frequencies. J. Neurosci. 32, 1395–1407 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Shmuel, A. & Leopold, D. A. Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: implications for functional connectivity at rest. Hum. Brain Mapp. 29, 751–761 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Yu-Feng, Z. et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev. 29, 83–91 (2007).

    Article  Google Scholar 

  33. Fair, D. A. & Yeo, B. T. T. Precision neuroimaging opens a new chapter of neuroplasticity experimentation. Neuron 107, 401–403 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Markicevic, M. et al. Cortical excitation:inhibition imbalance causes abnormal brain network dynamics as observed in neurodevelopmental disorders. Cereb. Cortex 30, 4922–4937 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Newbold, D. J. et al. Plasticity and spontaneous activity pulses in disused human brain circuits. Neuron 107, 580–589 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Hinton, E. A., Li, D. C., Allen, A. G. & Gourley, S. L. Social isolation in adolescence disrupts cortical development and goal-dependent decision-making in adulthood, despite social reintegration. eNeuro 6, ENEURO.0318-19.2019 (2019).

  37. Greifzu, F. et al. Environmental enrichment extends ocular dominance plasticity into adulthood and protects from stroke-induced impairments of plasticity. Proc. Natl Acad. Sci. USA 111, 1150–1155 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Favuzzi, E. et al. Activity-dependent gating of parvalbumin interneuron function by the perineuronal net protein brevican. Neuron 95, 639–655 (2017).

    Article  CAS  PubMed  Google Scholar 

  39. Tooley, U. A., Bassett, D. S. & Mackey, A. P. Environmental influences on the pace of brain development. Nat. Rev. Neurosci. 22, 372–384 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Colich, N. L., Rosen, M. L., Williams, E. S. & McLaughlin, K. A. Biological aging in childhood and adolescence following experiences of threat and deprivation: a systematic review and meta-analysis. Psychol. Bull. 146, 721–764 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  41. McDermott, C. L. et al. Early life stress is associated with earlier emergence of permanent molars. Proc. Natl Acad. Sci. USA 118, e2105304118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Baum, G. L. et al. Graded variation in T1w/T2w ratio during adolescence: measurement, caveats, and implications for development of cortical myelin. J. Neurosci. 42, 5681–5694 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. de Faria, O. et al. Periods of synchronized myelin changes shape brain function and plasticity. Nat. Neurosci. 24, 1508–1521 (2021).

    Article  PubMed  Google Scholar 

  44. Kato, D. et al. Motor learning requires myelination to reduce asynchrony and spontaneity in neural activity. Glia 68, 193–210 (2020).

    Article  PubMed  Google Scholar 

  45. Burt, J. B. et al. Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography. Nat. Neurosci. 21, 1251–1259 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hill, J. et al. Similar patterns of cortical expansion during human development and evolution. Proc. Natl Acad. Sci. USA 107, 13135–13140 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Moore, T. M. et al. Characterizing social environment’s association with neurocognition using census and crime data linked to the Philadelphia Neurodevelopmental Cohort. Psychol. Med. 46, 599–610 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. Tooley, U. A. et al. Associations between neighborhood SES and functional brain network development. Cereb. Cortex 30, 1–19 (2020).

    Article  PubMed  Google Scholar 

  49. O’Leary, D. D. M., Chou, S.-J. & Sahara, S. Area patterning of the mammalian cortex. Neuron 56, 252–269 (2007).

    Article  PubMed  Google Scholar 

  50. Charvet, C. J. & Finlay, B. L. Evo-devo and the primate isocortex: the central organizing role of intrinsic gradients of neurogenesis. Brain Behav. Evol. 84, 81–92 (2014).

    Article  PubMed  Google Scholar 

  51. Ackman, J. B., Burbridge, T. J. & Crair, M. C. Retinal waves coordinate patterned activity throughout the developing visual system. Nature 490, 219–225 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Benders, M. J. et al. Early brain activity relates to subsequent brain growth in premature infants. Cereb. Cortex 25, 3014–3024 (2015).

    Article  PubMed  Google Scholar 

  53. Winnubst, J., Cheyne, J. E., Niculescu, D. & Lohmann, C. Spontaneous activity drives local synaptic plasticity in vivo. Neuron 87, 399–410 (2015).

    Article  CAS  PubMed  Google Scholar 

  54. Takesian, A. E. & Hensch, T. K. in Progress in Brain Research, Vol. 207 (eds. Merzenich, M. M., Nahum, M. & Van Vleet, T. M.) Ch. 1 (Elsevier, 2013).

  55. Carulli, D. et al. Animals lacking link protein have attenuated perineuronal nets and persistent plasticity. Brain 133, 2331–2347 (2010).

    Article  PubMed  Google Scholar 

  56. Anderson, K. M. et al. Transcriptional and imaging-genetic association of cortical interneurons, brain function, and schizophrenia risk. Nat. Commun. 11, 2889 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Micheva, K. D. et al. A large fraction of neocortical myelin ensheathes axons of local inhibitory neurons. eLife 5, e15784 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Condé, F., Lund, J. S. & Lewis, D. A. The hierarchical development of monkey visual cortical regions as revealed by the maturation of parvalbumin-immunoreactive neurons. Brain Res. Dev. Brain Res. 96, 261–276 (1996).

    Article  PubMed  Google Scholar 

  59. Benoit, L. J. et al. Adolescent thalamic inhibition leads to long-lasting impairments in prefrontal cortex function. Nat. Neurosci. 25, 714–725 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Acevedo-Garcia, D. et al. Racial and ethnic inequities in children’s neighborhoods: evidence from the new child opportunity index 2.0. Health Aff. 39, 1693–1701 (2020).

    Article  Google Scholar 

  61. Norbom, L. B. et al. Parental socioeconomic status is linked to cortical microstructure and language abilities in children and adolescents. Dev. Cogn. Neurosci. 56, 101132 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Piekarski, D. J., Boivin, J. R. & Wilbrecht, L. Ovarian hormones organize the maturation of inhibitory neurotransmission in the frontal cortex at puberty onset in female mice. Curr. Biol. 27, 1735–1745 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kobayashi, Y., Ye, Z. & Hensch, T. K. Clock genes control cortical critical period timing. Neuron 86, 264–275 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Konstantinides, N. et al. A complete temporal transcription factor series in the fly visual system. Nature 604, 316–322 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Nielsen, A. N. et al. Maturation of large-scale brain systems over the first month of life. Cereb. Cortex https://doi.org/10.1093/cercor/bhac242 (2022).

  66. Satterthwaite, T. D. et al. Neuroimaging of the Philadelphia Neurodevelopmental Cohort. NeuroImage 86, 544–553 (2014).

    Article  PubMed  Google Scholar 

  67. Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111–116 (2019).

    Article  CAS  PubMed  Google Scholar 

  68. Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54, 2033–2044 (2011).

    Article  PubMed  Google Scholar 

  69. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage 9, 179–194 (1999).

    Article  CAS  PubMed  Google Scholar 

  70. Ciric, R. et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage 154, 174–187 (2017).

    Article  PubMed  Google Scholar 

  71. Ciric, R. et al. Mitigating head motion artifact in functional connectivity MRI. Nat. Protoc. 13, 2801–2826 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Schaefer, A. et al. Local–global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114 (2018).

    Article  PubMed  Google Scholar 

  74. Pham, D. D., Muschelli, J. & Mejia, A. F. ciftiTools: a package for reading, writing, visualizing, and manipulating CIFTI files in R. NeuroImage 250, 118877 (2022).

    Article  PubMed  Google Scholar 

  75. Cui, Z. et al. Individual variation in functional topography of association networks in Youth. Neuron 106, 340–353 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Covitz, S. et al. Curation of BIDS (CuBIDS): a workflow and software package for streamlining reproducible curation of large BIDS datasets. NeuroImage 263, 119609 (2022).

    Article  PubMed  Google Scholar 

  77. Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman and Hall/CRC, 2017).

  78. Simpson, G. L. Modelling palaeoecological time series using generalised additive models. Front. Ecol. Evol. 6, 149 (2018).

    Article  Google Scholar 

  79. Satterthwaite, T. D. et al. Impact of puberty on the evolution of cerebral perfusion during adolescence. Proc. Natl Acad. Sci. USA 111, 8643–8648 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Reardon, P. K. et al. Normative brain size variation and brain shape diversity in humans. Science 360, 1222–1227 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Vaishnavi, S. N. et al. Regional aerobic glycolysis in the human brain. Proc. Natl Acad. Sci. USA 107, 17757–17762 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C. & Wager, T. D. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8, 665–670 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Paquola, C. et al. The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging. eLife 10, e70119 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Diedenhofen, B. & Musch, J. cocor: a comprehensive solution for the statistical comparison of correlations. PLoS ONE 10, e0121945 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Garrett, D. D., Lindenberger, U., Hoge, R. D. & Gauthier, C. J. Age differences in brain signal variability are robust to multiple vascular controls. Sci. Rep. 7, 10149 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Adebimpe, A. et al. ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion. Nat. Methods 19, 683–686 (2022).

    Article  CAS  PubMed  Google Scholar 

  87. Alexander-Bloch, A. F. et al. On testing for spatial correspondence between maps of human brain structure and function. NeuroImage 178, 540–551 (2018).

    Article  PubMed  Google Scholar 

  88. Váša, F. et al. Adolescent tuning of association cortex in human structural brain networks. Cereb. Cortex 28, 281–294 (2018).

    Article  PubMed  Google Scholar 

Download references

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.

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bart Larsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jakob Seidlitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Azeez Adebimpe
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Aaron F. Alexander-Bloch
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dani S. Bassett
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Maxwell A. Bertolero
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Matthew Cieslak
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sydney Covitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yong Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Raquel E. Gur
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ruben C. Gur
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Allyson P. Mackey
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Tyler M. Moore
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. David R. Roalf
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Russell T. Shinohara
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Theodore D. Satterthwaite
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Ethics declarations

Competing interests

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.

Peer review

Peer review information

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

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-023-01282-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing