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Prediction of neurocognition in youth from resting state fMRI

Abstract

Difficulties with higher-order cognitive functions in youth are a potentially important vulnerability factor for the emergence of problematic behaviors and a range of psychopathologies. This study examined 2013 9–10 year olds in the first data release from the Adolescent Brain Cognitive Development 21-site consortium study in order to identify resting state functional connectivity patterns that predict individual-differences in three domains of higher-order cognitive functions: General Ability, Speed/Flexibility, and Learning/Memory. For General Ability scores in particular, we observed consistent cross-site generalizability, with statistically significant predictions in 14 out of 15 held-out sites. These results survived several tests for robustness including replication in split-half analysis and in a low head motion subsample. We additionally found that connectivity patterns involving task control networks and default mode network were prominently implicated in predicting differences in General Ability across participants. These findings demonstrate that resting state connectivity can be leveraged to produce generalizable markers of neurocognitive functioning. Additionally, they highlight the importance of task control-default mode network interconnections as a major locus of individual differences in cognitive functioning in early adolescence.

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References

  1. Paus T, Keshavan M, Giedd JN. Why do many psychiatric disorders emerge during adolescence? Nat Rev Neurosci. 2008;9:947–57.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Frith C, Dolan R. The role of the prefrontal cortex in higher cognitive functions. Cogn Brain Res. 1996;5:175–81.

    CAS  Google Scholar 

  3. Diamond A. Executive functions. Annu Rev Psychol. 2013;64:135–68.

    PubMed  Google Scholar 

  4. Casey BJ, Tottenham N, Fossella J. Clinical, imaging, lesion, and genetic approaches toward a model of cognitive control. Dev Psychobiol. 2002;40:237–54.

    CAS  PubMed  Google Scholar 

  5. Banich MT. Executive function: the search for an integrated account. Curr Dir Psychol Sci. 2009;18:89–94.

    Google Scholar 

  6. Barkley RA. Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol Bull. 1997;121:65.

    PubMed  Google Scholar 

  7. Pennington BF, Ozonoff S. Executive functions and developmental psychopathology. J Child Psychol Psychiatry. 1996;37:51–87.

    CAS  PubMed  Google Scholar 

  8. Ogilvie JM, Stewart AL, Chan RC, Shum DH. Neuropsychological measures of executive function and antisocial behavior: a meta‐analysis. Criminology. 2011;49:1063–107.

    Google Scholar 

  9. Fossati P, Ergis AM, Allilaire JF. Executive functioning in unipolar depression: a review. L’encéphale. 2002;28:97–107.

    CAS  PubMed  Google Scholar 

  10. Banich MT, Mackiewicz KL, Depue BE, Whitmer AJ, Miller GA, Heller W. Cognitive control mechanisms, emotion and memory: a neural perspective with implications for psychopathology. Neurosci Biobehav Rev. 2009;33:613–30.

    PubMed  Google Scholar 

  11. Barrett PM, Healy LJ. An examination of the cognitive processes involved in childhood obsessive–compulsive disorder. Behav Res Ther. 2003;41:285–99.

    PubMed  Google Scholar 

  12. Goodkind M, Eickhoff SB, Oathes DJ, Jiang Y, Chang A, Jones-Hagata LB, et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry. 2015;72:305–15.

    PubMed  PubMed Central  Google Scholar 

  13. McTeague LM, Huemer J, Carreon DM, Jiang Y, Eickhoff SB, Etkin A. Identification of common neural circuit disruptions in cognitive control across psychiatric disorders. Am J Psychiatry. 2017;174:676–85.

    PubMed  PubMed Central  Google Scholar 

  14. McTeague LM, Goodkind MS, Etkin A. Transdiagnostic impairment of cognitive control in mental illness. J Psychiatr Res. 2016;83:37–46.

    PubMed  PubMed Central  Google Scholar 

  15. Mill RD, Ito T, Cole MW. From connectome to cognition: the search for mechanism in human functional brain networks. NeuroImage. 2017;160:124–39.

    PubMed  Google Scholar 

  16. Rosenberg MD, Finn ES, Scheinost D, Papademetris X, Shen X, Constable RT, et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat Neurosci. 2016;19:165–71.

    CAS  PubMed  Google Scholar 

  17. Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci. 2015;18:1664–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Dubois J, Galdi P, Paul LK, Adolphs R A distributed brain network predicts general intelligence from resting-state human neuroimaging data. Philos Trans R Soc B Biol Sci. 2018;373. https://doi.org/10.1098/rstb.2017.0284.

  19. Cole MW, Reynolds JR, Power JD, Repovs G, Anticevic A, Braver TS. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat Neurosci. 2013;16:1348–55.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Cole MW, Schneider W. The cognitive control network: integrated cortical regions with dissociable functions. NeuroImage. 2007;37:343–60.

    PubMed  Google Scholar 

  21. Dosenbach NUF, Fair DA, Cohen AL, Schlaggar BL, Petersen SE. A dual-networks architecture of top-down control. Trends Cogn Sci. 2008;12:99–105.

    PubMed  PubMed Central  Google Scholar 

  22. Dosenbach NUF, Fair DA, Miezin FM, Cohen AL, Wenger KK, Dosenbach RAT, et al. Distinct brain networks for adaptive and stable task control in humans. Proc Natl Acad Sci USA. 2007;104:11073–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Sridharan D, Levitin DJ, Menon V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci USA. 2008;105:12569–74.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct. 2010;214:655–67.

    PubMed  PubMed Central  Google Scholar 

  25. Buckner RL, Andrews-Hanna JR, Schacter DL. The Brain’s Default Network. Ann N Y Acad Sci. 2008;1124:1–38.

    PubMed  Google Scholar 

  26. Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. Functional-anatomic fractionation of the brain’s default network. Neuron. 2010;65:550–62.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Spreng RN, Stevens WD, Chamberlain JP, Gilmore AW, Schacter DL. Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition. NeuroImage. 2010;53:303–17.

    PubMed  Google Scholar 

  28. Spreng RN, Sepulcre J, Turner GR, Stevens WD, Schacter DL. Intrinsic architecture underlying the relations among the default, dorsal attention, and frontoparietal control networks of the human brain. J Cogn Neurosci. 2012;25:74–86.

    PubMed  Google Scholar 

  29. Gerlach KD, Spreng RN, Gilmore AW, Schacter DL. Solving future problems: default network and executive activity associated with goal-directed mental simulations. NeuroImage. 2011;55:1816–24.

    PubMed  Google Scholar 

  30. Sonuga-Barke EJS, Castellanos FX. Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis. Neurosci Biobehav Rev. 2007;31:977–86.

    PubMed  Google Scholar 

  31. Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci. 2011;15:483–506.

    PubMed  Google Scholar 

  32. Anderson JS, Ferguson MA, Lopez-Larson M, Yurgelun-Todd D. Connectivity gradients between the default mode and attention control networks. Brain Connect. 2011;1:147–57.

    PubMed  PubMed Central  Google Scholar 

  33. Fair DA, Dosenbach NUF, Church JA, Cohen AL, Brahmbhatt S, Miezin FM, et al. Development of distinct control networks through segregation and integration. Proc Natl Acad Sci USA. 2007;104:13507–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Kessler D, Angstadt M, Sripada C. Brain network growth charting and the identification of attention impairment in youth. JAMA Psychiatry. 2016;73:481–9.

    PubMed  PubMed Central  Google Scholar 

  35. Luciana M, Bjork JM, Nagel B, Barch DM, Gonzalez R, Nixon SJ, et al. Adolescent neurocognitive development and impacts of substance use: overview of the adolescent brain cognitive development (ABCD) baseline neurocognition battery. Dev Cogn Neurosci. 2018;32:67–79.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Thompson WK, Barch DM, Bjork JM, Gonzalez R, Nagel BJ, Nixon SJ et al. The structure of cognition in 9 and 10 year-old children and associations with problem behaviors: findings from the ABCD study’s baseline neurocognitive battery. Dev Cogn Neurosci. 2019;36:100606.

  37. Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE. Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage. 2014;84:320–41.

    PubMed  Google Scholar 

  38. Sripada C, Angstadt M, Rutherford S, Kessler D, Kim Y, Yee M, et al. Basic units of inter-individual variation in resting state connectomes. Sci Rep. 2019;9:1900.

    PubMed  PubMed Central  Google Scholar 

  39. Sripada C, Angstadt M, Rutherford S. Towards a ‘treadmill test’ for cognition: reliable prediction of intelligence from whole-brain task activation patterns. bioRxiv 2018. https://doi.org/10.1101/412056.

  40. Casey BJ, Cannonier T, Conley MI, Cohen AO, Barch DM, Heitzeg MM, et al. The adolescent brain cognitive development (ABCD) study: imaging acquisition across 21 sites. Dev Cogn Neurosci. 2018;32:43–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Biswal BB, Kylen JV, Hyde JS. Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps. NMR Biomed. 1997;10:165–70.

    CAS  PubMed  Google Scholar 

  42. Buckner RL, Krienen FM, Yeo BTT. Opportunities and limitations of intrinsic functional connectivity MRI. Nat Neurosci. 2013;16:832–7.

    PubMed  Google Scholar 

  43. Smith SM, Vidaurre D, Beckmann CF, Glasser MF, Jenkinson M, Miller KL, et al. Functional connectomics from resting-state fMRI. Trends Cogn Sci. 2013;17:666–82.

    PubMed  PubMed Central  Google Scholar 

  44. Castellanos FX, Di Martino A, Craddock RC, Mehta AD, Milham MP. Clinical applications of the functional connectome. NeuroImage. 2013;80:527–40.

    CAS  PubMed  Google Scholar 

  45. Kaiser M. The potential of the human connectome as a biomarker of brain disease. Front Hum Neurosci. 2013; 7. https://doi.org/10.3389/fnhum.2013.00484.

  46. Cocchi L, Zalesky A, Fornito A, Mattingley JB. Dynamic cooperation and competition between brain systems during cognitive control. Trends Cogn Sci. 2013;17:493–501.

    PubMed  Google Scholar 

  47. Weissman DH, Roberts KC, Visscher KM, Woldorff MG. The neural bases of momentary lapses in attention. Nat Neurosci. 2006;9:971–8.

    CAS  PubMed  Google Scholar 

  48. Fassbender C, Zhang H, Buzy WM, Cortes CR, Mizuiri D, Beckett L, et al. A lack of default network suppression is linked to increased distractibility in ADHD. Brain Res. 2009;1273:114–28.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Fair DA, Cohen AL, Dosenbach NUF, Church JA, Miezin FM, Barch DM, et al. The maturing architecture of the brain’s default network. Proc Natl Acad Sci. 2008;105:4028–32.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Sripada C, Kessler D, Angstadt M. Lag in maturation of the brain’s intrinsic functional architecture in attention-deficit/hyperactivity disorder. Proc Natl Acad Sci USA. 2014;111:14259–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. van den Heuvel MI, Thomason ME. Functional connectivity of the human brain in utero. Trends Cogn Sci. 2016;20:931–9.

    PubMed  PubMed Central  Google Scholar 

  52. Shaw P, Eckstrand K, Sharp W, Blumenthal J, Lerch JP, Greenstein D, et al. Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proc Natl Acad Sci USA. 2007;104:19649–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Shaw P, Malek M, Watson B, Greenstein D, de Rossi P, Sharp W. Trajectories of cerebral cortical development in childhood and adolescence and adult attention-deficit/hyperactivity disorder. Biol Psychiatry. 2013;74:599–606.

    PubMed  PubMed Central  Google Scholar 

  54. Garavan H, Bartsch H, Conway K, Decastro A, Goldstein RZ, Heeringa S, et al. Recruiting the ABCD sample: design considerations and procedures. Dev Cogn Neurosci. 2018;32:16–22.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Luciana M, Bjork JM, Nagel B, Barch DM, Gonzalez R, Nixon SJ, et al. Adolescent neurocognitive development and impacts of substance use: Overview of the Adolescent Brain and Cognitive Development (ABCD) baseline neurocognition battery. Dev Cogn Neurosci. 2018;32:67–79.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Hagler DJ, Hatton SN, Makowski C, Cornejo MD, Fair DA, Dick AS, et al. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. bioRxiv. 2018;457739.

  57. Pruim RH, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage. 2015;112:267–77.

    PubMed  Google Scholar 

  58. Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage. 2007;37:90–101.

    PubMed  Google Scholar 

  59. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. Functional network organization of the human brain. Neuron. 2011;72:665–78.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Good P Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses 2nd edn. Springer, 2000.

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Acknowledgements

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9–10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093, and U01DA041025. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/Consortium_Members.pdf. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. This work was supported by the following grants from the United States National Institutes of Health, the National Institute on Drug Abuse, and the National Institute on Alcohol Abuse and Alcoholism: R01MH107741 (CS), U01DA041106 (CS, LH, MH), 1U24DA041123-01 (WT), U01DA041120 (ML), T32 AA007477 (AW). In addition, CS was supported by a grant from the Dana Foundation David Mahoney Neuroimaging Program. This research was supported in part through computational resources and services provided by Advanced Research Computing at the University of Michigan, Ann Arbor.

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Sripada, C., Rutherford, S., Angstadt, M. et al. Prediction of neurocognition in youth from resting state fMRI. Mol Psychiatry 25, 3413–3421 (2020). https://doi.org/10.1038/s41380-019-0481-6

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