Abstract
The Adolescent Brain Cognitive Development (ABCD) Study® is a 10-year longitudinal study of children recruited at ages 9 and 10. A battery of neuroimaging tasks are administered biennially to track neurodevelopment and identify individual differences in brain function. This study reports activation patterns from functional MRI (fMRI) tasks completed at baseline, which were designed to measure cognitive impulse control with a stop signal task (SST; N = 5,547), reward anticipation and receipt with a monetary incentive delay (MID) task (N = 6,657) and working memory and emotion reactivity with an emotional N-back (EN-back) task (N = 6,009). Further, we report the spatial reproducibility of activation patterns by assessing between-group vertex/voxelwise correlations of blood oxygen level-dependent (BOLD) activation. Analyses reveal robust brain activations that are consistent with the published literature, vary across fMRI tasks/contrasts and slightly correlate with individual behavioral performance on the tasks. These results establish the preadolescent brain function baseline, guide interpretation of cross-sectional analyses and will enable the investigation of longitudinal changes during adolescent development.
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Data availability
The ABCD Study anonymized data, including all assessment domains, are released annually to the research community. Information on how to access ABCD data through the NDA is available on the ABCD Study data-sharing webpage: https://abcdstudy.org/scientists_data_sharing.html. Instructions on how to create an NDA study are available at https://nda.nih.gov/training/modules/study.html. The ABCD data repository grows and changes over time.
The ABCD data used in this report came from https://doi.org/10.15154/1520620. DOIs can be found at https://doi.org/10.15154/1520620. The ABCD data used in this report also came from the fast-track data release. The raw data are available at https://nda.nih.gov/edit_collection.html?id=2573. Activation maps and spatial reproducibility data are available in Supplementary Data 1 and 2, respectively.
Code availability
The Python codes used to compute reproducibility curves undertaken as part of this study and that generate the figures are openly available in the Supplementary Data and at https://github.com/sahahn/ABCD_Consortium_Analysis. The following additional software packages used for this study are freely and openly available: PALM (v.alpha116), https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM/.
References
Membride, H. Mental health: early intervention and prevention in children and young people. Br. J. Nurs. 25, 552–557 (2016).
Verbruggen, F. et al. A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. eLife 8, e46323 (2019).
Zhang, R., Geng, X. & Lee, T. M. C. Large-scale functional neural network correlates of response inhibition: an fMRI meta-analysis. Brain Struct. Funct. 222, 3973–3990 (2017).
Dwyer, D. B. et al. Large-scale brain network dynamics supporting adolescent cognitive control. J. Neurosci. 34, 14096–14107 (2014).
Luna, B., Marek, S., Larsen, B., Tervo-Clemmens, B. & Chahal, R. An integrative model of the maturation of cognitive control. Annu. Rev. Neurosci. 38, 151–170 (2015).
Ferdinand, N. K., & Kray, J. Developmental changes in performance monitoring: how electrophysiological data can enhance our understanding of error and feedback processing in childhood and adolescence. Behav. Brain Res. 263, 122–132 (2014).
Segalowitz, S. J., Santesso, D. L. & Jetha, M. K. Electrophysiological changes during adolescence: a review. Brain Cogn. 72, 86–100 (2010).
Alahyane, N., Brien, D. C., Coe, B. C., Stroman, P. W. & Munoz, D. P. Developmental improvements in voluntary control of behavior: effect of preparation in the fronto-parietal network? Neuroimage 98, 103–117 (2014).
Ordaz, S. J., Foran, W., Velanova, K. & Luna, B. Longitudinal growth curves of brain function underlying inhibitory control through adolescence. J. Neurosci. 33, 18109–18124 (2013).
Yaple, Z. A., Stevens, W. D. & Arsalidou, M. Meta-analyses of the n-back working memory task: fMRI evidence of age-related changes in prefrontal cortex involvement across the adult lifespan. Neuroimage 196, 16–31 (2019).
Rottschy, C. et al. Modelling neural correlates of working memory: a coordinate-based meta-analysis. Neuroimage 60, 830–846 (2012).
Yaple, Z. & Arsalidou, M. N-back working memory task: meta-analysis of normative fMRI studies with children. Child Dev. 89, 2010–2022 (2018).
Satterthwaite, T. D. et al. Functional maturation of the executive system during adolescence. J. Neurosci. 33, 16249–16261 (2013).
Thomason, M. E. et al. Development of spatial and verbal working memory capacity in the human brain. J. Cogn. Neurosci. 21, 316–332 (2009).
O’Hare, E. D., Lu, L. H., Houston, S. M., Bookheimer, S. Y. & Sowell, E. R. Neurodevelopmental changes in verbal working memory load-dependency: an fMRI investigation. Neuroimage 42, 1678–1685 (2008).
Rosenberg, M. D. et al. Behavioral and neural signatures of working memory in childhood. J. Neurosci. 40, 5090–5104 (2020).
Schweizer, S. et al. The impact of affective information on working memory: a pair of meta-analytic reviews of behavioral and neuroimaging evidence. Psychol. Bull. 145, 566–609 (2019).
Barch, D. M. et al. Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013).
Gauthier, I. et al. The fusiform ‘face area’ is part of a network that processes faces at the individual level. J. Cogn. Neurosci. 12, 495–504 (2000).
Fuhrmann, D. et al. Perception and recognition of faces in adolescence. Sci. Rep. 6, 33497 (2016).
Cohen Kadosh, K. Differing processing abilities for specific face properties in mid-childhood and adulthood. Front. Psychol. 2, 400 (2012).
Scherf, K. S., Behrmann, M. & Dahl, R. E. Facing changes and changing faces in adolescence: a new model for investigating adolescent-specific interactions between pubertal, brain and behavioral development. Dev. Cogn. Neurosci. 2, 199–219 (2012).
Tahmasebi, A. M. et al. Creating probabilistic maps of the face network in the adolescent brain: a multicentre functional MRI study. Hum. Brain Mapp. 33, 938–957 (2012).
Kadosh, K. C. What can emerging cortical face networks tell us about mature brain organisation? Dev. Cogn. Neurosci. 1, 246–255 (2011).
Cohen Kadosh, K., Johnson, M. H., Henson, R. N. A., Dick, F. & Blakemore, S.-J. Differential face-network adaptation in children, adolescents and adults. Neuroimage 69, 11–20 (2013).
Scherf, K. S., Behrmann, M., Humphreys, K. & Luna, B. Visual category-selectivity for faces, places and objects emerges along different developmental trajectories. Dev. Sci. 10, F15–F30 (2007).
Silverman, M. H., Jedd, K. & Luciana, M. Neural networks involved in adolescent reward processing: an activation likelihood estimation meta-analysis of functional neuroimaging studies. Neuroimage 122, 427–439 (2015).
Cao, Z. et al. Mapping adolescent reward anticipation, receipt, and prediction error during the monetary incentive delay task. Hum. Brain Mapp. 40, 262–283 (2019).
Oldham, S. et al. The anticipation and outcome phases of reward and loss processing: a neuroimaging meta-analysis of the monetary incentive delay task. Hum. Brain Mapp. 39, 3398–3418 (2018).
Falk, E. B. et al. What is a representative brain? Neuroscience meets population science. Proc. Natl Acad. Sci. USA 110, 17615–17622 (2013).
Baker, M. 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016).
Song, X., Panych, L. P., Chou, Y.-H. & Chen, N.-K. A study of long-term fMRI reproducibility using data-driven analysis methods. Int. J. Imaging Syst. Technol. 24, 339–349 (2014).
Thirion, B. et al. Analysis of a large fMRI cohort: statistical and methodological issues for group analyses. Neuroimage 35, 105–120 (2007).
Turner, B. O., Paul, E. J., Miller, M. B. & Barbey, A. K. Small sample sizes reduce the replicability of task-based fMRI studies. Commun. Biol. 1, 1–10 (2018).
Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. Neuroimage 80, 62–79 (2013).
Owens, M. M., Duda, B., Sweet, L. H. & MacKillop, J. Distinct functional and structural neural underpinnings of working memory. Neuroimage 174, 463–471 (2018).
Rae, C. L., Hughes, L. E., Weaver, C., Anderson, M. C. & Rowe, J. B. Selection and stopping in voluntary action: a meta-analysis and combined fMRI study. Neuroimage 86, 381–391 (2014).
Hung, Y., Gaillard, S. L., Yarmak, P. & Arsalidou, M. Dissociations of cognitive inhibition, response inhibition, and emotional interference: voxelwise ALE meta-analyses of fMRI studies. Hum. Brain Mapp. 39, 4065–4082 (2018).
Swick, D., Ashley, V. & Turken, U. Are the neural correlates of stopping and not going identical? Quantitative meta-analysis of two response inhibition tasks. Neuroimage 56, 1655–1665 (2011).
Garavan, H., Ross, T. J., Murphy, K., Roche, R. A. P. & Stein, E. A. Dissociable executive functions in the dynamic control of behavior: inhibition, error detection, and correction. Neuroimage 17, 1820–1829 (2002).
Owen, A. M., McMillan, K. M., Laird, A. R. & Bullmore, E. N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum. Brain Mapp. 25, 46–59 (2005).
Wager, T. D. & Smith, E. E. Neuroimaging studies of working memory: a meta-analysis. Cogn. Affect. Behav. Neurosci. 3, 255–274 (2003).
Niendam, T. A. et al. Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn. Affect. Behav. Neurosci. 12, 241–268 (2012).
Buckner, R. L., Andrews-Hanna, J. R. & Schacter, D. L. The brain’s default network: anatomy, function, and relevance to disease. Ann. N. Y. Acad. Sci. 1124, 1–38 (2008).
Fox, M. D. et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl Acad. Sci. USA 102, 9673–9678 (2005).
Baird, A. A. et al. Functional magnetic resonance imaging of facial affect recognition in children and adolescents. J. Am. Acad. Child Adolesc. Psychiatry 38, 195–199 (1999).
Hare, T. A. et al. Biological substrates of emotional reactivity and regulation in adolescence during an emotional go-nogo task. Biol. Psychiatry 63, 927–934 (2008).
Heitzeg, M. M. et al. Effect of GABRA2 genotype on development of incentive-motivation circuitry in a sample enriched for alcoholism risk. Neuropsychopharmacology 39, 3077–3086 (2014).
Elliott, M. L. et al. What is the test–retest reliability of common task-functional MRI measures? New empirical evidence and a meta-analysis. Psychol. Sci. 31, 792–806 (2020).
Boehler, C. N., Appelbaum, L. G., Krebs, R. M., Hopf, J.-M. & Woldorff, M. G. The influence of different Stop-signal response time estimation procedures on behavior-behavior and brain-behavior correlations. Behav. Brain Res. 229, 123–130 (2012).
Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43–54 (2018).
Paus, T. Population Neuroscience. (Springer, 2013).
Garavan, H. et al. Recruiting the ABCD sample: design considerations and procedures. Dev. Cogn. Neurosci. 32, 16–22 (2018).
Petersen, A. C., Crockett, L., Richards, M. & Boxer, A. A self-report measure of pubertal status: reliability, validity, and initial norms. J. Youth Adolesc. 17, 117–133 (1988).
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
Hagler, D. J. et al. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. Neuroimage 202, 116091 (2019).
Logan, G. D., Schachar, R. J. & Tannock, R. Impulsivity and inhibitory control. Psychol. Sci. 8, 60–64 (1997).
Cohen, A. O., Conley, M. I., Dellarco, D. V. & Casey, B.J. The impact of emotional cues on short-term and long-term memory during adolescence. In Proc. Society for Neuroscience, San Diego, CA (2016).
Hoehl, S., Brauer, J., Brasse, G., Striano, T. & Friederici, A. D. Children’s processing of emotions expressed by peers and adults: an fMRI study. Soc. Neurosci. 5, 543–559 (2010).
Marusak, H. A., Carré, J. M. & Thomason, M. E. The stimuli drive the response: an fMRI study of youth processing adult or child emotional face stimuli. Neuroimage 83, 679–689 (2013).
Tottenham, N. et al. The NimStim set of facial expressions: judgments from untrained research participants. Psychiatry Res. 168, 242–249 (2009).
Conley, M. I. et al. The racially diverse affective expression (RADIATE) face stimulus set. Psychiatry Res. 270, 1059–1067 (2018).
Kanwisher, N. Neural events and perceptual awareness. Cognition 79, 89–113 (2001).
Park, S. & Chun, M. M. Different roles of the parahippocampal place area (PPA) and retrosplenial cortex (RSC) in panoramic scene perception. Neuroimage 47, 1747–1756 (2009).
Knutson, B., Westdorp, A., Kaiser, E. & Hommer, D. fMRI visualization of brain activity during a monetary incentive delay task. Neuroimage 12, 20–27 (2000).
Yau, W.-Y. W. et al. Nucleus accumbens response to incentive stimuli anticipation in children of alcoholics: relationships with precursive behavioral risk and lifetime alcohol use. J. Neurosci. 32, 2544–2551 (2012).
Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonancenNeuroimages. Comput. Biomed. Res. 29, 162–173 (1996).
Logan, G. D. & Cowan, W. B. On the ability to inhibit thought and action: a theory of an act of control. Psychol. Rev. 91, 295 (1984).
Winkler, A. M., Webster, M. A., Vidaurre, D., Nichols, T. E. & Smith, S. M. Multi-level block permutation. Neuroimage 123, 253–268 (2015).
Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53, 1–15 (2010).
Acknowledgements
Data used in the preparation of this article were obtained from the ABCD Study (https://abcdstudy.org) held in the NDA. This is a multisite, longitudinal study designed to recruit more than 10,000 children ages 9–10 years old 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 U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123 and U24DA041147. 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/. ABCD consortium investigators designed and implemented the study and/or provided data, but did not necessarily participate in the analysis or writing of this report. Most ABCD research sites rely on a central Institutional Review Board (IRB) at the University of California, San Diego, for the ethical review and approval of the research protocol, with a few sites obtaining local IRB approval. The views expressed in this manuscript are those of the authors and do not necessarily reflect the official views of the National Institutes of Health, the Department of Health and Human Services, the US federal government or ABCD consortium investigators. Computations were performed on the Vermont Advanced Computing Core supported, in part, by NSF award number OAC-1827314. A. Ivanciu and E. Pearson helped with the submission process. G. Dowling was substantially involved in all of the cited grants, M. Lopez and J. Matochik were substantially involved in U24DA041147, and S. Grant and A. Noronha were substantially involved in U24DA041123, consistent with their roles as Scientific Officers. All other Federal representatives contributed to the interpretation of the data and participated in the preparation, review and approval of the manuscript, consistent with their roles on the ABCD Federal Partners Group. The views and opinions expressed in this manuscript are those of the authors only and do not necessarily represent the views, official policy or position of the U.S. Department of Health and Human Services or any of its affiliated institutions or agencies.
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B.C., N.A., S.Hahn and H.P.G. performed neuroimaging data processing and analysis. B.C., M.M.O., A. Potter and H.P.G. prepared the manuscript. B.C., S. Adise, D.J.H., M.D.C., S. Hatton, A.M.D. and H.P.G. performed the quality control and preprocessing of behavioral and neuroimaging data. B.C., N.A., S. Hahn, S. Adise, M.M.O., A.C.J., D.K.Y., H.L., A. Ivanciu, M.D.A., J.D., S.M., J.L., M.I., D.J.H., M.D.C., S. Hatton, A.A., L. Aguinaldo, L. Ahonen, W.A., A.P.A., J.A., S. Avenevoli, D. Babcock, K.B., F.C.B., M.T.B., D.M.B., H.B., A.B., J.M.B., D. Blachman-Demmer, M.B., R.B., S.Y.B., F.B., S.B., F.J.C., V.C., B.J.C., L.C., D.B.C., C.C., R.T.C., K.C., R.C., L.B.C., S.C., R. K. Dagher, A.M.D., M.D., R. Delcarmen-Wiggins, A.S.D., E.K.D., N.U.D., G.J.D., S.E., T.M.E., D.A.F., C.C.F., E.F., S.W.F., P.F., J.J.F., E.G.F., N.P.F., S.F., B.F.F., A.G., D.G.G., J. Giedd, M. Glantz, P.G., J. Godino, M. Gonzalez, R.G., S.G., K.M.G., F.H., M.P.H., S. Hawes, A.C.H., S. Heeringa, M.M. Heitzeg, R.H., M.M. Herting, J.M.H., J.K.H., C.H., E.H., K.H., R.S.H., M.A.H., L.W.H., W.G.I., M.A.I., O.I., A. Isaiah, S.I., J.J., R.J., B.J., T.J., N.R.K., A. Kaufmann, B. Kelley, B. Kit, A. Ksinan, J.K., A.R. Laird, C. Larson, K. LeBlanc, C. Lessov-Schlagger, N.L., D.A.L., K. Lisdahl, A.R. Little, M. Lopez, M. Luciana, B.L., P.A.M., H.H.M., C. Makowski, A.T.M., M.J.M., J.M., B.D.M., E.M., I.M., G.M., A.M., C. Mulford, P.M., B.J.N., M.C.N., G.N., A. Nencka, A. Noronha, S.J.N., C.E.P., V.P., M.P.P., W.E.P., D. Pfefferbaum, C.P., A. Prescot, D. Prouty, L.I.P., N.R., K.M.R., G.R., P.F.R., M.C.R., P.R., M.R., M.D.R., M.J.R., M. Sanchez, C. Schrida, D.S., J. Schulenberg, K.J.S., C. Sheth, P.D.S., W.K.S., E.R.S., N.S., M. Spittel, L.M.S., C. Sripada, J. Steinberg, C. Striley, M.T.S., J.T., S.F.T., W.T., R.L.T., K.A.U., S.V., N.E.W., R.W., S.W., B.A.W., O.D.W., A. Wilbur, D. Wing, D. Wolff-Hughes, R.Y., D.A.Y., R.A.Z., A. Potter and H.P.G. contributed to the study design, collected the data and reviewed the manuscript.
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Supplementary information
Supplementary Information
Supplementary Notes 1–6, Supplementary Figs. 1–4 and Supplementary Tables 1–3.
Supplementary Software
Python scripts for group-level spatial reproducibility.
Supplementary Data 1
Activation map templates.
Supplementary Data 2
ABCD spatial reproducibility.
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Chaarani, B., Hahn, S., Allgaier, N. et al. Baseline brain function in the preadolescents of the ABCD Study. Nat Neurosci 24, 1176–1186 (2021). https://doi.org/10.1038/s41593-021-00867-9
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DOI: https://doi.org/10.1038/s41593-021-00867-9
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