Distinct brain network features predict internalizing and externalizing traits in children, adolescents and adults
Achenbach, T. M. The child behavior profile: I. Boys aged 6–11. J. Consult. Clin. Psychol. 46, 478–488 (1978).
Google Scholar
Achenbach, T. M. Manual for the Child Behavior Checklist/ 4–18 and 1991 Profile (Univ. Vermont, Department of Psychiatry, 1991).
Duprey, E. B., Oshri, A. & Liu, S. Developmental pathways from child maltreatment to adolescent suicide-related behaviors: the internalizing and externalizing comorbidity hypothesis. Dev. Psychopathol. 32, 945–959 (2020).
Google Scholar
Commisso, M. et al. Childhood externalizing, internalizing and comorbid problems: distinguishing young adults who think about suicide from those who attempt suicide. Psychol. Med. (2021).
Papachristou, E. & Flouri, E. The codevelopment of internalizing symptoms, externalizing symptoms, and cognitive ability across childhood and adolescence. Dev. Psychopathol. 32, 1375–1389 (2020).
Google Scholar
Narusyte, J., Ropponen, A., Alexanderson, K. & Svedberg, P. Internalizing and externalizing problems in childhood and adolescence as predictors of work incapacity in young adulthood. Soc. Psychiatry Psychiatr. Epidemiol. 52, 1159 (2017).
Google Scholar
Sripada, C. et al. Prediction of neurocognition in youth from resting state fMRI. Mol. Psychiatry 25, 3413–3421 (2019).
Google Scholar
Shannon, B. J. et al. Premotor functional connectivity predicts impulsivity in juvenile offenders. Proc. Natl Acad. Sci. USA 108, 11241–11245 (2011).
Google Scholar
Uddin, L. Q. et al. Salience network–based classification and prediction of symptom severity in children with autism. JAMA Psychiatry 70, 869 (2013).
Google Scholar
Lake, E. M. R. et al. The functional brain organization of an individual allows prediction of measures of social abilities transdiagnostically in autism and attention-deficit/hyperactivity disorder. Biol. Psychiatry 86, 315–326 (2019).
Google Scholar
Chen, J. et al. Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study. Nat. Commun. 13, 2217 (2022).
Google Scholar
Ooi, L. Q. R. et al. Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. Neuroimage 263, 119636 (2022).
Google Scholar
Sydnor, V. J. et al. Neurodevelopment of the association cortices: patterns, mechanisms, and implications for psychopathology. Neuron 109, 2820–2846 (2021).
Google Scholar
Hwang, K., Hallquist, M. N. & Luna, B. The development of hub architecture in the human functional brain network. Cereb. Cortex 23, 2380–2393 (2013).
Google Scholar
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).
Google Scholar
Volkow, N. D. et al. The conception of the ABCD study: from substance use to a broad NIH collaboration. Dev. Cogn. Neurosci. 32, 4–7 (2018).
Google Scholar
Alexander, L. M. et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci. Data 4, 170181 (2017).
Google Scholar
Van Essen, D. C. et al. The WU-minn human connectome project: An overview. Neuroimage 80, 62 (2013).
Google Scholar
Mackinnon, J. G. in Handbook of Computational Econometrics (eds Belsley, D. A. & Kontoghiorghes, E. J.) 183–213 (WIley, 2009); https://doi.org/10.1002/9780470748916.CH6
Achenbach, T. M. & Rescorla, L. A. Manual for ASEBA School-Age Forms & Profiles (ASEBA, 2001).
Achenbach, T. M. & Rescorla, L. A. Manual for the ASEBA Adult Forms & Profiles (ASEBA, 2003).
Schaefer, A. et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114 (2018).
Google Scholar
Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).
Google Scholar
Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654–660 (2022).
Google Scholar
Haufe, S. et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96–110 (2014).
Google Scholar
Dhamala, E., Jamison, K. W., Jaywant, A. & Kuceyeski, A. Shared functional connections within and between cortical networks predict cognitive abilities in adult males and females. Hum. Brain Mapp. 43, 1087–1102 (2022).
Google Scholar
Parkes, L. et al. Transdiagnostic dimensions of psychopathology explain individuals’ unique deviations from normative neurodevelopment in brain structure. Transl. Psychiatry 11, 232 (2021).
Google Scholar
Brislin, S. J. et al. Differentiated nomological networks of internalizing, externalizing, and the general factor of psychopathology (‘p factor’) in emerging adolescence in the ABCD study. Psychol. Med. 52, 3051–3061 (2022).
Google Scholar
Caspi, A. et al. The p factor: one general psychopathology factor in the structure of psychiatric disorders? Clin. Psychol. Sci. 2, 119 (2014).
Google Scholar
Yeo, B. T. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).
Google Scholar
Krueger, R. F., Markon, K. E., Patrick, C. J., Benning, S. D. & Kramer, M. D. Linking antisocial behavior, substance use, and personality: an integrative quantitative model of the adult externalizing spectrum. J. Abnorm. Psychol. 116, 645–666 (2007).
Google Scholar
Eaton, N. R. et al. An invariant dimensional liability model of gender differences in mental disorder prevalence: evidence from a national sample. J. Abnorm. Psychol. 121, 282–288 (2012).
Google Scholar
Achenbach, T. M. & Edelbrock, C. S. Psychopathology of childhood. Annu. Rev. Psychol. 35, 227–256 (2003).
Google Scholar
Kessler, R. C. et al. Development of lifetime comorbidity in the world health organization world mental health surveys. Arch. Gen. Psychiatry 68, 90–100 (2011).
Google Scholar
Ringwald, W. R., Forbes, M. K. & Wright, A. G. C. Meta-analytic tests of measurement invariance of internalizing and externalizing psychopathology across common methodological characteristics. J. Psychopathol. Clin. Sci. 131, 847–856 (2022).
Google Scholar
Rosenberg, M. D., Finn, E. S., Scheinost, D., Constable, R. T. & Chun, M. M. Characterizing attention with predictive network models. Trends Cogn. Sci. 21, 290–302 (2017).
Google Scholar
Rosenberg, M. D. et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci. 19, 165–171 (2015).
Google Scholar
Satterthwaite, T. D. et al. Connectome-wide network analysis of youth with psychosis-spectrum symptoms. Mol. Psychiatry 20, 1508–1515 (2015).
Google Scholar
Pornpattananangkul, N., Leibenluft, E., Pine, D. S. & Stringaris, A. Association between childhood anhedonia and alterations in large-scale resting-state networks and task-evoked activation. JAMA Psychiatry 76, 624–633 (2019).
Google Scholar
Karcher, N. R., O’Brien, K. J., Kandala, S. & Barch, D. M. Resting-state functional connectivity and psychotic-like experiences in childhood: results from the adolescent brain cognitive development study. Biol. Psychiatry 86, 7–15 (2019).
Google Scholar
He, T. et al. Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. Neuroimage 206, 116276 (2020).
Google Scholar
Narumoto, J., Okada, T., Sadato, N., Fukui, K. & Yonekura, Y. Attention to emotion modulates fMRI activity in human right superior temporal sulcus. Cogn. Brain Res. 12, 225–231 (2001).
Google Scholar
Mellem, M. S., Jasmin, K. M., Peng, C. & Martin, A. Sentence processing in anterior superior temporal cortex shows a social-emotional bias. Neuropsychologia 89, 217–224 (2016).
Google Scholar
Völlm, B. A. et al. Neuronal correlates of theory of mind and empathy: a functional magnetic resonance imaging study in a nonverbal task. Neuroimage 29, 90–98 (2006).
Google Scholar
Bigler, E. D. et al. Superior temporal gyrus, language function, and autism. Dev. Neuropsychol. 31, 217–238 (2007).
Google Scholar
Weinrich, M., Wise, S. P. & Mauritz, K. H. A neurophysiological study of the premotor cortex in the rhesus monkey. Brain 107, 385–414 (1984).
Google Scholar
Cunnington, R., Windischberger, C., Deecke, L. & Moser, E. The preparation and readiness for voluntary movement: a high-field event-related fMRI study of the Bereitschafts-BOLD response. Neuroimage 20, 404–412 (2003).
Google Scholar
Kwan, H. C., MacKay, W. A., Murphy, J. T. & Wong, Y. C. Spatial organization of precentral cortex in awake primates. II. Motor outputs. J. Neurophysiol. 41, 1120–1131 (1978).
Google Scholar
Penfield, W. & Boldrey, E. Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain 60, 389–443 (1937).
Google Scholar
Lees, B. et al. Altered neurocognitive functional connectivity and activation patterns underlie psychopathology in preadolescence. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 6, 387–398 (2021).
Google Scholar
Grayson, D. S. & Fair, D. A. Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature. Neuroimage 160, 15 (2017).
Google Scholar
Galván, A. & Tottenham, N. in Developmental Psychopathology 3rd edn (ed. Cicchetti, D.) Ch. 18 (Wiley, 2016); https://doi.org/10.1002/9781119125556.DEVPSY218
Casey, B. J., Galván, A. & Somerville, L. H. Beyond simple models of adolescence to an integrated circuit-based account: a commentary. Dev. Cogn. Neurosci. 17, 128 (2016).
Google Scholar
van Dijk, K. R. A., Sabuncu, M. R. & Buckner, R. L. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59, 431 (2012).
Google Scholar
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012).
Google Scholar
Li, J. et al. Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity. Sci. Adv. 8, 1812 (2022).
Google Scholar
Dhamala, E., Yeo, B. T. T. & Holmes, A. J. One size does not fit all: methodological considerations for brain-based predictive modeling in psychiatry. Biol. Psychiatry 93, 717–728 (2023).
Google Scholar
Dhamala, E. et al. Brain-based predictions of psychiatric illness-linked behaviors across the sexes. Biol. Psychiatry (2023).
Auchter, A. M. et al. A description of the ABCD organizational structure and communication framework. Dev. Cogn. Neurosci. 32, 8–15 (2018).
Google Scholar
Clark, D. B. et al. Biomedical ethics and clinical oversight in multisite observational neuroimaging studies with children and adolescents: the ABCD experience. Dev. Cogn. Neurosci. 32, 143–154 (2018).
Google Scholar
Li, J. et al. Global signal regression strengthens association between resting-state functional connectivity and behavior. Neuroimage 196, 126–141 (2019).
Google Scholar
Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43 (2018).
Google Scholar
Hagler, D. J. et al. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. Neuroimage 202, 116091 (2019).
Google Scholar
Fischl, B., Liu, A. & Dale, A. M. Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans. Med. Imaging 20, 70–80 (2001).
Google Scholar
Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).
Google Scholar
Fischl, B., Sereno, M. I., Tootell, R. B. H. & Dale, A. M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8, 272 (1999).
Google Scholar
Fischl, B., Sereno, M. I. & Dale, A. M. Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195–207 (1999).
Google Scholar
Ségonne, F. et al. A hybrid approach to the skull stripping problem in MRI. Neuroimage 22, 1060–1075 (2004).
Google Scholar
Ségonne, F., Pacheco, J. & Fischl, B. Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. IEEE Trans. Med. Imaging 26, 518–529 (2007).
Google Scholar
Greve, D. N. & Fischl, B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48, 63 (2009).
Google Scholar
FsFast – Free Surfer wiki. (2011).
Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).
Google Scholar
Gratton, C. et al. Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity. Neuroimage 217, 116866 (2020).
Google Scholar
Gordon, E. M. et al. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex 26, 288–303 (2016).
Google Scholar
Kong, R. et al. Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cereb. Cortex 29, 2533–2551 (2019).
Google Scholar
Power, J. D. et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84, 320–341 (2014).
Google Scholar
Yu, M. et al. Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Hum. Brain Mapp. 39, 4213 (2018).
Google Scholar
Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).
Google Scholar
Van Essen, D. C., Glasser, M. F., Dierker, D. L., Harwell, J. & Coalson, T. Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cereb. Cortex 22, 2241–2262 (2012).
Google Scholar
Jenkinson, M. & Smith, S. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–156 (2001).
Google Scholar
Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, S208–S219 (2004).
Google Scholar
Barch, D. M. et al. Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: rationale and description. Dev. Cogn. Neurosci. 32, 55–66 (2018).
Google Scholar
Finn, E. S. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664–1671 (2015).
Google Scholar
Kong, R. Schaefer2018 parcellation 400-region 17-network in fslr space. Figshare (2019).
Orban, C. Subcortical_regions.png. Figshare (2019).
link
