1932

Abstract

Poverty is associated with changes in brain development and elevates the risk for psychopathology in childhood, adolescence, and adulthood. Although the field is rapidly expanding, there are methodological challenges that raise questions about the validity of current findings. These challenges include the interrelated issues of reliability, effect size, interindividual heterogeneity, and replicability. To address these issues, we propose a multipronged approach that spans short-, medium-, and long-term solutions, including changes to data pipelines along with more comprehensive data acquisition of environment, brain, and mental health. Additional suggestions are to use open science approaches, more robust statistical analyses, and replication testing. Furthermore, we propose increased integration between advanced analytical approaches using large samples and neuroscience models in intervention research to enhance the interpretability of findings. Collectively, these approaches will expand the application of neuroimaging findings and provide a foundation for eventual policy changes designed to improve conditions for children in poverty.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-devpsych-011922-012402
2023-12-11
2024-04-25
Loading full text...

Full text loading...

/deliver/fulltext/devpsych/5/1/annurev-devpsych-011922-012402.html?itemId=/content/journals/10.1146/annurev-devpsych-011922-012402&mimeType=html&fmt=ahah

Literature Cited

  1. Akee RKQ, Copeland WE, Keeler G, Angold A, Costello EJ. 2010. Parents’ incomes and children's outcomes: a quasi-experiment using transfer payments from casino profits. Am. Econ. J. Appl. Econ. 2:186115
    [Google Scholar]
  2. Alexander LM, Escalera J, Ai L, Andreotti C, Febre K et al. 2017. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci. Data 4:1170181
    [Google Scholar]
  3. Arredondo MM. 2021. Shining a light on cultural neuroscience: recommendations on the use of fNIRS to study how sociocultural contexts shape the brain. Cult. Divers. Ethn. Minor. Psychol. 29:110617
    [Google Scholar]
  4. Arredondo MM, Garcini LM, McLaughlin KA. 2022. Integration of equity and diversity frameworks to advance biological psychiatry. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 7:12119899
    [Google Scholar]
  5. Axinn WG, Choi KW, Ghimire DJ, Cole F, Hermosilla S et al. 2022. Community-level social support infrastructure and adult onset of major depressive disorder in a South Asian postconflict setting. JAMA Psychiatry 79:324349
    [Google Scholar]
  6. Bandettini PA, Gonzalez-Castillo J, Handwerker D, Taylor P, Chen G et al. 2022. The challenge of BWAs: unknown unknowns in feature space and variance. Med 3:852631
    [Google Scholar]
  7. Birn RM, Molloy EK, Patriat R, Parker T, Meier TB et al. 2013. The effect of scan length on the reliability of resting-state fMRI connectivity estimates. NeuroImage 83:55058
    [Google Scholar]
  8. Biswal BB, Mennes M, Zuo X-N, Gohel S, Kelly C et al. 2010. Toward discovery science of human brain function. PNAS 107:10473439
    [Google Scholar]
  9. Biswal BB, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34:453741
    [Google Scholar]
  10. Bloom PA, VanTieghem M, Gabard-Durnam L, Gee DG, Flannery J et al. 2022. Age-related change in task-evoked amygdala—prefrontal circuitry: a multiverse approach with an accelerated longitudinal cohort aged 4–22 years. Hum. Brain Mapp. 43:10322144
    [Google Scholar]
  11. Boyce WT, Ellis BJ. 2005. Biological sensitivity to context: I. An evolutionary-developmental theory of the origins and functions of stress reactivity. Dev. Psychopathol. 17:2271301
    [Google Scholar]
  12. Brito NH, Noble KG. 2014. Socioeconomic status and structural brain development. Front. Neurosci. 8:276
    [Google Scholar]
  13. Brooks-Gunn J, Duncan GJ. 1997. The effects of poverty on children. Future Child. 7:25571
    [Google Scholar]
  14. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J et al. 2013. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14:536576
    [Google Scholar]
  15. Bzdok D, Meyer-Lindenberg A. 2018. Machine learning for precision psychiatry: opportunities and challenges. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3:322330
    [Google Scholar]
  16. Carp J. 2013. Better living through transparency: improving the reproducibility of fMRI results through comprehensive methods reporting. Cogn. Affect. Behav. Neurosci. 13:366066
    [Google Scholar]
  17. Casey BJ, Cannonier T, Conley MI, Cohen AO, Barch DM et al. 2018. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32:4354
    [Google Scholar]
  18. Chen G, Pine DS, Brotman MA, Smith AR, Cox RW et al. 2021. Trial and error: a hierarchical modeling approach to test-retest reliability. NeuroImage 245:118647
    [Google Scholar]
  19. Chen J, Tam A, Kebets V, Orban C, Ooi LQR et al. 2022. Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study. Nat Commun. 13:12217
    [Google Scholar]
  20. Chetty R, Hendren N, Katz LF. 2016. The effects of exposure to better neighborhoods on children: new evidence from the Moving to Opportunity experiment. Am. Econ. Rev. 106:4855902
    [Google Scholar]
  21. Chopra S, Dhamala E, Lawhead C, Ricard JA, Orchard ER et al. 2022. Reliable and generalizable brain-based predictions of cognitive functioning across common psychiatric illness. medRxiv 2228323. https://doi.org/10.1101/2022.12.08.22283232
  22. Cicchetti D, Rogosch FA. 1996. Equifinality and multifinality in developmental psychopathology. Dev. Psychopathol. 8:4597600
    [Google Scholar]
  23. Cohen AD, Yang B, Fernandez B, Banerjee S, Wang Y. 2021. Improved resting state functional connectivity sensitivity and reproducibility using a multiband multi-echo acquisition. NeuroImage 225:117461
    [Google Scholar]
  24. Conger RD, Wallace LE, Sun Y, Simons RL, McLoyd VC et al. 2002. Economic pressure in African American families: a replication and extension of the family stress model. Dev. Psychol. 38:217993
    [Google Scholar]
  25. Cosgrove KT, McDermott TJ, White EJ, Mosconi MW, Thompson WK et al. 2022. Limits to the generalizability of resting-state functional magnetic resonance imaging studies of youth: an examination of ABCD Study® baseline data. Brain Imaging Behav. 16:191925
    [Google Scholar]
  26. Costello EJ, Compton SN, Keeler G, Angold A. 2003. Relationships between poverty and psychopathology: a natural experiment. JAMA 290:15202329
    [Google Scholar]
  27. Dahl GB, Lochner L. 2012. The impact of family income on child achievement: evidence from the Earned Income Tax Credit. Am. Econ. Rev. 102:5192756
    [Google Scholar]
  28. Dhollander T, Clemente A, Singh M, Boonstra F, Civier O et al. 2021. Fixel-based analysis of diffusion MRI: methods, applications, challenges and opportunities. NeuroImage 241:118417
    [Google Scholar]
  29. Di Martino A, Yan C-G, Li Q, Denio E, Castellanos FX et al. 2014. The autism brain imaging data exchange: towards large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19:665967
    [Google Scholar]
  30. Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F et al. 2017. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 23:12838
    [Google Scholar]
  31. Duncan GJ, Magnuson KA. 2003. Off with Hollingshead: socioeconomic resources, parenting, and child development. Socioeconomic Status, Parenting, and Child Development MH Bornstein, RH Bradley 83106. Mahwah, NJ: Lawrence Erlbaum Assoc.
    [Google Scholar]
  32. Duncan GJ, Magnuson K, Votruba-Drzal E. 2014. Boosting family income to promote child development. Future Child. 24:199120
    [Google Scholar]
  33. Duncan GJ, Morris PA, Rodrigues C. 2011. Does money really matter? Estimating impacts of family income on young children's achievement with data from random-assignment experiments. Dev. Psychol. 47:126379
    [Google Scholar]
  34. Durkin K, Lipsey MW, Dale CF, Wiesen SE. 2022. Effects of a statewide pre-kindergarten program on children's achievement and behavior through sixth grade. Dev. Psychol. 58:347084
    [Google Scholar]
  35. Durstewitz D, Koppe G, Meyer-Lindenberg A. 2019. Deep neural networks in psychiatry. Mol. Psychiatry 24:11158398
    [Google Scholar]
  36. Elliott ML, Knodt AR, Cooke M, Kim MJ, Melzer TR et al. 2019. General functional connectivity: shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks. NeuroImage 189:51632
    [Google Scholar]
  37. Elliott ML, Knodt AR, Hariri AR. 2021. Striving toward translation: strategies for reliable fMRI measurement. Trends Cogn. Sci. 25:977687
    [Google Scholar]
  38. Elliott ML, Knodt AR, Ireland D, Morris ML, Poulton R et al. 2020. What is the test-retest reliability of common task-functional MRI measures? New empirical evidence and a meta-analysis. Psychol. Sci. 31:7792806
    [Google Scholar]
  39. Ellwood-Lowe ME, Whitfield-Gabrieli S, Bunge SA 2021. Brain network coupling associated with cognitive performance varies as a function of a child's environment in the ABCD study. Nat. Commun. 12:17183
    [Google Scholar]
  40. Evans GW. 2004. The environment of childhood poverty. Am. Psychol. 59:27792
    [Google Scholar]
  41. Fair DA, Dosenbach NUF, Moore AH, Satterthwaite TD, Milham MP. 2021. Developmental cognitive neuroscience in the era of networks and big data: strengths, weaknesses, opportunities, and threats. Annu. Rev. Dev. Psychol. 3:24975
    [Google Scholar]
  42. Falk EB, Hyde LW, Mitchell C, Faul J, Gonzalez R et al. 2013. What is a representative brain? Neuroscience meets population science. PNAS 110:441761522
    [Google Scholar]
  43. Feczko E, Miranda-Dominguez O, Marr M, Graham AM, Nigg JT et al. 2019. The heterogeneity problem: approaches to identify psychiatric subtypes. Trends Cogn. Sci. 23:7584601
    [Google Scholar]
  44. Feilong M, Guntupalli JS, Haxby JV. 2021. The neural basis of intelligence in fine-grained cortical topographies. eLife 10:e64058
    [Google Scholar]
  45. Ferschmann L, Bos MGN, Herting MM, Mills KL, Tamnes CK. 2022. Contextualizing adolescent structural brain development: environmental determinants and mental health outcomes. Curr. Opin. Psychol. 44:17076
    [Google Scholar]
  46. Finn ES. 2021. Is it time to put rest to rest?. Trends Cogn. Sci. 25:12102132
    [Google Scholar]
  47. Finn ES, Bandettini PA. 2021. Movie-watching outperforms rest for functional connectivity-based prediction of behavior. NeuroImage 235:117963
    [Google Scholar]
  48. Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J et al. 2015. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18:11166471
    [Google Scholar]
  49. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC et al. 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. PNAS 102:27967378
    [Google Scholar]
  50. Garavan H, Bartsch H, Conway K, Decastro A, Goldstein RZ et al. 2018. Recruiting the ABCD sample: design considerations and procedures. Dev. Cogn. Neurosci. 32:1622
    [Google Scholar]
  51. Garcini LM, Arredondo MM, Berry O, Church JA, Fryberg S et al. 2022. Increasing diversity in developmental cognitive neuroscience: a roadmap for increasing representation in pediatric neuroimaging research. Dev. Cogn. Neurosci. 58:101167
    [Google Scholar]
  52. Gard AM, Hyde LW, Heeringa SG, West BT, Mitchell C. 2023. Why weight? Analytic approaches for large-scale population neuroscience data. Dev. Cogn. Neurosci. 59:101196
    [Google Scholar]
  53. Garmezy N. 1993. Children in poverty: resilience despite risk. Psychiatry 56:112736
    [Google Scholar]
  54. Gershoff ET, Aber JL, Raver CC, Lennon MC. 2007. Income is not enough: incorporating material hardship into models of income associations with parenting and child development. Child Dev. 78:17095
    [Google Scholar]
  55. Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H et al. 1999. Brain development during childhood and adolescence: a longitudinal MRI study. Nat. Neurosci. 2:1086163
    [Google Scholar]
  56. Goetschius LG, Hein TC, McLanahan SS, Brooks-Gunn J, McLoyd VC et al. 2020a. Association of childhood violence exposure with adolescent neural network density. JAMA Netw. Open 3:9e2017850
    [Google Scholar]
  57. Goetschius LG, Hein TC, Mitchell C, Lopez-Duran NL, McLoyd VC et al. 2020b. Childhood violence exposure and social deprivation predict adolescent amygdala-orbitofrontal cortex white matter connectivity. Dev. Cogn. Neurosci. 45:100849
    [Google Scholar]
  58. Goodlad JK, Marcus DK, Fulton JJ. 2013. Lead and attention-deficit/hyperactivity disorder (ADHD) symptoms: a meta-analysis. Clin. Psychol. Rev. 33:341725
    [Google Scholar]
  59. Gordon EM, Laumann TO, Gilmore AW, Newbold DJ, Greene DJ et al. 2017. Precision functional mapping of individual human brains. Neuron 95:4791807.e7
    [Google Scholar]
  60. Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S et al. 2016. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci. Data 3:1160044
    [Google Scholar]
  61. Gratton C, Kraus BT, Greene DJ, Gordon EM, Laumann TO et al. 2020. Defining individual-specific functional neuroanatomy for precision psychiatry. Biol. Psychiatry 88:12839
    [Google Scholar]
  62. Gratton C, Laumann TO, Nielsen AN, Greene DJ, Gordon EM et al. 2018. Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron 98:243952.e5
    [Google Scholar]
  63. Gratton C, Nelson SM, Gordon EM. 2022. Brain-behavior correlations: two paths toward reliability. Neuron 110:9144649
    [Google Scholar]
  64. Greene AS, Gao S, Scheinost D, Constable RT. 2018. Task-induced brain state manipulation improves prediction of individual traits. Nat. Commun. 9:12807
    [Google Scholar]
  65. Greene AS, Shen X, Noble S, Horien C, Hahn CA et al. 2022. Brain-phenotype models fail for individuals who defy sample stereotypes. Nature 609:792510918
    [Google Scholar]
  66. Hackman DA, Farah MJ. 2009. Socioeconomic status and the developing brain. Trends Cogn. Sci. 13:26573
    [Google Scholar]
  67. Hair NL, Hanson JL, Wolfe BL, Pollak SD. 2015. Association of child poverty, brain development, and academic achievement. JAMA Pediatr. 169:982229
    [Google Scholar]
  68. Hanson JL, Hair N, Shen DG, Shi F, Gilmore JH et al. 2013. Family poverty affects the rate of human infant brain growth. PLOS ONE 8:12e80954
    [Google Scholar]
  69. Hardi FA, Goetschius LG, McLoyd V, Lopez-Duran NL, Mitchell C et al. 2023a. Adolescent functional network connectivity prospectively predicts adult anxiety symptoms related to perceived COVID-19 economic adversity. J. Child Psychol. Psychiatry 64:691829
    [Google Scholar]
  70. Hardi FA, Goetschius LG, Tillem S, McLoyd V, Brooks-Gunn J et al. 2023b. Early childhood household instability, adolescent structural neural network architecture, and young adulthood depression: a 21-year longitudinal study. Dev. Cogn. Neurosci. 61:101253
    [Google Scholar]
  71. Hardi FA, Goetschius LG, Peckins MK, Brooks-Gunn J, McLanahan SS et al. 2022. Differential developmental associations of material hardship exposure and adolescent amygdala–prefrontal cortex white matter connectivity. J. Cogn. Neurosci. 34:10186691
    [Google Scholar]
  72. Hardoon DR, Szedmak S, Shawe-Taylor J. 2004. Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16:12263964
    [Google Scholar]
  73. Hardt J, Rutter M. 2004. Validity of adult retrospective reports of adverse childhood experiences: review of the evidence. J. Child Psychol. Psychiatry 45:226073
    [Google Scholar]
  74. Harms MP, Somerville LH, Ances BM, Andersson J, Barch DM et al. 2018. Extending the Human Connectome Project across ages: imaging protocols for the Lifespan Development and Aging projects. NeuroImage 183:97284
    [Google Scholar]
  75. Haxby JV, Guntupalli JS, Connolly AC, Halchenko YO, Conroy BR et al. 2011. A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron 72:240416
    [Google Scholar]
  76. He T, An L, Chen P, Chen J, Feng J et al. 2022. Meta-matching as a simple framework to translate phenotypic predictive models from big to small data. Nat. Neurosci. 25:6795804
    [Google Scholar]
  77. Heeringa SG, West BT, Berglund PA. 2017. Applied Survey Data Analysis Boca Raton, FL: CRC Press
  78. Hein TC, Goetschius LG, McLoyd VC, Brooks-Gunn J, McLanahan SS et al. 2020. Childhood violence exposure and social deprivation are linked to adolescent threat and reward neural function. Soc. Cogn. Affect. Neurosci. 15:11125259
    [Google Scholar]
  79. Hensch TK, Bilimoria PM. 2012. Re-opening windows: manipulating critical periods for brain development. Cerebrum 2012:11
    [Google Scholar]
  80. Jensen SKG, Berens AE, Nelson CA. 2017. Effects of poverty on interacting biological systems underlying child development. Lancet Child Adolesc. Health 1:322539
    [Google Scholar]
  81. Jiang R, Calhoun VD, Cui Y, Qi S, Zhuo C et al. 2020. Multimodal data revealed different neurobiological correlates of intelligence between males and females. Brain Imaging Behav. 14:5197993
    [Google Scholar]
  82. Kaufman J, Birmaher B, Brent D, Rao U, Flynn C et al. 1997. Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J. Am. Acad. Child Adolesc. Psychiatry 36:798088
    [Google Scholar]
  83. Kessler RC, Duncan GJ, Gennetian LA, Katz LF, Kling JR et al. 2014. Associations of housing mobility interventions for children in high-poverty neighborhoods with subsequent mental disorders during adolescence. JAMA 311:993747
    [Google Scholar]
  84. Keyes KM, Gary D, O'Malley PM, Hamilton A, Schulenberg J. 2019. Recent increases in depressive symptoms among US adolescents: trends from 1991 to 2018. Soc. Psychiatry Psychiatr. Epidemiol. 54:898796
    [Google Scholar]
  85. Kragel PA, Han X, Kraynak TE, Gianaros PJ, Wager TD. 2021. Functional MRI can be highly reliable, but it depends on what you measure: a commentary on Elliott et al. 2020. Psychol. Sci. 32:462226
    [Google Scholar]
  86. Kundu P, Brenowitz ND, Voon V, Worbe Y, Vértes PE et al. 2013. Integrated strategy for improving functional connectivity mapping using multiecho fMRI. PNAS 110:401618792
    [Google Scholar]
  87. Lazzarino AI, Hamer M, Stamatakis E, Steptoe A. 2013. The combined association of psychological distress and socioeconomic status with all-cause mortality: a national cohort study. JAMA Intern. Med. 173:12227
    [Google Scholar]
  88. Li X, Ai L, Giavasis S, Jin H, Feczko E et al. 2022. Moving beyond processing and analysis-related variation in neuroscience. bioRxiv 470790. https://doi.org/10.1101/2021.12.01.470790
  89. Lindsay DS. 2015. Replication in psychological science. Psychol. Sci. 26:12182732
    [Google Scholar]
  90. Lynch CJ, Power JD, Scult MA, Dubin M, Gunning FM et al. 2020. Rapid precision functional mapping of individuals using multi-echo fMRI. Cell Rep. 33:12108540
    [Google Scholar]
  91. Månsson KNT, Waschke L, Manzouri A, Furmark T, Fischer H et al. 2022. Moment-to-moment brain signal variability reliably predicts psychiatric treatment outcome. Biol. Psychiatry 91:765866
    [Google Scholar]
  92. Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP et al. 2022. Reproducible brain-wide association studies require thousands of individuals. Nature 603:790265460
    [Google Scholar]
  93. Marquand AF, Kia SM, Zabihi M, Wolfers T, Buitelaar JK et al. 2019. Conceptualizing mental disorders as deviations from normative functioning. Mol. Psychiatry 24:10141524
    [Google Scholar]
  94. Masten AS, Lucke CM, Nelson KM, Stallworthy IC. 2021. Resilience in development and psychopathology: multisystem perspectives. Annu. Rev. Clin. Psychol. 17:52149
    [Google Scholar]
  95. McLaughlin KA, Sheridan MA. 2016. Beyond cumulative risk: a dimensional approach to childhood adversity. Curr. Dir. Psychol. Sci. 25:423945
    [Google Scholar]
  96. McLoyd VC. 1998. Socioeconomic disadvantage and child development. Am. Psychol. 20:185204
    [Google Scholar]
  97. McLoyd VC. 2019. How children and adolescents think about, make sense of, and respond to economic inequality: Why does it matter?. Dev. Psychol. 55:3592600
    [Google Scholar]
  98. Milne BJ, D'Souza S, Andersen SH, Richmond-Rakerd LS 2022. Use of population-level administrative data in developmental science. Annu. Rev. Dev. Psychol. 4:44768
    [Google Scholar]
  99. Miranda-Dominguez O, Mills BD, Carpenter SD, Grant KA, Kroenke CD et al. 2014. Connectotyping: model based fingerprinting of the functional connectome. PLOS ONE 9:11e111048
    [Google Scholar]
  100. Molenaar PCM. 2004. A manifesto on psychology as idiographic science: bringing the person back into scientific psychology, this time forever. Meas. Interdiscip. Res. Perspect. 2:420118
    [Google Scholar]
  101. Montag C, Quintana DS. 2023. Digital phenotyping in molecular psychiatry—a missed opportunity?. Mol. Psychiatry 28:169
    [Google Scholar]
  102. Mueller CW, Parcel TL. 1981. Measures of socioeconomic status: alternatives and recommendations. Child Dev. 52:11330
    [Google Scholar]
  103. Mueller S, Wang D, Fox MD, Yeo BTT, Sepulcre J et al. 2013. Individual variability in functional connectivity architecture of the human brain. Neuron 77:358695
    [Google Scholar]
  104. Natl. Inst. Child Health Hum. Dev. Early Child Care Res. Netw 2005. Duration and developmental timing of poverty and children's cognitive and social development from birth through third grade. Child Dev. 76:4795810
    [Google Scholar]
  105. Nielsen AN, Barch DM, Petersen SE, Schlaggar BL, Greene DJ. 2020. Machine learning with neuroimaging: evaluating its applications in psychiatry. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 5:879198
    [Google Scholar]
  106. Nikolaidis A, Chen AA, He X, Shinohara R, Vogelstein J et al. 2022. Suboptimal phenotypic reliability impedes reproducible human neuroscience. bioRxiv 501193. https://doi.org/10.1101/2022.07.22.501193
  107. Noble S, Spann MN, Tokoglu F, Shen X, Constable RT et al. 2017. Influences on the test-retest reliability of functional connectivity MRI and its relationship with behavioral utility. Cereb. Cortex 27:11541529
    [Google Scholar]
  108. Novak NM, Stein JL, Medland SE, Hibar DP, Thompson PM et al. 2012. EnigmaVis: online interactive visualization of genome-wide association studies of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. Twin Res. Hum. Genet. 15:341418
    [Google Scholar]
  109. Ocloo J, Matthews R. 2016. From tokenism to empowerment: progressing patient and public involvement in healthcare improvement. BMJ Qual. Saf. 25:862632
    [Google Scholar]
  110. Ooi LQR, Chen J, Shaoshi Z, Kong R, Tam A et al. 2022. Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. NeuroImage 263:119636
    [Google Scholar]
  111. Orrù G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. 2012. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. 36:4114052
    [Google Scholar]
  112. Parkes L, Fulcher B, Yücel M, Fornito A. 2018. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage 171:41536
    [Google Scholar]
  113. Poldrack RA, Baker CI, Durnez J, Gorgolewski KJ, Matthews PM et al. 2017. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci. 18:211526
    [Google Scholar]
  114. Poser BA, Versluis MJ, Hoogduin JM, Norris DG. 2006. BOLD contrast sensitivity enhancement and artifact reduction with multiecho EPI: parallel-acquired inhomogeneity-desensitized fMRI. Magn. Reson. Med. 55:6122735
    [Google Scholar]
  115. Price RB, Lane S, Gates K, Kraynak TE, Horner MS et al. 2017. Parsing heterogeneity in the brain connectivity of depressed and healthy adults during positive mood. Biol. Psychiatry 81:434757
    [Google Scholar]
  116. Pronk T, Molenaar D, Wiers RW, Murre J. 2022. Methods to split cognitive task data for estimating split-half reliability: a comprehensive review and systematic assessment. Psychon. Bull. Rev. 29:14454
    [Google Scholar]
  117. Rakesh D, Whittle S. 2021. Socioeconomic status and the developing brain—a systematic review of neuroimaging findings in youth. Neurosci. Biobehav. Rev. 130:379407
    [Google Scholar]
  118. Regier DA, Narrow WE, Clarke DE, Kraemer HC, Kuramoto SJ et al. 2013. DSM-5 field trials in the United States and Canada, part II: test-retest reliability of selected categorical diagnoses. Am. J. Psychiatry 170:15970
    [Google Scholar]
  119. Reichman NE, Teitler JO, Garfinkel I, McLanahan SS. 2001. Fragile Families: sample and design. Child. Youth. Serv. Rev. 23:430326
    [Google Scholar]
  120. Ricard JA, Parker TC, Dhamala E, Kwasa J, Allsop A et al. 2023. Confronting racially exclusionary practices in the acquisition and analyses of neuroimaging data. Nat. Neurosci. 26:1411
    [Google Scholar]
  121. Rutherford S. 2020. The promise of machine learning for psychiatry. Biol. Psychiatry 88:11E5355
    [Google Scholar]
  122. Rutherford S, Kia SM, Wolfers T, Fraza C, Zabihi M et al. 2022. The normative modeling framework for computational psychiatry. Nat. Protoc. 17:7171134
    [Google Scholar]
  123. Saez E, Zucman G. 2014. Wealth inequality in the United States since 1913: evidence from capitalized income tax data. Q. J. Econ. 131:251978
    [Google Scholar]
  124. Satterthwaite TD, Elliott MA, Ruparel K, Loughead J, Prabhakaran K et al. 2014. Neuroimaging of the Philadelphia Neurodevelopmental Cohort. NeuroImage 86:54453
    [Google Scholar]
  125. Schmaal L. 2022. The search for clinically useful neuroimaging markers of depression—a worthwhile pursuit or a futile quest?. JAMA Psychiatry 79:984546
    [Google Scholar]
  126. Schulz M-A, Bzdok D, Haufe S, Haynes J-D, Ritter K. 2022. Performance reserves in brain-imaging-based phenotype prediction. bioRxiv 470790. https://doi.org/10.1101/2021.12.01.470790
  127. Seider NA, Adeyemo B, Miller R, Newbold DJ, Hampton JM et al. 2022. Accuracy and reliability of diffusion imaging models. NeuroImage 254:119138
    [Google Scholar]
  128. Shankman SA, Funkhouser CJ, Klein DN, Davila J, Lerner D et al. 2018. Reliability and validity of severity dimensions of psychopathology assessed using the Structured Clinical Interview for DSM-5 (SCID). Int. J. Methods Psychiatr. Res. 27:1e1590
    [Google Scholar]
  129. Shavers VL. 2007. Measurement of socioeconomic status in health disparities research. J. Natl. Med. Assoc. 99:9101323
    [Google Scholar]
  130. Somerville LH, Bookheimer SY, Buckner RL, Burgess GC, Curtiss SW et al. 2018. The Lifespan Human Connectome Project in Development: a large-scale study of brain connectivity development in 5–21 year olds. NeuroImage 183:45668
    [Google Scholar]
  131. Spisak T, Bingel U, Wager T. 2022. Replicable multivariate BWAS with moderate sample sizes. . bioRxiv 497072. https://doi.org/10.1101/2022.06.22.497072
  132. Sui J, Jiang R, Bustillo J, Calhoun V. 2020. Neuroimaging-based individualized prediction of cognition and behavior for mental disorders and health: methods and promises. Biol. Psychiatry 88:1181828
    [Google Scholar]
  133. Sui J, Qi S, van Erp TGM, Bustillo J, Jiang R et al. 2018. Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion. Nat. Commun. 9:13028
    [Google Scholar]
  134. Taylor RL, Cooper SR, Jackson JJ, Barch DM. 2020. Assessment of neighborhood poverty, cognitive function, and prefrontal and hippocampal volumes in children. JAMA Netw. Open 3:11e2023774
    [Google Scholar]
  135. Tejavibulya L, Rolison M, Gao S, Liang Q, Peterson H et al. 2022. Predicting the future of neuroimaging predictive models in mental health. Mol. Psychiatry 27:8312937
    [Google Scholar]
  136. Tiego J, Martin EA, DeYoung CG, Hagan K, Cooper SE et al. 2023. Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology. Nat. Mental Health 1:30415
    [Google Scholar]
  137. Tomasi D, Volkow ND. 2021. Associations of family income with cognition and brain structure in USA children: prevention implications. Mol. Psychiatry 26:11661929
    [Google Scholar]
  138. Tooley UA, Bassett DS, Mackey AP. 2021. Environmental influences on the pace of brain development. Nat. Rev. Neurosci. 22:637284
    [Google Scholar]
  139. Tournier J-D, Calamante F, Connelly A. 2012. MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 22:15366
    [Google Scholar]
  140. Townsend L, Kobak K, Kearney C, Milham M, Andreotti C et al. 2020. Development of three web-based computerized versions of the Kiddie Schedule for Affective Disorders and Schizophrenia child psychiatric diagnostic interview: preliminary validity data. J. Am. Acad. Child Adolesc. Psychiatry 59:230925
    [Google Scholar]
  141. Troller-Renfree SV, Costanzo MA, Duncan GJ, Magnuson K, Gennetian LA et al. 2022. The impact of a poverty reduction intervention on infant brain activity. PNAS 119:5e2115649119
    [Google Scholar]
  142. Viruell-Fuentes EA, Miranda PY, Abdulrahim S. 2012. More than culture: structural racism, intersectionality theory, and immigrant health. Soc. Sci. Med. 75:122099106
    [Google Scholar]
  143. Volkow ND, Gordon JA, Freund MP. 2021. The Healthy Brain and Child Development Study—shedding light on opioid exposure, COVID-19, and health disparities. JAMA Psychiatry 78:5471
    [Google Scholar]
  144. Wang H-T, Smallwood J, Mourao-Miranda J, Xia CH, Satterthwaite TD et al. 2020. Finding the needle in a high-dimensional haystack: canonical correlation analysis for neuroscientists. NeuroImage 216:116745
    [Google Scholar]
  145. Winter NR, Leenings R, Ernsting J, Sarink K, Fisch L et al. 2022. Quantifying deviations of brain structure and function in major depressive disorder across neuroimaging modalities. JAMA Psychiatry 79:987988
    [Google Scholar]
  146. Xia CH, Ma Z, Ciric R, Gu S, Betzel RF et al. 2018. Linked dimensions of psychopathology and connectivity in functional brain networks. Nat. Commun. 9:13003
    [Google Scholar]
  147. Zelenina M, Pine D, Stringaris A, Nielson D. 2023. Validation of CBCL depression scores of adolescents in three independent datasets. Dev. Cogn. Neurosci. In press https://doi.org/10.31234/osf.io/z956k
    [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-devpsych-011922-012402
Loading
/content/journals/10.1146/annurev-devpsych-011922-012402
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error