Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-22T18:22:12.828Z Has data issue: false hasContentIssue false
  • English
  • Français

Translating RDoC to real-world impact in developmental psychopathology: A neurodevelopmental framework for application of mental health risk calculators

Published online by Cambridge University Press:  07 December 2021

Leigha A. MacNeill Open the ORCID record for Leigha A. MacNeill [Opens in a new window] ,
Norrina B. Allen ,
Roshaye B. Poleon ,
Teresa Vargas Open the ORCID record for Teresa Vargas [Opens in a new window] ,
K. Juston Osborne ,
Katherine S. F. Damme ,
Deanna M. Barch ,
Sheila Krogh-Jespersen ,
Ashley N. Nielsen  and
Elizabeth S. Norton
...Show all authors
Leigha A. MacNeill*
Affiliation:
Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, IL, USA
Norrina B. Allen
Affiliation:
Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, IL, USA Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
Roshaye B. Poleon
Affiliation:
Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, IL, USA
Teresa Vargas
Affiliation:
Department of Psychology, Northwestern University, Evanston, IL, USA
K. Juston Osborne
Affiliation:
Department of Psychology, Northwestern University, Evanston, IL, USA
Katherine S. F. Damme
Affiliation:
Department of Psychology, Northwestern University, Evanston, IL, USA
Deanna M. Barch
Affiliation:
Department of Psychological and Brain Sciences, Washington University, St. Louis, MO, USA Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
Sheila Krogh-Jespersen
Affiliation:
Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, IL, USA
Ashley N. Nielsen
Affiliation:
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
Elizabeth S. Norton
Affiliation:
Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, IL, USA Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
Christopher D. Smyser
Affiliation:
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
Cynthia E. Rogers
Affiliation:
Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
Joan L. Luby
Affiliation:
Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
Vijay A. Mittal
Affiliation:
Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, IL, USA Department of Psychology, Northwestern University, Evanston, IL, USA Department of Psychiatry, Northwestern University, Chicago, IL, USA Institute for Policy Research, Northwestern University, Evanston, IL, USA
Lauren S. Wakschlag
Affiliation:
Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, IL, USA
*
Author for Correspondence: Leigha MacNeill, PhD, Northwestern University Feinberg School of Medicine, 633 N. St. Clair St., Suite 1900, Chicago, IL60611, USA; E-mail: leigha.macneill@northwestern.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

The National Institute of Mental Health's Research Domain Criteria (RDoC) framework has prompted a paradigm shift from categorical psychiatric disorders to considering multiple levels of vulnerability for probabilistic risk of disorder. However, the lack of neurodevelopmentally based tools for clinical decision making has limited the real-world impact of the RDoC. Integration with developmental psychopathology principles and statistical methods actualize the clinical implementation of RDoC to inform neurodevelopmental risk. In this conceptual paper, we introduce the probabilistic mental health risk calculator as an innovation for such translation and lay out a research agenda for generating an RDoC- and developmentally informed paradigm that could be applied to predict a range of developmental psychopathologies from early childhood to young adulthood. We discuss methods that weigh the incremental utility for prediction based on intensity and burden of assessment, the addition of developmental change patterns, considerations for assessing outcomes, and integrative data approaches. Throughout, we illustrate the risk calculator approach with different neurodevelopmental pathways and phenotypes. Finally, we discuss real-world implementation of these methods for improving early identification and prevention of developmental psychopathology. We propose that mental health risk calculators can build a needed bridge between the RDoC multiple units of analysis and developmental science.

Keywords

developmental changeprevention/interventionpsychopathologyRDoCrisk calculator
Type
Special Issue Article
Information
Development and Psychopathology , Volume 33 , Special Issue 5: Applying RDoC-Informed Approaches to the Study of Development and Psychopathology , December 2021 , pp. 1665 - 1684
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adolph, K. E., Robinson, S. R., Young, J. W., & Gill-Alvarez, F. (2008). What is the shape of developmental change? Psychological Review, 115, 527–543. doi:10.1037/0033-295X.115.3.527CrossRefGoogle ScholarPubMed
Allen, N. B., Krefman, A. E., Labarthe, D., Greenland, P., Juonala, M., Kähönen, M., … Lloyd-Jones, D. M. (2020). Cardiovascular health trajectories from childhood through middle age and their association with subclinical atherosclerosis. JAMA Cardiology, 5, 557–566. doi:10.1001/jamacardio.2020.0140CrossRefGoogle ScholarPubMed
Allen, N. B., Siddique, J., Wilkins, J. T., Shay, C., Lewis, C. E., Goff, D. C., … Lloyd-Jones, D. (2014). Blood pressure trajectories in early adulthood and subclinical atherosclerosis in middle age. JAMA, 311, 490–497. doi:10.1001/jama.2013.285122CrossRefGoogle ScholarPubMed
Baltes, P. B., & Nesselroade, J. R. (1979). Longitudinal research in the study of behavior and development. New York, NY: Academic Press.Google Scholar
Barrett, L. F. (2012). Emotions are real. Emotion, 12, 413–429. doi:10.1037/a0027555CrossRefGoogle ScholarPubMed
Barrett, L. F., Mesquita, B., & Gendron, M. (2011). Context in emotion perception. Current Directions in Psychological Science, 20, 286–290. doi:doi.org/10.1177/0963721411422522CrossRefGoogle Scholar
Beauchaine, T. P. (2015). Future directions in emotion dysregulation and youth psychopathology. Journal of Clinical Child & Adolescent Psychology, 44, 875–896. doi:10.1080/15374416.2015.1038827CrossRefGoogle ScholarPubMed
Beauchaine, T. P., & Cicchetti, D. (2019). Emotion dysregulation and emerging psychopathology: A transdiagnostic, transdisciplinary perspective. Developmental Psychopathology, 31, 799–804. doi:10.1017/s0954579419000671CrossRefGoogle ScholarPubMed
Bell, E., Bryant, R. A., Boyce, P., Porter, R. J., & Malhi, G. S. (2021). Irritability through research domain criteria: An opportunity for transdiagnostic conceptualisation. BJPsych Open, 7, e36. doi:10.1192/bjo.2020.168CrossRefGoogle ScholarPubMed
Bernardini, F., Attademo, L., Cleary, S. D., Luther, C., Shim, R. S., Quartesan, R., & Compton, M. T. (2017). Risk prediction models in psychiatry: Toward a new frontier for the prevention of mental illnesses. The Journal of Clinical Psychiatry, 78, 572–583. doi:10.4088/JCP.15r10003CrossRefGoogle Scholar
Birmaher, B., Merranko, J. A., Goldstein, T. R., Gill, M. K., Goldstein, B. I., Hower, H., … Keller, M. B. (2018). A risk calculator to predict the individual risk of conversion from subthreshold bipolar symptoms to bipolar disorder I or II in youth. Journal of the American Academy of Child & Adolescent Psychiatry, 57, 755–763.e754. doi:10.1016/j.jaac.2018.05.023CrossRefGoogle ScholarPubMed
Boker, S. M., & Nesselroade, J. R. (2002). A method for modeling the intrinsic dynamics of intraindividual variability: Recovering the parameters of simulated oscillators in multi-wave panel data. Multivariate Behavioral Research, 37, 127–160. doi:10.1207/s15327906mbr3701_06CrossRefGoogle ScholarPubMed
Brotman, M. A., Kircanski, K., Stringaris, A., Pine, D. S., & Leibenluft, E. (2017). Irritability in youths: A translational model. American Journal of Psychiatry, 174, 520–532. doi:10.1176/appi.ajp.2016.16070839CrossRefGoogle ScholarPubMed
Buss, K. A., Davis, E. L., Ram, N., & Coccia, M. (2018). Dysregulated fear, social inhibition, and respiratory sinus arrhythmia: A replication and extension. Child Development, 89, e214–e228. doi:10.1111/cdev.12774CrossRefGoogle Scholar
Campbell, F., Conti, G., Heckman, J. J., Moon, S. H., Pinto, R., Pungello, E., & Pan, Y. (2014). Early childhood investments substantially boost adult health. Science, 343, 1478–1485. doi:10.1126/science.1248429CrossRefGoogle ScholarPubMed
Cannon, T. D., Cadenhead, K., Cornblatt, B., Woods, S. W., Addington, J., Walker, E., … Heinssen, R. (2008). Prediction of psychosis in youth at high clinical risk: A multisite longitudinal study in North America. Archives of General Psychiatry, 65, 28–37. doi:10.1001/archgenpsychiatry.2007.3CrossRefGoogle ScholarPubMed
Cannon, T. D., Chung, Y., He, G., Sun, D., Jacobson, A., van Erp, T. G., … Heinssen, R. (2015). Progressive reduction in cortical thickness as psychosis develops: A multisite longitudinal neuroimaging study of youth at elevated clinical risk. Biological Psychiatry, 77, 147–157. doi:10.1016/j.biopsych.2014.05.023CrossRefGoogle ScholarPubMed
Cannon, T. D., Yu, C., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., … Kattan, M. W. (2016). An individualized risk calculator for research in prodromal psychosis. The American Journal of Psychiatry, 173, 980–988. doi:10.1176/appi.ajp.2016.15070890CrossRefGoogle ScholarPubMed
Carrión, R. E., Cornblatt, B. A., Burton, C. Z., Tso, I. F., Auther, A. M., Adelsheim, S., … McFarlane, W. R. (2016). Personalized prediction of psychosis: External validation of the NAPLS-2 psychosis risk calculator with the EDIPPP project. The American Journal of Psychiatry, 173, 989–996. doi:10.1176/appi.ajp.2016.15121565CrossRefGoogle ScholarPubMed
Caspi, A., Houts, R. M., Belsky, D. W., Goldman-Mellor, S. J., Harrington, H., Israel, S., … Moffitt, T. E. (2014). The p factor: One general psychopathology factor in the structure of psychiatric disorders? Clinical Psychological Science, 2, 119–137. doi:10.1177/2167702613497473CrossRefGoogle Scholar
Caspi, A., & Moffitt, T. E. (2018). All for one and one for all: Mental disorders in one dimension. The American Journal of Psychiatry, 175, 831–844. doi:10.1176/appi.ajp.2018.17121383CrossRefGoogle ScholarPubMed
Caye, A., Agnew-Blais, J., Arseneault, L., Gonçalves, H., Kieling, C., Langley, K., … Rohde, L. A. (2020). A risk calculator to predict adult attention-deficit/hyperactivity disorder: Generation and external validation in three birth cohorts and one clinical sample. Epidemiology and Psychiatric Sciences, 29, 111–122. doi:10.1017/S2045796019000283Google Scholar
Chandler, R. K., Kahana, S. Y., Fletcher, B., Jones, D., Finger, M. S., Aklin, W. M., … Webb, C. (2015). Data collection and harmonization in HIV research: The seek, test, treat, and retain initiative at the national institute on drug abuse. American Journal of Public Health, 105, 2416–2422. doi:10.2105/ajph.2015.302788CrossRefGoogle ScholarPubMed
Ciarleglio, A. J., Brucato, G., Masucci, M. D., Altschuler, R., Colibazzi, T., Corcoran, C. M., … Girgis, R. R. (2019). A predictive model for conversion to psychosis in clinical high-risk patients. Psychological Medicine, 49, 1128–1137. doi:10.1017/S003329171800171XCrossRefGoogle ScholarPubMed
Cicchetti, D. (2008). Multi-level analysis perspective on research and development and psychopathology. In Beauchaine, T. P. & Hinshaw, S. P. (Eds.), Child and adolescent psychopathology (pp. 27–57). Hoboken, NJ: Wiley.Google Scholar
Cicchetti, D., & Dawson, G. (2002). Editorial: Multiple levels of analysis. Development and Psychopathology, 14, 417–420. doi:10.1017/S0954579402003012CrossRefGoogle ScholarPubMed
Collins, L. M. (2006). Analysis of longitudinal data: The integration of theoretical model, temporal design, and statistical model. Annual Review of Psychology, 57, 505–528. doi:10.1146/annurev.psych.57.102904.190146CrossRefGoogle ScholarPubMed
Cree, R. A., Bitsko, R. H., Robinson, L. R., Holbrook, J. R., Danielson, M. L., Smith, C., … Peacock, G. (2018). Health care, family, and community factors associated with mental, behavioral, and developmental disorders and poverty among children aged 2-8 years - United States, 2016. Morbidity and Mortality Weekly Report, 67, 1377–1383. doi:10.15585/mmwr.mm6750a1CrossRefGoogle ScholarPubMed
Curran, P. J., & Hussong, A. M. (2009). Integrative data analysis: The simultaneous analysis of multiple data sets. Psychological Methods, 14, 81–100. doi:10.1037/a0015914CrossRefGoogle ScholarPubMed
D'Agostino, R. B. Sr. (2012). Cardiovascular risk estimation in 2012: Lessons learned and applicability to the HIV population. The Journal of Infectious Diseases, 205, S362–S367. doi:10.1093/infdis/jis196CrossRefGoogle Scholar
D'Agostino, R. B. Jr., Griffith, J. L., Schmid, C. H., & Terrin, N. (1997). Measures for evaluating model performance. Proceedings of the Biometrics Section, American Statistical Association, Biometrics Section, Alexandria, VA, 253–258.Google Scholar
Damme, K. S. F., Wakschlag, L. S., Briggs-Gowan, M. J., Norton, E. S., & Mittal, V. A. (2021). Developmental patterning of irritability enhances prediction of psychopathology in pre-adolescence: Improving RDoC with developmental science. Preprint, doi:10.1101/2020.04.30.070714Google Scholar
Dawson, G., Ashman, S. B., & Carver, L. J. (2000). The role of early experience in shaping behavioral and brain development and its implications for social policy. Development and Psychopathology, 12, 695–712. doi:10.1017/s0954579400004089CrossRefGoogle ScholarPubMed
Dean, D. J., Orr, J. M., Bernard, J. A., Gupta, T., Pelletier-Baldelli, A., Carol, E. E., & Mittal, V. A. (2016). Hippocampal shape abnormalities predict symptom progression in neuroleptic-free youth at ultrahigh risk for psychosis. Schizophrenia Bulletin, 42, 161–169. doi:10.1093/schbul/sbv086Google ScholarPubMed
Eaton, K., Ohan, J. L., Stritzke, W. G. K., & Corrigan, P. W. (2016). Failing to meet the good parent ideal: Self-stigma in parents of children with mental health disorders. Journal of Child and Family Studies, 25, 3109–3123. doi:10.1007/s10826-016-0459-9CrossRefGoogle Scholar
Eaton, K., Stritzke, W., Corrigan, P., & Ohan, J. (2020). Pathways to self-stigma in parents of children with a mental health disorder. Journal of Child and Family Studies, 29, doi:10.1007/s10826-019-01579-2CrossRefGoogle Scholar
Finlay-Jones, A., Varcin, K., Leonard, H., Bosco, A., Alvares, G., & Downs, J. (2019). Very early identification and intervention for infants at risk of neurodevelopmental disorders: A transdiagnostic approach. Child Development Perspectives, 13, 97–103. doi:10.1111/cdep.12319CrossRefGoogle Scholar
Fortier, I., Raina, P., Van den Heuvel, E. R., Griffith, L. E., Craig, C., Saliba, M., … Burton, P. (2017). Maelstrom research guidelines for rigorous retrospective data harmonization. International Journal of Epidemiology, 46, 103–105. doi:10.1093/ije/dyw075Google ScholarPubMed
Fosco, G. M., Mak, H. W., Ramos, A., LoBraico, E., & Lippold, M. (2019). Exploring the promise of assessing dynamic characteristics of the family for predicting adolescent risk outcomes. Journal of Child Psychology and Psychiatry, 60, 848–856. doi:10.1111/jcpp.13052Google ScholarPubMed
Fowler, P. J., Tompsett, C. J., Braciszewski, J. M., Jacques-Tiura, A. J., & Baltes, B. B. (2009). Community violence: A meta-analysis on the effect of exposure and mental health outcomes of children and adolescents. Developmental Psychopathology, 21, 227–259. doi:10.1017/s0954579409000145CrossRefGoogle ScholarPubMed
Fusar-Poli, P., Bonoldi, I., Yung, A. R., Borgwardt, S., Kempton, M. J., Valmaggia, L., … McGuire, P. (2012). Predicting psychosis: Meta-analysis of transition outcomes in individuals at high clinical risk. Archives of General Psychiatry, 69, 220–229. doi:10.1001/archgenpsychiatry.2011.1472CrossRefGoogle ScholarPubMed
Fusar-Poli, P., Borgwardt, S., Bechdolf, A., Addington, J., Riecher-Rössler, A., Schultze-Lutter, F., … Yung, A. (2013). The psychosis high-risk state: A comprehensive state-of-the-art review. JAMA Psychiatry, 70, 107–120. doi:10.1001/jamapsychiatry.2013.269CrossRefGoogle ScholarPubMed
Fusar-Poli, P., Davies, C., Rutigliano, G., Stahl, D., Bonoldi, I., & McGuire, P. (2019). Transdiagnostic individualized clinically based risk calculator for the detection of individuals at risk and the prediction of psychosis: Model refinement including nonlinear effects of age. Frontiers in Psychiatry, 10, 313. doi:10.3389/fpsyt.2019.00313CrossRefGoogle ScholarPubMed
Fusar-Poli, P., Rutigliano, G., Stahl, D., Davies, C., Bonoldi, I., Reilly, T., & McGuire, P. (2017). Development and validation of a clinically based risk calculator for the transdiagnostic prediction of psychosis. JAMA Psychiatry, 74, 493–500. doi:10.1001/jamapsychiatry.2017.0284CrossRefGoogle ScholarPubMed
Fusar-Poli, P., Werbeloff, N., Rutigliano, G., Oliver, D., Davies, C., Stahl, D., … Osborn, D. (2019). Transdiagnostic risk calculator for the automatic detection of individuals at risk and the prediction of psychosis: Second replication in an independent national health service trust. Schizophrenia Bulletin, 45, 562–570. doi:10.1093/schbul/sby070CrossRefGoogle Scholar
Gaffrey, M. S., Barch, D. M., Bogdan, R., Farris, K., Petersen, S. E., & Luby, J. L. (2018). Amygdala reward reactivity mediates the association between preschool stress response and depression severity. Biological Psychiatry, 83, 128–136. doi:10.1016/j.biopsych.2017.08.020CrossRefGoogle ScholarPubMed
Gaffrey, M. S., Luby, J. L., Botteron, K., Repovš, G., & Barch, D. M. (2012). Default mode network connectivity in children with a history of preschool onset depression. Journal of Child Psychology and Psychiatry, 53, 964–972. doi:10.1111/j.1469-7610.2012.02552.xCrossRefGoogle ScholarPubMed
Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., … Rapoport, J. L. (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience, 2, 861–863. doi:10.1038/13158CrossRefGoogle ScholarPubMed
Gold, J. M., Corlett, P. R., Strauss, G. P., Schiffman, J., Ellman, L. M., Walker, E. F., … Mittal, V. A. (2020). Enhancing psychosis risk prediction through computational cognitive neuroscience. Schizophrenia Bulletin, 46, 1346–1352. doi:10.1093/schbul/sbaa091CrossRefGoogle ScholarPubMed
Goodman, S. H., Rouse, M. H., Connell, A. M., Broth, M. R., Hall, C. M., & Heyward, D. (2011). Maternal depression and child psychopathology: A meta-analytic review. Clinical Child and Family Psychology Review, 14, 1–27. doi:10.1007/s10567-010-0080-1CrossRefGoogle ScholarPubMed
Grabell, A. S., Li, Y., Barker, J. W., Wakschlag, L. S., Huppert, T. J., & Perlman, S. B. (2018). Evidence of non-linear associations between frustration-related prefrontal cortex activation and the normal:abnormal spectrum of irritability in young children. Journal of Abnormal Child Psychology, 46, 137–147. doi:10.1007/s10802-017-0286-5CrossRefGoogle ScholarPubMed
Helms, J. E., Jernigan, M., & Mascher, J. (2005). The meaning of race in psychology and how to change it: A methodological perspective. American Psychologist, 60, 27–38. doi:10.1037/0003-066X.60.1.27CrossRefGoogle ScholarPubMed
Hertzog, C., & Nesselroade, J. R. (2003). Assessing psychological change in adulthood: An overview of methodological issues. Psychology and Aging, 18, 639–657. doi:10.1037/0882-7974.18.4.639CrossRefGoogle ScholarPubMed
Hewlett, J., & Waisbren, S. E. (2006). A review of the psychosocial effects of false-positive results on parents and current communication practices in newborn screening. Journal of Inherited Metabolic Disease, 29, 677–682. doi:10.1007/s10545-006-0381-1CrossRefGoogle ScholarPubMed
Howell, B. R., Styner, M. A., Gao, W., Yap, P. T., Wang, L., Baluyot, K., … Elison, J. T. (2019). The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development. Neuroimage, 185, 891–905. doi:10.1016/j.neuroimage.2018.03.049CrossRefGoogle Scholar
Irving, J., Patel, R., Oliver, D., Colling, C., Pritchard, M., Broadbent, M., … Fusar-Poli, P. (2021). Using natural language processing on electronic health records to enhance detection and prediction of psychosis risk. Schizophrenia Bulletin, 47, 405–414. doi:10.1093/schbul/sbaa126CrossRefGoogle ScholarPubMed
Janssen, I., Krabbendam, L., Bak, M., Hanssen, M., Vollebergh, W., de Graaf, R., & van Os, J. (2004). Childhood abuse as a risk factor for psychotic experiences. Acta Psychiatrica Scandinavica, 109, 38–45. doi:10.1046/j.0001-690x.2003.00217.xCrossRefGoogle ScholarPubMed
Jones, P. B. (2013). Adult mental health disorders and their age at onset. The British Journal of Psychiatry, 54, s5–s10. doi:10.1192/bjp.bp.112.119164CrossRefGoogle ScholarPubMed
Kaat, A. J., Blackwell, C. K., Estabrook, R., Burns, J. L., Petitclerc, A., Briggs-Gowan, M. J., … Wakschlag, L. S. (2019). Linking the child behavior checklist (CBCL) with the multidimensional assessment profile of disruptive behavior (MAP-DB): Advancing a dimensional spectrum approach to disruptive behavior. Journal of Child and Family Studies, 28, 343–353. doi:10.1007/s10826-018-1272-4CrossRefGoogle ScholarPubMed
Karnieli-Miller, O., Perlick, D. A., Nelson, A., Mattias, K., Corrigan, P., & Roe, D. (2013). Family members’ of persons living with a serious mental illness: Experiences and efforts to cope with stigma. Journal of Mental Health, 22, 254–262. doi:10.3109/09638237.2013.779368CrossRefGoogle ScholarPubMed
Kaushik, A., Kostaki, E., & Kyriakopoulos, M. (2016). The stigma of mental illness in children and adolescents: A systematic review. Psychiatry Research, 243, 469–494. doi:10.1016/j.psychres.2016.04.042CrossRefGoogle ScholarPubMed
Keefe, R. S., Goldberg, T. E., Harvey, P. D., Gold, J. M., Poe, M. P., & Coughenour, L. (2004). The brief assessment of cognition in schizophrenia: Reliability, sensitivity, and comparison with a standard neurocognitive battery. Schizophrenia Research, 68, 283–297. doi:10.1016/j.schres.2003.09.011CrossRefGoogle ScholarPubMed
Kessel, E. M., Meyer, A., Hajcak, G., Dougherty, L. R., Torpey-Newman, D. C., Carlson, G. A., & Klein, D. N. (2016). Transdiagnostic factors and pathways to multifinality: The error-related negativity predicts whether preschool irritability is associated with internalizing versus externalizing symptoms at age 9. Development and Psychopathology, 28, 913–926. doi:10.1017/S0954579416000626CrossRefGoogle ScholarPubMed
Kim-Cohen, J., Caspi, A., Moffitt, T. E., Harrington, H., Milne, B. J., & Poulton, R. (2003). Prior juvenile diagnoses in adults with mental disorder: Developmental follow-back of a prospective-longitudinal cohort. Archives of General Psychiatry, 60, 709–717. doi:10.1001/archpsyc.60.7.709CrossRefGoogle ScholarPubMed
Kim, Y. S., & State, M. W. (2014). Recent challenges to the psychiatric diagnostic nosology: A focus on the genetics and genomics of neurodevelopmental disorders. International Journal of Epidemiology, 43, 465–475. doi:10.1093/ije/dyu037CrossRefGoogle Scholar
Kochanska, G., & Kim, S. (2013). Difficult temperament moderates links between maternal responsiveness and children's compliance and behavior problems in low-income families. Journal of Child Psychology and Psychiatry, 54, 323–332. doi:10.1111/jcpp.12002CrossRefGoogle ScholarPubMed
König, I. R., Malley, J. D., Weimar, C., Diener, H. C., & Ziegler, A. (2007). Practical experiences on the necessity of external validation. Statistics in Medicine, 26, 5499–5511. doi:10.1002/sim.3069CrossRefGoogle ScholarPubMed
Kotov, R., Krueger, R. F., Watson, D., Achenbach, T. M., Althoff, R. R., Bagby, R. M., … Zimmerman, M. (2017). The hierarchical taxonomy of psychopathology (HiTOP): A dimensional alternative to traditional nosologies. Journal of Abnormal Psychology, 126, 454–477. doi:10.1037/abn0000258CrossRefGoogle ScholarPubMed
Lee, T. Y., Hwang, W. J., Kim, N. S., Park, I., Lho, S. K., Moon, S. Y., … Kwon, J. S. (2020). Prediction of psychosis: Model development and internal validation of a personalized risk calculator. Psychological Medicine, doi:10.1017/S0033291720004675CrossRefGoogle ScholarPubMed
Liu, C., Moore, G. A., Beekman, C., Pérez-Edgar, K. E., Leve, L. D., Shaw, D. S., … Neiderhiser, J. M. (2018). Developmental patterns of anger from infancy to middle childhood predict problem behaviors at age 8. Developmental Psychology, 54, 2090–2100. doi:10.1037/dev0000589CrossRefGoogle ScholarPubMed
Lloyd-Jones, D. M. (2010). Cardiovascular risk prediction: Basic concepts, current status, and future directions. Circulation, 121, 1768–1777. doi:10.1161/circulationaha.109.849166CrossRefGoogle ScholarPubMed
Luby, J. L. (2012). Dispelling the “they'll grow out of it” myth: Implications for intervention. The American Journal of Psychiatry, 169, 1127–1129. doi:10.1176/appi.ajp.2012.12081037CrossRefGoogle ScholarPubMed
Luby, J., Allen, N., Estabrook, R., Pine, D. S., Rogers, C., Krogh-Jespersen, S., … Wakschlag, L. (2019). Mapping infant neurodevelopmental precursors of mental disorders: How synthetic cohorts & computational approaches can be used to enhance prediction of early childhood psychopathology. Behaviour Research and Therapy, 123, 103484. doi:10.1016/j.brat.2019.103484CrossRefGoogle ScholarPubMed
MacNeill, L. A., Ram, N., Bell, M. A., Fox, N. A., & Pérez-Edgar, K. (2018). Trajectories of infants’ biobehavioral development: Timing and rate of A-Not-B performance gains and EEG maturation. Child Development, 89, 711–724. doi:10.1111/cdev.13022CrossRefGoogle ScholarPubMed
Marceau, K., Ram, N., Houts, R. M., Grimm, K. J., & Susman, E. J. (2011). Individual differences in boys’ and girls’ timing and tempo of puberty: Modeling development with nonlinear growth models. Developmental Psychology, 47, 1389–1409. doi:10.1037/a0023838CrossRefGoogle ScholarPubMed
Martel, M. M., Pan, P. M., Hoffmann, M. S., Gadelha, A., do Rosário, M. C., Mari, J. J., … Salum, G. A. (2017). A general psychopathology factor (P factor) in children: Structural model analysis and external validation through familial risk and child global executive function. Journal of Abnormal Psychology, 126, 137–148. doi:10.1037/abn0000205CrossRefGoogle ScholarPubMed
McDaid, D., Park, A. L., & Wahlbeck, K. (2019). The economic case for the prevention of mental illness. Annual Review of Public Health, 40, 373–389. doi:10.1146/annurev-publhealth-040617-013629CrossRefGoogle ScholarPubMed
McGlashan, T., Walsh, B., & Woods, S. W. (2010). The psychosis-risk syndrome: Handbook for diagnosis and follow-up. Oxford: Oxford University Press.Google Scholar
McGorry, P. D., Purcell, R., Goldstone, S., & Amminger, G. P. (2011). Age of onset and timing of treatment for mental and substance use disorders: Implications for preventive intervention strategies and models of care. Current Opinion in Psychiatry, 24, 301–306. doi:10.1097/YCO.0b013e3283477a09CrossRefGoogle ScholarPubMed
Meehan, A. J., Latham, R. M., Arseneault, L., Stahl, D., Fisher, H. L., & Danese, A. (2020). Developing an individualized risk calculator for psychopathology among young people victimized during childhood: A population-representative cohort study. Journal of Affective Disorders, 262, 90–98. doi:10.1016/j.jad.2019.10.034CrossRefGoogle ScholarPubMed
Michelini, G., Gair, K., Tian, Y., Dougherty, L. R., Goldstein, B. L., MacNeill, L. A., … Kotov, R. (2021). Do general and specific factors of preschool psychopathology predict preadolescent outcomes? A transdiagnostic hierarchical approach. Unpublished manuscript [under review], University of California Los Angeles.Google Scholar
Mittal, V. A., & Wakschlag, L. S. (2017). Research domain criteria (RDoC) grows up: Strengthening neurodevelopment investigation within the RDoC framework. Journal of Affective Disorders, 216, 30–35. doi:10.1016/j.jad.2016.12.011CrossRefGoogle ScholarPubMed
Mittal, V. A., & Walker, E. F. (2019). Advances in the neurobiology of stress and psychosis. Schizophrenia Research, 213, 1–5. doi:10.1016/j.schres.2019.08.030CrossRefGoogle ScholarPubMed
Morawetz, C., Riedel, M., Salo, T., Berboth, S., Eickhoff, S., Laird, A., & Kohn, N. (2020). Multiple large-scale neural networks underlying emotion regulation. Neuroscience & Biobehavioral Reviews, 116, 382–395. doi:10.1016/j.neubiorev.2020.07.001CrossRefGoogle ScholarPubMed
Nagin, D. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Nelson, B. B., Chung, P. J., DuPlessis, H. M., Flores, L., Ryan, G. W., & Kataoka, S. H. (2011). Strengthening families of children with developmental concerns: Parent perceptions of developmental screening and services in head start. Ethnicity & Disease, 21, S1-89–S1-93.Google ScholarPubMed
Nielsen, A., Wakschlag, L., & Norton, E. (2021). Linking irritability and functional brain networks: A transdiagnostic case for expanding consideration of development and environment in RDoC. Unpublished manuscript [under review], Northwestern University.CrossRefGoogle Scholar
Ning, H., Krefman, A., Zhao, L., Wilkins, J. T., Lloyd-Jones, D. M., Siddique, J., & Allen, N. B. (in press). Development and validation of a large synthetic cohort for the study of cardiovascular health across the lifespan.Google Scholar
Norton, E. S., MacNeill, L. A., Harriott, E. M., Allen, N. B., Krogh-Jespersen, S., Smyser, C. D., … Wakschlag, L. S. (2021). EEG/ERP as a pragmatic method to expand the reach of infant-toddler neuroimaging in HBCD: Promises and challenges. Unpublished manuscript [under review], Northwestern University.CrossRefGoogle Scholar
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366, 447–453. doi:10.1126/science.aax2342CrossRefGoogle Scholar
Ofonedu, M. E., Belcher, H. M. E., Budhathoki, C., & Gross, D. A. (2017). Understanding barriers to initial treatment engagement among underserved families seeking mental health services. Journal of Child and Family Studies, 26, 863–876. doi:10.1007/s10826-016-0603-6CrossRefGoogle ScholarPubMed
Oliver, D., Spada, G., Colling, C., Broadbent, M., Baldwin, H., Patel, R., … Fusar-Poli, P. (2021). Real-world implementation of precision psychiatry: Transdiagnostic risk calculator for the automatic detection of individuals at-risk of psychosis. Schizophrenia Research, 227, 52–60. doi:10.1016/j.schres.2020.05.007CrossRefGoogle ScholarPubMed
Oliver, D., Wong, C. M. J., Bøg, M., Jönsson, L., Kinon, B., Wehnert, A., … Fusar-Poli, P. (2020). Transdiagnostic individualized clinically-based risk calculator for the automatic detection of individuals at-risk and the prediction of psychosis: External replication in 2,430,333 US patients. Translational Psychiatry, 10, 364. doi:10.1038/s41398-020-01032-9CrossRefGoogle ScholarPubMed
Osborne, K. J., & Mittal, V. A. (2019). External validation and extension of the NAPLS-2 and SIPS-RC personalized risk calculators in an independent clinical high-risk sample. Psychiatry Research, 279, 9–14. doi:10.1016/j.psychres.2019.06.034CrossRefGoogle Scholar
Ozonoff, S. (2015). Editorial: Early detection of mental health and neurodevelopmental disorders: The ethical challenges of a field in its infancy. Journal of Child Psychology and Psychiatry, 56, 933–935. doi:10.1111/jcpp.12452CrossRefGoogle ScholarPubMed
Patel, P. K., Leathem, L. D., Currin, D. L., & Karlsgodt, K. H. (2021). Adolescent neurodevelopment and vulnerability to psychosis. Biological Psychiatry, 89, 184–193. doi:10.1016/j.biopsych.2020.06.028CrossRefGoogle ScholarPubMed
Pencina, M. J., & D'Agostino, R. B. Sr. (2012). Thoroughly modern risk prediction? Science Translational Medicine, 4, 131fs110. doi:10.1126/scitranslmed.3004127CrossRefGoogle ScholarPubMed
Pencina, M. J., D'Agostino, R. B. Sr, D'Agostino, R. B. Jr., & Vasan, R. S. (2008). Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Statistics in Medicine, 27, 157–172; 207–112. doi:10.1002/sim.2929CrossRefGoogle ScholarPubMed
Perlman, S. B., Jones, B. M., Wakschlag, L. S., Axelson, D., Birmaher, B., & Phillips, M. L. (2015). Neural substrates of child irritability in typically developing and psychiatric populations. Developmental Cognitive Neuroscience, 14, 71–80. doi:10.1016/j.dcn.2015.07.003CrossRefGoogle ScholarPubMed
Pool, L. R., Ning, H., Wilkins, J., Lloyd-Jones, D. M., & Allen, N. B. (2018). Use of long-term cumulative blood pressure in cardiovascular risk prediction models. JAMA Cardiology, 3, 1096–1100. doi:10.1001/jamacardio.2018.2763CrossRefGoogle ScholarPubMed
Rajji, T. K., Ismail, Z., & Mulsant, B. H. (2009). Age at onset and cognition in schizophrenia: Meta-analysis. British Journal of Psychiatry, 195, 286–293. doi:10.1192/bjp.bp.108.060723CrossRefGoogle ScholarPubMed
Ram, N., & Gerstorf, D. (2009). Time-structured and net intraindividual variability: Tools for examining the development of dynamic characteristics and processes. Psychology and Aging, 24, 778–791. doi:10.1037/a0017915CrossRefGoogle ScholarPubMed
Ram, N., & Grimm, K. J. (2007). Using simple and complex growth models to articulate developmental change: Matching theory to method. International Journal of Behavioral Development, 31, 303–316. doi:10.1177/0165025407077751CrossRefGoogle Scholar
Reardon, T., Harvey, K., Baranowska, M., O'Brien, D., Smith, L., & Creswell, C. (2017). What do parents perceive are the barriers and facilitators to accessing psychological treatment for mental health problems in children and adolescents? A systematic review of qualitative and quantitative studies. European Child & Adolescent Psychiatry, 26, 623–647. doi:10.1007/s00787-016-0930-6CrossRefGoogle ScholarPubMed
Roberts, R. E., Attkisson, C. C., & Rosenblatt, A. (1998). Prevalence of psychopathology among children and adolescents. The American Journal of Psychiatry, 155, 715–725. doi:10.1176/ajp.155.6.715Google ScholarPubMed
Rocha, T. B. M., Fisher, H. L., Caye, A., Silva, L. A. D. D., Arseneault, L., Barros, F. C. L. F. D., … Kieling, C. C. (2021). Identifying adolescents at risk for depression: A prediction score performance in cohorts based in 3 different continents. Journal of the American Academy of Child & Adolescent Psychiatry, 60, 262–273. doi:10.1016/j.jaac.2019.12.004CrossRefGoogle ScholarPubMed
Rogers, C. E., Sylvester, C. M., Mintz, C., Kenley, J. K., Shimony, J. S., Barch, D. M., & Smyser, C. D. (2017). Neonatal amygdala functional connectivity at rest in healthy and preterm infants and early internalizing symptoms. Journal of the American Academy of Child & Adolescent Psychiatry, 56, 157–166. doi:10.1016/j.jaac.2016.11.005CrossRefGoogle ScholarPubMed
Rothbart, M. K., & Bates, J. E. (1998). Temperament. In Damon, W. & Eisenberg, N. (Eds.), Handbook of child psychology: Social, emotional, and personality development, (5th ed., Vol. 3, pp. 105–176). Hoboken, NJ: Wiley.Google Scholar
Rutter, M. (1987). Psychosocial resilience and protective mechanisms. The American Journal of Orthopsychiatry, 57, 316–331. doi:10.1111/j.1939-0025.1987.tb03541.xCrossRefGoogle ScholarPubMed
Sameroff, A. J., Seifer, R., Barocas, R., Zax, M., & Greenspan, S. (1987). Intelligence quotient scores of 4-year-old children: Social-environmental risk factors. Pediatrics, 79, 343–350.CrossRefGoogle ScholarPubMed
Sameroff, A., Seifer, R., & McDonough, S. C. (2004). Contextual contributors to the assessment of infant mental health. In R. DelCarmen-Wiggins & A. Carter (Eds.), Handbook of infant, toddler, and preschool mental health assessment (pp. 61–76). New York, NY: Oxford University Press.Google Scholar
Scott, K., & Schulz, L. (2017). Lookit (part 1): A new online platform for developmental research. Open Mind, 1, 4–14. doi:10.1162/OPMI_a_00002CrossRefGoogle Scholar
Shaw, D. S., & Taraban, L. (2016). New directions and challenges in preventing conduct problems in early childhood. Child Development Perspectives, 11, 85–89. doi:10.1111/cdep.12212.CrossRefGoogle ScholarPubMed
Sheldrick, R. C., Merchant, S., & Perrin, E. C. (2011). Identification of developmental-behavioral problems in primary care: A systematic review. Pediatrics, 128, 356–363. doi:10.1542/peds.2010-3261CrossRefGoogle ScholarPubMed
Shonkoff, J. P. (2010). Building a new biodevelopmental framework to guide the future of early childhood policy. Child Development, 81, 357–367. doi:10.1111/j.1467-8624.2009.01399.xCrossRefGoogle ScholarPubMed
Shonkoff, J. P., Boyce, W. T., Levitt, P., Martinez, F. D., & McEwen, B. (2021). Leveraging the biology of adversity and resilience to transform pediatric practice. Pediatrics, 147, e20193845. doi:10.1542/peds.2019-3845CrossRefGoogle ScholarPubMed
Siddique, J., Reiter, J. P., Brincks, A., Gibbons, R. D., Crespi, C. M., & Brown, C. H. (2015). Multiple imputation for harmonizing longitudinal non-commensurate measures in individual participant data meta-analysis. Statistics in Medicine, 34, 3399–3414. doi:10.1002/sim.6562CrossRefGoogle ScholarPubMed
Silva Ribeiro, J., Pereira, D., Salagre, E., Coroa, M., Santos Oliveira, P., Santos, V., … Vieta, E. (2020). Risk calculators in bipolar disorder: A systematic review. Brain Sciences, 10, 525. doi:10.3390/brainsci10080525CrossRefGoogle ScholarPubMed
Simon, R. (2006). A checklist for evaluating reports of expression profiling for treatment selection. Clinical Advances in Hematology and Oncology, 4, 219–224.Google ScholarPubMed
Smith, J. D., Berkel, C., Jordan, N., Atkins, D. C., Narayanan, S. S., Gallo, C., … Bruening, M. M. (2018). An individually tailored family-centered intervention for pediatric obesity in primary care: Study protocol of a randomized type II hybrid effectiveness-implementation trial (raising healthy children study). Implementation Science, 13, 11. doi:10.1186/s13012-017-0697-2CrossRefGoogle Scholar
Smith, J. D., Cruden, G. H., Rojas, L. M., Van Ryzin, M., Fu, E., Davis, M. M., … Brown, C. H. (2020). Parenting interventions in pediatric primary care: A systematic review. Pediatrics, 146, e20193548. doi:10.1542/peds.2019-3548CrossRefGoogle ScholarPubMed
Smith, J. D., & Dishion, T. J. (2014). Mindful parenting in the development and maintenance of youth psychopathology. In J. Ehrenreich-May & B. C. Chu (Eds.), Transdiagnostic treatments for children and adolescents: Principles and practice (pp. 138–158). New York, NY: Guilford Press.Google Scholar
Smith, J. D., Wakschlag, L., Krogh-Jespersen, S., Walkup, J. T., Wilson, M. N., Dishion, T. J., & Shaw, D. S. (2019). Dysregulated irritability as a window on young children's psychiatric risk: Transdiagnostic effects via the family check-up. Development and Psychopathology, 31, 1887–1899. doi:10.1017/s0954579419000816CrossRefGoogle Scholar
Sroufe, L. A. (1990). Considering normal and abnormal together: The essence of developmental psychopathology. Development and Psychopathology, 2, 335–347. doi:10.1017/S0954579400005769CrossRefGoogle Scholar
Steyerberg, E. W., Moons, K. G., van der Windt, D. A., Hayden, J. A., Perel, P., Schroter, S., … Altman, D. G. (2013). Prognosis research strategy (PROGRESS) 3: Prognostic model research. PLOS Medicine, 10, e1001381. doi:10.1371/journal.pmed.1001381CrossRefGoogle ScholarPubMed
Studerus, E., Beck, K., Fusar-Poli, P., & Riecher-Rössler, A. (2020). Development and validation of a dynamic risk prediction model to forecast psychosis onset in patients at clinical high risk. Schizophrenia Bulletin, 46, 252–260. doi:10.1093/schbul/sbz059Google ScholarPubMed
Teti, D. M., O'Connell, M. A., & Reiner, C. D. (1996). Parenting sensitivity, parental depression and child health: The mediational role of parental self-efficacy. Infant and Child Development, 5, 237–250. doi:10.1002/(sici)1099-0917(199612)5:4Google Scholar
Vargas, T., Conley, R. E., & Mittal, V. A. (2020). Chronic stress, structural exposures and neurobiological mechanisms: A stimulation, discrepancy and deprivation model of psychosis. International Review of Neurobiology, 152, 41–69. doi:10.1016/bs.irn.2019.11.004CrossRefGoogle ScholarPubMed
Vargas, T., & Mittal, V. A. (2018). Issues affecting reliable and valid assessment of early life stressors in psychosis. Schizophrenia Research, 192, 465–466. doi:10.1016/j.schres.2017.04.021CrossRefGoogle ScholarPubMed
Vargas, T., Zou, D. S., Conley, R. E., & Mittal, V. A. (2019). Assessing developmental environmental risk factor exposure in clinical high risk for psychosis individuals: Preliminary results using the individual and structural exposure to stress in psychosis-risk states scale. Journal of Clinical Medicine, 8, doi:10.3390/jcm8070994CrossRefGoogle ScholarPubMed
Wakschlag, L. S., Estabrook, R., Petitclerc, A., Henry, D., Burns, J. L., Perlman, S. B., … Briggs-Gowan, M. L. (2015). Clinical implications of a dimensional approach: The normal:abnormal spectrum of early irritability. Journal of the American Academy of Child & Adolescent Psychiatry, 54, 626–634. doi:10.1016/j.jaac.2015.05.016CrossRefGoogle ScholarPubMed
Wakschlag, L. S., Luby, J. L., Pool, L. R., MacNeill, L. A., Adam, H., Barch, D. M., … Allen, N. B. (2021). Closing the research-to-practice gap in early identification: Proof-of-concept for a personalized early childhood mental health risk calculator. Unpublished manuscript [under review], Northwestern University.Google Scholar
Wakschlag, L. S., Perlman, S. B., Blair, R. J., Leibenluft, E., Briggs-Gowan, M. J., & Pine, D. S. (2018). The neurodevelopmental basis of early childhood disruptive behavior: Irritable and callous phenotypes as exemplars. The American Journal of Psychiatry, 175, 114–130. doi:10.1176/appi.ajp.2017.17010045CrossRefGoogle ScholarPubMed
Wakschlag, L. S., Roberts, M. Y., Flynn, R. M., Smith, J. D., Krogh-Jespersen, S., Kaat, A. J., … Davis, M. M. (2019). Future directions for early childhood prevention of mental disorders: A road map to mental health, earlier. Journal of Clinical Child & Adolescent Psychology, 48, 539–554. doi:10.1080/15374416.2018.1561296CrossRefGoogle ScholarPubMed
Wakschlag, L. S., Tolan, P. H., & Leventhal, B. L. (2010). Research review: ‘ain't misbehavin’: Towards a developmentally-specified nosology for preschool disruptive behavior. Journal of Child Psychology and Psychiatry, 51, 3–22. doi:10.1111/j.1469-7610.2009.02184.xCrossRefGoogle ScholarPubMed
Walker, E., Trotman, H., Goulding, S., Holtzman, C., Ryan, A., Macdonald, A., … Brasfield, J. (2013). Developmental mechanisms in the prodrome to psychosis. Development and Psychopathology, 25, 1585–1600. doi:10.1017/S0954579413000783CrossRefGoogle ScholarPubMed
Walkup, J. T., Mathews, T., & Green, C. M. (2017). Transdiagnostic behavioral therapies in pediatric primary care: Looking ahead. JAMA Psychiatry, 74, 557–558. doi:10.1001/jamapsychiatry.2017.0448CrossRefGoogle ScholarPubMed
Waller, R., Gardner, F., Viding, E., Shaw, D. S., Dishion, T. J., Wilson, M. N., & Hyde, L. W. (2014). Bidirectional associations between parental warmth, callous unemotional behavior, and behavior problems in high-risk preschoolers. Journal of Abnormal Child Psychology, 42, 1275–1285. doi:10.1007/s10802-014-9871-zCrossRefGoogle ScholarPubMed
Wiggins, J. L., Briggs-Gowan, M. J., Brotman, M. A., Leibenluft, E., & Wakschlag, L. S. (2020). Toward a developmental nosology for disruptive mood dysregulation disorder in early childhood. Journal of the American Academy of Child & Adolescent Psychiatry, 60, 388–397. doi:10.1016/j.jaac.2020.04.015CrossRefGoogle Scholar
Wiggins, J. L., Briggs-Gowan, M. J., Estabrook, R., Brotman, M. A., Pine, D. S., Leibenluft, E., & Wakschlag, L. S. (2018). Identifying clinically significant irritability in early childhood. Journal of the American Academy of Child & Adolescent Psychiatry, 57, 191–199. doi:10.1016/j.jaac.2017.12.008CrossRefGoogle ScholarPubMed
Wiggins, J. L., Ureña Rosario, A., Krogh-Jespersen, S., MacNeill, L. A., Walkup, J. T., Smith, J. D., … Wakschlag, L. S. (2021). Advancing early irritability as a pragmatic, transdiagnostic marker of early-onset, chronic mental disorder: Prevalence, stability, and predictive utility. Unpublished manuscript [under review], San Diego State University.Google Scholar
Wilson-Mendenhall, C. D., Barrett, L. F., Simmons, W. K., & Barsalou, L. W. (2011). Grounding emotion in situated conceptualization. Neuropsychologia, 49, 1105–1127. doi:10.1016/j.neuropsychologia.2010.12.032CrossRefGoogle ScholarPubMed
Worthington, M. A., Walker, E. F., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., … Cannon, T. D. (2021). Incorporating cortisol into the NAPLS2 individualized risk calculator for prediction of psychosis. Schizophrenia Research, 227, 95–100. doi:10.1016/j.schres.2020.09.022CrossRefGoogle ScholarPubMed
Yates, T. M., Egeland, B., & Sroufe, L. A. (2003). Rethinking resilience: A developmental process perspective. In resilience and vulnerability: Adaptation in the context of childhood adversities (pp. 243–266). New York, NY: Cambridge University Press.Google Scholar
Zhang, T., Li, H., Tang, Y., Niznikiewicz, M. A., Shenton, M. E., Keshavan, M. S., … Wang, J. (2018). Validating the predictive accuracy of the NAPLS-2 psychosis risk calculator in a clinical high-risk sample from the SHARP (Shanghai At-Risk for Psychosis) program. The American Journal of Psychiatry, 175, 906–908. doi:10.1176/appi.ajp.2018.18010036CrossRefGoogle Scholar
Zhang, T., Xu, L., Tang, Y., Li, H., Tang, X., Cui, H., … Wang, J. (2019). Prediction of psychosis in prodrome: Development and validation of a simple, personalized risk calculator. Psychological Medicine, 49, 1990–1998. doi:10.1017/s0033291718002738CrossRefGoogle ScholarPubMed