Data-Driven Identification of Brain–Behavioral and Sociodemographic Predictors of Anxiety Severity in Children Using Machine Learning
We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as living with a disability. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science.
Authors
- Kate D. Fitzgerald (ORCID: https://orcid.org/0000-0002-6502-7031)
- Ann M. Iturra‐Mena (ORCID: https://orcid.org/0000-0001-9485-7539)
- Katherine Lisa Rosenblum (ORCID: https://orcid.org/0000-0001-6952-9672)
- Jason S. Moser (ORCID: https://orcid.org/0000-0003-4732-7695)
- Maria Muzik (ORCID: https://orcid.org/0000-0001-9888-8519)
- Melanie M. Wall (ORCID: https://orcid.org/0000-0001-6673-3467)
- Sherry Y.H. Chen (ORCID: https://orcid.org/0009-0002-5674-4060)
Institutions
- University of Michigan (US)
- New York Psychoanalytic Society and Institute (US)
- New York State Psychiatric Institute
- Columbia University (US)
- Michigan State University (US)
Publication Details
- Journal
- JAACAP Open
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1016/j.jaacop.2025.05.005
- Primary Topic
- Child and Adolescent Psychosocial and Emotional Development
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institute of Mental Health