Hierarchical taxonomy of psychopathology and personalized mental health treatment selection
Bibliographic Data
| ID | 15528769 |
|---|---|
| Authors | Evangelia Argyriou (0000-0001-9113-0334, Indiana University – Purdue University Indianapolis, corresponding author), Christiana J Prestigiacomo (0000-0002-1025-4755, University of Indianapolis), Christiana Prestigiacomo, Douglas B Samuel (0000-0003-3592-1523, Purdue University West Lafayette), John C Stewart (0000-0001-7377-8823, University of Indianapolis), Jesse C Stewart, Wei Wu (0000-0003-1901-9134, University of Indianapolis), Melissa A Cyders (0000-0003-1990-8337, University of Indianapolis) |
| Year | 2025 |
| Volume | 16 |
| Pages | 1597879-1597879 |
| Publication date | 2025-10-29 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2025.1597879 |
| PMID | 41234426 |
| OpenAlex | W4415649233 |
| Language | EN |
| References cited | 144 |
Accumulated research has shown considerable heterogeneity in mental health treatment response. Precision mental health approaches aim to leverage this heterogeneity to tailor treatment selection to individual needs. The goals of this manuscript are to 1) present theoretical rationale for the potential usefulness of the Hierarchical Taxonomy of Psychopathology (HiTOP) to optimize treatment selection and 2) conduct a scoping review of the role of individual psychopathology components that map onto HiTOP on differential psychotherapy response, both as a proof-of-concept analysis, as well as to identify gaps and concrete recommendations for future application. We focus our review on treatment for internalizing disorders as a candidate class of disorders, and on Cognitive-Behavioral Therapies given their empirical support for this disorder class. Overall, the reviewed literature provides preliminary evidence about the potential usefulness of HiTOP dimensions of differing levels of specificity for personalized treatment selection that can guide future research. Gaps and limitations were identified, including limited research in several HiTOP domains, strict inclusion/exclusion criteria shrinking individual heterogeneity, large variability in HiTOP dimension measurement, risk of Type I and Type II error, and other methodological limitations for assessing personalized treatment response. The translation of this research to clinical decision making has a long way to go. Nonetheless, we view the application of HiTOP-relevant dimensions to personalized mental health approaches as a viable and exciting direction that offers many avenues for research for the improvement of patient outcomes
Leverage (statistics · Mental health · Personalized medicine · Psychopathology · Research Domain Criteria · Selection (genetic algorithm · Taxonomy (biology · Digital Mental Health Interventions · Personality Disorders and Psychopathology · Treatment of Major Depression
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| Highly cited | No |
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