Reconstructing Psychopathology: A Data-Driven Reorganization of the Symptoms in the <i>Diagnostic and Statistical Manual of Mental Disorders</i>

In this study, we reduced the <i>DSM-5</i> to its constituent symptoms and reorganized them based on patterns of covariation in individuals' (<i>n</i> = 14,762) self-reported experiences of the symptoms to form an empirically derived hierarchical framework of clinical phenomena. Specifically, we used the points of agreement among hierarchical principal components analyses and hierarchical clustering, as well as between the randomly split primary (<i>n</i> = 11,762) and hold-out (<i>n</i> = 3,000) samples, to identify the robust constructs that emerged to form a hierarchy ranging from symptoms and syndromes up to very broad superspectra of psychopathology. The resulting model had noteworthy convergence with the upper levels of the Hierarchical Taxonomy of Psychopathology (HiTOP) framework and substantially expands on HiTOP's current coverage of dissociative, elimination, sleep-wake, trauma-related, neurodevelopmental, and neurocognitive disorder symptoms. We also mapped some exemplar <i>DSM-5</i> disorders onto our hierarchy; some formed coherent syndromes, whereas others were notably heterogeneous.

Authors

Institutions

Publication Details

Journal
Clinical Psychological Science
Published
2024-10-17
DOI
https://doi.org/10.1177/21677026241268345
Citations
12
Primary Topic
Personality Disorders and Psychopathology
Type
article
Field-Weighted Citation Impact
7.67

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reconstructing Psychopathology: A Data-Driven Reorganization of the Symptoms in the Diagnostic and Statistical Manual of Mental Disorders

Ashley L. Watts, Louise R. Mewton, Craig Rodriguez‐Seijas, Elizabeth Pellicano et al.
12 citations
Clinical Psychological Science
Personality Disorders and Psychopathology
7.67
article

Reconstructing Psychopathology: A Data-Driven Reorganization of the Symptoms in the Diagnostic and Statistical Manual of Mental Disorders

Ashley L. Watts, Louise R. Mewton, Craig Rodriguez‐Seijas, Elizabeth Pellicano, Lee Anna Clark, Kristian E. Markon, Philip J. Batterham, Matthew Sunderland, Miriam K. Forbes, David Watson, Aidan G.C. Wright, Alison L. Calear, Andrew Baillie, Matthew Roberts, Roman Kotov, Robert F. Krueger
article en
12 citations

Abstract

In this study, we reduced the <i>DSM-5</i> to its constituent symptoms and reorganized them based on patterns of covariation in individuals' (<i>n</i> = 14,762) self-reported experiences of the symptoms to form an empirically derived hierarchical framework of clinical phenomena. Specifically, we used the points of agreement among hierarchical principal components analyses and hierarchical clustering, as well as between the randomly split primary (<i>n</i> = 11,762) and hold-out (<i>n</i> = 3,000) samples, to identify the robust constructs that emerged to form a hierarchy ranging from symptoms and syndromes up to very broad superspectra of psychopathology. The resulting model had noteworthy convergence with the upper levels of the Hierarchical Taxonomy of Psychopathology (HiTOP) framework and substantially expands on HiTOP's current coverage of dissociative, elimination, sleep-wake, trauma-related, neurodevelopmental, and neurocognitive disorder symptoms. We also mapped some exemplar <i>DSM-5</i> disorders onto our hierarchy; some formed coherent syndromes, whereas others were notably heterogeneous.

Clinical Psychological ScienceVol. 13(3)
University of Notre Dame (US), Australian National University (AU), University of Iowa (US), The University of Sydney (AU), University of Minnesota (US), Vanderbilt University (US), University of Michigan (US), Centre for Mental Health (GB), Sydney Local Health District (AU), University College London (GB), Stony Brook University (US), Macquarie University (AU)
Medical Research Council, National Health and Medical Research Council
Openalex Percentile: Top 2%
Personality Disorders and Psychopathology
7.67
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.