Predicting long-term outcome in anorexia nervosa: A machine learning analysis of brain structure at different stages of weight recovery
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Background Anorexia nervosa (AN) is characterized by sizable, widespread gray matter (GM) reductions in the acutely underweight state. However, evidence for persistent alterations after weight-restoration has been surprisingly scarce despite high relapse rates, frequent transitions to other psychiatric disorders, and generally unfavorable outcome. While most studies investigated brain regions separately (univariate analysis), psychiatric disorders can be conceptualized as brain network disorders characterized by multivariate alterations with only subtle local effects. We tested for persistent multivariate structural brain alterations in weight-restored individuals with a history of AN, investigated their putative biological substrate and relation with 1-year treatment outcome. Methods We trained machine learning models on regional GM measures to classify healthy controls (HC) (N = 289) from individuals at three stages of AN: underweight patients starting intensive treatment (N = 165, used as baseline), patients after partial weight-restoration (N = 115), and former patients after stable and full weight-restoration (N = 89). Alterations after weight-restoration were related to treatment outcome and characterized both anatomically and functionally. Results Patients could be classified from HC when underweight (ROC-AUC = 0.90) but also after partial weight-restoration (ROC-AUC = 0.64). Alterations after partial weight-restoration were more pronounced in patients with worse outcome and were not detected in long-term weight-recovered individuals, i.e. those with favorable outcome. These alterations were more pronounced in regions with greater functional connectivity, not merely explained by body mass index, and even increases in cortical thickness were observed (insula, lateral orbitofrontal, temporal pole). Conclusions Analyzing persistent multivariate brain structural alterations after weight-restoration might help to develop personalized interventions after discharge from inpatient treatment.
Details
Original language | English |
---|---|
Pages (from-to) | 7827-7836 |
Number of pages | 10 |
Journal | Psychological medicine |
Volume | 53 (2023) |
Issue number | 16 |
Publication status | Published - 9 Aug 2023 |
Peer-reviewed | Yes |
External IDs
Mendeley | 4c41bb5b-f904-38f9-be5f-fe777c97a713 |
---|---|
ORCID | /0000-0002-2864-5578/work/145695546 |
ORCID | /0000-0003-2132-4445/work/145696024 |
ORCID | /0000-0001-8029-8270/work/145696303 |
ORCID | /0000-0002-5112-405X/work/145697981 |
ORCID | /0000-0002-5026-1239/work/145698594 |
PubMed | 37554008 |
Keywords
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- Anorexia nervosa, brain structure, machine learning, treatment outcome prediction, Body Mass Index, Magnetic Resonance Imaging, Brain/diagnostic imaging, Humans, Thinness/psychology, Anorexia Nervosa/psychology