Predicting long-term outcome in anorexia nervosa: a machine learning analysis of brain structure at different stages of weight recovery Article Swipe
Dominic Arold
,
Fabio Bernardoni
,
Daniel Geisler
,
Arne Doose
,
Volkan Uen
,
Ilka Boehm
,
Veit Roessner
,
Joseph A. King
,
Stefan Ehrlich
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1017/s0033291723001861
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1017/s0033291723001861
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.
Related Topics
Concepts
Underweight
Anorexia nervosa
Multivariate analysis
Body mass index
Eating disorders
Weight loss
Multivariate statistics
Univariate analysis
Orbitofrontal cortex
Internal medicine
Psychology
Weight gain
Medicine
Pediatrics
Psychiatry
Body weight
Obesity
Cognition
Overweight
Prefrontal cortex
Mathematics
Statistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1017/s0033291723001861
- https://www.cambridge.org/core/services/aop-cambridge-core/content/view/8CC82FCC73FB66CE03BCC8A45958608B/S0033291723001861a.pdf/div-class-title-predicting-long-term-outcome-in-anorexia-nervosa-a-machine-learning-analysis-of-brain-structure-at-different-stages-of-weight-recovery-div.pdf
- OA Status
- hybrid
- Cited By
- 18
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385684908
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385684908Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1017/s0033291723001861Digital Object Identifier
- Title
-
Predicting long-term outcome in anorexia nervosa: a machine learning analysis of brain structure at different stages of weight recoveryWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-09Full publication date if available
- Authors
-
Dominic Arold, Fabio Bernardoni, Daniel Geisler, Arne Doose, Volkan Uen, Ilka Boehm, Veit Roessner, Joseph A. King, Stefan EhrlichList of authors in order
- Landing page
-
https://doi.org/10.1017/s0033291723001861Publisher landing page
- PDF URL
-
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/8CC82FCC73FB66CE03BCC8A45958608B/S0033291723001861a.pdf/div-class-title-predicting-long-term-outcome-in-anorexia-nervosa-a-machine-learning-analysis-of-brain-structure-at-different-stages-of-weight-recovery-div.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/8CC82FCC73FB66CE03BCC8A45958608B/S0033291723001861a.pdf/div-class-title-predicting-long-term-outcome-in-anorexia-nervosa-a-machine-learning-analysis-of-brain-structure-at-different-stages-of-weight-recovery-div.pdfDirect OA link when available
- Concepts
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Underweight, Anorexia nervosa, Multivariate analysis, Body mass index, Eating disorders, Weight loss, Multivariate statistics, Univariate analysis, Orbitofrontal cortex, Internal medicine, Psychology, Weight gain, Medicine, Pediatrics, Psychiatry, Body weight, Obesity, Cognition, Overweight, Prefrontal cortex, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
18Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 13, 2024: 5Per-year citation counts (last 5 years)
- References (count)
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56Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5000599118 |
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| corresponding_institution_ids | https://openalex.org/I78650965 |
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