A systematic review of structural neuroimaging markers of psychotherapeutic and pharmacological treatment for obsessive-compulsive disorder

被引:0
|
作者
Moreau, Allison L. [1 ]
Hansen, Isabella [1 ]
Bogdan, Ryan [1 ]
机构
[1] Washington Univ St Louis, Dept Psychol & Brain Sci, St Louis, MO 63130 USA
来源
FRONTIERS IN PSYCHIATRY | 2025年 / 15卷
基金
美国国家卫生研究院;
关键词
structural magnetic resonance imaging; treatment; psychotherapy; pharmacotherapy; neuromarkers; obsessive-compulsive disorder; COGNITIVE-BEHAVIORAL THERAPY; GRAY-MATTER VOLUME; PREFRONTAL CORTEX; ORBITOFRONTAL CORTEX; STATISTICAL-ANALYSES; CELL-PROLIFERATION; TREATMENT RESPONSE; PEDIATRIC-PATIENTS; THALAMUS VOLUMES; DIFFUSION TENSOR;
D O I
10.3389/fpsyt.2024.1432253
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Identifying individual difference factors associated with treatment response and putative mechanisms of therapeutic change may improve treatment for Obsessive Compulsive Disorder (OCD). Our systematic review of structural neuroimaging markers (i.e., morphometry, structural connectivity) of psychotherapy and medication treatment response for OCD identified 26 eligible publications from 20 studies (average study total n=54 +/- 41.6 [range: 11-175]; OCD group n=29 +/- 19) in child, adolescent, and adult samples evaluating baseline brain structure correlates of treatment response as well as treatment-related changes in brain structure. Findings were inconsistent across studies; significant associations within the anterior cingulate cortex (3/5 regional, 2/8 whole brain studies) and orbitofrontal cortex (5/10 regional, 2/7 whole brain studies) were most common, but laterality and directionality were not always consistent. Structural neuroimaging markers of treatment response do not currently hold clinical utility. Given increasing evidence that associations between complex behavior and brain structure are characterized by small, but potentially meaningful, effects, much larger samples are likely needed. Multivariate approaches (e.g., machine learning) may also improve the clinical predictive utility of neuroimaging data.
引用
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页数:28
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