Quantitative Expression of Latent Disease Factors in Individuals Associated with Psychopathology Dimensions and Treatment Response.
10.1007/s12264-024-01224-z
- Author:
Shaoling ZHAO
1
;
Qian LV
2
;
Ge ZHANG
3
;
Jiangtao ZHANG
4
;
Heqiu WANG
4
;
Jianmin ZHANG
4
;
Meiyun WANG
5
;
Zheng WANG
6
Author Information
1. Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.
2. School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
3. Department of Medical Imaging, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, 450003, China.
4. Tongde Hospital of Zhejiang Province (Zhejiang Mental Health Center), Zhejiang Office of Mental Health, Hangzhou, 310012, China.
5. Department of Medical Imaging, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, 450003, China. mywang@ha.edu.cn.
6. School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China. zheng.wang@pku.edu.cn.
- Publication Type:Journal Article
- Keywords:
Latent disease factor;
Psychiatric comorbidity;
Psychopathology dimension;
Quantitative diagnosis;
Treatment outcome
- MeSH:
Humans;
Male;
Female;
Attention Deficit Disorder with Hyperactivity;
Obsessive-Compulsive Disorder;
Adult;
Autism Spectrum Disorder;
Bayes Theorem;
Adolescent;
Young Adult;
Magnetic Resonance Imaging;
Middle Aged;
Child;
Brain/metabolism*;
Cohort Studies;
Comorbidity
- From:
Neuroscience Bulletin
2024;40(11):1667-1680
- CountryChina
- Language:English
-
Abstract:
Psychiatric comorbidity is common in symptom-based diagnoses like autism spectrum disorder (ASD), attention/deficit hyper-activity disorder (ADHD), and obsessive-compulsive disorder (OCD). However, these co-occurring symptoms mediated by shared and/or distinct neural mechanisms are difficult to profile at the individual level. Capitalizing on unsupervised machine learning with a hierarchical Bayesian framework, we derived latent disease factors from resting-state functional connectivity data in a hybrid cohort of ASD and ADHD and delineated individual associations with dimensional symptoms based on canonical correlation analysis. Models based on the same factors generalized to previously unseen individuals in a subclinical cohort and one local OCD database with a subset of patients undergoing neurosurgical intervention. Four factors, identified as variably co-expressed in each patient, were significantly correlated with distinct symptom domains (r = -0.26-0.53, P < 0.05): behavioral regulation (Factor-1), communication (Factor-2), anxiety (Factor-3), adaptive behaviors (Factor-4). Moreover, we demonstrated Factor-1 expressed in patients with OCD and Factor-3 expressed in participants with anxiety, at the degree to which factor expression was significantly predictive of individual symptom scores (r = 0.18-0.5, P < 0.01). Importantly, peri-intervention changes in Factor-1 of OCD were associated with variable treatment outcomes (r = 0.39, P < 0.05). Our results indicate that these data-derived latent disease factors quantify individual factor expression to inform dimensional symptom and treatment outcomes across cohorts, which may promote quantitative psychiatric diagnosis and personalized intervention.
