北京大学Zheng Wang等研究人员合作发现，个体潜在疾病因素的定量表达与精神病理学维度和治疗反应有关。相关论文于2024年6月6日在线发表在《神经科学通报》杂志上。

研究人员表示，在自闭症谱系障碍（ASD）、注意力/缺陷多动障碍（ADHD）和强迫症（OCD）等以症状为基础的诊断中，精神疾病合并症很常见。然而，这些由共同和/或不同神经机制介导的共存症状很难在个体水平上进行剖析。

利用分层贝叶斯框架下的无监督机器学习，研究人员从ASD和ADHD混合队列的静息态功能连接数据中推导出了潜在疾病因素，并根据典型相关性分析确定了个体与维度症状的关联。基于相同因素的模型可推广到亚临床队列中以前未见过的个体，以及一个包含接受神经外科干预的患者子集的本地强迫症数据库。在每位患者身上共同表达的四个因子与不同的症状领域显著相关（r=-0.26-0.53，P<0.05）：行为调节（因子-1）、沟通（因子-2）、焦虑（因子-3）、适应行为（因子-4）。

此外，研究人员还发现强迫症患者体内表达的因子-1和焦虑症参与者体内表达的因子-3在一定程度上可显著预测个体症状评分（r=0.18-0.5，P<0.01）。重要的是，强迫症因子-1在干预期间的变化与不同的治疗结果相关（r=0.39，P<0.05）。这些研究结果表明，这些数据衍生的潜在疾病因子量化了个体因子的表达，为不同队列的维度症状和治疗结果提供了信息，这可能会促进精神疾病的量化诊断和个性化干预。

附：英文原文

Title: Quantitative Expression of Latent Disease Factors in Individuals Associated with Psychopathology Dimensions and Treatment Response

Author: Zhao, Shaoling, Lv, Qian, Zhang, Ge, Zhang, Jiangtao, Wang, Heqiu, Zhang, Jianmin, Wang, Meiyun, Wang, Zheng

Issue&Volume: 2024-06-06

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.

DOI: 10.1007/s12264-024-01224-z

Source: https://link.springer.com/article/10.1007/s12264-024-01224-z