Xiao Xiang, Hammond Christopher, Salmeron Betty Jo, Wang Danni, Gu Hong, Zhai Tianye, Nguyen Hieu, Lu Hanbing, Ross Thomas J, Yang Yihong
Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland.
Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland.
Biol Psychiatry. 2024 Jun 15;95(12):1081-1090. doi: 10.1016/j.biopsych.2023.08.028. Epub 2023 Sep 26.
Cognitive function and general psychopathology are two important classes of human behavior dimensions that are individually related to mental disorders across diagnostic categories. However, whether these two transdiagnostic dimensions are linked to common or distinct brain networks that convey resilience or risk for the development of psychiatric disorders remains unclear.
The current study is a longitudinal investigation with 11,875 youths from the Adolescent Brain Cognitive Development (ABCD) Study at ages 9 to 10 years at the onset of the study. A machine learning approach based on canonical correlation analysis was used to identify latent dimensional associations of the resting-state functional connectome with multidomain behavioral assessments including cognitive functions and psychopathological measures. For the latent resting-state functional connectivity factor showing a robust behavioral association, its ability to predict psychiatric disorders was assessed using 2-year follow-up data, and its genetic association was evaluated using twin data from the same cohort.
A latent functional connectome pattern was identified that showed a strong and generalizable association with the multidomain behavioral assessments (5-fold cross-validation: ρ = 0.68-0.73 for the training set [n = 5096]; ρ = 0.56-0.58 for the test set [n = 1476]). This functional connectome pattern was highly heritable (h = 74.42%, 95% CI: 56.76%-85.42%), exhibited a dose-response relationship with the cumulative number of psychiatric disorders assessed concurrently and at 2 years post-magnetic resonance imaging scan, and predicted the transition of diagnosis across disorders over the 2-year follow-up period.
These findings provide preliminary evidence for a transdiagnostic connectome-based measure that underlies individual differences in the development of psychiatric disorders during early adolescence.
认知功能和一般精神病理学是人类行为维度的两个重要类别,它们分别与跨诊断类别的精神障碍相关。然而,这两个跨诊断维度是否与传达精神疾病发展的恢复力或风险的共同或不同脑网络相关联仍不清楚。
本研究是一项纵向调查,研究对象为青少年大脑认知发展(ABCD)研究中的11875名9至10岁的青少年。基于典型相关分析的机器学习方法用于识别静息态功能连接组与多领域行为评估(包括认知功能和精神病理学测量)之间的潜在维度关联。对于显示出强大行为关联的潜在静息态功能连接性因子,使用2年随访数据评估其预测精神疾病的能力,并使用来自同一队列的双胞胎数据评估其遗传关联。
确定了一种潜在的功能连接组模式,该模式与多领域行为评估显示出强烈且可推广的关联(五折交叉验证:训练集[n = 5096]的ρ = 0.68 - 0.73;测试集[n = 1476]的ρ = 0.56 - 0.58)。这种功能连接组模式具有高度遗传性(h = 74.42%,95% CI:56.76% - 85.42%),与同时评估的以及磁共振成像扫描后2年时评估的精神疾病累积数量呈现剂量反应关系,并在2年随访期内预测了跨疾病诊断的转变。
这些发现为一种基于跨诊断连接组的测量方法提供了初步证据,该方法是青春期早期精神疾病发展中个体差异的基础。