Abstract
Background Cognitive dysfunction and high-order psychopathologic dimensions are two main classes of transdiagnostic factors related to psychiatric disorders. They may link to common or distinct core brain networks underlying developmental risk of psychiatric disorders.
Method The current study is a longitudinal investigation with 11,875 youths aged 9-to 10-years-old at study onset, from the Adolescent Brain Cognitive Development study. A machine-learning approach based on canonical correlation analysis was used to identify latent dimensional associations of the resting-state functional connectome with multi-domain behavioral assessments of cognitive functions and psychopathological problems. For the latent rsFC factor showing a robust behavioral association, its ability to predict psychiatric disorders was assessed using two-year follow-up data and its genetic association was evaluated using twin data from the same cohort.
Result A latent functional connectome pattern was identified that showed a strong and generalizable association with the multi-domain 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 (h2 = 74.42%, 95% CI: 56.76%-85.42%), exhibited a dose-response relationship with cumulative number of psychiatric disorders assessed concurrently and 2-years post-MRI-scan, and predicted the transition of diagnosis across disorders over the 2-year follow-up period.
Conclusion These findings provide preliminary evidence for a transdiagnostic connectome-based measure that underlies individual differences in developing psychiatric disorders in early adolescence.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Competing Interest Statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
results of the longitudinal analysis are updated in this version.