Predicting clinical outcome from reward circuitry function and white matter structure in behaviorally and emotionally dysregulated youth

Abstract

Behavioral and emotional dysregulation in childhood may be understood as prodromal to adult psychopathology. Additionally, there is a critical need to identify biomarkers reflecting underlying neuropathological processes that predict clinical/behavioral outcomes in youth. We aimed to identify such biomarkers in youth with behavioral and emotional dysregulation in the Longitudinal Assessment of Manic Symptoms (LAMS) study. We examined neuroimaging measures of function and white matter in the whole brain using 80 youth aged 14.0 (s.d.=2.0) from three clinical sites. Linear regression using the LASSO (Least Absolute Shrinkage and Selection Operator) method for variable selection was used to predict severity of future behavioral and emotional dysregulation measured by the Parent General Behavior Inventory-10 Item Mania Scale (PGBI-10M)) at a mean of 14.2 months follow-up after neuroimaging assessment. Neuroimaging measures, together with near-scan PGBI-10M, a score of manic behaviors, depressive behaviors and sex, explained 28% of the variance in follow-up PGBI-10M. Neuroimaging measures alone, after accounting for other identified predictors, explained ~1/3 of the explained variance, in follow-up PGBI-10M. Specifically, greater bilateral cingulum length predicted lower PGBI-10M at follow-up. Greater functional connectivity in parietal-subcortical reward circuitry predicted greater PGBI-10M at follow-up. For the first time, data suggest that multimodal neuroimaging measures of underlying neuropathologic processes account for over a third of the explained variance in clinical outcome in a large sample of behaviorally and emotionally dysregulated youth. This may be an important first step toward identifying neurobiological measures with the potential to act as novel targets for early detection and future therapeutic interventions.

Figure 1. LASSO plots generated in GLMNET

A. Plot of variable fit. Each curve corresponds to an independent variable in the full model prior to optimization. Curves indicate the path of each variable coefficient as λ varies. Lambda.min corresponds to the λ which minimizes mean squared error in the model and was used for the selection of the seven predictor variables B. Plot of non-zero variable fit after cross validation. Representation of the 10-fold cross validation performed in LASSO that chooses the optimal λ. Lambda.min corresponds to the λ which minimizes mean squared error and was used for variable selection. Lambda.1se corresponds to the λ that is one standard error from the lambda.min.

Figure 2. Representation of neural variables showing nonzero relationships with TIME2:PGBI-10M after LASSO regression and scatter plots of the linear relationships of these variables

All statistical analyses assumed an underlying Poisson distribution. A. Representation of bilateral cingulum tracts in a standard brain. Blue diamonds and trend line represent the relationship between left cingulum length and TIME2:PGBI-10M scores. Red squares and trend line represent the relationship between right cingulum length and TIME2:PGBI-10M scores. B. Representation of ventral striatum- right parietal functional connectivity (right parietal target region: mni: 48, −46, 52, k=314) in a standard brain. Scatter plot and trend line represent the relationship between vs-right parietal functional connectivity and TIME2:PGBI-10M scores.