Can Emotional and Behavioral Dysregulation in Youth Be Decoded from Functional Neuroimaging?

INTRODUCTION: High comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points. METHODS: A sample of fifty-seven youth (mean age: 14.5 years; 32 males) was selected from a multi-site study of youth with parent-reported behavioral and emotional dysregulation. Participants performed a block-design reward paradigm during functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Relevance Vector Regression (RVR) and two cross-validation strategies implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Medication was treated as a binary confounding variable. Decoded and actual clinical scores were compared using Pearson's correlation coefficient (r) and mean squared error (MSE) to evaluate the models. Permutation test was applied to estimate significance levels. RESULTS: Relevance Vector Regression identified patterns of neural activity associated with symptoms of behavioral and emotional dysregulation at the initial study screen and close to the fMRI scanning session. The correlation and the mean squared error between actual and decoded symptoms were significant at the initial study screen and close to the fMRI scanning session. However, after controlling for potential medication effects, results remained significant only for decoding symptoms at the initial study screen. Neural regions with the highest contribution to the pattern regression model included cerebellum, sensory-motor and fronto-limbic areas. CONCLUSIONS: The combination of pattern regression models and neuroimaging can help to determine the severity of behavioral and emotional dysregulation in youth at different time points.

Parsing dimensional vs diagnostic category-related patterns of reward circuitry function in behaviorally and emotionally dysregulated youth in the Longitudinal Assessment of Manic Symptoms study.

IMPORTANCE: Pediatric disorders characterized by behavioral and emotional dysregulation pose diagnostic and treatment challenges because of high comorbidity, suggesting that they may be better conceptualized dimensionally rather than categorically. Identifying neuroimaging measures associated with behavioral and emotional dysregulation in youth may inform understanding of underlying dimensional vs disorder-specific pathophysiologic features. OBJECTIVE: To identify, in a large cohort of behaviorally and emotionally dysregulated youth, neuroimaging measures that (1) are associated with behavioral and emotional dysregulation pathologic dimensions (behavioral and emotional dysregulation measured with the Parent General Behavior Inventory 10-Item Mania Scale [PGBI-10M], mania, depression, and anxiety) or (2) differentiate diagnostic categories (bipolar spectrum disorders, attention-deficit/hyperactivity disorder, anxiety, and disruptive behavior disorders). DESIGN, SETTING, AND PARTICIPANTS: A multisite neuroimaging study was conducted from February 1, 2011, to April 15, 2012, at 3 academic medical centers: University Hospitals Case Medical Center, Cincinnati Children's Hospital Medical Center, and University of Pittsburgh Medical Center. Participants included a referred sample of behaviorally and emotionally dysregulated youth from the Longitudinal Assessment of Manic Symptoms (LAMS) study (n = 85) and healthy youth (n = 20). MAIN OUTCOMES AND MEASURES: Region-of-interest analyses examined relationships among prefrontal-ventral striatal reward circuitry during a reward paradigm (win, loss, and control conditions), symptom dimensions, and diagnostic categories. RESULTS: Regardless of diagnosis, higher PGBI-10M scores were associated with greater left middle prefrontal cortical activity (r = 0.28) and anxiety with greater right dorsal anterior cingulate cortical (r = 0.27) activity to win. The 20 highest (t = 2.75) and 20 lowest (t = 2.42) PGBI-10M-scoring youth showed significantly greater left middle prefrontal cortical activity to win compared with 20 healthy youth. Disruptive behavior disorders were associated with lower left ventrolateral prefrontal cortex activity to win (t = 2.68) (all P < .05, corrected). CONCLUSIONS AND RELEVANCE: Greater PGBI-10M-related left middle prefrontal cortical activity and anxiety-related right dorsal anterior cingulate cortical activity to win may reflect heightened reward sensitivity and greater attention to reward in behaviorally and emotionally dysregulated youth regardless of diagnosis. Reduced left ventrolateral prefrontal cortex activity to win may reflect reward insensitivity in youth with disruptive behavior disorders. Despite a distinct reward-related neurophysiologic feature in disruptive behavior disorders, findings generally support a dimensional approach to studying neural mechanisms in behaviorally and emotionally dysregulated youth.