Longitudinal relationships among activity in attention redirection neural circuitry and symptom severity in youth

Abstract

Background: Changes in neural circuitry function may be associated with longitudinal changes in psychiatric symptom severity. Identification of these relationships may aid in elucidating the neural basis of psychiatric symptom evolution over time. We aimed to distinguish these relationships using data from the Longitudinal Assessment of Manic Symptoms (LAMS) cohort.

Methods: Forty-one youth completed two study visits (mean=21.3 months). Elastic-net regression (Multiple response Gaussian family) identified emotional regulation neural circuitry that changed in association with changes in depression, mania, anxiety, affect lability, and positive mood and energy dysregulation, accounting for clinical and demographic variables.

Results: Non-zero coefficients between change in the above symptom measures and change in activity over the inter-scan interval were identified in right amygdala and left ventrolateral prefrontal cortex. Differing patterns of neural activity change were associated with changes in each of the above symptoms over time. Specifically, from Scan1 to Scan2, worsening affective lability and depression severity were associated with increased right amygdala and left ventrolateral prefrontal cortical activity. Worsening anxiety and positive mood and energy dysregulation were associated with decreased right amygdala and increased left ventrolateral prefrontal cortical activity. Worsening mania was associated with increased right amygdala and decreased left ventrolateral prefrontal cortical activity. These changes in neural activity between scans accounted for 13.6% of the variance; that is 25% of the total explained variance (39.6%) in these measures.

Conclusions: Distinct neural mechanisms underlie changes in different mood and anxiety symptoms overtime.

Keywords: Elastic-net; behaviorally and emotionally dysregulated youth; emotional regulation; longitudinal; neural mechanism; penalized regression.

Figure 1. Elastic net 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. B. Plot of non-zero variable fit after cross validation. Representation of the 10-fold cross validation performed in GLMNET that calculates 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

Relationships between our outcome measures with both right amygdala activity (green triangles) and left ventrolateral prefrontal cortical activity (blue diamonds). Trend lines denote the direction of the relationships. Y axis is the change in clinical outcome measure-a positive change score indicates worsening over time and a negative change score indicates improvement over time. X axis is the change in neural activity- a positive change score indicates increased activity and a negative change score indicates decreased activity. Representation of entire region of interest used in analysis, highlighted regions are right amygdala and left ventrolateral prefrontal cortex areas that show non-zero coefficients with change in five measures of affective dysregulation (depression, mania, anxiety, positive mood and energy, and affective lability) 21.3 months apart.