Functional connectivity during frustration: a preliminary study of predictive modeling of irritability in youth

Abstract

Irritability cuts across many pediatric disorders and is a common presenting complaint in child psychiatry; however, its neural mechanisms remain unclear. One core pathophysiological deficit of irritability is aberrant responses to frustrative nonreward. Here, we conducted a preliminary fMRI study to examine the ability of functional connectivity during frustrative nonreward to predict irritability in a transdiagnostic sample. This study included 69 youths (mean age = 14.55 years) with varying levels of irritability across diagnostic groups: disruptive mood dysregulation disorder (n = 20), attention-deficit/hyperactivity disorder (n = 14), anxiety disorder (n = 12), and controls (n = 23). During fMRI, participants completed a frustrating cognitive flexibility task. Frustration was evoked by manipulating task difficulty such that, on trials requiring cognitive flexibility, "frustration" blocks had a 50% error rate and some rigged feedback, while "nonfrustration" blocks had a 10% error rate. Frustration and nonfrustration blocks were randomly interspersed. Child and parent reports of the affective reactivity index were used as dimensional measures of irritability. Connectome-based predictive modeling, a machine learning approach, with tenfold cross-validation was conducted to identify networks predicting irritability. Connectivity during frustration (but not nonfrustration) blocks predicted child-reported irritability (ρ = 0.24, root mean square error = 2.02, p = 0.03, permutation testing, 1000 iterations, one-tailed). Results were adjusted for age, sex, medications, motion, ADHD, and anxiety symptoms. The predictive networks of irritability were primarily within motor-sensory networks; among motor-sensory, subcortical, and salience networks; and between these networks and frontoparietal and medial frontal networks. This study provides preliminary evidence that individual differences in irritability may be associated with functional connectivity during frustration, a phenotype-relevant state.

Trial registration: ClinicalTrials.gov NCT00006177 NCT00018057 NCT00025935.

Fig. 1. Trial timing and structure during the modified change-signal task.

ITI intertrial interval.

Fig. 2. Correlation between observed (x-axis) and predicted (y-axis) irritability generated using CPM.

RMSE root mean square error. Shaded area represents 95% confidence interval.

Fig. 3. CPM predicts irritability.

A Edges that contributed to the CPM model organized by macroscopic brain regions. To help visualizing these complex networks, edges only belonging to nodes with five or more edges (degree ≥ 5; middle) and 10 or more edges (degree ≥ 10; right) are also shown. B Visualization of node degree (i.e., the sum of predictive edges for a node for the positive networks). Darker color indicates higher degree. C Within and between network connectivity for the positive network. Cells represent the total number of edges connecting nodes within and between each network, with darker colors indicating a greater number of edges. As the negative network did not contribute to prediction, only the positive network is shown in all visualizations. Visualization created using BioImage Suite Web, http://bisweb.yale.edu/. MF medial frontal, FP frontoparietal, DMN default mode network, Mot motor/sensory, VI visual A, VII visual B, VAs visual association, SAL salience, SC subcortical, CBL cerebellum.