Neural and behavioral markers of inhibitory control predict symptom improvement during internet-delivered cognitive behavioral therapy for depression

Abstract

Poor inhibitory control contributes to deficits in emotion regulation, which are often targeted by treatments for major depressive disorder (MDD), including cognitive behavioral therapy (CBT). Brain regions that contribute to inhibitory control and emotion regulation overlap; thus, inhibitory control might relate to response to CBT. In this study, we examined whether baseline inhibitory control and resting state functional connectivity (rsFC) within overlapping emotion regulation-inhibitory control regions predicted treatment response to internet-based CBT (iCBT). Participants with MDD were randomly assigned to iCBT (N = 30) or a monitored attention control (MAC) condition (N = 30). Elastic net regression was used to predict post-treatment Patient Health Questionnaire-9 (PHQ-9) scores from baseline variables, including demographic variables, PHQ-9 scores, Flanker effects (interference, sequential dependency, post-error slowing), and rsFC between the dorsal anterior cingulate cortex, bilateral anterior insula (AI), and right temporoparietal junction (TPJ). Essential prognostic predictor variables retained in the elastic net regression included treatment group, gender, Flanker interference response time (RT), right AI-TPJ rsFC, and left AI-right AI rsFC. Prescriptive predictor variables retained included interactions between treatment group and baseline PHQ-9 scores, age, gender, Flanker RT, sequential dependency effects on accuracy, post-error accuracy, right AI-TPJ rsFC, and left AI-right AI rsFC. Inhibitory control and rsFC within inhibitory control-emotion regulation regions predicted reduced symptom severity following iCBT, and these effects were stronger in the iCBT group than in the MAC group. These findings contribute to a growing literature indicating that stronger inhibitory control at baseline predicts better outcomes to psychotherapy, including iCBT.

Fig. 1. Locations of regions of interest (ROIs).

Note. Blue: dorsal anterior cingulate cortex (dACC). Red: right anterior insula (AI). Green: left AI. Yellow: right temporoparietal junction (TPJ). ROIs were derived from Langner et al. [42], (Towards a human self-regulation system: Common and distinct neural signatures of emotional and behavioural control: http://anima.fz-juelich.de/studies/Langner_2018). The conjunction map for meta-analyses of cognitive emotion regulation (CER) and cognitive action regulation (CAR) was downloaded (CER_and_CAR_cFWE05.nii.gz, from http://anima.fz-juelich.de/studies/Langner_2018).

Fig. 2. Plot of linear regression coefficients for model predicting post-treatment PHQ-9 scores, including essential variables retained in the elastic net regression (Stage 3).

Note. Tx treatment, PHQ-9 patient health questionnaire-9, RT response time, AI anterior insula, rsFC resting state functional connectivity, TPJ temporoparietal junction.

Fig. 3. Partial regressions predicting post-treatment depression by treatment group by Flanker RT, Gratton accuracy, post-error accuracy, right AI-TPJ rsFC, and right AI-left AI rsFC.

Note. Partial regressions predicting post-treatment depression by (A) Flanker RT by treatment group interaction, (B) Gratton accuracy by treatment group interaction, (C) post-error accuracy by treatment group interaction, (D) right AI-TPJ rsFC by treatment group interaction, and (E) right AI-left AI rsFC by treatment group interaction. MAC monitored attention control, iCBT internet-based cognitive behavioral therapy, PHQ-9 patient health questionnaire-9, RT response time, AI anterior insula, TPJ temporoparietal junction, rsFC resting state functional connectivity.