Towards personalized, brain-based behavioral intervention for transdiagnostic anxiety: Transient neural responses to negative images predict outcomes following a targeted computer-based intervention

Abstract

Objective: Clinical anxiety is prevalent, highly comorbid with other conditions, and associated with significant medical morbidity, disability, and public health burden. Excessive attentional deployment toward threat is a transdiagnostic dimension of anxiety seen at both initial and sustained stages of threat processing. However, group-level observations of these phenomena mask considerable within-group heterogeneity that has been linked to treatment outcomes, suggesting that a transdiagnostic, individual differences approach may capture critical, clinically relevant information.

Method: Seventy clinically anxious individuals were randomized to receive 8 sessions of attention bias modification (ABM; n = 41 included in analysis), a computer-based mechanistic intervention that specifically targets initial stages of threat processing, or a sham control (n = 21). Participants completed a mixed block/event-related functional MRI task optimized to discriminate transient from sustained neural responses to threat.

Results: Larger transient responses across a wide range of cognitive-affective regions (e.g., ventrolateral prefrontal cortex, anterior cingulate cortex, amygdala) predicted better clinical outcomes following ABM, in both a priori anatomical regions and whole-brain analyses; sustained responses did not. A spatial pattern recognition algorithm using transient threat responses successfully discriminated the top quartile of ABM responders with 68% accuracy.

Conclusions: Neural alterations occurring on the relatively transient timescale that is specifically targeted by ABM predict favorable clinical outcomes. Results inform how to expand on the initial promise of neurocognitive treatments like ABM by fine-tuning their clinical indications (e.g., through personalized mechanistic intervention relevant across diagnoses) and by increasing the range of mechanisms that can be targeted (e.g., through synergistic treatment combinations and/or novel neurocognitive training protocols designed to tackle identified predictors of nonresponse). (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Trial registration: ClinicalTrials.gov NCT02303691.

Conflict of interest statement

Conflict of Interest Disclosures. All authors report no biomedical financial interests or potential conflicts of interest.

Figures

Figure 1

CONSORT diagram for study.

Figure 2

Clusters where negative transient AUC values predict CAPS-Vigilance residual scores (from whole-brain analysis). From left to right, panels display the following clusters from Table 2 whole-brain analysis: 1) R VLPFC, R insula/IFG, R middle/superior temporal cortex, R DLPFC; 2) pgACC, dACC, thalamus; 3) R middle/superior temporal cortex, R amygdala/hippocampus, thalamus; 4) R DLPFC, dACC/dmPFC, L DLPFC, L dorsal postcentral gyrus. Lower row of figures depicts representative linear relationships between negative transient AUC values from selected ROIs and residual CAPS-vigilance scores in the ABM and control groups.

Figure 3

Discrimination map and classifier results from spatial pattern recognition analysis using whole-brain map of negative transient AUC values to classify individuals as high (best quartile) vs. poor (worst quartile) ABM responders. Discrimination information was strongest in vmPFC and bilateral VLFPC (dark blue shading).