Multi‐voxel pattern analysis of amygdala functional connectivity at rest predicts variability in posttraumatic stress severity. Issue 8 (11th June 2020)

• Record Type: Journal Article
• Authors: Fitzgerald, Jacklynn M.; Belleau, Emily L.; Miskovich, Tara A.; Pedersen, Walker S.; Larson, Christine L.
• Is Part Of: Brain and behavior. Volume 10:Issue 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-11
• Subjects: functional magnetic resonance imaging -- machine learning -- multi‐voxel pattern analysis -- posttraumatic stress disorder -- resting state -- trauma; Neurology -- Periodicals; Neurosciences -- Periodicals; Psychology -- Periodicals; Psychiatry -- Periodicals; 616.8005
• DOI: 10.1002/brb3.1707
• Languages: English
• ISSNs: 2162-3279

Abstract

Abstract: Introduction: Resting state functional magnetic resonance imaging (rsfMRI) studies demonstrate that individuals with posttraumatic stress disorder (PTSD) exhibit atypical functional connectivity (FC) between the amygdala, involved in the generation of emotion, and regions responsible for emotional appraisal (e.g., insula, orbitofrontal cortex [OFC]) and regulation (prefrontal cortex [PFC], anterior cingulate cortex). Consequently, atypical amygdala FC within an emotional processing and regulation network may be a defining feature of PTSD, although altered FC does not seem constrained to one brain region. Instead, altered amygdala FC involves a large, distributed brain network in those with PTSD. The present study used a machine‐learning data‐driven approach, multi‐voxel pattern analysis (MVPA), to predict PTSD severity based on whole‐brain patterns of amygdala FC. Methods: Trauma‐exposed adults ( N = 90) completed the PTSD Checklist‐Civilian Version to assess symptoms and a 5‐min rsfMRI. Whole‐brain FC values to bilateral amygdala were extracted and used in a relevance vector regression analysis with a leave‐one‐out approach for cross‐validation with permutation testing (1, 000) to obtain significance values. Results: Results demonstrated that amygdala FC predicted PCL‐C scores with statistically significant accuracy ( r = .46, p = .001; mean sum of squares = 130.46, p = .001; R 2 = 0.21, p = .001). Prediction was based on whole‐brain amygdala FC, although regions that informed prediction (top 10%) included the OFC, amygdala, and dorsolateral PFC. Conclusion: Findings demonstrate the utility of MVPA based on amygdala FC to predict individual severity of PTSD symptoms and that amygdala FC within a fear acquisition and regulation network contributed to accurate prediction. Abstract : We examined patterns of whole‐brain connectivity with the amygdala as a predictor of posttraumatic stress disorder (PTSD) severity using a machine‐learning approach. A total of n = 90 civilian trauma survivors completed a functional magnetic resonance imaging (fMRI) resting state scan and provided stress severity symptoms. Results showed that multi‐voxel patterns of amygdala connectivity accurately predicted severity of PTSD symptoms, while amygdala connectivity with regions involved in regulation (prefrontal cortex) contributed highly to accurate prediction.