Abstract

Posttraumatic growth (PTG) is the advantageous change some people report following the struggle to overcome traumatic life circumstances. As neural understanding of PTG is limited, debate persists regarding whether PTG represents “real” or “illusory” change. This study presents a novel supervised machine learning examination, predicting high versus low PTG from electroencephalographic (EEG) data collected from 66 trauma-exposed individuals. Alpha and gamma EEG frequency power accurately classified PTG and demonstrated the disruptive neural influence of posttraumatic stress disorder. Results provide objectively measurable neural evidence of the existence of PTG and the first whole-brain, high-density EEG scalp topographies of PTG in known literature.

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