DACC Resting State Functional Connectivity as a Predictor of Pain Symptoms Following Motor Vehicle Crash: A Preliminary Investigation.

There is significant heterogeneity in pain outcomes following motor vehicle crashes (MVCs), such that a sizeable portion of individuals develop symptoms of chronic pain months after injury while others recover. Despite variable outcomes, the pathogenesis of chronic pain is currently unclear. Previous neuroimaging work implicates the dorsal anterior cingulate cortex (dACC) in adaptive control of pain, while prior resting state functional magnetic resonance imaging studies find increased functional connectivity (FC) between the dACC and regions involved in pain processing in those with chronic pain. Hyper-connectivity of the dACC to regions that mediate pain response may therefore relate to pain severity. The present study completed rsfMRI scans on N = 22 survivors of MVCs collected within 2 weeks of the incident to test whole-brain dACC-FC as a predictor of pain severity 6 months later. At 2 weeks, pain symptoms were predicted by positive connectivity between the dACC and the premotor cortex. Controlling for pain symptoms at 2 weeks, pain symptoms at 6 months were predicted by negative connectivity between the dACC and the precuneus. Previous research implicates the precuneus in the individual subjective awareness of pain. Given a relatively small sample size, approximately half of which did not experience chronic pain at 6 months, findings warrant replication. Nevertheless, this study provides preliminary evidence of enhanced dACC connectivity with motor regions and decreased connectivity with pain processing regions as immediate and prospective predictors of pain following MVC. PERSPECTIVE: This article presents evidence of distinct neural vulnerabilities that predict chronic pain in MVC survivors based on whole-brain connectivity with the dorsal anterior cingulate cortex.

Amygdala functional connectivity in the acute aftermath of trauma prospectively predicts severity of posttraumatic stress symptoms.

Understanding neural mechanisms that confer risk for posttraumatic stress disorder (PTSD) is critical for earlier intervention, yet longitudinal work has been sparse. The amygdala is part of a core network consistently implicated in PTSD symptomology. Most neural models of PTSD have focused on the amygdala's interactions with the dorsal anterior cingulate cortex, ventromedial prefrontal cortex, and hippocampus. However, an increasing number of studies have linked PTSD symptoms to aberrations in amygdala functional connections with other brain regions involved in emotional information processing, self-referential processing, somatosensory processing, visual processing, and motor control. In the current study, trauma-exposed individuals (N = 54) recruited from the emergency department completed a resting state fMRI scan as well as a script-driven trauma recall fMRI task scan two-weeks post-trauma along with demographic, PTSD, and other clinical symptom questionnaires two-weeks and six-months post-trauma. We examined whether amygdala-whole brain functional connectivity (FC) during rest and task could predict six-month post-trauma PTSD symptoms. More negative amygdala-cerebellum and amygdala-postcentral gyrus FC during rest as well as more negative amygdala-postcentral gyrus and amygdala-midcingulate cortex during recall of the trauma memory predicted six-month post-trauma PTSD after controlling for scanner type. Follow-up multiple regression sensitivity analyses controlling for several other relevant predictors of PTSD symptoms, revealed that amygdala-cerebellum FC during rest and amygdala-postcentral gyrus FC during trauma recall were particularly robust predictors of six-month PTSD symptoms. The results extend cross-sectional studies implicating abnormal FC of the amygdala with other brain regions involved in somatosensory processing, motor control, and emotional information processing in PTSD, to the prospective prediction of risk for chronic PTSD. This work may contribute to earlier identification of at-risk individuals and elucidate potential intervention targets.