Brain Network Disruption in Whiplash

Abstract

Background and purpose: Whiplash-associated disorders frequently develop following motor vehicle collisions and often involve a range of cognitive and affective symptoms, though the neural correlates of the disorder are largely unknown. In this study, a sample of participants with chronic whiplash injuries were scanned by using resting-state fMRI to assess brain network changes associated with long-term outcome metrics.

Materials and methods: Resting-state fMRI was collected for 23 participants and used to calculate network modularity, a quantitative measure of the functional segregation of brain region communities. This was analyzed for associations with whiplash-associated disorder outcome metrics, including scales of neck disability, traumatic distress, depression, and pain. In addition to these clinical scales, cervical muscle fat infiltration was quantified by using Dixon fat-water imaging, which has shown promise as a biomarker for assessing disorder severity and predicting recovery in chronic whiplash.

Results: An association was found between brain network structure and muscle fat infiltration, wherein lower network modularity was associated with larger amounts of cervical muscle fat infiltration after controlling for age, sex, body mass index, and scan motion (t = -4.02, partial R ² = 0.49, P < .001).

Conclusions: This work contributes to the existing whiplash literature by examining a sample of participants with whiplash-associated disorder by using resting-state fMRI. Less modular brain networks were found to be associated with greater amounts of cervical muscle fat infiltration suggesting a connection between disorder severity and neurologic changes, and a potential role for neuroimaging in understanding the pathophysiology of chronic whiplash-associated disorders.

Fig 1.

Plot of average modularity (Q) versus MFI (%) in the cervical spine. Range represents 95% confidence interval via R function im (ggplot2).

Fig 2.

Network structure in the patients with lowest and highest MFI scores. Node colors show communities, green lines show edges within communities, red lines show edges between. The top row exhibits a high level of modularity (high within community connectivity), while the bottom row demonstrates a low level of modularity (fewer communities and more between community connections).