Longitudinal Resting State Functional Connectivity Predicts Clinical Outcome in Mild Traumatic Brain Injury

Abstract

Mild traumatic brain injury (mTBI) affects about 42 million people worldwide. It is often associated with headache, cognitive deficits, and balance difficulties but rarely shows any abnormalities on conventional computed tomography (CT) or magnetic resonance imaging (MRI). Although in most mTBI patients the symptoms resolve within 3 months, 10-15% of patients continue to exhibit symptoms beyond a year. Also, it is known that there exists a vulnerable period post-injury, when a second injury may exacerbate clinical prognosis. Identifying this vulnerable period may be critical for patient outcome, but very little is known about the neural underpinnings of mTBI and its recovery. In this work, we used advanced functional neuroimaging to study longitudinal changes in functional organization of the brain during the 3-month recovery period post-mTBI. Fractional amplitude of low frequency fluctuations (fALFF) measured from resting state functional MRI (rs-fMRI) was found to be associated with symptom severity score (SSS, r = -0.28, p = 0.002). Decreased fALFF was observed in specific functional networks for patients with higher SSS, and fALFF returned to higher values when the patient recovered (lower SSS). In addition, functional connectivity of the same networks was found to be associated with concurrent SSS, and connectivity immediately after injury (<10 days) was capable of predicting SSS at a later time-point (3 weeks to 3 months, p < 0.05). Specific networks including motor, default-mode, and visual networks were found to be associated with SSS (p < 0.001), and connectivity between these networks predicted 3-month clinical outcome (motor and visual: p < 0.001, default-mode: p < 0.006). Our results suggest that functional connectivity in these networks comprise potential biomarkers for predicting mTBI recovery profiles and clinical outcome.