Exploring Variations in Functional Connectivity of the Resting State Default Mode Network in Mild Traumatic Brain Injury

Abstract

A definitive diagnosis of mild traumatic brain injury (mTBI) is difficult due to the absence of biomarkers in standard clinical imaging. The brain is a complex network of interconnected neurons and subtle changes can modulate key networks of cognitive function. The resting state default mode network (DMN) has been shown to be sensitive to changes induced by pathology. This study seeks to determine whether quantitative measures of the DMN are sensitive in distinguishing mTBI subjects. Resting state functional magnetic resonance imaging data were obtained for healthy (n=12) and mTBI subjects (n=15). DMN maps were computed using dual-regression Independent Component Analysis (ICA). A goodness-of-fit (GOF) index was calculated to assess the degree of spatial specificity and sensitivity between healthy controls and mTBI subjects. DMN regions and neuropsychological assessments were examined to identify potential relationships. The resting state DMN maps indicate an increase in spatial coactivity in mTBI subjects within key regions of the DMN. Significant coactivity within the cerebellum and supplementary motor areas of mTBI subjects were also observed. This has not been previously reported in seed-based resting state network analysis. The GOF suggested the presence of high variability within the mTBI subject group, with poor sensitivity and specificity. The neuropsychological data showed correlations between areas of coactivity within the resting state network in the brain with a number of measures of emotion and cognitive functioning. The poor performance of the GOF highlights the key challenge associated with mTBI injury: the high variability in injury mechanisms and subsequent recovery. However, the quantification of the DMN using dual-regression ICA has potential to distinguish mTBI from healthy subjects, and provide information on the relationship of aspects of cognitive and emotional functioning with their potential neural correlates.

Detection of Mild Traumatic Brain Injury by Machine Learning Classification Using Resting State Functional Network Connectivity and Fractional Anisotropy

“…Four of the significant changes in rsFNC observed in our analysis involved connectivity between cerebellum and sensorimotor RSNs. A similar result has been observed before by Nathan and coworkers 24 in which the functional connectivity between cerebellum and SMA showed an increase in patients with TBI. Another study found functional connectivity of the cerebellum among those correlated with post-concussive complaints.…”

“…21,22 Other areas where connectivity disruption has been found in subjects with mTBI include the primary visual, hippocampal, and dorsolateral prefrontal cortexes 23 as well as increased connectivity in the cerebellum. 24 Studying the brain in a more comprehensive way, it has been suggested that stronger connectivity among functional brain networks is a normal response to brain injury. 25 Collectively, these results suggest that methods to exploit resting state functional connectivity as a biomarker should be comprehensive rather than specific when including relevant brain areas.…”

“…We hypothesized that subjects with mTBI would exhibit functional connectivity changes within the DMN 18,32 and abnormalities between: DMN and task positive networks, 17,18 cerebellum and sensorimotor areas, 24,33 cortical and subcortical regions (thalamus), 21,22 DMN and basal ganglia, attentional and sensorimotor areas, attentional and frontal regions, within the sensorimotor areas. 20 …”

The effect of preprocessing in dynamic functional network connectivity used to classify mild traumatic brain injury

“…(2013) reported increased connectivity between salience and task‐positive networks. The connectivity between cerebellum and the SMA has been found to be stronger in mTBI patients (Nathan et al., 2014; Vergara et al., 2015). The evidence points to a pattern of increased connectivity involving salience, sensorial, auditory, and visual areas (Mayer et al., 2011, 2014).…”

“…Connectivity changes in the supplementary motor area and the cerebellum have been reported by Nathan et al. (2014) using a seed‐based approach. While changes in the DMN of mTBI patients were found in the study by Vakhtin et al.…”