Exploring variations in functional connectivity of the resting state default mode network in mild traumatic brain injury.

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.

Examining intrinsic thalamic resting state networks using graph theory analysis: implications for mTBI detection.

A major challenge associated with understanding mild traumatic brain injury (mTBI) is the absence of biomarkers in standard clinical imaging modalities. Furthermore, the inhomogeneity of mTBI location and intensity, combined with latent symptoms further complicates identification and treatment. A growing body of evidence suggests that the thalamus may be injured or susceptible to change as the result of mTBI. A significant number of connections to and from cortical, subcortical, cerebellar and brain stem regions converge at the thalamus. Furthermore, the thalamus is also involved with information processing, integration and the regulation of specific behaviors. We use graph theory analysis to evaluate intrinsic functional networks of the left and right thalamus in mTBI subjects (N=15) and neurologically intact healthy controls (N=12). We also explore neural correlates of the thalamic network architecture with clinical assessments. Our results suggest the presence of distinct unilateral thalamic differences in mTBI subjects. We also observe correlations of the thalamic changes with clinical assessments. The findings from this study have implications for functional networks in the thalamus and its projections for application as a potential biomarker for mTBI detection.