Chapter 31 - Imaging connectivity and functional brain networks in mild traumatic brain injury

Abstract

Mild traumatic brain injury (mTBI) has become a major public health problem today, affecting both civilian and military populations. A sizeable fraction of individuals who sustain mTBI develop long-term neurocognitive sequelae, despite normal conventional neuroimaging findings. Resting state fMRI (rsfMRI) is a useful and sensitive technique to detect the subtle changes in functional connectivity in the brain after sustaining injury. It detects the spontaneous, temporally correlated changes in brain activity at rest, which occur in brain regions that are spatially distinct, but functionally related. The resultant pattern of synchronous activity is representative of the functional neuronal networks of the brain. Disruption in connectivity in several neuronal networks, such as the default mode network, central executive network, fronto-parietal network, etc., has been observed in mTBI. These changes evolve with time and show an association with the severity of post-concussion symptoms and cognitive deficits. Due to the diversity in the population affected, nature of insult and response to injury, as well as in data analysis techniques employed, there is sizeable heterogeneity in the pattern of network disruption observed in different studies. RsfMRI is a useful technique to identify the neural substrates underlying the neurocognitive sequelae of TBI and shows promise as an imaging biomarker for its diagnosis, prognostication and therapeutic monitoring. Besides functional connectivity changes, the TBI spectrum also includes diffuse axonal injury, alterations in perfusion, metabolic changes etc. Integration of rsfMRI into a multi-modal and multi-parametric neuroimaging protocol which addresses all these factors would provide a more complete understanding of the pathophysiology of TBI. Combining imaging data with molecular markers appears to be a promising approach which would further enhance our ability to detect and predict long term outcome.