Disentangling Disorders of Consciousness: Insights from Diffusion Tensor Imaging and Machine Learning

Abstract: Previous studies have suggested that disorders of consciousness (DOC) after severe brain injury may result from disconnections of the thalamo-cortical system. However, thalamo-cortical connectivity differences between vegetative state (VS), minimally conscious state minus (MCS-,i.e., low-level behavior such as visual pursuit), and minimally conscious state plus (MCS+, i.e., high-level behavior such as language processing) remain unclear. We employed probabilistic tractography in a sample of 25 DOC patients to assess whether structural connectivity in various thalamo-cortical circuits could differentiate between VS, MCS-, and MCS+ patients. First, we individually segmented the thalamus into seven clusters based on patterns of cortical connectivity and tested for univariate differences across groups. Second, reconstructed wholebrain thalamic tracks were used as features in a multivariate searchlight analysis to identify regions along the tracks that were most informative in distinguishing among groups. At the univariate level, we found that VS patients displayed reduced connectivity in most thalamocortical circuits of interest, including frontal, temporal, and sensorimotor connections, as compared to MCS+, but showed more pulvinar-occipital connections when compared to MCS-.Moreover, MCS- exhibited significantly less thalamo-premotor and thalamo-temporal connectivity than MCS+. At the multivariate level, we found that thalamic tracks reaching frontal, parietal, and sensorimotor regions, could discriminate, up to 100% accuracy, across each pairwise group comparison. Together, these findings highlight the role of thalamo-cortical connections in patients’ behavioral profile and level of consciousness. Diffusion tensor imaging combined with machine learning algorithms could thus potentially facilitate diagnostic distinctions in DOC and shed light on the neural correlates of consciousness.

Disentangling Disorders of Consciousness: Insights from Diffusion Tensor Imaging and Machine Learning — Human Brain Mapping (2016)