Multimodal FDG-PET and EEG assessment improves diagnosis and prognostication of disorders of consciousness

The complex diagnosis of disorders of consciousness (DoC) diagnosis increasingly relies on brain-imaging techniques for their ability to detect residual signs of consciousness in otherwise unresponsive patients. However, few of these techniques have been validated on external datasets. Here, we show that the FDG-PET glucose metabolic index of the best preserved hemisphere has robust in-sample and out-sample performances to diagnose DoC, slightly outperforming EEG-based classification. We further show that a multimodal assessment combining both FDG-PET and EEG not only improved diagnostic performances, but also allowed to identify covert cognition and to predict 6-month responsiveness in initially unresponsive patients. Lastly, we show that DoC heterogeneity reflects a sum of regional cortical metabolic differences, and their corresponding behavioral patterns, rather than a binary contrast between conscious and unconscious states. In total, we show that FDG-PET and EEG provide complementary information on DoC physiopathology and that their combination improves DoC diagnosis and prognostication.