Integrative Electrophysiology and Neuroimaging Approach in Assessing Disorders of Consciousness: A Multimodal Multicentric Machine Learning Study

Severely brain-injured patients may enter a spectrum of conditions collectively known as disorders of consciousness (DoC). This spectrum includes clinical categories such as unresponsive wakefulness syndrome or minimally conscious state, where the behavioral assessment of consciousness can often be deceptive. To bridge this dissociation, neuroimaging techniques are employed to look for the residual brain functions. Each neuroimaging modality imperfectly captures distinct aspects of brain preservation - functional, anatomical, or both. In this study, we adopt a comprehensive approach by integrating the neurophysiology and neuroimaging modalities available from the standard and advanced clinical assessment through interpretable machine learning (ML). The electrophysiological modalities included high-density electroencephalography (EEG) (resting state and task), whereas neuroimaging modalities included anatomical and resting-state functional magnetic resonance imaging (MRI), diffusion MRI, and 18F-fluoro-deoxy-glucose positron emission tomography (FDG PET). Our investigation reveals that specific modalities, such as functional assessments provide comprehensive insights into the currently evaluated state of consciousness - the diagnosis of the patients. Conversely, structural modalities offer valuable information about the patients evolution within the consciousness spectrum. We validate the proposed analysis with data coming from other centers with different acquisition parameters. Importantly, we show that there is an improved model performance with the increase in the number of modalities. We observe a higher inter-modality disagreement for MCS patients and those patients who improve. Lastly, we observe a difference in feature importances in diagnosis and prognosis. This integrative multimodal and ML methodology presents a promising avenue for a more nuanced understanding of DoC, contributing to enhanced diagnostic precision and prognostic capabilities in clinical practice.