Investigation of brain changes in neurological patients with varying degrees of loss of consciousness

Abstract

Despite our best medical advances, the mechanisms of the brain dynamics that underlie induction and maintenance of disorders of consciousness (DoC) remain poorly understood. This gap in understanding complicates even further the appropriate diagnosis, prognosis and the design of a treatment plan for the patients. Novel methods are required to complement bedside diagnosis and to provide more information about the underlying brain state of the patient. A growing body of studies has been using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to successfully identify and study regions and networks in the brain that are related to consciousness. On this basis, fMRI/EEG have been used to better understand how brain activity and connectivity is affected under coma and explored as techniques that may provide means for detecting signs of covert conscious awareness in patients that are behaviorally unresponsive. Furthermore, these imaging techniques seek to provide insights into the neural correlates of consciousness, identify neurophysiologic signatures of covert consciousness and identify possible biomarkers of recovery. The objective of this work is, through a secondary analysis of available clinical MRI and/or EEG datasets, to study the brain states associated with different DoC and to understand the interplay between abnormality of neural parameters (e.g., functional resting state connectivity) and pathologies of consciousness. More specifically, this project aims to investigate different methods of analysis of structural and/or functional neuroimaging data of patients diagnosed with DoC at different stages of the pathology. Several approaches are available to analyze this data. For structural data, there are methods based on volumetric, morphological or multi atlas analysis, among others. For functional data, there are methods such as seed-based analysis, independent component analysis (ICA) and graph analysis. Each method has its own set of tradeoffs and extracts different types of information from the data. Therefore, it is important to explore these different types of analyses and investigate what they can tell us about the underlying brain dynamics.