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Eeg-based Graph Theory indices to Support the Clinical Diagnosis of Disorders of Consciousness

Severe acquired brain injury often leads to a disorder of consciousness (DOC) which can be classified in vegetative state (VS) or minimally consciousness state (MCS) according to its severity. While the standardized Coma Recovery Scale Revised (CRS-R) is considered the gold-standard for the diagnosis of DOCs, fluctuations in the level of awareness and/or operator-dependence variation may hinder diagnostic accuracy (up to 40% of misdiagnosis for VS). Here we aimed at providing reliable EEG-based indices extracted from resting state networks that can corroborate clinical diagnosis with high level of accuracy, even in absence of behavioral signs of consciousness. Advanced methodologies for connectivity estimation and graph theory were applied to EEG resting state data from 15 DOC patients (2 groups: 6 VS and 9 MCS). Indices describing the global properties of the resting networks and the information flows between anterior and posterior brain regions resulted significantly different between the two groups. Moreover, they allowed the discrimination between VS from MCS with accuracy above 80%. These findings boost the role of EEG synthetic indices as valuable and reliable tool to support DOC clinical diagnosis. INTRODUCTION Disorders of Consciousness (DOCs) after severe acquired brain injury include, in the acute phase, coma and in the post-acute phase, vegetative state (VS) and minimally conscious state (MCS). The VS is a condition that follows coma, when the patient recovers vigilance (eyes opening), but not awareness, defined as the ability to interact with the surroundings, in spite of eyes opening and partial recovery of the sleep-wake circadian cycle [1]. More recently, the European Task Force has introduced the definition of “unresponsive wakefulness syndrome” (UWS) [2] to replace the term “vegetative state,” although it has not been universally accepted [3]. Here, we will use the term VS/UWS. The MCS has been described as a condition in which the patient recovers eye tracking ability or fluctuating commands, while remaining unable to communicate [4]. The current gold-standard in diagnosis of DOC patients is the JFK Coma Recovery Scale Revised (CRSR) which allows the clinical assessment of residual visual, auditory, motor, verbal functions, patients’ communication ability and awareness [5]. Reliance on behavioral assessment presents however, significant challenges and may lead to a misdiagnosis up to 40% (in VS) with evident impact on DOCs assistance and rehabilitation [6]. In light of this, several studies investigating neuroelectrical and hemodynamical brain signals at rest and during batteries of auditory, visual and tactile stimuli have been conducted in DOC patients in order to isolate quantitative markers of awareness independently of behavior [7]–[9]. The most promising studies appears to be those based on the analysis of alterations in DOC resting state networks, particularly in the so-called Default Mode Network, with respect to healthy subjects [10]. Hemodynamical and neuroelectrical measures have recently gained growing interest as a tool that may—in perspective—support the diagnosis of patients with different DOC by circumventing the need for behavioral responses. The networks defined by the statistical relationship between different signals (and their properties at different frequencies) can be seen as indirect correlates of the information processing by the patient’s brain. EEG shows invaluable advantages with respect to other neuroimaging techniques, both at the theoretical and at the practical level: it allows to capture the dynamics of brain connectivity and its spectral distribution, by keeping it viable to handle severely disabled patients even with bedside testing, being therefore eligible for routine clinical application. Previous studies have identified markers derived from a combination of connectivity estimators and graph theory able to classify MCS patients from VS/UWS and healthy subjects with an accuracy slightly above chance [11], [12]. In this study, we employed Partial Directed Coherence, as spectral multivariate connectivity estimator [13] combined with asymptotic statistic method to assess patterns of connectivity [14] and graph theory to extract EEG indices describing the topology of Proceedings of the 7th Graz Brain-Computer Interface Conference 2017 DOI: 10.3217/978-3-85125-533-1-89