PCI & Auditory ERPs for the diagnosis of disorders of consciousness Hello everyone, My name is Leandro Sanz I will introduce you to our research which compares two EEG-based methods for the diagnosis of patients with disorders of consciousness Leandro Sanz, MD Coma Science Group GIGA Consciousness University & Hospital of Liège, Belgium 4th Congress of the EAN Lisbon June 17th 2018 www.comascience.org 1

Disorders of consciousness Differential diagnosis Introduction Disorders of consciousness Wakefulness Variable awareness & communication Unresponsive Wakefulness Syndrome Only reflexive behavior Minimally Conscious State Reproducible non-reflexive behaviors Emergence from the Minimally Conscious State Functional communication or use of object After coma, patients can develop DOC where they are awake but show variable awareness & communication skills. While unresponsive patients only show reflexive behaviours, MCS patients exhibit reproducible behaviours such as command following. They emerge from the MCS when they are able to functionally communicate or use an object. The clinical assessment of these patients yields up to 40% of misdiagnosis, even though an accurate diagnosis is crucial to predict prognosis, to offer adequate treatment and guide end-of-life decisions. Therefore, other non-invasive tools such as EEG-based methods are needed to complement the behavioral evaluation. Differential diagnosis Clinical misdiagnosis up to 40% Crucial for prognosis, treatment, end-of-life decisions EEG-based methods as non-invasive tools to detect consciousness

Methods 2 EEG-based methods 1. Transcranial magnetic stimulation combined with EEG The Perturbational Complexity Index (PCI) The first approach we used is transcranial magnetic stimulation, a technique where we measure the EEG response of the cortex to a magnetic focal perturbation through the scalp. By integrating the complexity of the response into a single metric, the PCI, we can predict the patient’s level of consciousness. PCI=0.31

Methods 2 EEG-based methods 1. Transcranial magnetic stimulation combined with EEG The Perturbational Complexity Index (PCI) We see here an unresponsive patient with local, short, simple response, which yields a PCI under the threshold of consciousness. PCI=0.20 Unresponsive

Methods 2 EEG-based methods Minimally conscious 1. Transcranial magnetic stimulation combined with EEG The Perturbational Complexity Index (PCI) Whereas this minimally conscious patient exhibits a far more complex, widespread and long-lasting response which classifies him above the threshold of consciousness. PCI=0.41 Minimally conscious

2. Auditory evoked potentials Methods 2 EEG-based methods 2. Auditory evoked potentials Local-Global oddball paradigm  Machine-Learning approach 120 EEG markers Evoked potentials Spectral measures Information Connectivity  Multivariate classifier calibrated on a dataset of 130 patients (68 unresponsive; 62 minimally conscious) Regularity 1) ..... Local Standard Global Standard Local Deviant Global Deviant Regularity For the second method, EEG was recorded while during a standardized auditory oddball paradigm, and 120 EEG markers were computed using a machine-learning approach. These markers reflected either evoked potentials, spectral measures, information or connectivity. From these markers, a multivariate classifier was used to evaluate the level the consciousness, which was calibrated on a dataset of 130 previous patients. 2) ..... Local Deviant Global Standard Local Standard Global Deviant

Results 25 patients (4 unresponsive; 16 minimally conscious; 5 emergent) PCI correlates with some individual EEG markers 25 patients with a clinical diagnosis of disorders of consciousness underwent both assessments. On the x axis, you can see individual markers from the machine learning approach, while the y axis shows the PCI value from transcranial magnetic stimulation. The color code indicates the clinical diagnosis. We see that some markers correlate better than others with the PCI value, but there is still some overlap between the different colors. Kolmogorov complexity Permutational entropy (θ) Weighted symbolic mutual information (θ)

Results & Discussion The probability from the multivariate classifier correlates best with the PCI Congruent results for 19 patients (76%) Positive correlation between 2 methods Correct diagnosis for all unresponsive and emergent patients EEG signatures of consciousness  reliable predictive models for diagnosis Using the multivariate classifier with the 120 markers, we observe a better correlation with the PCI, as well as a clear distinction between unresponsive and emergent patients. However, minimally conscious ones are still spread across the graph which might indicate greater variability in their capacity for consciousness. The correlation between these two independent measures provides evidence that EEG-based methods can be reliably used to detect signatures of consciousness and that further tools should be developed to increase the diagnosis of disorders of consciousness. Thank you for your attention. leandro.sanz@uliege.be