[en] Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitié-Salpêtrière and Liège). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ~0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liège datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liège remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.
Disciplines :
Neurology
Author, co-author :
Engemann, D. A. ✱; Parietal project-team, INRIA Saclay - Île de France, France, Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, NeuroSpin center, Université Paris-Sud ,Université Paris-Saclay, Gif sur Yvette, France, Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
King, J.-R.; Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, NeuroSpin center, Université Paris-Sud ,Université Paris-Saclay, Gif sur Yvette, France, New York University, 6 Washington Place, New York, NY, USA, Frankfurt Institute for Advanced Studies, Frankfurt, Germany
Rohaut, B.; Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France, Department of Neurology, Columbia University, New York, NY, USA
Louppe, Gilles ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data
Faugeras, F.; Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
Annen, Jitka ; Université de Liège - ULiège > GIGA : Coma Group
Cassol, Helena ; Université de Liège - ULiège > GIGA : Coma Group
Fernandez-Slezak, D.; Laboratorio de Inteligencia Artificial Aplicada, Departamento de Computación FCEyN, UBA, Argentina, CONICET - Universidad de Buenos Aires, Instituto de Investigación en Ciencias de la Computación, Godoy Cruz 2290, C1425FQB, Ciudad Autónoma de Buenos Aires, Argentina
Laureys, Steven ; Université de Liège - ULiège > GIGA : Coma Group
Naccache, Lionel; Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France, Faculté de Médecine Pitié-Salpêtrière, Sorbonne Universités ,UPMC Université Paris 06, Paris, France
Dehaene, S.; Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, NeuroSpin center, Université Paris-Sud ,Université Paris-Saclay, Gif sur Yvette, France, Collège de France, Paris, France
Sitt, J. D.; Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France, Faculté de Médecine Pitié-Salpêtrière, Sorbonne Universités ,UPMC Université Paris 06, Paris, France
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