Multimodal multicentre investigation of diagnostic and prognostic markers in disorders of consciousness.
Authors
Affiliations (24)
Affiliations (24)
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Sorbonne Université, Paris 75013, France.
- Université Paris Cité, Paris 75006, France.
- Laboratorio de Inteligencia Artificial Aplicada, Instituto de Ciencias de la Computación, Universidad de Buenos Aires, Buenos Aires C1053, Argentina.
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ministry of Science, Technology and Innovation, Buenos Aires C1053, Argentina.
- Department of Neurology, University Hospital of the Ludwig-Maximilians-Universität München, Munich 82152, Germany.
- Therapiezentrum Burgau, Hospital for Neurological Rehabilitation, Burgau 89331, Germany.
- Clinical and Biological Psychology, Institute of Psychology and Education, Ulm University, Ulm 89081, Germany.
- Department of Biomedical and Clinical Sciences, University of Milano, Milan 20157, Italy.
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan 20148, Italy.
- Inserm 1266, Institute of Psychiatry and Neurosciences of Paris, Université Paris Cité, Paris F-75014, France.
- Medical Intensive Care Unit, HEGP Hôpital, Assistance Publique - Hôpitaux de Paris-Centre (APHP-Centre), Paris 75014, France.
- Centre Mémoire de Ressources et de Recherche, Paris Nord/Université Paris-Cité, Paris 75006, France.
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona 08005, Spain.
- Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg 86156, Germany.
- Diagnostic and Interventional Neuroradiology, Faculty of Medicine, University of Augsburg, Augsburg 86156, Germany.
- BRAINTALE SAS, Paris 75013, France.
- Institució Catalana de la Recerca I Estudis Avançats (ICREA), Barcelona 08010, Spain.
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago 7941169, Chile.
- GRC 29, AP-HP, DMU DREAM, Department of Anaesthesiology and Critical Care Medicine, Pitié-Salpêtrière Hospital, Sorbonne University, Paris 75013, France.
- AP-HP, Hôpital de la Pitié Salpêtrière, Neuro ICU, DMU Neurosciences, Paris 75013, France.
- AP-HP, Hôpital Pitié - Salpêtrière, Service de Neurophysiologie Clinique, Paris 75013, France.
- Department of Medical Neurobiology, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel.
- Department of Cognitive and Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel.
- Department of Neurorehabilitation, Medical Faculty, University of Augsburg, Augsburg 86156, Germany.
Abstract
Severely brain-injured patients may enter a spectrum of conditions collectively known as disorders of consciousness. This spectrum includes clinical conditions such as unresponsive wakefulness syndrome or minimally conscious state, where the behavioural assessment of consciousness can often be deceptive. To bridge this dissociation, neuroimaging techniques are employed to identify 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 assessments through interpretable machine learning. The electrophysiological modalities included high-density EEG (resting state and task), whereas neuroimaging modalities included anatomical and resting-state functional MRI, diffusion MRI and 18F-fluorodeoxyglucose 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 patient's evolution within the consciousness spectrum. We validate the proposed analysis with data coming from other centres with different acquisition parameters. Importantly, we demonstrate that model performance improves with an increase in the number of modalities. We observe a higher inter-modality disagreement for minimally conscious state patients and those patients who improve. Lastly, we observe a difference in feature importances between diagnosis and prognosis, with an interaction between modality and anatomical structures: some subcortical markers tend to contribute more to prognosis, while other cortical markers are more informative for diagnosis. This integrative multimodal and machine learning methodology presents a promising avenue for a more nuanced understanding of disorders of consciousness, contributing to enhanced diagnostic precision, prognostic capabilities and the personalization of rehabilitative strategies in clinical practice.