Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness

• Published in: NeuroImage (Orlando, Fla.) Vol. 275; p. 120162
• Main Authors: Luppi, Andrea I., Cabral, Joana, Cofre, Rodrigo, Mediano, Pedro A.M., Rosas, Fernando E., Qureshi, Abid Y., Kuceyeski, Amy, Tagliazucchi, Enzo, Raimondo, Federico, Deco, Gustavo, Shine, James M., Kringelbach, Morten L., Orio, Patricio, Ching, ShiNung, Sanz Perl, Yonatan, Diringer, Michael N., Stevens, Robert D., Sitt, Jacobo Diego
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.07.2023; Elsevier Limited; Elsevier; Academic Press
• Subjects: Biophysical models; Brain Injuries; Brain Injuries - complications; Brain injury; Brain research; Coma; Computational models; Computational neuroscience; Computer Simulation; Consciousness; Consciousness - physiology; Consciousness Disorders; Consciousness Disorders - diagnostic imaging; Disorders of consciousness; Generative models; Human health and pathology; Humans; Life Sciences; Machine learning; Mathematical models; Medical imaging; Nervous system; Neuroimaging; Neurosciences; Patients; Statistical models; Unconsciousness; Statistical models; Biophysical models; Generative models; Computational models; Disorders of consciousness; Machine learning; Disorders of consciousness Computational models Generative models Statistical models Biophysical models Machine learning
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2023.120162

•Overview of the wide range of modelling strategies for disorders of consciousness.•Descriptive and generative statistical models, biophysical computational models.•Gap analysis of challenges to DOC modelling and recommendations to overcome them.•Towards personalised models for diagnosis and treatment of DOC with multimodal data.•“Phase Zero” in silico clinical trials of potential treatments via brain modelling. Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges.