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

TitleComputational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.
Publication TypeJournal Article
Year of Publication2023
AuthorsLuppi AI, Cabral J, Cofre R, Mediano PAM, Rosas FE, Qureshi AY, Kuceyeski A, Tagliazucchi E, Raimondo F, Deco G, Shine JM, Kringelbach ML, Orio P, Ching SN, Perl YSanz, Diringer MN, Stevens RD, Sitt JDiego
JournalNeuroimage
Volume275
Pagination120162
Date Published2023 Jul 15
ISSN1095-9572
KeywordsBrain Injuries, Computer Simulation, Consciousness, Consciousness Disorders, Humans, Neuroimaging
Abstract

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.

DOI10.1016/j.neuroimage.2023.120162
Alternate JournalNeuroimage
PubMed ID37196986
PubMed Central IDPMC10262065
Grant ListR01 NS102646 / NS / NINDS NIH HHS / United States
R01 NS130693 / NS / NINDS NIH HHS / United States
RF1 MH123232 / MH / NIMH NIH HHS / United States
Related Institute: 
Brain Health Imaging Institute (BHII)

Weill Cornell Medicine
Department of Radiology
525 East 68th Street New York, NY 10065