Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.
| Publication Type | Academic Article |
| Authors | Luppi A, Cabral J, Cofre R, Mediano P, Rosas F, Qureshi A, Kuceyeski A, Tagliazucchi E, Raimondo F, Deco G, Shine J, Kringelbach M, Orio P, Ching S, Sanz Perl Y, Diringer M, Stevens R, Sitt J |
| Journal | Neuroimage |
| Volume | 275 |
| Pagination | 120162 |
| Date Published | 05/15/2023 |
| ISSN | 1095-9572 |
| Keywords | Consciousness, Brain Injuries |
| 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. |
| DOI | 10.1016/j.neuroimage.2023.120162 |
| PubMed ID | 37196986 |
| PubMed Central ID | PMC10262065 |