TY  - JOUR
AU  - Luppi, Andrea I.
AU  - Cabral, Joana
AU  - Cofre, Rodrigo
AU  - Mediano, Pedro A. M.
AU  - Rosas, Fernando E.
AU  - Qureshi, Abid
AU  - Kuceyeski, Amy
AU  - Tagliazucchi, Enzo
AU  - Raimondo, Federico
AU  - Deco, Gustavo
AU  - Shine, James
AU  - Kringelbach, Morten L.
AU  - Orio, Patricio
AU  - Ching, ShiNung
AU  - Perl, Yonatan Sanz
AU  - Diringer, Michael N.
AU  - Stevens, Robert D.
AU  - Sitt, Jaco
TI  - Computational modelling in disorders of consciousness: closing the gap towards personalised models for restoring consciousness
JO  - NeuroImage
VL  - 275
SN  - 1053-8119
CY  - Orlando, Fla.
PB  - Academic Press
M1  - FZJ-2023-01294
SP  - 120162
PY  - 2023
AB  - 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.
LB  - PUB:(DE-HGF)16
DO  - DOI:10.1016/j.neuroimage.2023.120162
UR  - https://juser.fz-juelich.de/record/1003878
ER  -