[en] Introduction: In healthy conditions, group-level fMRI resting state analyses identify ten
resting state networks (RSNs) of cognitive relevance. Here, we aim to assess the tennetwork
model in severely brain-injured patients suffering from disorders of consciousness
and to identify those networks which will be most relevant to discriminate between
patients and healthy subjects.
Methods: 300 fMRI volumes were obtained in 27 healthy controls and 53 patients in minimally
conscious state (MCS), vegetative state/unresponsive wakefulness syndrome (VS/
UWS) and coma. Independent component analysis (ICA) reduced data dimensionality. The
ten networks were identified by means of a multiple template-matching procedure and
were tested on neuronality properties (neuronal vs non-neuronal) in a data-driven way.
Univariate analyses detected between-group differences in networks’ neuronal properties
and estimated voxel-wise functional connectivity in the networks, which were significantly
less identifiable in patients. A nearest-neighbor “clinical” classifier was used to
determine the networks with high between-group discriminative accuracy.
Results: Healthy controls were characterized by more neuronal components compared to
patients in VS/UWS and in coma. Compared to healthy controls, fewer patients in MCS and
VS/UWS showed components of neuronal origin for the left executive control network,
default mode network (DMN), auditory, and right executive control network. The “clinical”
classifier indicated the DMN and auditory network with the highest accuracy (85.3%) in
discriminating patients from healthy subjects.
Disciplines :
Neurosciences & comportement
Auteur, co-auteur :
Demertzi, Athina ✱; Université de Liège - ULiège > Centre de recherches du cyclotron
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