Abstract
A major challenge in medicine is the rehabilitation of brain-injured patients with poor neurological outcomes who experience chronic impairment of consciousness, termed minimally conscious state or vegetative state. Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is easy-to-acquire and holds the promise of large-range biomarkers. Previous rs-fMRI studies in monkeys and humans have highlighted that different consciousness levels are characterized by the relative prevalence of different functional connectivity patterns - also referred to as brain states - which conform closely to the underlying structural connectivity. Results suggest that changes in consciousness lead to changes in connectivity patterns, not only at the level of the co-activation strength between regions but also at the level of entire networks. In this work, a four-stage framework is proposed to identify interpretable spatial signature of consciousness, by i) defining brain regions of interest (ROIs) from atlases, ii) filtering and extracting the time series associated with these ROIs, iii) recovering disjoint networks and associated connectivities, and iv) performing pairwise non-parametric tests between network activities grouped by acquisition conditions. Our approach yields tailored networks, spatially consistent and symmetric. They will be helpful to study spontaneous recovery from disorders of consciousness known to be accompanied by a functional restoration of several networks.
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Grigis, A., Gomez, C., Frouin, V., Uhrig, L., Jarraya, B. (2022). Interpretable Signature of Consciousness in Resting-State Functional Network Brain Activity. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_25
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