Research reportMultiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations
Introduction
Cumulative research on hemodynamic activity measured with functional MRI (fMRI) in resting state conditions suggests that the healthy brain can be organized in large-scale resting state networks (RSNs) of cognitive-behavioral relevance. More particularly, the default mode network (DMN), right and left executive control, auditory, salience, sensorimotor, cerebellum and three visual networks (lateral, medial, occipital) are consistently identified in healthy subjects (Damoiseaux et al., 2006, Laird et al., 2011, Smith et al., 2009) and show differential connectivity changes as the level of consciousness diminishes, such as in light (Horovitz et al., 2008) and deep sleep (Boly et al., 2012, Horovitz et al., 2009), anesthesia (Boveroux et al., 2010, Guldenmund et al., 2013, Martuzzi et al., 2010, Schrouff et al., 2011) and hypnosis (Demertzi et al., 2011, Hoeft et al., 2012, McGeown et al., 2009). In clinical conditions, like dementia, coma and related disorders of consciousness, fMRI resting state investigations have mainly focused on the DMN which shows decreases in functional connectivity as a function of the level of consciousness (e.g., Buckner et al., 2008, Greicius et al., 2004, Norton et al., 2012, Vanhaudenhuyse et al., 2010). To date, a system-level characterization of fMRI baseline activity in patients with consciousness alterations is pending. Here, we aimed to assess the ten-cognitive-RSN model in severely brain-injured patients suffering from disorders of consciousness, namely in coma, vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS). By means of an automatic data-driven approach, we further aimed to identify those RSNs, which were most relevant to discriminate unconscious and minimally conscious patients from healthy volunteers.
Section snippets
Methods
A widely-used methodology to investigate fMRI resting state functional connectivity is independent components analysis (ICA). ICA is a multivariate data-driven procedure, which evaluates the coherence of activity in multiple distributed voxels that are organized in maximally statistical independent components (Cole, Smith, & Beckmann, 2010). By this way, ICA is also able to isolate cortical connectivity maps from non-neuronal signals, such as head motion, cardiac pulsation and respiration (
Results
Twenty-seven healthy volunteers (14 women; mean age: 47 ± 16 years; range: 20–72) and 53 patients with disorders of consciousness (24 in MCS, 24 in VS/UWS, 5 in coma; 20 women; mean age: 50 ± 18 years, range: 14–87; 34 of non-traumatic etiology of whom anoxic: 11, cerebrovascular accident: 12, hemorrhage: 9, seizure: 1, metabolic: 1; 17 of traumatic, and 2 of mixed etiology; 31 patients assessed in the chronic setting, i.e., ≥50 days post-insult) were included in the analysis Supplementary
Discussion
We here aimed to assess the ten RSN model, typically observed in healthy conditions (e.g., Damoiseaux et al., 2006, Laird et al., 2011, Smith et al., 2009), in severely brain-injured patients suffering from disorders of consciousness. By means of a multiple template-matching procedure and a “neuronality” test, we first found that patients with consciousness alterations exhibit fewer networks of neuronal origin compared to healthy subjects. The notion of reduced neural function in disorders of
Conclusions
FMRI multiple-network connectivity is disrupted in severely brain-injured patients suffering from disorders of consciousness. When performing ICA, the identification of the “right” network may not be feasible due to structural and temporal alterations in patients' BOLD signal. By multiple-network testing and controlling for neuronal properties of the identified RSN, we can advance fMRI system-level characterization. Automatic data-driven patient classifications based on fMRI resting state
Acknowledgments
This work was supported by the Belgian National Funds for Scientific Research (FNRS), tinnitus Prize 2011 (FNRS 9.4501.12), the European Commission, the James McDonnell Foundation, the European Space Agency, Mind Science Foundation, the French Speaking Community Concerted Research Action, the Belgian interuniversity attraction pole, the Public Utility Foundation “Université Européenne du Travail”, “Fondazione Europea di Ricerca Biomedica” and the University and University Hospital of Liège. The
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Authors contributed equally to this work.