Disrupted intrinsic functional connectivity in the vegetative state

  • Cauda F
  • Micon B
  • Sacco K
 et al. 
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It is debatable as to whether the spontaneous blood-oxygen-level dependent fluctuations that are observed in the resting brain in turn reflect consciously directed mental activity or, alternatively, constitute an intrinsic property of functional brain organisation persisting in the absence of consciousness. This report shows for the first time, in three patients, that the persistent vegetative state (PVS) is marked by a dysfunctional default mode network, with decreased connectivity in several brain regions, including the dorsolateral prefrontal cortex and anterior cingulated cortex, especially in the right hemi-sphere. This finding supports the view that the resting state is involved in self-consciousness, and that the right-hemisphere default state may play a major role in conscious processes. It is speculated that the default state may act as a surrogate marker of PVS with awareness contents and, therefore, could replace a more complex activation paradigm. In neuroimaging studies, the persistent vegetative state (PVS) is characterised by the hypometabolism of several associated areas (precuneus, posterior cingulate cortex, BA 19, 22, 30 and 39), with impaired thalamocortical and corticocortical effec-tive connectivity. 1 2 A similar pattern is seen during anaesthesia. 3 These areas are believed to be a part of a default mode network (DMN), which may, or may not, support self-awareness. 4 To investigate this issue, we studied the resting state (RS) of patients in PVS for the first time. We used functional magnetic resonance imaging (fMRI) to measure the resting state (RS) activity of three patients in PVS, two of which followed traumatic brain injury 20 months previously (one female aged 19: Disability Rating Scale (DRS) 25/ Cat. 9, one male aged 21: DRS 23/ Cat. 8) and one of mixed origin (a female aged 78: DRS 28/ Cat. 9; see Supplementary Online Material for full details), and a control group of six healthy subjects. These patients were enrolled in an approved coma-recovery cortical stimulation protocol. Data acquisition was performed on a 1.5 T scanner (Philips Medical Systems). The resting state functional T 2 -weighted images were acquired using EPI sequences. A total of 300 volumes were acquired, each consisting of 25 axial slices, covering the whole brain. In the same session, a set of 3D high-resolution T 1 -weighted structural images was acquired for each participant. Functional connec-tivity was measured via independent component analysis (ICA), which is a statistical technique that separates a set of signals into independent uncor-related and non-Gaussian spatio-temporal compo-nents, wherein the ICA decomposition was calculated using the single-subject ICA plug-in that corresponded to a C++ implementation of the fast-ICA algorithm. For each subject, the initial dimensions of the functional data-set were reduced from 300 (number of time points) to 50 using the principal-component analysis (PCA) technique; for each subject, the 50 independent components were estimated using the fast-ICA method. Two criteria were used to select the components that most closely matched the default-mode network. 1. Only components with a signal frequency in the 0.01–0.1 Hz range were included. 2. A spatial template of the default mode net-work was used to select the best-fit of the remaining low-frequency components. In particular, we spatially correlated all the components with a default mode mask. This mask contained the posterior parietal cortex (BA 7), frontal pole (BA 10) and occipitoparietal junction (BA 39), as well as the posterior cingulate and precuneus. The component that spatially corre-lated most significantly with the template was selected as the default mode component. The group components were calculated as random effects maps. A two-sample t test was used to compare the healthy subject and patient group maps. Significant clusters of resting state activity for the two sample t tests were determined by using a (p,0.05) threshold corrected at the whole-brain level using the False Discovery Rate. 5 In order to quantify the degree of impoverishment in the resting state, we used various procedures. First, we compared the observed resting state activity maps with the reference template: a spatial correlation index was obtained providing the spatial template to the ICA plug-in in Brain Voyager (Brain Innovation, Maastricht, The Netherlands). Second, we examined the maps and then split them, by visual inspection, in specific neural functional connectivity networks. Third, to better characterise the asymmetry of the resting state activity we also computed a lateralisation index, using the formula: (right –left) / (right + left), where ''right'' denotes the active voxels situated to the right of the commissural sagittal plane, and ''left'' denotes the active voxels situated to the left of the commissural sagittal plane; the index can vary from –1 to 1, where 1 indicates a total right lateralisation, –1 total left and 0 total symmetry. See Supplementary Online Material for full details. In patients, DMNs were partially impaired compared with the healthy controls. On qualita-tive analysis, the more severe the clinical condition, the more impaired was the DMN. In the male patient (fig. S1b), the DMN was impaired, show-ing only the dorsolateral prefrontal cortex

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  • F Cauda

  • B M Micon

  • K Sacco

  • S Duca

  • F D 'agata

  • G Geminiani

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