Functional brain network changes associated with clinical and biochemical measures of the severity of hepatic encephalopathy

Electroencephalogram variability in patients with cirrhosis associates with the presence and severity of hepatic encephalopathy

“…The agreement between the EEG characteristics and patients categorized using conventional clinical and psychometric criteria was not absolute, which was to be expected as each modality accesses different domains of cerebral activity. The findings in the present study are echoed in a recent study of brain networks in patients with cirrhosis using functional magnetic resonance imaging and graph theoretical analysis, which showed that changes in cerebral networks were evident before the advent of either clinical changes suggestive of HE or disturbed psychometric performance [33]. Taken together, the newly derived EEG parameters [mym1]may identify abnormal cortical processing at an earlier stage than current available psychometric methods and, as such, they may be useful for its earlier identification.…”

Architecture of the Mouse Brain Synaptome

“…The clustering coefficient (CC) in Figure 4C is calculated (Watts and Strogatz, 1998) as the fraction of triangles around a node and small-world networks usually have high clustering compared with random network (Bullmore and Sporns, 2009). The modularity in Figure 4C measures the degree to which the network can be decomposed into a set of non-overlapping subnets, each of which comprises a number of densely inter-connected nodes that are sparsely connected to the noses in the other subnets (Jao et al., 2015). The small worldness (Bullmore and Sporns, 2009) in Figure 4C is calculated as the ratio of the clustering coefficient and average path length normalized by the random network,$\mathrm{\sigma }=\left({\text{C}}_{\text{net}}/{\text{C}}_{\text{rand}}\right)/\left(l_{\text{net}}/l_{\text{rand}}\right).$where C net , C rand represents the CCs of the synaptome and random network respectively, and the l net , l rand are the path length of the synaptome and random network, respectively.…”

Reorganisation of Brain Hubs across Altered States of Consciousness

“…Given prior reports that demonstrated differences in the hubness of network nodes with loss of consciousness 10,24,33 , we next calculated the hub disruption index across three experimental conditions with the aim of investigating the potential reorganisation of brain hubs in response to systematic reductions in consciousness. Hub disruption index is a summary metric that characterises regional changes in the hubness of network nodes in response to external manipulations 24,25 .…”

“…Following up on previous research that has indicated the selective influence of pharmacologically-induced sedation on brain regions comprising high levels of functional connectivity 10,33 , we investigated changes in the profile of whole-brain functional connectivity hubs in response to propofol-induced light and moderate sedation. For this purpose, we applied the hub disruption index (κ) 24,25 , which provides a systematic characterization of changes in the overall organization of brain hubs across experimental conditions. In the present study, κ was calculated as the slope of a linear fit to the scatterplot of group average nodal strengths (sum of Pearson correlation values) between a chosen condition, and the difference between this condition and either of the subsequent experimental conditions for each participant.…”

“…In agreement with this notion, a recent study comparing patients in coma to healthy participants revealed a marked reorganisation of both transmodal and unimodal brain hubs 24 . Similarly, progressively greater disturbances to the connectivity strength of both primary somatosensory and association cortices were observed in hepatic encephalopathy patients with systematic differences in their levels of consciousness, that ultimately resulted in gross reorganisation of brain network topology 25 . Further adding to these findings, loss of consciousness with pharmacological interventions in healthy participants has been linked to reduced functional integration in the brain, affecting both the frontal and parietal cortices 26 .…”