Brain network motifs are markers of loss and recovery of consciousness

Abstract

Motifs are patterns of inter-connections between nodes of a network, and have been investigated as building blocks of directed networks. This study explored the re-organization of 3-node motifs during loss and recovery of consciousness. Nine healthy subjects underwent a 3-hour anesthetic protocol while 128-channel electroencephalography (EEG) was recorded. In the alpha (8)(9)(10)(11)(12)(13) band, five-minute epochs of EEG were extracted for: baseline; induction; unconsciousness; 30-, 10-and 5-minutes pre-recovery of consciousness; 30-and 180-minutes post-recovery of consciousness. We constructed a functional brain network using both the weighted and the directed phase lag index, on which we calculated graph theoretical network properties, and the frequency and topography of 3-node motifs. Three motifs (motifs 1, 2 and 5) were significantly present across participants and epochs, when compared to random networks (p<0.05). While graph theoretical properties varied inconsistently across unconscious epochs (p-values<0.05), the topography of motifs 1 and 5 changed significantly between conscious and unconscious states (p<0.01). Our results suggest that anesthetic-induced unconsciousness is associated with a topographic re-organization of network motifs. As motif topographic re-organization may precede (motif 1) or accompany (motif 5) the behavioral return of consciousness, motifs could contribute to the understanding of the neural building blocks of consciousness.representing the spurious connectivity. The wPLI and dPLI values of the original, non-shuffled EEG data were compared to this distribution of surrogate data using a Wilcoxon signed rank test and were set to 0 (wPLI) or 0.5 (dPLI) if they did not achieve statistical significance. Statistical significance was set to p < 0.05. Construction and analysis of brain networksThe functional brain network was constructed using the wPLI of all pairwise combinations of electrode channels. We constructed a binary adjacency matrix Aij using a threshold of 35%: if the wPLIij value of nodes i and j was within the top 35% of all wPLI values, Aij = 1; otherwise, Aij = 0. This threshold was selected because it was previously shown to be the optimum threshold to avoid an isolated node in the EEG network during baseline [47]. From the binary adjacency matrix, we calculated basic graph theoretical network properties, including global efficiency, clustering coefficient, and modularity (Fig. 6). Global efficiency is the inverse of the average shortest path length ( ! ! ! ), where Lw is the average of the shortest path lengths (Lij) between all pairs of nodes in the network [48]. The clustering coefficient, calculated by averaging the clustering coefficients of all individual nodes (Ci), represents the degree to which nodes of a graph tend to cluster together, such that higher values imply networks with highly clustered or regular structures [49]. The modularity of the network represents the strength of division of a network into modules, such that high modularity implies a network w...

Brain network motif topography may predict emergence from disorders of consciousness: a case series

“… Topographic network properties in the alpha band distinguish between consciousness and anesthetic-induced unconsciousness in healthy controls; adapted from Duclos et al (2020) . In a cohort of nine healthy controls, the average topographies of alpha motif frequency, node degree, and power undergo anterior–posterior shifts across stages of anesthetic-induced unconsciousness.…”

“…Global and topographic network properties in DOC patients were compared against nine healthy controls undergoing anesthetic-induced unconsciousness, induced by propofol and maintained for 3 h using isoflurane ( Maier et al 2017 ; Duclos et al 2020 ). Similarly to DOC patients, high-density EEG was recorded during baseline, anesthesia exposure, and recovery.…”

“…Motifs may reveal granular changes in network composition that are associated with the capacity of a network to sustain consciousness ( Shin et al 2013 ; Kafashan et al 2016 ), and thus may have strong prognostic value for DOC patients. Our group has demonstrated in healthy participants that three-node functional motifs exhibit topographically distinct patterns associated with states of anesthetic-induced unconsciousness ( Duclos et al 2020 ). We have also demonstrated the feasibility of using anesthesia to perturb the brain networks of a DOC patient, allowing us to measure changes in the network that herald the recovery of consciousness ( Blain-Moraes et al 2016 ).…”