Hubs of brain functional networks are radically reorganized in comatose patients

Abstract

Human brain networks have topological properties in common with many other complex systems, prompting the following question: what aspects of brain network organization are critical for distinctive functional properties of the brain, such as consciousness? To address this question, we used graph theoretical methods to explore brain network topology in resting state functional MRI data acquired from 17 patients with severely impaired consciousness and 20 healthy volunteers. We found that many global network properties were conserved in comatose patients. Specifically, there was no significant abnormality of global efficiency, clustering, small-worldness, modularity, or degree distribution in the patient group. However, in every patient, we found evidence for a radical reorganization of high degree or highly efficient "hub" nodes. Cortical regions that were hubs of healthy brain networks had typically become nonhubs of comatose brain networks and vice versa. These results indicate that global topological properties of complex brain networks may be homeostatically conserved under extremely different clinical conditions and that consciousness likely depends on the anatomical location of hub nodes in human brain networks.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Global functional connectivity and topological properties of brain networks in healthy volunteers (white) and comatose patients (gray). (A) Mean wavelet correlation, a measure of functional connectivity on average over all pairs of regions in the brain. (B) Global efficiency, a topological measure of integrative information transfer inversely related to characteristic path length. (C) Clustering, a topological measure of segregated information transfer. (D) Maximum modularity, a global measure of the near-decomposability of the network into a community structure of sparsely interconnected modules. (E) Degree distribution, the probability distribution of the degree of a node in the network (patients in red and healthy volunteers in black). Corresponding results for other global metrics (betweenness centrality, participation coefficient, degree distribution parameters), other wavelet scales, spatial parcellation scales, and graph connection densities are in Figs. S1 and S6–S9.

Fig. 2.

Hub disruption of functional networks in comatose patients. (A) The mean degree of each node in the healthy volunteer group (x axis) is plotted versus the difference between groups in mean degree of each node (y axis). Normal hub nodes have a high mean degree in the healthy group and an abnormal reduction of degree in the comatose group, e.g., precuneus or fusiform gyrus, whereas nodes that are normally nonhubs have a low degree in healthy volunteers and an abnormal increase of degree in patients, e.g., angular gyrus. The slope of the (red) line fitted to the data is the hub disruption index, ; using the same color scale as in B, the color of the points denotes the difference between groups in mean degree of each node. (B) Cortical surface representation of the difference in mean degree between patient and volunteer groups; red denotes increased degree, on average, in patients compared with healthy volunteers; blue denotes abnormally decreased degree in comatose patients. (C) Cortical surface representation of nodes that demonstrated significant between-group difference in nodal degree; permutation test, two-tailed ; red denotes significantly increased degree and blue denotes significantly decreased degree in the patients on average. Corresponding results for other measures of hubness (functional connectivity, global efficiency, clustering, betweenness centrality, and participation coefficient) are shown in Figs. S3 and S6; for further detail on the estimation of the hub disruption index, see Fig. S4.

Fig. 3.

Individual comatose patients consistently demonstrate hub disruption of functional brain networks. (A and C) The hub disruption indices and were estimated for each patient as the gradient of a straight (red) line fitted to the scatterplots of the individual differences in nodal degree (D) or functional connectivity (S) vs. the healthy group mean nodal degree or connectivity. The black horizontal line shows the equivalent function estimated for the individual differences of each healthy volunteer vs. the healthy group mean (error bars = SD). (B and D) Boxplots of the individually estimated hub disruption indices for the healthy volunteer group (white) and the comatose patient group (gray). For both and , the healthy group mean is approximately zero, whereas for the patient group, it is closer to −1. The between-group differences in κ are significant by t test () and by permutation test (; Fig. S4). Corresponding results for other hubness measures are shown in Fig. S3.

Fig. 4.

The community structure of functional networks is abnormally variable between comatose patients. (Right) Each matrix element represents the similarity between the community structure (modular decomposition) for a pair of participants, as measured by normalised mutual information (NMI). The first 20 rows/columns represent healthy volunteers, whereas the next 17 rows/columns correspond to the comatose patients; insets show group means (and SEMs) for NMI. The NMI values on the diagonal were set to zero (instead of their natural value of 1) for clarity of visualization. (Left) Cortical surface representations of the community structure of the healthy volunteer group on average (Top), a single representative healthy volunteer (Middle), and a single representative comatose patient, with median modularity in the patient group (Bottom). It is evident that the normal affiliation of specific cortical regions to topological modules (color coded on the cortical surface) is extensively disrupted in the comatose patient.