pubmed.ncbi.nlm.nih.gov

Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis - PubMed

  • ️Fri Jan 01 2010

Comparative Study

Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis

Martijn P van den Heuvel et al. J Neurosci. 2010.

Abstract

Brain regions are not independent. They are interconnected by white matter tracts, together forming one integrative complex network. The topology of this network is crucial for efficient information integration between brain regions. Here, we demonstrate that schizophrenia involves an aberrant topology of the structural infrastructure of the brain network. Using graph theoretical analysis, complex structural brain networks of 40 schizophrenia patients and 40 human healthy controls were examined. Diffusion tensor imaging was used to reconstruct the white matter connections of the brain network, with the strength of the connections defined as the level of myelination of the tracts as measured through means of magnetization transfer ratio magnetic resonance imaging. Patients displayed a preserved overall small-world network organization, but focusing on specific brain regions and their capacity to communicate with other regions of the brain revealed significantly longer node-specific path lengths (higher L) of frontal and temporal regions, especially of bilateral inferior/superior frontal cortex and temporal pole regions. These findings suggest that schizophrenia impacts global network connectivity of frontal and temporal brain regions. Furthermore, frontal hubs of patients showed a significant reduction of betweenness centrality, suggesting a less central hub role of these regions in the overall network structure. Together, our findings suggest that schizophrenia patients have a less strongly globally integrated structural brain network with a reduced central role for key frontal hubs, resulting in a limited structural capacity to integrate information across brain regions.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.

The brain network, a graph, and graph organizational measurements. Each individual structural brain network was represented as a graph (a). A graph is a mathematical description of a network (b), consisting of a collection of nodes (blue dots) and a collection of connections between the nodes (green lines). A weighted graph (c) is a network in which the strength of the connections is taken into account, expressing a tighter connection between two nodes as one with a higher weight. From a graph, a number of key organizational characteristics can be computed describing the local and global organization of the connections and the overall structure of the network. The connectivity strength Si of a graph (d) is measured as the sum of the weights of each node i and reflects how strong node i is connected to the rest of the network. The node-specific clustering coefficient Ci (e) is computed as the weight intensities of the local subnetwork Gi of node i in ratio of the total number of possible connections and provides information about how strongly node i is connected locally in the graph. Furthermore, the node-specific travel distance Li (f) reflects the average distance from node i to every other node in the network. Li is computed as the shortest travel distance between node i and j in the network, averaged over all nodes j, i.e., all connections that have to be crossed to travel from node i to node j in the network. In a weighted graph, the shortest distance between node i and node j is typically defined as the sum of the inverse of the weights of the connections that have to be passed to travel from node i to node j. The betweenness centrality Bi of node i (g) is computed as the number of shortest routes between two nodes h and j in the graph that pass through node i, expressing how central node i is located in the network. Finally, measures of overall organization can be computed by averaging the node-specific values Si, Ci, and Li, over all nodes i in the network.

Figure 2.
Figure 2.

Schematic overview of formation of individual structural brain networks. For each individual dataset, a structural brain network was constructed. First, for each dataset (a) from the AAL template consisting of 108 unique brain regions (54 each hemisphere), two regions, i and j, were selected (b). From the total collection of reconstructed white matter tracts (based on the DTI data), the white matter fibers interconnecting region i and region j were selected by selecting those tracts that touched both region i and region j. If this selection procedure revealed fibers between i and j (c), node i and node j in the brain network were interconnected by a connection. Furthermore, the weight wij of the connection between node i and j was selected as the MTR value along the selected fibers, reflecting the level of myelination of the selected tract (d), and wij was included in cell cij of the weighted connectivity matrix M. If no tract was found between region i and j, no connection was included in graph G, resulting in cij value of 0 in M. These steps were repeated for all regions i and j in the AAL template (e), resulting in a filled weighted connectivity matrix M, representing a weighted undirected graph G (f). Next, from the resulting brain network overall organizational characteristics (g) and node-specific organizational characteristics (h) (see supplemental materials and Fig. 1) were computed and compared between patients and healthy controls (i).

Figure 3.
Figure 3.

Overall graph organizational measures S, gamma and lambda values of controls and schizophrenia patients. Overall measures of normalized clustering coefficient gamma (a) and normalized path length lambda (b) of the patient and healthy control group. Bars express standard deviation over the group of subjects. Figure shows no significant differences in overall organization of the brain networks of schizophrenia patients and healthy participants on gamma or lambda.

Figure 4.
Figure 4.

Node-specific hub scores. Figure illustrates region-specific “hub scores” of the average brain network of the group of healthy controls. Hub scores were computed as the number of times a node belonged to the 20% of nodes having a high Ki, a high Bi, a low Ci, and/or a low Li. High scores mark nodes that take a central position in the brain network (see Materials and Methods). Hubs are also shown in supplemental Movie 1.

Figure 5.
Figure 5.

Group differences of node-specific Li and Ci values between healthy controls and schizophrenia patients. Upper figures show p values of significant between-group increases in node-specific Li (corrected for within group Si) for the left and right hemisphere respectively, marking significantly increased path length Li of olfactory, medial, and superior frontal, occipital, and medial temporal pole brain regions in patients with schizophrenia compared with brain networks of the matched healthy controls (see also supplemental Movie 2). Lower figures mark significantly decreased node-specific clustering coefficient Ci values in patients with schizophrenia, with significantly decreased clustering coefficient of right paracentral lobule and right hippocampus. The Li effects, together with the low number of Ci effects, tend to suggest that schizophrenia merely affects the global organization of the brain network, leaving the local organization relatively intact. Yellow dotted line expresses the critical FDR threshold (q = 0.05; see Materials and Methods).

Figure 6.
Figure 6.

Group differences of node-specific Bi between healthy controls and schizophrenia patients. Figure shows p values of node-specific decreases of Bi (corrected for within group Si) in patients for the left and right hemisphere (left and right image), illustrating decreased betweenness centrality Bi of bilateral superior frontal and anterior cingulate regions in patients with schizophrenia (see also supplemental Movie 3). Yellow dotted line expresses the critical FDR threshold (q = 0.05; see Materials and Methods).

Figure 7.
Figure 7.

Overlap between hub nodes and brain regions affected by schizophrenia. c shows the intersection between the collection of hub regions (hub-score 2 and more) (a) and the union of the brain regions that showed a significant increase in path length Li (b, upper parts) and/or a decrease in centrality Bi (b, lower parts). Overlap marks the hub regions that are impacted by disease.

Comment in

Similar articles

Cited by

References

    1. Achard S, Bullmore E. Efficiency and cost of economical brain functional networks. PLoS Comput Biol. 2007;3:e17. - PMC - PubMed
    1. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci. 2006;26:63–72. - PMC - PubMed
    1. Andersson JL, Skare S. A model-based method for retrospective correction of geometric distortions in diffusion-weighted EPI. Neuroimage. 2002;16:177–199. - PubMed
    1. Andreasen NC, Flaum M, Arndt S. The Comprehensive Assessment of Symptoms and History (CASH): an instrument for assessing diagnosis and psychopathology. Arch Gen Psychiatry. 1992;49:615–623. - PubMed
    1. Arnone D, Cavanagh J, Gerber D, Lawrie SM, Ebmeier KP, McIntosh AM. Magnetic resonance imaging studies in bipolar disorder and schizophrenia: meta-analysis. Br J Psychiatry. 2009;195:194–201. - PubMed

Publication types

MeSH terms