Nodal centrality of functional network in the differentiation of schizophrenia

Abstract

A disturbance in the integration of information during mental processing has been implicated in schizophrenia, possibly due to faulty communication within and between brain regions. Graph theoretic measures allow quantification of functional brain networks. Functional networks are derived from correlations between time courses of brain regions. Group differences between SZ and control groups have been reported for functional network properties, but the potential of such measures to classify individual cases has been little explored. We tested whether the network measure of betweenness centrality could classify persons with schizophrenia and normal controls. Functional networks were constructed for 19 schizophrenic patients and 29 non-psychiatric controls based on resting state functional MRI scans. The betweenness centrality of each node, or fraction of shortest-paths that pass through it, was calculated in order to characterize the centrality of the different regions. The nodes with high betweenness centrality agreed well with hub nodes reported in previous studies of structural and functional networks. Using a linear support vector machine algorithm, the schizophrenia group was differentiated from non-psychiatric controls using the ten nodes with the highest betweenness centrality. The classification accuracy was around 80%, and stable against connectivity thresholding. Better performance was achieved when using the ranks as feature space as opposed to the actual values of betweenness centrality. Overall, our findings suggest that changes in functional hubs are associated with schizophrenia, reflecting a variation of the underlying functional network and neuronal communications. In addition, a specific network property, betweenness centrality, can classify persons with SZ with a high level of accuracy.

Keywords: Betweenness centrality; Functional network; Machine learning; Resting state fMRI; Schizophrenia; Support vector machine.

Figure 1

Betweenness centrality as a function of ranks. The graph clearly shows two zones where BC values increases linearly with ranks. Zone 1 is approximately from rank 1 – 200; zone 2 is from rank 269–278. The top 10% nodes with highest BC are marked with solid circles.

Figure 2

Comparison of both mean translational (blue) and rotational (red) motions between the SZ and NC groups (A); standard errors of motion within each group are indicated by the error bars. Correlation coefficients between motion parameters and BC values are shown in (B).

Figure 3

Mean functional networks with 5% edges retained. The location of the nodes represents the coordinates of parcellated regions in MNI template. The size of the nodes is proportional to the square root of their BC values.

Figure 4

Top 10% nodes with highest betweenness centrality displayed on the MNI template. The Z coordinates in mm are shown on top of corresponding slices. Ten nodes with highest betweenness centrality used for SVM analysis are colored in red, all others are colored in blue.

Figure 5

Classification accuracy using ranks of betweenness centrality of all nodes (A); values of betweenness centrality of all nodes (B); ranks of betweenness centrality of top ten hub nodes (C); and values of betweenness centrality of top ten hub nodes (D).

Figure 6

(A) shows classification accuracy using different sets of nodes in SVM while keeping the same number of features. The figure shows the prediction rates as we choose every 10 nodes with order of betweenness centrality starting from 9. Although many node sets give rise to high overall accuracy rate, only three sets achieve accuracy rates above 0.6 in predicting SZ. (B) shows the classification accuracy for 100 trials by randomly mixing SZ and NC subjects and performing SVM classification while still using the original labeling. The classification accuracy dramatically drops.

Figure 7

Motion effects on the SVM classification. (A) and (B) compare translational motion and rotational motion on those classified as NC and those classified as SZ. (C) and (D) compare translational motion and rotational motion on those classified correctly and those classified incorrectly. Results were from the network with threshold of 0.30.