Signal quality as Achilles' heel of graph theory in functional magnetic resonance imaging in multiple sclerosis

Abstract

Graph-theoretical analysis is a novel tool to understand the organisation of the brain.We assessed whether altered graph theoretical parameters, as observed in multiple sclerosis (MS), reflect pathology-induced restructuring of the brain's functioning or result from a reduced signal quality in functional MRI (fMRI). In a cohort of 49 people with MS and a matched group of 25 healthy subjects (HS), we performed a cognitive evaluation and acquired fMRI. From the fMRI measurement, Pearson correlation-based networks were calculated and graph theoretical parameters reflecting global and local brain organisation were obtained. Additionally, we assessed metrics of scanning quality (signal to noise ratio (SNR)) and fMRI signal quality (temporal SNR and contrast to noise ratio (CNR)). In accordance with the literature, we found that the network parameters were altered in MS compared to HS. However, no significant link was found with cognition. Scanning quality (SNR) did not differ between both cohorts. In contrast, measures of fMRI signal quality were significantly different and explained the observed differences in GTA parameters. Our results suggest that differences in network parameters between MS and HS in fMRI do not reflect a functional reorganisation of the brain, but rather occur due to reduced fMRI signal quality.

Figure 1

Illustration of the fMRI quality metrics. The red and blue signal represent brain activity and background noise respectively. $\overline{S}$ is the mean of the brain activity, σ_S is the standard deviation of the brain activity, σ_N is the standard deviation of the background noise, and A is the amplitude of the maximum peak of the brain activity.

Figure 2

Graphs (a) to (d) show the global network parameters and (e) to (g) show the local network parameters. The median value and interquartile range of PwMS (red) and HS (blue) are presented. The x-axis represents the thresholds from the adjacency matrix from which networks were extracted. The background is shown in grey for the regions where the permutation test between the two groups showed a significant difference (p < 0.05).

Figure 3

(a) the adjacency matrix of HS in the upper triangle matrix and PwMS in the lower triangle matrix. (b) results of the permutation tests between all edges, PwMS relative to HS (c) distribution of all effect sizes of the permutations with p-value < 0.05.

Figure 4

Group distributions of scanning quality (SNR) and signal quality (tSNR and CNR) of the fMRI scans in standard space. * indicates statistical significance (p < 0.05).

Figure 5

Graphs (a) to (d) show the global network parameters and e to g show the local network parameters. The median value and interquartile range of the GTA parameters, corrected for CNR and tSNR, are presented for PwMS (red) and HS (blue). The x-axis represents the thresholds from the adjacency matrix from which networks were extracted. The background is shown in grey and yellow for CNR and tSNR respectively to indicate at what thresholds they contributed significantly.