Table 3.

Correlation Between Network Metrics Weighted for Intraneurite Volume Fraction/Neurite Volume Fraction, and Expanded Disability Status Scale

		Estimate	SE	t value	Pr(>|t|)
ICVF Multiple R2: 0.592; Adjusted R2: 0.535	(Intercept)	−23.745	22.859	−1.039	0.303
	Density	31.724	34.456	0.921	0.361
	Efficiency	−7.735	69.011	−0.112	0.911
	Modularity	33.539	13.553	2.475	0.016
	Clustering coefficient	52.857	49.289	1.072	0.288
	Mean strength	−0.673	1.121	−0.600	0.551
	Gender	0.151	0.351	0.430	0.668
	Age	0.069	0.014	5.034	<0.001
	Disease duration	0.008	0.010	0.744	0.460
INTRA Multiple R2: 0.584; Adjusted R2: 0.526	(Intercept)	−23.546	23.784	−0.990	0.326
	Density	33.357	35.799	0.932	0.355
	Efficiency	3.083	76.846	0.040	0.968
	Modularity	28.678	12.690	2.260	0.028
	Clustering coefficient	48.160	52.987	0.909	0.367
	Mean strength	−0.798	1.187	−0.672	0.504
	Gender	0.070	0.349	0.201	0.841
	Age	0.072	0.014	5.196	<0.001
	Disease duration	0.007	0.010	0.677	0.501

The statistically significant results are highlighted in bold.

Robust linear models to identify the contribution of each network metrics in explaining the EDSS. Age, gender, and disease duration are included as covariates. For compactness, only the maps that show significant results are presented. In the upper part we have the model corresponding to ICVF, whereas in the bottom we have the model corresponding to INTRA. Both models explain ∼53% of our data. In the two models, in addition to age that describes most of EDSS, modularity also seems to contribute to explaining the worsening of the disease, highlighting that EDSS is related to the segregation of the network.

SE, standard error.