. 2022 Aug 4;12(6):502–514. doi: 10.1089/brain.2021.0037

Table 2.

Model Performance of Each Network

Network model	Sample	Network	r_s	p	MSE	Prediction R²
MS-wmCPM	Internal	High	0.47	0.011^a,^*
		Low	0.50	0.008^a,^**
		Full	0.47	0.008^a,^**
MS-wmCPM	Validation	High	0.026	0.88	0.006	−2.93%
		Low	0.084	0.63	0.006	−0.90%
		Full	−0.047	0.78	0.006	−2.21%
wmCPM	Internal	High	0.20	0.24	0.010	3.39%
		Low	−0.33	0.046^*	0.009	16.8%
		Full	0.33	0.049^*	0.009	13.4%
wmCPM	Validation	High	0.41	0.013^*	0.005	12.7%
		Low	−0.51	0.002^**	0.005	17.8%
		Full	0.46	0.005^**	0.005	16.9%

The Spearman rank correlations between predicted network strength and observed scores are presented here. For the MS-wmCPM internal sample, correlations are reported between predicted and observed WM scores. The “Network Model” indicates which model was applied to which “Sample” data using which “Network.”

^{^a}

Indicates permuted p^* = p < 0.05, ^** = p < 0.01.

MS, multiple sclerosis; MSE, mean squared error; MS-wmCPM = WM connectome-based predictive model derived in the internal sample of individuals with multiple sclerosis using linear regression and leave-one-out cross-validation; WM = Working Memory; wmCPM = WM connectome-based predictive model derived in healthy young adults by Avery et al. (2020).