Table 2. Benchmarking results.

Deviation (Z) score column shows the performance using deviation scores (AUC for classification, the total number of regions with significant group differences FDR-corrected p<0.05 for case versus control, mean squared error for regression), Raw column represents the performance when using the raw data, and Difference column shows the difference between the deviation scores and raw data (Deviation - Raw). Higher AUC, higher count, and lower MSE represent better performance. Positive values in the Difference column show that there is better performance when using deviation scores as input features for classification and group difference tasks, and negative performance difference values for the regression task show there is a better performance using the deviation scores. *=statistically significant difference between Z and Raw established using permutation testing (10 k perms).

Benchmark	Modality	Normative ModelingDeviation Score Data	Raw Data	PerformanceDifference
Group Difference	Cortical thickness	117/187	0/187	117*
Group Difference	Functional Networks	50/136	0/136	50*
Classification	Cortical thickness	0.87	0.43	0.44*
Classification	Functional Networks	0.69	0.68	0.01
Regression	Cortical thickness	0.699	0.708	–0.008
Regression	Functional Networks	0.877	0.890	–0.013