TABLE 3.

(a) Each Dataset Compared With Model 1; (b) Each Dataset Compared With Model 2

Dataset	1	2	3	4	5	6	7	8	9	10	“11”
CorrCoef (a)	0.8553	0.8766	0.8283	0.7626	0.6876	0.8025	0.4512	0.4778	0.4589	0.7502	−0.0218
z-score	6.831	7.059	6.664	6.188	5.511	6.535	3.641	3.845	3.724	6.065	−0.1670
P-value	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	0.8674
CorrCoef (b)	0.5998	0.5733	0.6254	0.5911	0.8026	0.6515	0.5987	0.6251	0.6401	0.5590	0.0069
z-score	4.908	4.596	5.025	4.838	6.535	5.231	4.816	5.067	5.164	4.523	0.068
P-value	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	0.9458

To test the validity of each pattern of functional connectivity represented by the models, 10,000 permutations of each model were correlated with each dataset to create a distribution of correlation coefficients. Reported are the correlation coefficients of the model to the dataset, its z-score, and its resulting P-value. Since the models were built off of observations from dataset 1, the fit of dataset 1 to the models was expected and the correlation coefficients of dataset 1 to the models are simply reported for descriptive purposes. Each dataset was significantly correlated with both models. The fake “dataset 11” did not correlate with either model.