Table 1.

Estimates for the data shown in Fig. 3 with the model predicting the accuracy as a function of the number of ROIs. Chi-square estimate (${\chi }^2$) shows the goodness-of-fit under the null hypothesis that the data follows the specified model. In our problem, the critical ${\chi }^2$ to reach a significance level of 0.01 must be less than or equal to 0.115 (3 degrees of freedom). The scores ${\mathrm{AR}}_{0.5\%}/{\mathrm{AR}}_{0.25\%}$ represent the threshold at which adding an extra ROI would increase the accuracy by $<0.5\%/0.25$%, respectively.

Neuroimaging contrast	$\alpha$	$\gamma$	${\chi }^2$	${\mathrm{AR}}_{0.5\%}$	${\mathrm{AR}}_{0.25\%}$
HbO	88	0.05	$9\times {10}^{-4}$	44	58
HbR	100	0.027	$3\times {10}^{-2}$	63	89
HbT	98	0.046	$8\times {10}^{-3}$	48	63
BOLD	100	0.043	$3\times {10}^{-2}$	50	67