After computing the normalized sensitivity (normSens), as described in Methods section, the brain sensitivity (brainSens) of a given channel (ch) with respect to a head atlas (h) is calculated. This is done by summing normSens from all voxels (i) classified as gray and white matter during tissue segmentation27, as follows:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$brainSens(ch)=\sum _{i}^{brain}normSens(ch,i)$$\end{document}brainSens(ch)=∑ibrainnormSens(ch,i)To better assess the influence of a given region of interest (i.e. set of voxels) inside the brain in respect to brainSens(ch), we define “specificity” as follows:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Specificit{y}_{ROI}(ch)=100\times \sum _{j}^{ROI}\frac{normSens(ch,j)}{brainSens(ch)}$$\end{document}SpecificityROI(ch)=100×∑jROInormSens(ch,j)brainSens(ch)Equation 2 can be translated as a weighted mean of the voxels (j) within a given region of interest (ROI), in which the weight corresponds to the normalized sensitivity (normSens), normalized by the sensitivity to the brain (brainSens) to percentage. Thus, it provides the anatomical specificity of a channel (ch) to the region-of-interest (ROI).