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. Author manuscript; available in PMC: 2013 Jun 24.
Published in final edited form as: J Cogn Neurosci. 2011 Jun 14;23(12):4022–4037. doi: 10.1162/jocn_a_00077

Figure 1.

Figure 1

The data processing pipeline included four steps. Step 1: Peak coordinates in BrainMap were smoothed (12 mm FWHM) to generate 8637 modeled activation images. Step 2: ICA was applied to this 4D data using FSL’s MELODIC to decompose the experiment images into 20 spatially independent components. Step 3: The matrix that quantifies the relationship between components and BrainMap experiments was utilized to compute a set of matrices that corresponded to 14 independent metadata fields, each with n classes. The relative salience as computed for each field and the two fields with the highest salience were selected for further analysis: behavioral domain and paradigm. Step 4: HCA was performed on the concatenated behavioral domain and paradigm matrix (125 metadata classes × 20 networks). Clustering was first performed on the combined matrix to determine groupings across metadata classes; subsequently, the matrix was transposed and the analysis repeated to quantify similarity across networks.