The question of how many functionally distinct networks were apparent within MD cortex was addressed using exploratory factor analysis. Voxels within MD cortex (Figure 1A) were transformed into 12 vectors, one for each task, and these were examined using principal components analysis (PCA),

a factor analysis technique that extracts orthogonal linear components from the 12-by-12 matrix of task-task bivariate correlations. The results revealed two “significant” principal components, each of which explained more variability in brain activation than was contributed by any one task. These components accounted for ∼90% of the total variance in task-related activation http://www.selleckchem.com/products/ipi-145-ink1197.html across MD cortex (Table S1). After orthogonal rotation with the Varimax algorithm, the strengths of the task-component loadings were highly variable and easily comprehensible (Table 1 and Figure 1B). Specifically, www.selleckchem.com/products/Cisplatin.html all of the tasks in which information had to be actively maintained in short-term

memory, for example, spatial working memory, digit span, and visuospatial working memory, loaded heavily on one component (MDwm). Conversely, all of the tasks in which information had to be transformed in mind according to logical rules, for example, deductive reasoning, grammatical reasoning, spatial rotations, and color-word remapping, loaded heavily on the other component (MDr). When factor scores were generated at each voxel using regression and projected back onto the brain, two clearly defined functional networks were rendered (Figure 1D). Thus, the insula/frontal operculum (IFO), the superior frontal sulcus (SFS),

and the ventral portion of the anterior cingulate cortex/ presupplementary motor area (ACC/preSMA) had greater MDwm component scores, whereas the inferior frontal sulcus (IFS), inferior parietal cortex (IPC), and the dorsal portion of the ACC/preSMA had greater MDr component scores. When the PCA was rerun with spherical regions of interest (ROIs) centered on each the MD subregion, with radii that varied from 10 to 25 mm in 5 mm steps and excluding voxels that were on average deactivated, the task loadings correlated with those from the MD mask at r > 0.95 for both components and at all radii. Thus, the PCA solution was robust against variations in the extent of the ROIs. When data from the whole brain were analyzed using the same method, three significant components were generated, the first two of which correlated with those from the MD cortex analysis (MDr r = 0.76, MDwm r = 0.83), demonstrating that these were the most prominent active-state networks in the brain. The factor solution was also reliable at the individual subject level. Rerunning the same PCA on each individual’s data generated solutions with two significant components in 13/16 cases. There was one three-component solution and two four-component solutions.