(a) Matrix multiplication for modeling cortical patterns in individual brains (t2(Bi)) by multiplying a pattern vector (t2) in the model space (M) and the transpose of individual-specific transformation matrices (RiT). (b) Illustration of topographic basis functions in two subjects, S1 and S2, for model dimensions (d1, d2, d3, …, dm), which are comprised of weights (w’s in RiT) across vertices in overlapping but non-coextensive patches of cortex. This image is illustrative and not based on experimental data. Two hypothetical subjects’ basis functions are illustrated to emphasize that these functions are individual-specific and support the same shared information from M in variable topographic patterns in individual brains. Transformation matrix weights (w) are illustrated as colored circles of varying intensity at vertices of a stylized triangular cortical mesh. The pattern of weights for each basis function varies across brains. These topographic basis functions are combined as a weighted sum, using the weights from a pattern vector (e.g., t2) in the model data matrix (y1,2, y2,2, y3,2, …, ym,2 in M) to model or predict a topographic pattern (t̂2(Bi)) in an individual subject’s cortex. The same weights in the model pattern vector predict different patterns of response in each individual brain. The predicted topographic patterns (t̂2(Bi)) are illustrated as values at each vertex of a triangular cortical mesh using a typical color scale with negative values in shades of blue and positive values in shades of red, orange, and yellow.