Viewing Selleck AZD9291 a natural movie evokes local brain responses that show synchrony across subjects in a large expanse of cortex, including visual areas in the occipital, ventral temporal, and lateral temporal cortices ( Hasson et al., 2004, Bartels and Zeki, 2004 and Sabuncu et al., 2010). In contrast to earlier univariate analyses of local synchrony, we took a multivariate approach to analyze the time-varying patterns of response evoked by this rich, dynamic stimulus. We reasoned that in the brains of two individuals viewing the same dynamic visual stimulus, such as a full-length

action movie, the trajectories of VT response-pattern vectors over time reflect similar visual information, but the coordinate systems for their respective representational spaces, in which each dimension is one voxel, are poorly aligned. Hyperalignment uses Procrustean transformations ( Schönemann, 1966) iteratively over pairs of subjects to derive a group coordinate system

Crenolanib chemical structure in which subjects’ vector trajectories are in optimal alignment. The Procrustean transformation is an orthogonal transformation (rotations and reflections) that minimizes the Euclidean distance between two sets of paired vectors. After hyperalignment, we reduced the dimensionality of the common space by performing a principal components analysis (PCA) and determined the subspace that is sufficient to capture the full range of response-pattern distinctions. We tested the validity of the common the model by performing between-subject MVP classification of responses to a wide range of visual stimuli—time segments from the movie and still images of seven categories of faces and objects and six animal species. For between-subject classification (BSC), the response vectors for one subject were classified using a classifier model based on other subjects’ response vectors. We compared BSC performance for response vectors that had been transformed into the common model space to BSC for data that were aligned across subjects based on anatomy and to within-subject classification (WSC), in which the response vectors for a subject were

classified using an individually tailored classifier model based on response vectors from the same subject. Results showed that BSC accuracies for response-pattern vectors in common model space were markedly higher than BSC accuracies for anatomically aligned response-pattern vectors and equivalent to WSC accuracies. More than 20 dimensions were needed to achieve this level of accuracy. Here we present a common model space with 35 dimensions. Thus, the representational space in VT cortex can be modeled with response-tuning functions that are common across subjects. These response-tuning functions are associated with cortical topographies that serve as basis patterns for modeling patterns of response to stimuli and can be examined in each individual’s VT cortex.