A Model of Representational Spaces in Human Cortex

作者：Guntupalli J Swaroop; Hanke Michael; Halchenko Yaroslav O; Connolly Andrew C; Ramadge Peter J; Haxby James V^*

来源：Cerebral Cortex, 2016, 26(6): 2919-2934.

DOI：10.1093/cercor/bhw068

• functional magnetic resonance imaging (fMRI)
• hyperalignment
• multivariate pattern analysis (MVPA)
• neural decoding
• representational similarity analysis (RSA)

Current models of the functional architecture of human cortex emphasize areas that capture coarse-scale features of cortical topography but provide no account for population responses that encode information in fine-scale patterns of activity. Here, we present a linear model of shared representational spaces in human cortex that captures fine-scale distinctions among population responses with response-tuning basis functions that are common across brains and models cortical patterns of neural responses with individual-specific topographic basis functions. We derive a common model space for the whole cortex using a new algorithm, searchlight hyperalignment, and complex, dynamic stimuli that provide a broad sampling of visual, auditory, and social percepts. The model aligns representations across brains in occipital, temporal, parietal, and prefrontal cortices, as shown by between-subject multivariate pattern classification and intersubject correlation of representational geometry, indicating that structural principles for shared neural representations apply across widely divergent domains of information. The model provides a rigorous account for individual variability of well-known coarse-scale topographies, such as retinotopy and category selectivity, and goes further to account for fine-scale patterns that are multiplexed with coarse-scale topographies and carry finer distinctions.

• 出版日期2016-6