Search for: All records

Category selectivity is a fundamental principle of organization of perceptual brain regions. Human occipitotemporal cortex is subdivided into areas that respond preferentially to faces, bodies, artifacts, and scenes. However, observers need to combine information about objects from different categories to form a coherent understanding of the world. How is this multicategory information encoded in the brain? Studying the multivariate interactions between brain regions of male and female human subjects with fMRI and artificial neural networks, we found that the angular gyrus shows joint statistical dependence with multiple category-selective regions. Adjacent regions show effects for the combination of scenes and each other category, suggesting that scenes provide a context to combine information about the world. Additional analyses revealed a cortical map of areas that encode information across different subsets of categories, indicating that multicategory information is not encoded in a single centralized location, but in multiple distinct brain regions. SIGNIFICANCE STATEMENTMany cognitive tasks require combining information about entities from different categories. However, visual information about different categorical objects is processed by separate, specialized brain regions. How is the joint representation from multiple category-selective regions implemented in the brain? Using fMRI movie data and state-of-the-art multivariate statistical dependence based on artificial neural networks, we identified the angular gyrus encoding responses across face-, body-, artifact-, and scene-selective regions. Further, we showed a cortical map of areas that encode information across different subsets of categories. These findings suggest that multicategory information is not encoded in a single centralized location, but at multiple cortical sites which might contribute to distinct cognitive functions, offering insights to understand integration in a variety of domains. 

Cognitive tasks engage multiple brain regions. Studying how these regions interact is key to understand the neural bases of cognition. Standard approaches to model the interactions between brain regions rely on univariate statistical dependence. However, newly developed methods can capture multivariate dependence. Multivariate pattern dependence (MVPD) is a powerful and flexible approach that trains and tests multivariate models of the interactions between brain regions using independent data. In this article, we introduce PyMVPD: an open source toolbox for multivariate pattern dependence. The toolbox includes linear regression models and artificial neural network models of the interactions between regions. It is designed to be easily customizable. We demonstrate example applications of PyMVPD using well-studied seed regions such as the fusiform face area (FFA) and the parahippocampal place area (PPA). Next, we compare the performance of different model architectures. Overall, artificial neural networks outperform linear regression. Importantly, the best performing architecture is region-dependent: MVPD subdivides cortex in distinct, contiguous regions whose interaction with FFA and PPA is best captured by different models. 

Category-selectivity is a fundamental principle of organization of perceptual brain regions. Human occipitotemporal cortex is subdivided into areas that respond preferentially to faces, bodies, artifacts, and scenes. However, observers need to combine information about objects from different categories to form a coherent understanding of the world. How is this multi-category information encoded in the brain? Studying the multivariate interactions between brain regions with fMRI and artificial neural networks, we found that the angular gyrus shows joint statistical dependence with multiple category-selective regions. Additional analyses revealed a cortical map of areas that encode information across different subsets of categories, indicating that multi-category information is not encoded in a single stage at a centralized location, but in multiple distinct brain regions.