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Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies
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DOI：10.7554/eLife.56601.png)
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Abstract
En 中文
Information that is shared across brains is encoded in idiosyncratic fine-scale functional topographies. Hyperalignment captures shared information by projecting pattern vectors for neural responses and connectivities into a common, high-dimensional information space, rather than by aligning topographies in a canonical anatomical space. Individual transformation matrices project information from individual anatomical spaces into the common model information space, preserving the geometry of pairwise dissimilarities between pattern vectors, and model cortical topography as mixtures of overlapping, individual-specific topographic basis functions, rather than as contiguous functional areas. The fundamental property of brain function that is preserved across brains is information content, rather than the functional properties of local features that support that content. In this Perspective, we present the conceptual framework that motivates hyperalignment, its computational underpinnings for joint modeling of a common information space and idiosyncratic cortical topographies, and discuss implications for understanding the structure of cortical functional architecture.
Keywords:
BRAIN ACTIVITY
REPRESENTATIONAL SPACES
PATTERN-ANALYSIS
TEMPORAL CORTEX
NATURAL IMAGES
SEMANTIC SPACE
FMRI
OBJECT
NEURONS
PARCELLATION