Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies

Elife. 2020 Jun 2;9:e56601. doi: 10.7554/eLife.56601.

Abstract

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: cortex; functional connectivity; hyperalignment; individual differences; neuroscience; population response; topography.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Review

MeSH terms

  • Algorithms
  • Cerebral Cortex / anatomy & histology*
  • Cerebral Cortex / diagnostic imaging
  • Cerebral Cortex / physiology*
  • Connectome
  • Electroencephalography
  • Humans
  • Magnetoencephalography
  • Models, Neurological*
  • Nerve Net / anatomy & histology*
  • Nerve Net / diagnostic imaging
  • Nerve Net / physiology*