September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Model-based functional segmentation of the human lateral geniculate nucleus
Author Affiliations
  • Kevin DeSimone
    Department of Psychology, York University
    Centre for Vision Research, York University
  • Keith Schneider
    Centre for Vision Research, York University
    Department of Biology, York University
Journal of Vision August 2017, Vol.17, 584. doi:
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      Kevin DeSimone, Keith Schneider; Model-based functional segmentation of the human lateral geniculate nucleus. Journal of Vision 2017;17(10):584. doi:

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      © ARVO (1962-2015); The Authors (2016-present)

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The lateral geniculate nucleus of the thalamus (LGN) is somewhat unique in the visual pathway in that there is a clear separation of structure and function at a spatial scale that is resolvable by contemporary functional imaging techniques. Therefore, it provides a unique opportunity for developing and testing models of neural function, visual perception, and information flow throughout the brain. For instance, one prevailing theory of dyslexia contends that a malfunction in the M system throughout the brain is responsible for the behavioral deficits observed in dyslexics (Stein, 2001; Stein and Walsh, 1997). The LGN receives input from retinal ganglion cells and projectors directly to primary visual cortex, and can be functionally subdivided into layers on the basis of the response properties of the neurons contained therein. Neurons in the magnocellular (M) and parvocellular (P) layers of the LGN have distinct and complimentary spatial and temporal tuning properties. However, functionally segmenting the LGN on the basis of these neural response properties using functional brain imaging techniques has proven difficult. Recent attempts have been made to segment the LGN into its M and P subdivisions (Denison et al., 2014; Zhang et al., 2015) using fMRI. In these experiments, researchers took advantage of the complementarity of the response properties of M and P neurons to differentially drive the BOLD activity during the presentation of various stimulus features (i.e., contrast, spatial frequency, temporal frequency, color sensitivity). Here, we present a new spatiotemporal population receptive field (pRF) model that leverages the differences in the temporal frequency tuning and neural discharge patterns among M and P neural populations. This spatiotemporal pRF model estimates provide activation maps describing the LGN both in terms of its retinotopic organization and temporal response profile, and so affords an avenue for differentiating the M and P layers of the LGN.

Meeting abstract presented at VSS 2017


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