September 2019
Volume 19, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2019
Behavioural profiles and neural correlates of higher-level vision after posterior cerebral artery stroke
Author Affiliations & Notes
  • Grace E Rice
    MRC Cognition and Brain Sciences Unit, University of Cambridge, UK
  • Sheila J Kerry
    Institute of Cognitive Neuroscience, University College London, UK
  • Ro Julia Robotham
    Department of Psychology, University of Copenhagen, Denmark
  • Alex P Leff
    Institute of Cognitive Neuroscience, University College London, UK
  • Matthew A Lambon Ralph
    MRC Cognition and Brain Sciences Unit, University of Cambridge, UK
  • Randi Starrfelt
    Department of Psychology, University of Copenhagen, Denmark
Journal of Vision September 2019, Vol.19, 21c. doi:https://doi.org/10.1167/19.10.21c
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      Grace E Rice, Sheila J Kerry, Ro Julia Robotham, Alex P Leff, Matthew A Lambon Ralph, Randi Starrfelt; Behavioural profiles and neural correlates of higher-level vision after posterior cerebral artery stroke. Journal of Vision 2019;19(10):21c. doi: https://doi.org/10.1167/19.10.21c.

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

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Abstract

The presence and degree of category-selective responses in the human brain remains a central research question in visual neuroscience. Evidence for category-selectivity in higher-level vision primarily stems from neuro-imaging studies of healthy participants. Converging evidence also exists in patients after brain injury, however they often focus either on in-depth analysis of single case-studies or behavioural testing of one category, for example faces or words. Here we adopt a novel approach to studying higher-level vision after brain injury by exploring the largest sample of posterior cerebral artery stroke patients currently available (n = 64; 33 left hemisphere, 23 right hemisphere, 8 bilateral). Patients were tested using an in-depth behavioural battery encompassing both low-level visual tests (e.g., visual field, visual acuity, contrast sensitivity) and higher-level visual tests of word, object, and face processing. A data-driven approach (principal component analysis) was used to establish a pattern of co-occurrence within higher-level vision. The data revealed two principal components underlying patients’ performance. The first component included tests with a verbal (written word) input. The second component included tests with a non-verbal (picture) input, including face and object processing. This behavioural model was mapped onto the patients’ lesion profiles using voxel-based lesion symptom mapping. The two components had unique lesion correlates: The verbal input component with damage in the left inferior occipital and posterior temporal lobe, and the non-verbal input component with damage in the right occipital and medial temporal lobe. This approach to studying higher-level vision after brain injury using a data-driven approach suggests that patient’s behavioural performance did not reflect strict category-selective responses.

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