Topographic maps representing haptic numerosity reveals distinct sensory representations in supramodal networks
Dedicated maps for cognitive quantities such as timing, size and numerosity support the view that topography is a general principle of brain organization. To date, however, all of these maps were driven by the visual system. Here, we ask whether there are supramodal topographic maps representing cog...
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Published in | Nature communications Vol. 12; no. 1; p. 221 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
11.01.2021
Nature Publishing Group Nature Portfolio |
Subjects | |
Online Access | Get full text |
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Summary: | Dedicated maps for cognitive quantities such as timing, size and numerosity support the view that topography is a general principle of brain organization. To date, however, all of these maps were driven by the visual system. Here, we ask whether there are supramodal topographic maps representing cognitive dimensions irrespective of the stimulated sensory modality. We measured haptically and visually driven numerosity-selective neural responses using model-based analyses and ultra-high field (7T) fMRI. We found topographically organized neural populations tuned to haptic numerosity. The responses to visual or haptic numerosity shared a similar cortical network. However, the maps of the two modalities only partially overlap. Thus, although both visual and haptic numerosities are processed in a similar supramodal functional network, the underlying neural populations may be related, but distinct. Therefore, we hypothesize that overlap between modality-specific maps facilitates cross-modal interactions and supramodal representation of cognitive quantities.
Topographically organized tuned responses to haptic numerosity were found in the human brain. The responses to visual or haptic numerosity shared a similar large-scale cortical network, yet the maps of the two modalities only partially overlapped, suggesting distinct underlying neural populations. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-020-20567-5 |