Subject-specific, functionally defined areas are conventionally estimated with functional localizers and a simplecontrast analysis between responses to different stimulus categories. Compared with functional localizers, natu-ralistic stimuli provide several advantages such as stronger and widespread brain activation, greater engagement,and increased subject compliance. In this study we demonstrate that a subject’s idiosyncratic functional topog-raphy can be estimated with highfidelity from that subject’s fMRI data obtained while watching a naturalisticmovie using hyperalignment to project other subjects’localizer data into that subject’s idiosyncratic corticalanatomy. Thesefindings lay the foundation for developing an efficient tool for mapping functional topographiesfor a wide range of perceptual and cognitive functions in new subjects based only on fMRI data collected whilewatching an engaging, naturalistic stimulus and other subjects’localizer data from a normative sample

Predicting individual face-selective topography using naturalistic stimuli

M Ida Gobbini
2020

Abstract

Subject-specific, functionally defined areas are conventionally estimated with functional localizers and a simplecontrast analysis between responses to different stimulus categories. Compared with functional localizers, natu-ralistic stimuli provide several advantages such as stronger and widespread brain activation, greater engagement,and increased subject compliance. In this study we demonstrate that a subject’s idiosyncratic functional topog-raphy can be estimated with highfidelity from that subject’s fMRI data obtained while watching a naturalisticmovie using hyperalignment to project other subjects’localizer data into that subject’s idiosyncratic corticalanatomy. Thesefindings lay the foundation for developing an efficient tool for mapping functional topographiesfor a wide range of perceptual and cognitive functions in new subjects based only on fMRI data collected whilewatching an engaging, naturalistic stimulus and other subjects’localizer data from a normative sample
Guo Jiahui, Ma Feilong, Matteo Visconti di Oleggio Castello, J Swaroop Guntupalli, Vassiki Chauhan, James V Haxby, M Ida Gobbini
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/719335
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