Predicting individual face-selective topography using naturalistic stimuli

• Published in: NeuroImage (Orlando, Fla.) Vol. 216; p. 116458
• Main Authors: Jiahui, Guo, Feilong, Ma, Visconti di Oleggio Castello, Matteo, Guntupalli, J. Swaroop, Chauhan, Vassiki, Haxby, James V., Gobbini, M. Ida
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2020; Elsevier Limited; Elsevier
• Subjects: Adult; Algorithms; Brain; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Cognitive ability; Datasets; Faces; Facial Recognition - physiology; Female; Forecasting; Functional magnetic resonance imaging; Functional topography; Hotels & motels; Humans; Hyperalignment; Localizer; Magnetic Resonance Imaging - methods; Male; Mapping; Motion Pictures; Naturalistic stimuli; Photic Stimulation - methods; Scanners; Topography; Young Adult; Hyperalignment; Functional topography; Localizer; Faces; Naturalistic stimuli
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2019.116458

Subject-specific, functionally defined areas are conventionally estimated with functional localizers and a simple contrast analysis between responses to different stimulus categories. Compared with functional localizers, naturalistic 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 topography can be estimated with high fidelity from that subject’s fMRI data obtained while watching a naturalistic movie using hyperalignment to project other subjects’ localizer data into that subject’s idiosyncratic cortical anatomy. These findings lay the foundation for developing an efficient tool for mapping functional topographies for a wide range of perceptual and cognitive functions in new subjects based only on fMRI data collected while watching an engaging, naturalistic stimulus and other subjects’ localizer data from a normative sample. •Hyperalignment estimates topographic maps from others’ movie and localizer data.•Only movie data is needed to estimate functional topographies in new participants.•Hyperalignment affords estimation of individual topographies in great detail.•New one-step hyperalignment algorithm improves estimates of topography.