An efficient multifunction fMRI localizer for high-level visual, auditory, and cognitive regions in humans

One of the most robust findings in human cognitive neuroscience is the discovery that many regions of the cortex are engaged in distinctive, often very specific, functions. Although these regions are found in approximately the same location in almost all typical participants, their exact location varies from one individual to the next. Thus, the first step in studying these regions is to identify them in each participant individually. Standard functional localizers have been devised to accomplish this goal, but most localizers identify only a few regions. Many important questions in modern neuroscience can only be answered by measuring the responses of multiple cortical regions at the same time. Here, we introduce a new Efficient Multifunction fMRI localizer (EMFL) in which visual and auditory conditions are presented simultaneously, enabling the identification, in just 14 minutes of fMRI scan time, of 14 of the most widely-studied cortical regions: those selectively engaged in perceiving faces, places, bodies, words, objects, and speech sounds; understanding language and other people's thoughts; and engaging broadly in demanding cognitive tasks (the "multiple demand" system). We validate the EMFL by showing that it identifies the major functional regions of interest as well as the standard localizers do, in a quarter of the scan time. The stimuli and presentation code for this new localizer are publicly available, enabling future studies to efficiently identify the major functional regions of interest with the same procedure across labs.

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Publication Details

Journal
Imaging Neuroscience
Published
2025-01-01
DOI
https://doi.org/10.1162/imag.a.905
Citations
3
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
4.29
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article

An efficient multifunction fMRI localizer for high-level visual, auditory, and cognitive regions in humans

Nancy Kanwisher, Tamar I. Regev, S. M. Hutchinson, Emily M. Chen et al.
3 citations
Imaging Neuroscience
EEG and Brain-Computer Interfaces
4.29
article

An efficient multifunction fMRI localizer for high-level visual, auditory, and cognitive regions in humans

Nancy Kanwisher, Tamar I. Regev, S. M. Hutchinson, Emily M. Chen, Rebecca Saxe, Evelina Fedorenko, Frederik S. Kamps, Ammar Marvi
article en
3 citations

Abstract

One of the most robust findings in human cognitive neuroscience is the discovery that many regions of the cortex are engaged in distinctive, often very specific, functions. Although these regions are found in approximately the same location in almost all typical participants, their exact location varies from one individual to the next. Thus, the first step in studying these regions is to identify them in each participant individually. Standard functional localizers have been devised to accomplish this goal, but most localizers identify only a few regions. Many important questions in modern neuroscience can only be answered by measuring the responses of multiple cortical regions at the same time. Here, we introduce a new Efficient Multifunction fMRI localizer (EMFL) in which visual and auditory conditions are presented simultaneously, enabling the identification, in just 14 minutes of fMRI scan time, of 14 of the most widely-studied cortical regions: those selectively engaged in perceiving faces, places, bodies, words, objects, and speech sounds; understanding language and other people's thoughts; and engaging broadly in demanding cognitive tasks (the "multiple demand" system). We validate the EMFL by showing that it identifies the major functional regions of interest as well as the standard localizers do, in a quarter of the scan time. The stimuli and presentation code for this new localizer are publicly available, enabling future studies to efficiently identify the major functional regions of interest with the same procedure across labs.

Imaging NeuroscienceVol. 3
Harvard University (US), McGovern Institute for Brain Research (US), Institute of Cognitive and Brain Sciences (US), Massachusetts Institute of Technology (US), Stanford University (US), University of Edinburgh (GB)
Openalex Percentile: Top 5%
EEG and Brain-Computer Interfaces
4.29
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