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Divide and conquer: A defense of functional localizers
Abstract
Numerous functionally distinct regions of cortex (e.g., V1, MT, the fusiform face area) can be easily identified in any normal human subject in just a few minutes of fMRI scanning. However, the locations of these regions vary across subjects. Investigations of these regions have therefore often used a functional region of interest (fROI) approach in which the region is first identified functionally in each subject individually, before subsequent scans in the same subjects test specific hypotheses concerning that region. This fROI method, which resembled long-established practice in visual neurophysiology, has methodological, statistical, and theoretical advantages over standard alternatives (such as whole-brain analyses of group data): (i) because functional properties are more consistently and robustly associated with fROIs than with locations in stereotaxic space, functional hypotheses concerning fROIs are often the most straightforward to frame, motivate, and test, (ii) because hypotheses are tested in only a handful of fROIs (instead of in tens of thousands of voxels), advance specification of fROIs provides a massive increase in statistical power over whole-brain analyses, and (iii) some fROIs may serve as candidate distinct components of the mind/brain worth investigation as such. Of course fROIs can be productively used in conjunction with other complementary methods. Here, we explain the motivation for and advantages of the fROI approach, and we rebut the criticism of this method offered by Friston et al. (Friston, K., Rotshtein, P., Geng, J., Sterzer, P., Henson, R., in press. A critique of functional localizers. NeuroImage).
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“In addition, because of a constrained search volume, fROI analyses typically have higher statistical power than whole-brain analyses (Fedorenko, Hsieh, Nieto-Castañón, Whitfield-Gabrieli, & Kanwisher, 2010;Saxe, Brett, & Kanwisher, 2006).”
From Functional Specificity and Sex Differences in the Neural Circuits Supporting the Inhibition of Automatic Imitation (2018) · cites this paper as Saxe, Brett, & Kanwisher, 2006
In the citing paper
“Definitively addressing the question of neural overlap between linguistic and nonlinguistic functions requires examining overlap within individual subjects (27, 28), a data analysis strategy that has almost never been applied in neuroimaging investigations of high-level linguistic processing.”
From Functional specificity for high-level linguistic processing in the human brain (2011) · cites this paper as [28]
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Metadata
- Authors
- Saxe, R., Brett, M., & Kanwisher, N.
- Affiliations
- Massachusetts Institute of Technology (US)
- MRC Cognition and Brain Sciences Unit (GB)
- McGovern Institute for Brain Research (US)
- Corresponding author
- Saxe, Rebecca
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2006-05
- Volume
- 30
- Issue
- 4
- Pages
- 1088-1096
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2084332617
- Cited by
- 544
- References
- 49
- Citations by year
- 2026: 26 · 2025: 21 · 2024: 25 · 2023: 29 · 2022: 23 · 2021: 26 · 2020: 26 · 2019: 50 · 2018: 16 · 2017: 24 · 2016: 25 · 2015: 33 · 2014: 20 · 2013: 22 · 2012: 38
- FWCI
- 13.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- functional localizer, statistical power, whole-brain analysis, stereotaxic space, visual cortex, fusiform face area, motion processing
- MeSH terms
- Brain Mapping, Cerebral Cortex, Dominance, Cerebral, Fourier Analysis, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Mathematical Computing, Sensitivity and Specificity, Radiosurgery, Mind-Body Relations, Metaphysical, Surgery, Computer-Assisted
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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