New method for fMRI investigations of language: defining ROIs functionally in individual subjects
Open accessAbstract
Previous neuroimaging research has identified a number of brain regions sensitive to different aspects of linguistic processing, but precise functional characterization of these regions has proven challenging. We hypothesize that clearer functional specificity may emerge if candidate language-sensitive regions are identified functionally within each subject individually, a method that has revealed striking functional specificity in visual cortex but that has rarely been applied to neuroimaging studies of language. This method enables pooling of data from corresponding functional regions across subjects rather than from corresponding locations in stereotaxic space (which may differ functionally because of the anatomical variability across subjects). However, it is far from obvious a priori that this method will work as it requires that multiple stringent conditions be met. Specifically, candidate language-sensitive brain regions must be identifiable functionally within individual subjects in a short scan, must be replicable within subjects and have clear correspondence across subjects, and must manifest key signatures of language processing (e.g., a higher response to sentences than nonword strings, whether visual or auditory). We show here that this method does indeed work: we identify 13 candidate language-sensitive regions that meet these criteria, each present in ≥80% of subjects individually. The selectivity of these regions is stronger using our method than when standard group analyses are conducted on the same data, suggesting that the future application of this method may reveal clearer functional specificity than has been evident in prior neuroimaging research on language.
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Because of a constrained search volume, fROI analyses typically have higher statistical power than whole-brain analyses.
“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 Fedorenko, Hsieh, Nieto-Castañón, Whitfield-Gabrieli, & Kanwisher, 2010
The language network supports both comprehension (spoken, written, and signed) and production.
“This language network supports both comprehension (spoken, written, and signed) [30, 31, 32, 33] and production [34, 35]”
From Dissociating language and thought in large language models (2024) · cites this paper as [31]
The human language network is sensitive to abstract hierarchical rules across isolated phrases, naturalistic narratives, and syntactically well-formed but semantically empty stimuli.
“it is sensitive to abstract hierarchical rules in isolated phrases and sentences [31, 108, 109, 110], in naturalistic narratives [111, 112, 113, 114], and in syntactically well-formed but semantically empty (”
From Dissociating language and thought in large language models (2024) · cites this paper as [31]
The language localizer was previously shown to robustly and reliably identify each of the key brain regions previously implicated in high-level linguistic processing in at least 80% of subjects individually.
“This localizer was previously shown (29) to robustly and reliably identify each of the key brain regions previously implicated in high-level linguistic processing (30, 31) (Fig. 2 and Fig. S1) in at least 80% of subjects individually”
From Functional specificity for high-level linguistic processing in the human brain (2011) · cites this paper as [29]
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Self-citation
- Authors
- Fedorenko, E., Hsieh, P.-J., Nieto-Castañón, A., Whitfield-Gabrieli, S., & Kanwisher, N.
- Affiliations
- McGovern Institute for Brain Research (US)
- McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts
- Corresponding author
- Fedorenko, Evelina
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2010-08
- Volume
- 104
- Issue
- 2
- Pages
- 1177-1194
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- PMC
- PMC2934923
- OpenAlex
- W2155034734
- Cited by
- 686
- References
- 93
- Citations by year
- 2026: 66 · 2025: 90 · 2024: 86 · 2023: 68 · 2022: 48 · 2021: 56 · 2020: 44 · 2019: 45 · 2018: 29 · 2017: 29 · 2016: 30 · 2015: 23 · 2014: 22 · 2013: 18 · 2012: 18
- FWCI
- 9.50
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5k99hd057522-02)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (K99 HD-057522)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Keywords
- language processing, functional MRI, region of interest selection, anatomical variation, sentence processing, visual cortex, functional specificity
- MeSH terms
- Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Language, Functional Laterality, Magnetic Resonance Imaging, Neuropsychological Tests, Oxygen, Photic Stimulation, Reaction Time, Reading, Time Factors, Reproducibility of Results
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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.
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