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Updated: Aug 15, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
Published on: October 13, 2023
A language network in the individualized functional connectomes of 1199 human brains doing arbitrary tasks
Cory Shain1, Evelina Fedorenko2
1Department of Linguistics, Stanford University, Stanford, CA, USA. cashain@stanford.edu.
Individualized functional connectomics (iFC) reveals a stable, left-dominant language network in the brain. This neuroimaging approach can identify language areas regardless of task, aiding neuroscience and neurosurgery.
Area of Science:
- Neuroscience
- Cognitive Neuroscience
- Neuroimaging
Background:
- Neuroscience has numerous theories on language's neurobiology.
- Disagreement stems from conceptual issues and individual brain differences.
Purpose of the Study:
- To test the hypothesis that a language network emerges from functional connectomics.
- To evaluate its utility for task-agnostic language localization.
Main Methods:
- Applied individualized functional connectomics (iFC) to 1,957 fMRI scans from 1,199 individuals.
- Analyzed task-free and task-regressed activation timecourses.
Main Results:
- iFC identified a left-hemisphere-dominant frontotemporal language network.
- This network showed higher stability within individuals than between them.
- The network was robust to parcellation granularity and selective for language.
Conclusions:
- Results support the hypothesis of a bottom-up emergent language network.
- iFC can recover this network retrospectively from diverse fMRI data.
- Findings have implications for neuroscience, neurosurgery, and neural engineering.
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