Evidence map›Paper›PMID 42595752›Full record

ArticleNature communications2026

A language network in the individualized functional connectomes of 1199 human brains doing arbitrary tasks.

Cory Shain, Evelina Fedorenko

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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  9. The cerebellar components of the human language network.bioRxiv : the preprint server for biology · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Cory ShainDepartment of Linguistics, Stanford University, Stanford, CA, USA. cashain@stanford.edu.ORCID http://orcid.org/0000-0002-2704-7197
Evelina FedorenkoDepartment of Brain & Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-3823-514X

Funding

Computational Neuroscience of Language Processing in the Human BrainU01NS121471 · NINDS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI FEDORENKO, EVELINA, RICHARDSON, ROBERT MARK · 2021 to 2025
$3.2M
NINDS NIH HHS U01 NS121471U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) NS121471
6 · The paper itself

Abstract

A century and a half of neuroscience has yielded many divergent theories of the neurobiology of language. Two factors that likely contribute to this situation include (a) conceptual disagreement about language and its component processes, and (b) intrinsic inter-individual variability in the topography of language areas. Recent functional magnetic resonance imaging (fMRI) studies of small numbers of intensively scanned individuals have argued that a language-selective brain network emerges bottom-up from correlations (individualized functional connectomics, iFC) in task-free (e.g., rest) or task-regressed activation timecourses. Here we tested this hypothesis at scale and evaluated its practical utility for task-agnostic language localization: we apply iFC separately to each of 1,957 (fMRI) scanning sessions (1,199 unique brains), each consisting of diverse tasks. We found that iFC indeed revealed a largely left-hemisphere-dominant frontotemporal network that was more stable within individuals than between them, robust to the granularity of the parcellation, and selective for language. These results support the hypothesis that this network is a key structure in the functional organization of the adult brain and show that it can be recovered retrospectively from arbitrary imaging data, with implications for neuroscience, neurosurgery, and neural engineering.

Indexed as

BrainConnectomeLanguageNerve NetAdultBrain MappingFemaleHumansMagnetic Resonance ImagingMaleYoung Adult

Identifiers

PMID42595752
PMCPMC13473096

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.