Evidence map›Paper›PMID 42169456›Full record

ArticleNeural plasticity2026

Resting-State Coactivation Patterns of Language Reorganization in Brain Tumors.

Antonio Napolitano, Leonardo Spitoni, Mehrnaz Jenabi, Kyung K Peck, Andrei I Holodny, Jennifer Moliterno, Todd Constable, Luca Pasquini

Abstract read
In one paragraph

Article in Neural plasticity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Antonio NapolitanoDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA, yale.edu.
Leonardo SpitoniMedical Physics Department, Bambino Gesù Pediatric Hospital, Rome, Italy.
Mehrnaz JenabiRadiology Department, Memorial Sloan Kettering Cancer Center, New York City, New York, USA, mskcc.org.
Kyung K PeckRadiology Department, Memorial Sloan Kettering Cancer Center, New York City, New York, USA, mskcc.org.
Andrei I HolodnyRadiology Department, Memorial Sloan Kettering Cancer Center, New York City, New York, USA, mskcc.org.
Jennifer MoliternoDepartment of Neurosurgery, Yale School of Medicine, New Haven, Connecticut, USA, yale.edu.
Todd ConstableDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA, yale.edu.
Luca PasquiniDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA, yale.edu.ORCID https://orcid.org/0000-0003-1679-6958

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
American Roentgen Ray SocietyNCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

We investigated the temporal dynamics of resting-state brain activity associated with language reorganization in patients with left-hemisphere brain tumors using coactivation pattern (CAP) analysis of functional MRI. This retrospective study included 106 right-handed patients who underwent both resting-state and task-based fMRI before surgery. Language dominance was determined using a phonemic fluency task, and CAP analysis identified recurring whole-brain activation patterns. Dynamic properties of CAPs, including persistence, transitions, in-degree, and out-degree, were compared between patients with typical and atypical language dominance and between those with and without postoperative aphasia. Six distinct CAPs corresponding to known functional networks were identified. Patients with atypical language dominance showed higher out-degree (p = 0.004) and transitions (p = 0.006) in the dorsal default mode network (DMN; CAP2) and lower persistence in the visuospatial network (VSN; CAP3; p = 0.002) compared with patients with typical dominance. Patients without postoperative aphasia demonstrated higher transitions in VSN CAP6 (p = 0.024). Logistic regression analyses were performed to evaluate the prediction of postoperative aphasia. A model including CAP metrics and the language laterality index (LI) demonstrated good predictive performance (area under the curve [AUC] = 0.851, accuracy = 82.4%, sensitivity = 71.9%, and specificity = 88.1%), outperforming a model based on LI alone (AUC = 0.640 and accuracy = 67.0%). No significant differences were observed by tumor type or volume. These findings indicate that CAP dynamics capture distinct neural states associated with language dominance and postoperative outcomes. Specific CAP metrics may serve as potential imaging biomarkers of functional reorganization, supporting prognostic evaluation and surgical planning in patients with brain tumors.

Indexed as

BrainBrain NeoplasmsLanguageNeuronal PlasticityAdultAgedAphasiaBrain MappingFemaleFunctional LateralityHumansMagnetic Resonance ImagingMaleMiddle AgedRestRetrospective Studiesbrain tumorscoactivation pattern analysisfunctional MRIlanguage reorganizationresting-state fMRI

Identifiers

PMID42169456
PMCPMC13195184

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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.