Evidence map›Paper›PMID 41857530›Full record

ArticleBMC medicine2026

Cortical functional hierarchy disruption following subcortical stroke.

Ke Wu, Gang Liu, Xinyu Liang, Jinsheng Zeng, Gaolang Gong

Abstract read
In one paragraph

Article in BMC medicine, 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

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

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

5 authors.

Ke Wu *State Key Laboratory of Cognitive Neuroscience and Learning & IDG, McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Gang Liu *Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou, 510080, China.
Xinyu LiangThe Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, China.
Jinsheng ZengDepartment of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou, 510080, China. zengjs@pub.guangzhou.gd.cn.
Gaolang GongState Key Laboratory of Cognitive Neuroscience and Learning & IDG, McGovern Institute for Brain Research, Beijing Normal University, Beijing, China. gaolang.gong@bnu.edu.cn.

Funding

Fundamental Research Funds for the Central Universities 2233200020Guangdong Province International Cooperation Base for Early Intervention and Functional Rehabilitation of Neurological Diseases 2020A0505020004Guangdong Provincial Clinical Research Center for Neurological Diseases 2020B1111170002Guangzhou Major Difficult and Rare Diseases Project 2024MDRD02National Natural Science Foundation of China 82021004, 82271300, 82471258National Natural Science Foundation of China T2325006, 82172016Natural Science Foundation of Guangdong Province 2023A1515012739Science and Technology Program of Guangzhou 2023B03J0466
6 · The paper itself

Abstract

backgroundSubcortical structures are pivotal for cortico-subcortical communication, yet are highly vulnerable to stroke. Damage to these structures can produce widespread cortical dysfunction, but how subcortical stroke reshapes the macroscale hierarchical organization of cortical networks remains poorly understood. Clarifying this relationship is critical for understanding subcortical contributions to brain-wide communication and for identifying reliable neural markers of clinical outcome.

methodsWe analyzed resting-state fMRI data from two independent cohorts of patients with subcortical stroke (discovery, 55 patients, 49 controls; replication, 23 patients, 26 controls). Cortical functional organization was mapped using diffusion map embedding to derive connectivity gradients, and group differences were quantified at both global and regional levels. Gradient metrics were further compared with conventional functional connectivity strength and tested for predictive utility using multivariate ridge regression against neurological outcome measures.

resultsAcross cohorts, patients consistently exhibited compression of the principal unimodal-to-transmodal gradient, reflecting diminished hierarchical differentiation between sensory and association cortices. These alterations were stable over the first three months of recovery and spatially aligned with normative maps of dopaminergic, GABAergic, glutamatergic, serotonergic and cannabinoid systems. By contrast, conventional functional connectivity strength metrics showed poor reproducibility. Gradient features outperformed connectivity strength in predicting overall neurological severity (NIHSS scores) and generalized robustly across cohorts, although predictive power for motor-specific deficits (FMA scores) was limited.

conclusionsSubcortical stroke disrupts cortical functional hierarchy in a reproducible and neurochemically constrained manner, and this disruption serves as a robust biomarker of global neurological impairment. These findings establish cortical gradients as a mechanistically informative and clinically relevant tool for probing subcortical-cortical interactions, with potential to inform prognosis and targeted neurorehabilitation.

Indexed as

Cerebral CortexStrokeAgedBrain MappingFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedFunctional connectivityFunctional gradientNeurotransmitter systemsResting-state fMRISubcortical stroke

Identifiers

PMID41857530
PMCPMC13122868

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.