Evidence map›Paper›PMID 39363797›Full record

ArticleBrain and behavior2024

Combining Quantitative Susceptibility Mapping With the Gray Matter Volume to Predict Neurological Deficits in Patients With Small Artery Occlusion.

Xuelian Tang, Zhenzhen He, Qian Yang, Tao Yang, Yusheng Yu, Jinan Chen

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In one paragraph

Article in Brain and behavior, 2024. 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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xuelian TangDepartment of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.ORCID https://orcid.org/0009-0000-7362-7988
Zhenzhen HeDepartment of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Qian YangDepartment of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Tao YangDepartment of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Yusheng YuDepartment of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Jinan ChenDepartment of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCurrently, there is still a lack of valuable neuroimaging markers to assess the clinical severity of stroke patients with small artery occlusion (SAO). Quantitative susceptibility mapping (QSM) is a quantitative processing method for neuroradiological diagnostics. Gray matter (GM) volume changes in stroke patients are also proved to be associated with neurological deficits. This study aims to explore the predictive value of QSM and GM volume in neurological deficits of patients with SAO.

methodsAs neurological deficits, the National Institutes of Health Stroke Scale (NIHSS) was used. Sixty-six SAO participants within 24 h of first onset were enrolled and divided into mild and moderate groups based on NIHSS. QSM values of infarct area and GM volume were calculated from magnetic resonance imaging (MRI) data. Two-sample t-tests were used to compare differences in QSM value and GM volume between the two groups, and the diagnostic efficacy of the combination of QSM value and GM volume was evaluated.

resultsThe results revealed both the QSM value and GM volume within the infarct area of the moderate group were lower compared to the mild group. Moderate group exhibited lower GM volume in some specific gyrus compared with mild group in the case of voxel-wise GM volume on whole-brain voxel level. The support vector machine (SVM) classifier's analysis showed a high power for the combination of QSM value, GM volume within the infarct area, and voxel-wise GM volume.

conclusionOur research first reported the combination of QSM value, GM volume within the infarct area, and voxel-wise GM volume could be used to predict neurological impairment of patients with SAO, which provides new insights for further understanding the SAO stroke.

Indexed as

Gray MatterMagnetic Resonance ImagingAgedArterial Occlusive DiseasesFemaleHumansMaleMiddle AgedSeverity of Illness IndexStrokeSupport Vector Machinegray matter volume | neurological deficits | quantitative susceptibility mapping | small artery occlusion

Identifiers

PMID39363797
PMCPMC11450255

What Socratic holds

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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 Socratic graph.