Evidence map›Paper›PMID 36118689›Full record

ArticleFrontiers in aging neuroscience2022

Abnormal regional homogeneity in right caudate as a potential neuroimaging biomarker for mild cognitive impairment: A resting-state fMRI study and support vector machine analysis.

Yujun Gao, Xinfu Zhao, JiChao Huang, Sanwang Wang, Xuan Chen, Mingzhe Li, Fengjiao Sun, Gaohua Wang, Yi Zhong

Open access · goldAbstract read
In one paragraph

Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.1field-weighted citation impact, top 8% of its field
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

12 citing papers in PubMed, 27 citations in OpenAlex.

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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

9 authors at 8 institutions in 2 countries.

Yujun GaoDepartment of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
Xinfu ZhaoWuxi Mental Health Center, Nanjing Medical University, Wuxi, China.
JiChao HuangAffiliated Shuyang Hospital, Nanjing University of Chinese Medicine, Suqian, China.
Sanwang WangDepartment of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
Xuan ChenDepartment of Psychiatry, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.
Mingzhe LiPeking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.
Fengjiao SunMedical Research Center, Binzhou Medical University Hospital, Binzhou, China.
Gaohua WangDepartment of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
Yi ZhongDepartment of Neuroscience, City University of Hong Kong, Kowloon, Hong Kong SAR, China.
Wuhan University · CNBinzhou University · CNChinese Institute for Brain Research · CNNanjing University of Chinese Medicine · CNPeking University Sixth Hospital · CNRenmin Hospital of Wuhan University · CNWuxi People's Hospital · CNXinxiang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Mild cognitive impairment (MCI) is a heterogeneous syndrome characterized by cognitive impairment on neurocognitive tests but accompanied by relatively intact daily activities. Due to high variation and no objective methods for diagnosing and treating MCI, guidance on neuroimaging is needed. The study has explored the neuroimaging biomarkers using the support vector machine (SVM) method to predict MCI. Methods: In total, 53 patients with MCI and 68 healthy controls were involved in scanning resting-state functional magnetic resonance imaging (rs-fMRI). Neurocognitive testing and Structured Clinical Interview, such as Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) test, Activity of Daily Living (ADL) Scale, Hachinski Ischemic Score (HIS), Clinical Dementia Rating (CDR), Montreal Cognitive Assessment (MoCA), and Hamilton Rating Scale for Depression (HRSD), were utilized to assess participants' cognitive state. Neuroimaging data were analyzed with the regional homogeneity (ReHo) and SVM methods. Results: Compared with healthy comparisons (HCs), ReHo of patients with MCI was decreased in the right caudate. In addition, the SVM classification achieved an overall accuracy of 68.6%, sensitivity of 62.26%, and specificity of 58.82%. Conclusion: The results suggest that abnormal neural activity in the right cerebrum may play a vital role in the pathophysiological process of MCI. Moreover, the ReHo in the right caudate may serve as a neuroimaging biomarker for MCI, which can provide objective guidance on diagnosing and managing MCI in the future.

Indexed as

biomarkerfMRImachine learningmild cognitive impairmentneuroimagingregional homogeneitysupport vector machine

Identifiers

PMID36118689
PMCPMC9475111
OpenAlexW4294324934

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LicenceCC BY
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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.