Evidence map›Paper›PMID 33132832›Full record

ArticleFrontiers in neuroscience2020

Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View.

Xiaowen Xu, Weikai Li, Mengling Tao, Zhongfeng Xie, Xin Gao, Ling Yue, Peijun Wang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 3 pooled it
5.5field-weighted citation impact, top 4% 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

24 citing papers in PubMed, 3 syntheses or guidelines pooled it, 45 citations in OpenAlex.

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  9. [Research on classification method of multimodal magnetic resonance images of Alzheimer's disease based on generalized convolutional neural networks].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023
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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

7 authors at 5 institutions in 1 country.

Xiaowen XuDepartment of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Weikai LiCollege of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China.
Mengling TaoDepartment of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Zhongfeng XieDepartment of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Xin GaoUniversal Medical Imaging Diagnostic Center, Shanghai, China.
Ling YueDepartment of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Peijun WangDepartment of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Tongji University · CNChongqing Jiaotong University · CNShanghai Jiao Tong University · CNShanghai Medical Information Center · CNTongji Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Subjective cognitive decline (SCD) is considered the earliest preclinical stage of Alzheimer's disease (AD) that precedes mild cognitive impairment (MCI). Effective and accurate diagnosis of SCD is crucial for early detection of and timely intervention in AD. In this study, brain functional connectome (i.e., functional connections and graph theory metrics) based on the resting-state functional magnetic resonance imaging (rs-fMRI) provided multiple information about brain networks and has been used to distinguish individuals with SCD from normal controls (NCs). The consensus connections and the discriminative nodal graph metrics selected by group least absolute shrinkage and selection operator (LASSO) mainly distributed in the prefrontal and frontal cortices and the subcortical regions corresponded to default mode network (DMN) and frontoparietal task control network. Nodal efficiency and nodal shortest path showed the most significant discriminative ability among the selected nodal graph metrics. Furthermore, the comparison results of topological attributes suggested that the brain network integration function was weakened and network segregation function was enhanced in SCD patients. Moreover, the combination of brain connectome information based on multiple kernel-support vector machine (MK-SVM) achieved the best classification performance with 83.33% accuracy, 90.00% sensitivity, and an area under the curve (AUC) of 0.927. The findings of this study provided a new perspective to combine machine learning methods with exploration of brain pathophysiological mechanisms in SCD and offered potential neuroimaging biomarkers for diagnosis of early-stage AD.

Indexed as

functional connectiongraph theorymultiple kernel learningresting-state functional magnetic resonance imagingsubjective cognitive impairment

Identifiers

PMID33132832
PMCPMC7550635
OpenAlexW3089341061

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.