Evidence map›Paper›PMID 39099649›Full record

ArticlePeerJ2024

Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification.

Lizhong Liang, Zijian Zhu, Hui Su, Tianming Zhao, Yao Lu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Lizhong LiangSchool of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
Zijian ZhuSchool of Public Health, Guangdong Medical University, Dongguan, China.
Hui SuShandong Liaocheng Intelligent Vocational Technical School, Liaocheng, China.
Tianming ZhaoDalian University, Dalian, China.
Yao LuSchool of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.

Funding

China Department of Science and TechnologyGuangdong Province Key Laboratory of Computational Science at the Sun Yat-sen UniversityGuangzhou Science and Technology bureauKey-Area Research and Development Program of Guangdong ProvinceR&D project of Pazhou Lab (HuangPu)Science and Technology Innovative Project of Guangdong Province
6 · The paper itself

Abstract

The adoption and growth of functional magnetic resonance imaging (fMRI) technology, especially through the use of Pearson's correlation (PC) for constructing brain functional networks (BFN), has significantly advanced brain disease diagnostics by uncovering the brain's operational mechanisms and offering biomarkers for early detection. However, the PC always tends to make for a dense BFN, which violates the biological prior. Therefore, in practice, researchers use hard-threshold to remove weak connection edges or introduce

Indexed as

BrainCognitive DysfunctionMagnetic Resonance ImagingAlgorithmsBrain MappingHumansNerve NetBrain functional networkMild cognitive impairmentNeighborhood structurePearson’s correlationSparse representation

Identifiers

PMID39099649
PMCPMC11296305

What OpenQuestion holds

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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.