Evidence map›Paper›PMID 32684926›Full record

ArticleNeural plasticity2020

A Brain Network Constructed on an L1-Norm Regression Model Is More Sensitive in Detecting Small World Network Changes in Early AD.

Hao Liu, Haimeng Hu, Huiying Wang, Jiahui Han, Yunfei Li, Huihui Qi, Meimei Wang, Sisi Zhang, Huijin He, Xiaohu Zhao

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

Article in Neural plasticity, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 30% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. 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

10 authors at 4 institutions in 1 country.

Hao LiuDepartment of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Haimeng HuDepartment of Imaging, Huashan Hospital, Fudan University, Shanghai, China.
Huiying WangOphthalmology Department, Huashan Hospital, Fudan University, Shanghai, China.
Jiahui HanCollege of Optical and Electronic Technology, China Jiliang University, Hangzhou, China.
Yunfei LiDepartment of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai, China.
Huihui QiDepartment of Imaging, Shanghai Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Meimei WangDepartment of Imaging, Shanghai Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Sisi ZhangDepartment of Imaging, Shanghai Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China.
Huijin HeDepartment of Imaging, Huashan Hospital, Fudan University, Shanghai, China.
Xiaohu ZhaoDepartment of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai, China.ORCID 0000-0002-3630-0879
Fudan University · CNTongji University · CNChina Jiliang University · CNShanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most previous imaging studies have used traditional Pearson correlation analysis to construct brain networks. This approach fails to adequately and completely account for the interaction between adjacent brain regions. In this study, we used the L1-norm linear regression model to test the small-world attributes of the brain networks of three groups of patients, namely, those with mild cognitive impairment (MCI), Alzheimer's disease (AD), and healthy controls (HCs); we attempted to identify the method that may detect minor differences in MCI and AD patients. Twenty-four AD patients, 33 MCI patients, and 27 HC elderly subjects were subjected to functional MRI (fMRI). We applied traditional Pearson correlation and the L1-norm to construct the brain networks and then tested the small-world attributes by calculating the following parameters: clustering coefficient (Cp), path length (Lp), global efficiency (Eg), and local efficiency (Eloc). As expected, L1 could detect slight changes, mainly in MCI patients expressing higher Cp and Eloc; however, no statistical differences were found between MCI patients and HCs in terms of Cp, Lp, Eg, and Eloc, using Pearson correlation. Compared with HCs, AD patients expressed a lower Cp, Eloc, and Lp and an increased Eg using both connectivity metrics. The statistical differences between the groups indicated the brain networks constructed by the L1-norm were more sensitive to detect slight small-world network changes in early stages of AD.

Indexed as

AgedAlzheimer DiseaseBrainBrain MappingCognitive DysfunctionDisease ProgressionFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedNerve Net

Identifiers

PMID32684926
PMCPMC7351016
OpenAlexW3039914092

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.