Evidence map›Paper›PMID 31552901›Full record

ArticleNeural regeneration research2020

Alteration of functional connectivity in patients with Alzheimer's disease revealed by resting-state functional magnetic resonance imaging.

Jie Zhao, Yu-Hang Du, Xue-Tong Ding, Xue-Hu Wang, Guo-Zun Men

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Article in Neural regeneration research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–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

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Connectivity Regression.Biostatistics (Oxford, England) · 2025
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  20. Synthesis of New 3-Arylcoumarins BearingFrontiers in chemistry · 2021
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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

5 authors.

Jie ZhaoSchool of Electronic and Information Engineering, Hebei University; Research Center of Machine Vision Engineering & Technology of Hebei Province; Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei Province, China.
Yu-Hang DuSchool of Electronic and Information Engineering, Hebei University, Baoding, Hebei Province, China.
Xue-Tong DingSchool of Electronic and Information Engineering, Hebei University, Baoding, Hebei Province, China.
Xue-Hu WangSchool of Electronic and Information Engineering, Hebei University; Research Center of Machine Vision Engineering & Technology of Hebei Province; Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei Province, China.
Guo-Zun MenSchool of Economics, Hebei University, Baoding, Hebei Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The main symptom of patients with Alzheimer's disease is cognitive dysfunction. Alzheimer's disease is mainly diagnosed based on changes in brain structure. Functional connectivity reflects the synchrony of functional activities between non-adjacent brain regions, and changes in functional connectivity appear earlier than those in brain structure. In this study, we detected resting-state functional connectivity changes in patients with Alzheimer's disease to provide reference evidence for disease prediction. Functional magnetic resonance imaging data from patients with Alzheimer's disease were used to show whether particular white and gray matter areas had certain functional connectivity patterns and if these patterns changed with disease severity. In nine white and corresponding gray matter regions, correlations of normal cognition, early mild cognitive impairment, and late mild cognitive impairment with blood oxygen level-dependent signal time series were detected. Average correlation coefficient analysis indicated functional connectivity patterns between white and gray matter in the resting state of patients with Alzheimer's disease. Functional connectivity pattern variation correlated with disease severity, with some regions having relatively strong or weak correlations. We found that the correlation coefficients of five regions were 0.3-0.5 in patients with normal cognition and 0-0.2 in those developing Alzheimer's disease. Moreover, in the other four regions, the range increased to 0.45-0.7 with increasing cognitive impairment. In some white and gray matter areas, there were specific connectivity patterns. Changes in regional white and gray matter connectivity patterns may be used to predict Alzheimer's disease; however, detailed information on specific connectivity patterns is needed. All study data were obtained from the Alzheimer's Disease Neuroimaging Initiative Library of the Image and Data Archive Database.

Indexed as

Alzheimer’s diseaseblood oxygen level-dependent signalcorrelation coefficientfunctional connectivity patternfunctional magnetic resonance imaginggray matterresting statewhite matter

Identifiers

PMID31552901
PMCPMC6905343

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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.