Evidence map›Paper›PMID 30825712›Full record

ArticleNeuroImage. Clinical2019

Connectome-wide network analysis of white matter connectivity in Alzheimer's disease.

Chenfei Ye, Susumu Mori, Piu Chan, Ting Ma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Connectomic analysis of Alzheimer's disease using percolation theory.Network neuroscience (Cambridge, Mass.) · 2022
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. COMPENSATORY BRAIN CONNECTION DISCOVERY IN ALZHEIMER'S DISEASE.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020
    Article
  15. 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

4 authors.

Chenfei YeDepartment of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong Province, China; Peng Cheng Laboratory, Shenzhen, Guangdong, China.
Susumu MoriThe Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
Piu ChanNational Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China; Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing Institute of Geriatrics, Beijing, China; Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, China; Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, China.
Ting MaDepartment of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong Province, China; Peng Cheng Laboratory, Shenzhen, Guangdong, China; National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China. Electronic address: tmahit@outlook.com.

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
NIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

A multivariate analytical strategy may pinpoint the structural connectivity patterns associated with Alzheimer's disease (AD) pathology in connectome-wide association studies. Diffusion magnetic resonance imaging data from 161 participants including subjects with healthy controls, AD, stable and converting mild cognitive impairment, were selected for group-wise comparisons. A multivariate distance matrix regression (MDMR) analysis was performed to detect abnormality in brain structural network along with disease progression. Based on the seed regions returned by the MDMR analysis, supervised learning was applied to evaluate the disease predictive performance. Nine brain regions, including the left orbital part of superior and middle frontal gyrus, the bilateral supplementary motor area, the bilateral insula, the left hippocampus, the left putamen, and the left thalamus demonstrated extremely significant structural pattern changes along with the progression of AD. The disease classification was more efficient when based on the key connectivity related to these seed regions than when based on whole-brain structural connectivity. MDMR analysis reveals brain network reorganization caused by AD pathology. The key structural connectivity detected in this study exhibits promising distinguishing capability to predict prodromal AD patients.

Indexed as

AgedAged, 80 and overAlzheimer DiseaseConnectomeCross-Sectional StudiesDiffusion Tensor ImagingFemaleHumansMaleMiddle AgedNerve NetWhite MatterAlzheimer's diseaseConnectomeDiffusion tensor imagingMild cognitive impairmentMultivariate regressionTractography

Identifiers

PMID30825712
PMCPMC6396432

What OpenQuestion holds

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Registered trials

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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.