Evidence map›Paper›PMID 34177548›Full record

ArticleFrontiers in aging neuroscience2021

Interactions Between Aging and Alzheimer's Disease on Structural Brain Networks.

Zhanxiong Wu, Yunyuan Gao, Thomas Potter, Julia Benoit, Jian Shen, Paul E Schulz, Yingchun Zhang, and The Alzheimer’s Disease Neuroimaging Initiative

Abstract read
In one paragraph

Article in Frontiers in aging neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Caudal and Thalamic Segregation in White Matter Brain Network Communities in Alzheimer's Disease Population.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2024
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Review
  15. Article
  16. Article
  17. 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

8 authors.

Zhanxiong WuSchool of Electronic Information, Hangzhou Dianzi University, Hangzhou, China.
Yunyuan GaoDepartment of Intelligent Control and Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou, China.
Thomas PotterDepartment of Biomedical Engineering, University of Houston, Houston, TX, United States.
Julia BenoitTexas Institute for Measurement Evaluation and Statistics, Department of Basic Vision Sciences, University of Houston, Houston, TX, United States.
Jian ShenNeurosurgery Department, The First Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.
Paul E SchulzDepartment of Neurology, The McGovern Medical School of UTHealth-Houston, Houston, TX, United States.
Yingchun ZhangDepartment of Biomedical Engineering, University of Houston, Houston, TX, United States.
and The Alzheimer’s Disease Neuroimaging Initiative

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

Normative aging and Alzheimer's disease (AD) propagation alter anatomical connections among brain parcels. However, the interaction between the trajectories of age- and AD-linked alterations in the topology of the structural brain network is not well understood. In this study, diffusion-weighted magnetic resonance imaging (MRI) datasets of 139 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used to document their structural brain networks. The 139 participants consist of 45 normal controls (NCs), 37 with early mild cognitive impairment (EMCI), 27 with late mild cognitive impairment (LMCI), and 30 AD patients. All subjects were further divided into three subgroups based on their age (56-65, 66-75, and 71-85 years). After the structural connectivity networks were built using anatomically-constrained deterministic tractography, their global and nodal topological properties were estimated, including network efficiency, characteristic path length, transitivity, modularity coefficient, clustering coefficient, and betweenness. Statistical analyses were then performed on these metrics using linear regression, and one- and two-way ANOVA testing to examine group differences and interactions between aging and AD propagation. No significant interactions were found between aging and AD propagation in the global topological metrics (network efficiency, characteristic path length, transitivity, and modularity coefficient). However, nodal metrics (clustering coefficient and betweenness centrality) of some cortical parcels exhibited significant interactions between aging and AD propagation, with affected parcels including left superior temporal, right pars triangularis, and right precentral. The results collectively confirm the age-related deterioration of structural networks in MCI and AD patients, providing novel insight into the cross effects of aging and AD disorder on brain structural networks. Some early symptoms of AD may also be due to age-associated anatomic vulnerability interacting with early anatomic changes associated with AD.

Indexed as

Alzheimer’s diseasecognitive impairmentdiffusion-weighted magnetic resonance imagingnerve fiber trackingstructural network

Identifiers

PMID34177548
PMCPMC8222527

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.