Evidence map›Paper›PMID 29053778›Full record

ArticleBrain : a journal of neurology2017

Influence of cerebrovascular disease on brain networks in prodromal and clinical Alzheimer's disease.

Joanna Su Xian Chong, Siwei Liu, Yng Miin Loke, Saima Hilal, Mohammad Kamran Ikram, Xin Xu, Boon Yeow Tan, Narayanaswamy Venketasubramanian, Christopher Li-Hsian Chen, Juan Zhou

Open access · bronzeAbstract read
In one paragraph

Article in Brain : a journal of neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 1 pooled it
4.2field-weighted citation impact, top 5% 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

41 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.

  1. Pooled it
  2. Trial
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  9. Spectral normative modeling of brain structure.medRxiv : the preprint server for health sciences · 2025
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  20. White matter network damage mediates association between cerebrovascular disease and cognition.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2021
    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 6 institutions in 1 country.

Joanna Su Xian ChongCentre for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore Medical School, Singapore.
Siwei LiuCentre for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore Medical School, Singapore.
Yng Miin LokeCentre for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore Medical School, Singapore.
Saima HilalDepartment of Pharmacology, Clinical Research Centre, National University Health System, National University of Singapore, Singapore.
Mohammad Kamran IkramMemory Ageing and Cognition Centre, National University Health System, Singapore.
Xin XuDepartment of Pharmacology, Clinical Research Centre, National University Health System, National University of Singapore, Singapore.
Boon Yeow TanSt. Luke's Hospital, Singapore.
Narayanaswamy VenketasubramanianRaffles Neuroscience Centre, Raffles Hospital, Singapore.
Christopher Li-Hsian ChenDepartment of Pharmacology, Clinical Research Centre, National University Health System, National University of Singapore, Singapore.
Juan ZhouCentre for Cognitive Neuroscience, Neuroscience and Behavioural Disorders Programme, Duke-National University of Singapore Medical School, Singapore.
National University Health System · SGNational Neuroscience Institute · SGNational University of Singapore · SGAgency for Science, Technology and Research · SGRaffles Institution · SGSt Luke's Hospital · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Network-sensitive neuroimaging methods have been used to characterize large-scale brain network degeneration in Alzheimer's disease and its prodrome. However, few studies have investigated the combined effect of Alzheimer's disease and cerebrovascular disease on brain network degeneration. Our study sought to examine the intrinsic functional connectivity and structural covariance network changes in 235 prodromal and clinical Alzheimer's disease patients with and without cerebrovascular disease. We focused particularly on two higher-order cognitive networks-the default mode network and the executive control network. We found divergent functional connectivity and structural covariance patterns in Alzheimer's disease patients with and without cerebrovascular disease. Alzheimer's disease patients without cerebrovascular disease, but not Alzheimer's disease patients with cerebrovascular disease, showed reductions in posterior default mode network functional connectivity. By comparison, while both groups exhibited parietal reductions in executive control network functional connectivity, only Alzheimer's disease patients with cerebrovascular disease showed increases in frontal executive control network connectivity. Importantly, these distinct executive control network changes were recapitulated in prodromal Alzheimer's disease patients with and without cerebrovascular disease. Across Alzheimer's disease patients with and without cerebrovascular disease, higher default mode network functional connectivity z-scores correlated with greater hippocampal volumes while higher executive control network functional connectivity z-scores correlated with greater white matter changes. In parallel, only Alzheimer's disease patients without cerebrovascular disease showed increased default mode network structural covariance, while only Alzheimer's disease patients with cerebrovascular disease showed increased executive control network structural covariance compared to controls. Our findings demonstrate the differential neural network structural and functional changes in Alzheimer's disease with and without cerebrovascular disease, suggesting that the underlying pathology of Alzheimer's disease patients with cerebrovascular disease might differ from those without cerebrovascular disease and reflect a combination of more severe cerebrovascular disease and less severe Alzheimer's disease network degeneration phenotype.

Indexed as

Executive FunctionAgedAged, 80 and overAlzheimer DiseaseBrainCase-Control StudiesCerebrovascular DisordersCognitive DysfunctionFemaleFunctional NeuroimagingHippocampusHumansMagnetic Resonance ImagingMaleNeural PathwaysOrgan SizeAlzheimer’s diseasecerebrovascular diseaseintrinsic functional connectivityprodromal stagestructural covariance

Identifiers

PMID29053778
PMCPMC5841199
OpenAlexW2755032587

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.