Evidence map›Paper›PMID 34379638›Full record

ArticlePLoS computational biology2021

A large-scale brain network mechanism for increased seizure propensity in Alzheimer's disease.

Luke Tait, Marinho A Lopes, George Stothart, John Baker, Nina Kazanina, Jiaxiang Zhang, Marc Goodfellow

Abstract read
In one paragraph

Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Bifurcations and bursting in the Epileptor.PLoS computational biology · 2024
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. The fate of interneurons, GABABrain pathology (Zurich, Switzerland) · 2023
    Review
  13. Review
  14. Whole-Brain Network Models: From Physics to Bedside.Frontiers in computational neuroscience · 2022
    Review
  15. Review
  16. 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

7 authors.

Luke TaitCardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom.ORCID 0000-0002-2351-5328
Marinho A LopesCardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom.
George StothartDepartment of Psychology, University of Bath, Bath, United Kingdom.
John BakerDementia Research Centre, Queen Square Institute of Neurology, UCL, London, United Kingdom.
Nina KazaninaSchool of Psychological Science, University of Bristol, Bristol, United Kingdom.
Jiaxiang ZhangCardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom.ORCID 0000-0002-4758-0394
Marc GoodfellowLiving Systems Institute, University of Exeter, Exeter, United Kingdom.ORCID 0000-0002-7282-7280

Funding

Wellcome TrustWellcome Trust WT105618MA
6 · The paper itself

Abstract

People with Alzheimer's disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies.

Indexed as

Models, NeurologicalAgedAged, 80 and overAlgorithmsAlzheimer DiseaseBrainCase-Control StudiesComputational BiologyComputer SimulationDisease SusceptibilityElectroencephalographyElectrophysiological PhenomenaFemaleHumansMaleNerve Net

Identifiers

PMID34379638
PMCPMC8382184

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.