Evidence map›Paper›PMID 40845134›Full record

ArticlePloS one2025

Multiplex nodal modularity: A novel network metric for the regional analysis of amnestic mild cognitive impairment during a working memory binding task.

Avalon Campbell-Cousins, Federica Guazzo, Mark E Bastin, Mario A Parra, Javier Escudero

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Avalon Campbell-CousinsSchool of Engineering, Institute for Imaging, Data and Communications, University of Edinburgh, Edinburgh, Scotland, United Kingdom.ORCID https://orcid.org/0009-0005-2263-0593
Federica GuazzoHuman Cognitive Neuroscience, Psychology, University of Edinburgh, Edinburgh, Scotland, United Kingdom.
Mark E BastinCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom.
Mario A ParraDepartment of Psychology, University of Strathclyde, Glasgow, Scotland, United Kingdom.
Javier EscuderoSchool of Engineering, Institute for Imaging, Data and Communications, University of Edinburgh, Edinburgh, Scotland, United Kingdom.ORCID https://orcid.org/0000-0002-2105-8725

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modularity is a well-established concept for assessing community structures in various single and multi-layer networks, including those in biological and social domains. Brain networks are known to exhibit community structure at a variety of scales-local, meso, and global scale. However, modularity, while useful in describing mesoscale brain organization, is limited as a metric to a global scale describing the overall strength of community structure. This approach, while valuable, overlooks important variations in community structure at node level. To address this limitation, we extended modularity to individual nodes. This novel measure of nodal modularity (nQ) captures both mesoscale and local-scale changes in modularity. We hypothesized that nQ would illuminate granular changes in the brain due to diseases such as Alzheimer's disease (AD), which are known to disrupt the brain's modular structure. We explored nQ in multiplex networks of a visual short-term memory binding task in fMRI and DTI data in the early stages of AD. While limited by sample size, changes in nQ for individual regions of interest (ROIs) in our fMRI networks were predominantly observed in visual, limbic, and paralimbic systems in the brain, aligning with known AD trajectories and linked to amyloid-β and tau deposition. Furthermore, observed changes in white-matter microstructure in our DTI networks in parietal and frontal regions may compliment studies of white-matter integrity in poor memory binders. Additionally, nQ clearly differentiated MCI from MCI converters indicating that nQ may be sensitive to this key turning point of AD. Our findings demonstrate the utility of nQ as a measure of localized group structure, providing novel insights into task and disease-related variability at the node level. Given the widespread application of modularity as a global measure, nQ represents a significant advancement, providing a granular measure of network organization applicable to a wide range of disciplines.

Indexed as

AmnesiaBrainCognitive DysfunctionMemory, Short-TermNerve NetAgedAlzheimer DiseaseDiffusion Tensor ImagingFemaleHumansMagnetic Resonance ImagingMaleMiddle Aged

Identifiers

PMID40845134
PMCPMC12373287

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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.