Evidence map›Paper›PMID 40779429›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2025

Topological and geometric signatures of brain network dynamics in Alzheimer's disease.

Luopeiwen Yi, Michael William Lutz, Yutong Wu, Yang Li, Tananun Songdechakraiwut

Abstract read
In one paragraph

Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Topological and geometric signatures of brain network dynamics in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    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

5 authors.

Luopeiwen YiSocial Science Research Institute (SSRI), Duke University, Durham, North Carolina, USA.
Michael William LutzDepartments of Neurology and Pathology, Duke University School of Medicine, Durham, North Carolina, USA.
Yutong WuDepartment of Computer Science, Duke University, Durham, North Carolina, USA.
Yang LiDepartment of Computer Science, Duke University, Durham, North Carolina, USA.
Tananun SongdechakraiwutDepartment of Computer Science, Duke University, Durham, North Carolina, USA.ORCID 0009-0008-5637-2811

Funding

Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5M
The natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 2005 to 2025
$49.5M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
Research Education ComponentP30AG066444 · NIA · WASHINGTON UNIVERSITY · PI Susan Lynn Stark · 2020 to 2026
$28.7M
Research Education Component CoreP30AG072958 · NIA · DUKE UNIVERSITY · PI Heather E. Whitson · 2021 to 2026
$24.1M
THE XNAT IMAGING INFORMATICS PLATFORMR01EB009352 · NIBIB · WASHINGTON UNIVERSITY · PI Daniel Scott Marcus · 2009 to 2026
$9.3M
DRIVING PERFORMANCE IN PRECLINICAL ALZHEIMER'S DISEASER01AG043434 · NIA · WASHINGTON UNIVERSITY · PI ROE, CATHERINE M · 2012 to 2016
$2.4M
Bass Connections at Duke UniversityDuke Science and Technology LAUNCHNational Institute on Aging, National Institutes of Health (NIA, NIH) P30AG072958NCATS NIH HHS UL1 TR000448NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P30 AG066444NIA NIH HHS P30 AG072958NIA NIH HHS R01 AG043434NIBIB NIH HHS R01 EB009352
6 · The paper itself

Abstract

introductionThis study explores magnetic resonance imaging (MRI) as a promising non-invasive approach to monitor Alzheimer's disease (AD) and related dementias. We investigate whether dynamic functional connectivity (dFC), which captures time-varying neural interactions, can reveal sex-specific brain network disruptions in AD that conventional static connectivity analyses may miss.

methodsWe analyzed dFC in the Open Access Series of Imaging Studies (OASIS-3) dataset across three diagnostic groups (normal cognition, mild cognitive impairment, dementia), stratified by sex, and regressed out age. We evaluated group differences using multiple distance metrics sensitive to various aspects of network structure, with statistical significance assessed via permutation testing.

resultsDistinct sex-specific patterns emerged across diagnostic groups, with each metric sensitive to different aspects of network disruption. Peak connectivity states, rather than mean levels, more effectively reflected brain network dynamics. DISCUSSION: By emphasizing network dynamics, our findings highlight promising signatures for early detection and longitudinal biomarkers. Future work will explore metrics tailored to specific demographic or clinical subpopulations. HIGHLIGHTS: Dynamic connectivity reveals sex-specific brain disruptions in Alzheimer's disease (AD). Peak-based analysis improves sensitivity over mean-based connectivity measures. Topological and geometric metrics capture distinct network disruptions by sex. Mild cognitive impairment shows less consistent connectivity changes due to diagnostic instability. Findings support dynamic magnetic resonance imaging (MRI) metrics as early AD biomarkers in future studies.

Indexed as

Alzheimer DiseaseBrainCognitive DysfunctionNerve NetAgedFemaleHumansMagnetic Resonance ImagingMaleNeural PathwaysAlzheimer's diseasebrain network dynamicsnetwork topologyneuroimaging biomarkerspermutation testingpersistent graph homologysex differences in network structure

Identifiers

PMID40779429
PMCPMC12333878

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.