Evidence map›Paper›PMID 39814858›Full record

ArticleCommunications medicine2025

Revealing excitation-inhibition imbalance in Alzheimer's disease using multiscale neural model inversion of resting-state functional MRI.

Guoshi Li, Li-Ming Hsu, Ye Wu, Andrea C Bozoki, Yen-Yu Ian Shih, Pew-Thian Yap

Abstract read
In one paragraph

Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
  2. Excitation-inhibition imbalance links amyloid pathophysiology to cognition in non-demented individuals.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  3. Article
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  6. Review
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  12. Excitation-inhibition homeostasis in Alzheimer's disease: a selective multiscale review of mechanisms, sex differences, and therapeutic opportunities.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Review
  13. Article
  14. Article
  15. Review
  16. A Large-scale Neural Model Inversion Framework for Effective Connectivity Estimation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026
    Article
  17. Article
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  20. Review
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

6 authors.

Guoshi LiDepartment of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID http://orcid.org/0000-0002-8984-4722
Li-Ming HsuDepartment of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Ye WuDepartment of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Andrea C BozokiDepartment of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Yen-Yu Ian ShihBiomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID http://orcid.org/0000-0001-6529-911X
Pew-Thian YapDepartment of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. ptyap@med.unc.edu.ORCID http://orcid.org/0000-0003-1489-2102

Funding

Longitudinal Mapping of Human Brain Development in the First Years of LifeR01EB008374 · NIBIB · UNIV OF NORTH CAROLINA CHAPEL HILL · PI YAP, PEW-THIAN · 2009 to 2024
$5.1M
Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRIR01EB006733 · NIBIB · UNIV OF NORTH CAROLINA CHAPEL HILL · PI YAP, PEW-THIAN · 2008 to 2020
$4.5M
Computational Diffusion MRI for Studying Early Human Brain DevelopmentR01MH125479 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI YAP, PEW-THIAN · 2021 to 2025
$2.1M
Comprehensive MR Fingerprinting for Infants and Young Children at Risk for Developmental Delays.R01HD112923 · NICHD · DUKE UNIVERSITY · PI Dan Ma, DEANNE E WILSON-COSTELLO · 2024 to 2026
$1.7M
Accurate and Individualized Prediction of Excitation-Inhibition Imbalance in Alzheimer's Disease using Data-driven Neural ModelR21AG083589 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, GUOSHI, SHIH, YEN-YU IAN · 2023 to 2023
$428k
NIA NIH HHS R21 AG083589NIBIB NIH HHS R01 EB006733NIBIB NIH HHS R01 EB008374NICHD NIH HHS R01 HD112923NIMH NIH HHS R01 MH125479U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) R01EB006733U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) R01EB008374U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH125479
6 · The paper itself

Abstract

backgroundAlzheimer's disease (AD) is a serious neurodegenerative disorder without a clear understanding of pathophysiology. Recent experimental data have suggested neuronal excitation-inhibition (E-I) imbalance as an essential element of AD pathology, but E-I imbalance has not been systematically mapped out for either local or large-scale neuronal circuits in AD, precluding precise targeting of E-I imbalance in AD treatment.

methodIn this work, we apply a Multiscale Neural Model Inversion (MNMI) framework to the resting-state functional MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to identify brain regions with disrupted E-I balance in a large network during AD progression.

resultsWe observe that both intra-regional and inter-regional E-I balance is progressively disrupted from cognitively normal individuals, to mild cognitive impairment (MCI) and to AD. Also, we find that local inhibitory connections are more significantly impaired than excitatory ones and the strengths of most connections are reduced in MCI and AD, leading to gradual decoupling of neural populations. Moreover, we reveal a core AD network comprised mainly of limbic and cingulate regions. These brain regions exhibit consistent E-I alterations across MCI and AD, and thus may represent important AD biomarkers and therapeutic targets. Lastly, the E-I balance of multiple brain regions in the core AD network is found to be significantly correlated with the cognitive test score.

conclusionsOur study constitutes an important attempt to delineate E-I imbalance in large-scale neuronal circuits during AD progression, which may facilitate the development of new treatment paradigms to restore physiological E-I balance in AD.

Identifiers

PMID39814858
PMCPMC11735810

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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.