Evidence map›Paper›PMID 41364237›Full record

ArticleNeuroradiology2026

The association between individualized functional connectivity disruption and metabolic abnormality in alzheimer's disease and mild cognitive impairment: insights from multimodal neuroimaging.

Ya Gao, Shiwei Lin, Mei Jiang, Peiqi Wu, Jinghua Wang, Zhongxian Yang, Yubao Liu

Abstract read
In one paragraph

Article in Neuroradiology, 2026. 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Ya GaoMedical imaging center, Shenzhen Hospital, Southern Medical University, Shenzhen, China.
Shiwei LinDepartment of Radiology, Shenzhen Nanshan People's Hospital, Shenzhen, China.
Mei JiangDepartment of Nuclear Medicine, Shenzhen Nanshan People's Hospital, Shenzhen, China.
Peiqi WuDepartment of Radiology, Southern University of Science and Technology Yantian Hospital (Shenzhen Yantian District People's Hospital), Shenzhen, China.
Jinghua WangMedical imaging center, Shenzhen Hospital, Southern Medical University, Shenzhen, China.
Zhongxian YangMedical imaging center, Shenzhen Hospital, Southern Medical University, Shenzhen, China.
Yubao LiuMedical imaging center, Shenzhen Hospital, Southern Medical University, Shenzhen, China. ybliu28@163.com.

Funding

Major Science and Technology Project of Nanshan District, Shenzhen NSZD2024022Shenzhen Science and Technology Program JCYJ20230807142308018
6 · The paper itself

Abstract

purposeThe aim of this study was to leverage fluorodeoxyglucose-positron emission tomography (FDG-PET) and blood oxygen level dependent-functional magnetic resonance imaging (BOLD-fMRI) to perform a comprehensive multi-modal analysis of metabolic alteration and individualized functional connectivity in Alzheimer's Disease (AD) and mild cognitive impairment (MCI) and characterize the relationship of these alterations with neurocognitive scores.

methodsWe analyzed data from 71 subjects, including those with AD, MCI and Health Control (HC), using FDG-PET and BOLD-fMRI acquired from Alzheimer's Disease Neuroimaging Initiative (ADNI). We examined network functional connectivities (FC) base on Independent Component Analysis (ICA), analyzed regional standardized uptake value ratios (SUVR) and their relationships with neurocognitive scores.

resultsBoth AD and MCI showed metabolic and functional connectivity abnormalities in Default Mode Network (DMN) region. We also found abnormalities in the somatomotor system in AD, which may be an early predictive indicator of AD. In MCI, both metabolic and functional connectivity abnormalities appear in precuneus, and these two modes were closely related, indicating that the precuneus may be a core region in the transition of healthy individuals to MCI.

conclusionThis study demonstrated that the individual brain network technology based on ICA, combined with the metabolic characteristics of FDG-PET, facilitates the development of personalized early diagnosis for AD/MCI, enhances our understanding of the underlying neuropathological mechanisms, and also promotes the development of interdisciplinary technologies.

Indexed as

Alzheimer DiseaseCognitive DysfunctionMagnetic Resonance ImagingMultimodal ImagingPositron-Emission TomographyAgedFemaleFluorodeoxyglucose F18HumansMaleRadiopharmaceuticalsFluorodeoxyglucose F18RadiopharmaceuticalsAlzheimer's diseaseBOLD-fMRI.FDG-PETIndependent component analysisIndividualized functional connectivityMild cognitive impairment

Identifiers

PMID41364237
PMCPMC13021722

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.