Neuroradiology · 2025
The association between individualized functional connectivity disruption and metabolic abnormality in alzheimer's disease and mild cognitive impairment: insights from multimodal neuroimaging.
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Gao Ya, Lin Shiwei, Jiang Mei, Wu Peiqi, Wang Jinghua, Yang Zhongxian, Liu Yubao
Аннотация
PURPOSE: The 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.
METHODS: We 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.
RESULTS: Both 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.
CONCLUSION: This 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.
METHODS: We 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.
RESULTS: Both 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.
CONCLUSION: This 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.