Whole-brain Connectome in Alzheimer's Disease spectrum: A Multimodal Bayesian Meta-Analysis
Poster No:
1214
Submission Type:
Abstract Submission
Authors:
Wenxiong Liu1,2,3,4, Chao Zuo1, Li Chen1, Huan Lan1, Graham Kemp5, Su Lui1,4,3, Xueling Suo1,4,3, Qiyong Gong1,3,6,4
Institutions:
1Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital, Chengdu, China, 2Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, 3Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China, 4Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China, 5Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Scienc, Liverpool, UK, 6Xiamen Key Lab of Psychoradiology and Neuromodulation, Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China
First Author:
Wenxiong Liu
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital|Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College|Research Unit of Psychoradiology, Chinese Academy of Medical Sciences|Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University
Chengdu, China|Beijing, China|Chengdu, China|Chengdu, China
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital|Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College|Research Unit of Psychoradiology, Chinese Academy of Medical Sciences|Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University
Chengdu, China|Beijing, China|Chengdu, China|Chengdu, China
Co-Author(s):
Chao Zuo
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital
Chengdu, China
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital
Chengdu, China
Li Chen
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital
Chengdu, China
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital
Chengdu, China
Huan Lan
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital
Chengdu, China
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital
Chengdu, China
Graham Kemp
Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Scienc
Liverpool, UK
Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Scienc
Liverpool, UK
Su Lui
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital|Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University|Research Unit of Psychoradiology, Chinese Academy of Medical Sciences
Chengdu, China|Chengdu, China|Chengdu, China
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital|Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University|Research Unit of Psychoradiology, Chinese Academy of Medical Sciences
Chengdu, China|Chengdu, China|Chengdu, China
Xueling Suo
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital|Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University|Research Unit of Psychoradiology, Chinese Academy of Medical Sciences
Chengdu, China|Chengdu, China|Chengdu, China
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital|Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University|Research Unit of Psychoradiology, Chinese Academy of Medical Sciences
Chengdu, China|Chengdu, China|Chengdu, China
Qiyong Gong
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital|Research Unit of Psychoradiology, Chinese Academy of Medical Sciences|Xiamen Key Lab of Psychoradiology and Neuromodulation, Department of Radiology, West China Xiamen Hospital of Sichuan University|Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University
Chengdu, China|Chengdu, China|Xiamen, China|Chengdu, China
Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital|Research Unit of Psychoradiology, Chinese Academy of Medical Sciences|Xiamen Key Lab of Psychoradiology and Neuromodulation, Department of Radiology, West China Xiamen Hospital of Sichuan University|Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University
Chengdu, China|Chengdu, China|Xiamen, China|Chengdu, China
Introduction:
Alzheimer's disease (AD) is a debilitating neurodegenerative disorder that leads to cognitive decline, as well as various behavioral and psychological symptoms, accounting for 60–70% of dementia cases worldwide (World Health Organization, 2023). The hallmark pathological feature of AD is the loss of neurons and synapses, along with reduced neuronal connectivity, which disrupts both structural and functional brain networks (Yu et al., 2021). Recent research indicates that, much like other chronic conditions, pathophysiological changes in AD begin years before clinical symptoms manifest. This suggests that the progression from a clinically asymptomatic phase to severe cognitive impairment is a continuous process, often referred to as the AD spectrum (Aisen et al., 2017; Anon, 2023; Nemy et al., 2023). Increasingly, the AD spectrum is being viewed as a progressive network-disconnection syndrome (Mito et al., 2018). Neuroimaging studies using graph theoretical analysis (GTA) have found alterations in the topological properties of whole-brain structural and functional connectomes in both preclinical AD patients and those with AD (Chen et al., 2024; Filippi et al., 2020; Heo et al., 2024); however, the findings have been inconsistent. Our aim was to conduct a comprehensive literature review to identify robust changes in multimodal GTA metrics across the AD spectrum.
Methods:
We performed Bayesian random-effects meta-analyses on studies using GTA to assess whole-brain network segregation and integration in AD, preclinical AD, and healthy controls. A comprehensive search of PubMed, Embase, and Web of Science was conducted up to August 2024, following PRISMA guidelines, and included studies in English that reported on whole-brain structural or functional network metrics. The search included terms related to AD, graph theoretical analysis, and structural neuroimaging (diffusion magnetic resonance imaging) or functional neuroimaging (e.g., functional magnetic resonance imaging, electroencephalography, and magnetoencephalography). Heterogeneity effects were evaluated through moderator analyses.
Results:
A total of 53 studies met the inclusion criteria, comprising 1743 patients with AD, 1502 patients with preclinical AD, and 1824 healthy controls. For the structural network, compared to healthy controls, patients with AD exhibited reduced clustering coefficient (Hedges' g = -0.943, 95% CI: -1.704 to -0.079) and local efficiency (Hedges' g = -0.800, 95% CI: -1.435 to -0.097), along with increased characteristic path length (Hedges' g = 1.018, 95% CI: 0.239 to 1.696) and normalized characteristic path length (Hedges' g = 0.636, 95% CI: 0.219 to 1.057). In patients with preclinical AD, reductions in clustering coefficient (Hedges' g = -0.525, 95% CI: -0.883 to -0.159) and local efficiency (Hedges' g = -0.477, 95% CI: -0.843 to -0.127), as well as increased characteristic path length (Hedges' g = 0.558, 95% CI: 0.148 to 0.970), were observed. For the functional network, patients with AD showed decreased clustering coefficient (Hedges' g = -0.342, 95% CI: -0.643 to -0.046). The quality scores of the included studies ranged from 9.5 to 12.5, indicating a high overall quality. In moderator analysis, methodological factors such as neuroimaging technique, definition of node and edge, and network type were potential moderators of the effect outcomes, but further validation is required.
·Figure 1. PRISMA flow diagram of the literature search and selection criteria.
·Figure 2. Inverted forest plot of the overall effect sizes.
Conclusions:
We demonstrated that structural and functional network topology properties are altered in patients with AD, while in patients with preclinical AD, changes are limited to structural network topology with relatively preserved functional topology. These findings support the progressive disconnection hypothesis in the AD spectrum and suggest that structural network alterations may precede functional network changes. Furthermore, the results help clarify inconsistencies in previous studies and highlight the utility of graph-based metrics as biomarkers for staging AD progression.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Modeling and Analysis Methods:
Bayesian Modeling
Connectivity (eg. functional, effective, structural) 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Keywords:
Degenerative Disease
Meta- Analysis
Other - Graph theoretical analysis
1|2Indicates the priority used for review
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Provide references using APA citation style.
Anonymous. (2023). Alzheimer's disease facts and figures. Alzheimer's & Dementia, 19, 1598–1695.
Chen, Y. (2024). Disrupted morphological brain network organization in subjective cognitive decline and mild cognitive impairment. Brain Imaging and Behavior, 18, 387–395.
Filippi, M. (2020). Changes in functional and structural brain connectome along the Alzheimer's disease continuum. Molecular Psychiatry, 25, 230–239.
Heo, S. (2024). Alterations of structural network efficiency in early-onset and late-onset Alzheimer's disease. Journal of Clinical Neurology, 20, 265–275.
Mito, R. (2018). Fibre-specific white matter reductions in Alzheimer's disease and mild cognitive impairment. Brain, 141, 888–902.
Nemy, M. (2023). Cholinergic white matter pathways along the Alzheimer's disease continuum. Brain, 146, 2075–2088.
World Health Organization. (n.d.). Dementia.
Yu, M. (2021). The human connectome in Alzheimer disease: Relationship to biomarkers and genetics. Nature Reviews Neurology, 17, 545–563.