Exploring causal links between brain functional networks and neurodegenerative disease risk using Mendelian randomization

利用孟德尔随机化方法探索大脑功能网络与神经退行性疾病风险之间的因果关系

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Abstract

BACKGROUND: Resting-state functional magnetic resonance imaging (rsfMRI) is pivotal for mapping alterations in brain functional networks associated with neurodegenerative diseases, particularly Alzheimer's disease (AD). However, the causal mechanisms linking such network dysfunction to disease pathogenesis remain unresolved. OBJECTIVE: This study aimed to elucidate bidirectional causal relationships between 191 resting-state fMRI phenotypes (derived from 34,691 individuals) and six neurodegenerative diseases, specifically AD, amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), multiple sclerosis (MS), dementia with Lewy bodies (DLB), and Parkinson's disease (PD), using disease-specific GWAS data from European-ancestry cohorts. METHODS: Bidirectional two-sample Mendelian randomization (MR) was performed using rsfMRI phenotypes from Zhao et al. (2022) and GWAS summary statistics (AD: ieu-b-5067/ebi-a-GCST90027158, ALS: ebi-a-GCST90027164, FTD: ieu-b-43, MS: ieu-b-18, DLB: ebi-a-GCST90001390, PD: ieu-b-7). Instrumental variables were filtered for significance (p < 5 × 10^-8), with sensitivity analyses (MR-PRESSO, Cochran's Q, MR-Egger) to ensure robustness. RESULTS: Forward MR identified 26 rsfMRI phenotypes causally linked to neurodegenerative diseases. AD risk was associated with reduced cerebellum-subcortical connectivity (OR = 0.957, p = 0.004), while heightened cerebellar activity increased DLB risk (OR = 2.58, p = 0.0063). Reverse MR revealed 64 disease-to-network effects: AD altered default mode network connectivity (OR = 0.965, p = 0.034), and PD disrupted salience-central executive network interactions (OR = 0.950, p = 0.00011). CONCLUSIONS: This study establishes robust bidirectional causal pathways between brain functional networks and neurodegenerative diseases, with AD showing unique vulnerability in cerebellar-subcortical and default mode circuits. These findings highlight network-specific therapeutic targets for AD and related disorders.

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