Exploring the Causal Relationships Between Brain Functional Networks and Psychiatric Disorders: A Mendelian Randomization Approach

Abstract

Background: Numerous studies have reported brain functional network impairments in individuals with psychiatric disorders; however, the causal relationships between the two remain unclear. We aimed to investigate the potential causal relationships between resting-state functional magnetic resonance imaging (rsfMRI) phenotypes and psychiatric disorders via Mendelian randomization (MR) analysis. Method: Employing a bidirectional two-sample MR analysis approach, this study assessed the associations between 191 rsfMRI phenotypes and 9 psychiatric disorders. Genetic variations were utilized as instrumental variables, ensuring the minimization of confounding factors in accordance with Mendel's laws of inheritance. Causal inferences were drawn by selecting genetic variants that were directly associated with the exposure variables and excluding those that might influence outcomes via alternative pathways. The study employed various statistical methods, including inverse variance weighting, the weighted median, and the MR Egger method, to evaluate causal relationships and adjusted for false discovery rates among outcomes. Results: The study identified significant causal associations between 21 rsfMRI phenotypes and five psychiatric disorders. For instance, in anxiety disorders, increased neural activity intensity in the parietal, frontal, and temporal lobes, along with enhanced functional connectivity between the attention, central executive, and default mode networks, are significantly associated with an increased risk of anxiety disorders. With respect to dementia, increased activity in the frontal lobe region was associated with a higher risk of dementia, and increased functional connectivity between the salience network and the central executive network was also linked to an increased risk of dementia. Conclusion: The findings of this study support the causal relationships between rsfMRI and psychiatric disorders, offering new insights for future prevention and treatment strategies.