Dissecting Causal Relationships Between gut microbiota imbalance, inflammatory cytokines, and structural connectivity in the brain: A Mendelian Randomization Study

Dissecting Causal Relationships Between gut microbiota imbalance, inflammatory cytokines, and structural connectivity in the brain: A Mendelian Randomization Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dissecting Causal Relationships Between gut microbiota imbalance, inflammatory cytokines, and structural connectivity in the brain: A Mendelian Randomization Study Qianling Guo, Dongli Yang, Aamir Fahira, Jiahao Yang, Kai Zhuang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6197499/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Growing evidence indicates that the imbalances in gut microbiota influence brain structural connectivity, a key component of the microbiota-gut-brain axis. However, a deeper understanding of this complex bidirectional relationship remains elusive. This study aims to deepen our understanding of this bidirectional relationship by examining the underlying causal relationship and the mediating role of inflammatory cytokines. Methods : This study analyzed GWAS data from 18,340 participants for gut microbiota composition and MRI data from 82,382 participants for brain structural connectivity. We conducted a bidirectional two-sample Mendelian randomization (MR) to explore potential causal relationships between 211 gut microbiota taxa and 206 brain connectivity features. A two-step mediation analysis involving 41 inflammatory cytokines was performed, using the inverse variance weighted (IVW) method as the main analytical approach, supplemented by sensitivity analyses and reverse MR to check for robustness, reverse causation, heterogeneity, and horizontal pleiotropy. Results : After Bonferroni correction, MR analysis identified significant correlations between 11 pairs of gut microbiota taxa and brain connectivity traits, with 6 positive and 5 negative associations. Reverse MR confirmed positive associations in nine pairs. Sensitivity analyses found no evidence of horizontal pleiotropy, heterogeneity, or reverse causality. Inflammatory cytokines, such as RANTES, HGF, and IL-13, mediated 10–30% of these relationships, mainly through JAK-STAT, IL-17, and MAPK pathways. Conclusion : This research establishes potential causal links between gut microbiota and brain structural connectivity, bridging a crucial gap in the microbiota-gut-brain axis research. These findings enhance our understanding of the axis and suggest new therapeutic targets for neurological disorders. Population Genetics Molecular Genetics Molecular Epidemiology Bioinformatics Microbiota-Gut-Brain Axis Gut Microbiota Brain Structural Connectivity Mendelian Randomization Magnetic Resonance Imaging Figures Figure 1 Figure 2 Figure 3 Introduction The gut microbiota, a complex microbial ecosystem, influences brain development and behavioral performance through various mechanisms. This microbial community communicates extensively with the central nervous system via the microbiota-gut-brain axis, which involves neural, endocrine, and immune pathways (Cryan et al., 2019 ). Studies have indicated that specific microbiota can indirectly influence brain structure and function by affecting the barrier function of the gut epithelium and producing metabolic products such as short-chain fatty acids and bile acids (Caspani and Swann, 2019 ). Observational studies have previously identified an association between gut microbiota imbalance and structural changes in the brain related to various neurodegenerative and psychiatric disorders including Alzheimer’s disease (AD) (Sochocka et al., 2019 ), Parkinson’s disease (PD) (Sampson et al., 2016 ), autism spectrum disorder (Fetissov et al., 2019 ), epilepsy (Dahlin and Prast-Nielsen, 2019 ), and major depressive disorder (Zheng et al., 2016 ). Concisely, patients with Alzheimer's disease often exhibit significant differences in gut microbiota (Li et al., 2024 ), accompanied by structural changes such as hippocampal atrophy (Zhao et al., 2017 ). Despite these observational studies providing preliminary evidence of gut-brain interactions, they are often confounded by various factors, making it challenging to establish causal relationships. Therefore, further investigation is needed to elucidate the causal relationship between gut microbiota and changes in brain structure. Inflammation plays a pivotal role in shaping both brain structure and function, with inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) serving as key mediators (Bagyinszky et al., 2017 ; Smith et al., 2012 ). Inflammation is implicated in structural brain changes underlying neuropsychiatric disorders via microglia and astrocytic function, leading to disordered synaptic pruning and the subsequent effects on gray matter volume (GMV) (Khandaker et al., 2020 ). The gut microbiota and its metabolites can regulate the function of local immune cells in the brain, thereby influencing neural responses and altering brain structure. Briefly, short-chain fatty acids (SCFAs), can modulate cytokine production to regulate blood-brain barrier permeability and modulate microglial activity, playing a critical role in maintaining brain health (Agirman et al., 2021 ). Recent Mendelian randomization (MR) analyses have provided evidence suggesting a potential causal relationship between inflammation and changes in brain structures (Williams et al., 2022 ). The intricate interplay between the gut microbiota and brain structural connectivity, mediated by inflammation, is crucial for advancing our understanding of immunomodulatory mechanisms in gastrointestinal and neurological disorders. However, the specific role of the gut microbiota in modulating brain structure through inflammatory mediators remains unexplored. This study aims to address this gap by investigating these relationships through Mendelian randomization and mediation analysis. Mendelian randomization has emerged as a powerful tool for uncovering potential biological causal relationships. This technique relies on genetic variation as a naturally randomized instrumental variable, using the correlation of these variations with exposure to assess potential causal effects (Davey Smith and Hemani, 2014 ). Recently, Mendelian randomization has been widely applied to investigate the causal relationship between gut microbiota and various diseases. Concisely, this method has been used to study the relationship between gut microbiota and neurological diseases, with results supporting the notion that gut microbiota imbalance may increase the risk of depression (Qi et al., 2021 ). However, despite extensive research on the causal links between gut microbiota and systemic health conditions, no studies have yet explored the direct causal relationship between gut microbiota and brain structural connectivity using Mendelian randomization. Given the complexity of the microbiota-gut-brain axis and its potential role in neurodegenerative and mental health diseases, investigating how gut microbiota influences brain structural connectivity will fill an important knowledge gap and may provide a foundation for developing new therapeutic strategies. This study investigates the causal relationship between gut microbiota and brain structural connectivity using a two-sample Mendelian randomization approach. By elucidating these causal links, the findings contribute to a deeper understanding of the microbiota-gut-brain axis and its role in brain health. Moreover, this research may pave the way for novel diagnostic and therapeutic strategies targeting the gut microbiota, offering potential interventions for neurodegenerative diseases and other related neurological disorders Materials and methods Study design The flowchart of the study is shown in Fig. 1 . The data analyzed in this study were publicly available from existing, published genome-wide association studies (GWAS, https://www.ebi.ac.uk/gwas/ ). Therefore, all original research was ethically approved, and informed consent was obtained. The study utilized 206 brain structural connectivity phenotypes and 211 taxonomic units of aggregated gut microbiome data from GWAS. Before MR analysis, the instrumental variables (IVs) were rigorously screened. Three core assumptions must be met for MR which comprise i) The assumption of correlation, i.e., there is a strong genetic correlation between the IVs and the exposure; ii) The assumption of independence, i.e., the IVs are not associated with any confounding factors influencing the exposure and the outcome; and iii) The assumption of exclusion restriction, i.e., the IVs influence the outcome variable only through the exposure and not through other pathways (Davey Smith and Hemani, 2014 ). We then performed bidirectional two-sample MR analyses as well as sensitivity analyses. Furthermore, a two-step Mendelian Randomization approach was employed to screen for potential mediators among 41 inflammatory cytokines and to quantify their mediating effects in the causal associations between gut microbiota and brain structural connectivity. These comprehensive analyses not only ensure the validity of the causal inferences drawn from the MR approach but also provide a deeper understanding of the underlying mechanisms, particularly by identifying potential mediators and quantifying their effects. This thorough investigation enhances the reliability of the findings and offers new insights into the causal pathways linking gut microbiota and brain structural connectivity. Data sources for gut microbiota Single nucleotide polymorphisms (SNPs) associated with the composition of the human gut microbiome were selected as IVs from the GWAS dataset of the MiBioGen international consortium ( https://mibiogen.gcc.rug.nl/ ). This dataset encompasses 18,340 European participants from 24 independent cohorts. Microbial composition was analyzed by targeting three different variable regions of the 16S rRNA gene, which resulted in an estimated 5,717,754 SNPs. Two hundred and eleven taxa (9 phyla, 16 orders, 20 orders, 35 families, 131 genera) that fit the mbQTL (microbial quantitative trait loci) mapping analysis were included in this study (Kurilshikov et al., 2021). Data sources for brain structural connectivity features This study utilized structural brain connectivity data sourced from the UK Biobank encompassing 26,333 individuals of European genetic ancestry (Wainberg et al., 2024 ). Briefly, Wainberg et al (Wainberg et al., 2024 ) conducted Magnetic resonance imaging (MRI) scans, incorporating both T1-weighted and diffusion-weighted sequences. T1 scans were employed for structural imaging, providing surface model files and additional structural segmentation. Conversely, diffusion-weighted MRI scans were utilized to capture white matter structural connections. Furthermore, quality control metrics such as signal-to-noise ratio, contrast-to-noise ratio, and assessment of head motion were employed to ensure data reliability. Only scans that met these quality control criteria were included in subsequent analyses, thereby ensuring the integrity of the imaging data used to characterize brain connectivity. The dataset encompasses three categories of structural brain connectivity measures, totaling 206 in all. These include hemisphere-level cortical-to-cortical connectivity (3 measures), network-level cortical-to-cortical connectivity within and between each of the 14 hemisphere-specific "Yeo 7" (Yeo et al., 2011 ) networks (105 measures), and cortical-to-subcortical connectivity between each "Yeo 7" network and 7 subcortical structures (98 measures) (Wainberg et al., 2024 ). Statistical data are publicly available in the GWAS catalog under accession numbers GCST90302648 through GCST90302853. Data sources for inflammatory cytokines The GWAS conducted by Ahola-Olli et al. (Ahola-Olli et al., 2017 ) provided data on circulating cytokines and growth factors. This study uses a dataset containing the genome-wide meta-analysis summary statistics data of 41 inflammatory cytokines, conducted within three Finnish cohorts (YFS and FINRISK 1997 and 2002), encompassing 8,293 individuals with European ancestry (Corbin and Timpson, 2020 ). The dataset provides comprehensive genetic mapping of cytokines implicated in inflammatory processes, offering valuable insights into their regulatory mechanisms. This dataset was used in mediation analyses to explore the relationships between gut microbiota, inflammatory cytokines, and brain structural connectivity, aiming to uncover the pathways linking the gut microbiota to changes in brain structure. Selection of instrumental variables To delve into the relationship between the gut microbiome and brain structural connectivity, we expanded the gut microbiome significance threshold to P < 1 × 10 − 5 as the literature (Li, P. et al., 2022 ) reported. In addition, for brain structural connectivity and inflammatory cytokines, we used SNPs with a significance threshold of P < 5 × 10 − 6 (Zheng et al., 2024 ) as genetic tools. To ensure the selection of independent SNP locus, we performed linkage disequilibrium (LD) analysis using the “clump_data” function of the R package "TwoSampleMR" (Hemani et al., 2018 ). The screening criteria for the gut microbiota were set at r 2 = 0. 1 and kb = 500, SNPs with r 2 greater than 0.1 to the most significant SNP in the range of 500 kb were excluded (Hemani et al., 2018 ). For brain structural connectivity and inflammatory cytokines, the screening criteria were set at r 2 = 0.01 and kb = 10,000. The R package "TwoSampleMR” was used to analyze the association between the exposure factors and the outcome phenotypes. Consistent analysis of the effect alleles of SNPs associated with both exposure and outcome phenotypes was conducted to ensure consistent effect alleles and to exclude SNPs with palindromic structures. Furthermore, to assess the strength of selected SNPs, the following formula (Levin et al., 2020 ; Palmer et al., 2012 ) was used to compute the R 2 and F-statistic corresponding to each SNP, and SNPs with an F-statistic less than 10 were excluded to avoid the introduction of a weak instrumental variable bias Table S1-Table S3 . $$\:\text{F}\:=\:\frac{{\text{R}}^{2}\times\:(\text{N}\:-2)}{1-{\text{R}}^{2}}$$ R 2 denotes the IV explanation of exposure, also known as PVE (phenotypic variance explained), and N denotes the sample size. MR analysis The primary analysis of this study uses the inverse-variance weighted method (IVW) (Burgess et al., 2015 ) to assess the causal relationship between gut microbiota and brain structural connectivity. For each association, the odds ratio (OR) and 95% confidence intervals (CI) were then calculated. Specifically, effect sizes and standard errors for both exposure and outcome were obtained for each genetic variant. A weighted sum of the effects, represented by the genetic instruments, is computed to determine the overall effect size. In addition, multiple tests were conducted, such as MR-PRESSO (Verbanck et al., 2018 ), weighted median (Bowden et al., 2016 ), weighted mode (Hartwig et al., 2017 ), and MR-Egger (Burgess and Thompson, 2017 ). Moreover, a bi-directional MR analysis (Hemani et al., 2018 ) was conducted to investigate the presence of reverse causal relationships. Mediation MR analysis A two-step MR analysis (Relton and Davey Smith, 2012 ) was performed to explore the potential mediating role of inflammatory cytokines in the association between the gut microbiota and brain structural connectivity. In the first step, univariable MR (UVMR) was employed to assess the causal effect of the genetically determined gut microbiota and inflammatory cytokines (β1). The second step involved estimating the causal impact of each inflammatory cytokine as a mediator on brain structural connectivity (β2), assuming that the mediator is causally linked to the UVMR outcome. The mediation proportion of each mediator in the association between the gut microbiota and brain structural connectivity was calculated by the following formula (VanderWeele, 2016 ): $$\:{\text{M}\text{P}}_{\text{n}}=\frac{{{\beta\:}}_{\text{n}1}\:\times\:\:{{\beta\:}}_{\text{n}2}}{{{\beta\:}}_{\text{n}\text{t}\text{o}\text{t}\text{a}\text{l}}}$$ where β n1 represents the causal effect for each gut microbiota-inflammatory cytokines pair, β n2 represents the causal effect for each inflammatory cytokines/brain structural connectivity, β ntotal represents the total causal effect for each gut microbiota/brain structural connectivity pair, and MP n represents the mediation proportion for each pair (Vanderweele, 2015 ). Confidence intervals were estimated using the delta method (Kendall et al., 1994 ). Enrichment analysis Functional enrichment analysis of 41 cell cycle factors was performed using the Metascape database ( https://metascape.org/ ), focusing on Gene Ontology (GO), KEGG, and Reactome pathways. To visualize the results, a circular plot was employed, effectively highlighting the enriched terms. Significant terms were further subjected to hierarchical clustering based on Kappa-statistical similarities (Cohen and J., 1960) among their associated gene sets. Using a Kappa score threshold of 0.3, the hierarchical tree was partitioned into distinct term clusters, facilitating the identification of functional modules. The P -value cutoff was set at 0.01 to ensure statistical significance, and the minimum enrichment threshold was defined as 1 to include all relevant terms. MR sensitivity analysis To assess the robustness of the results, a series of sensitivity analyses were conducted. Concisely, Heterogeneity was evaluated using Cochran's Q test (Greco et al., 2015 ), with a P-value < 0.05 indicating the presence of heterogeneity. MR pleiotropy residual sum and outlier (MR-PRESSO) (Verbanck et al., 2018 ) were performed to further explore the stability of the results. When the global test P -values in the MR-PRESSO analysis were less than 0.05, the estimates were adjusted for outliers. The MR-Egger intercept test and MR-PRESSO global test were utilized to detect the influence of pleiotropy on causal association estimates, with a P-value < 0.05 indicating the presence of horizontal pleiotropy (Bowden et al., 2015 ). The reliability of the association was assessed through leave-one-out analysis, funnel plots, and scatter plots. Briefly, a leave-one-out analysis (Burgess et al., 2017 ) was performed to ensure that no bias was caused by a specific SNP. Scatter plots demonstrate that the results are not influenced by outliers. Funnel plots (Burgess et al., 2017 ) are utilized to evaluate the reliability of the association. Furthermore, to minimize the risk of false positives, the Bonferroni correction was applied. This adjustment accounted for the number of bacterial taxa in the gut microbiome, setting a more stringent significance threshold: 2.37 × 10 − 4 (0.05/211) for forward analysis and gut microbiota to mediation in mediation analysis. Reverse causality analyses were then performed to investigate whether brain structural connectivity could influence the gut microbiota, with a Bonferroni correction of 2.43 × 10 − 4 (0.05/206) applied for reverse analysis. Additionally, MR analysis was conducted between mediation and brain structural connectivity, with a Bonferroni-corrected significance threshold of 0.0012 (0.05/41). Statistical analysis All analyses were performed in R software version 4.3.1 ( https://www.r-project.org/ ). The IVW, weighted median, MR-PRESSO, MR-Egger, and sensitivity analyses were conducted using the “TwoSampleMR” package (version 0.5.7) and “MR-PRESSO package (version 1.0)”. Results The causal effect of gut microbiota on brain structural connectivity A total of 11 gut microbiota were found to be significantly associated with brain structural connectivity, as determined by the Bonferroni correction with the IVW method as the primary analytical approach. Concisely, four taxa comprising order Desulfovibrionales , family Desulfovibrionaceae , genus Escherichia Shigella , and genus Veillonella were positively associated with brain structural connectivity (Table 1 and Fig. 2 ). Notably, the order Desulfovibrionales demonstrated a strong positive effect on the connectivity between the left-hemisphere somatomotor network and the right-hemisphere dorsal attention network (OR:1.17, 95%CI:1.09–1.26, P-value = 1.33 \(\:\times\:\) 10 −5 ), This result was corroborated by additional MR methods (Table 1 and Fig. 2 ). In contrast, MR analysis indicated that five gut microbiota taxa comprising genus Senegalimassilia , order Rhodospirillales , family Rhodospirillaceae , genus Ruminococcus gnavus , and genus Howardella were associated with decreased risk development of brain structural connectivity (Table 1 and Fig. 2 ). Among these, the genus Ruminococcus gnavus was the most notably associated with reduced connectivity between the left-hemisphere limbic network and the right-hemisphere default mode network (OR:0.89, 95%CI:0.85–0.94, P-value = 1.69 \(\:\times\:\) 10 −5 ) (Table 1 and Fig. 2 ). Comprehensive details on the associations between gut microbiotas and brain structural connectivity are presented in Table S4 . Table 1 Significant associations of gut microbiota with the brain structural connectivity: Findings from Mendelian randomization analyses. Exposure Outcome nSNP Methods OR (95%CI) P-value Order Desulfovibrionales Left-hemisphere somatomotor network to right-hemisphere dorsal attention network white-matter structural connectivity 12 IVW 1.175(1.093 to 1.263) 1.33×10 − 5 12 Weighted median 1.164(1.051 to 1.290) 0.0036 12 MR Egger 1.199(0.989 to 1.454) 0.094 12 Simple mode 1.226(1.047 to 1.435) 0.028 12 Weighted mode 1.176(1.017 to 1.360) 0.051 Left-hemisphere somatomotor network to right-hemisphere somatomotor network white-matter structural connectivity 12 IVW 1.147(1.071 to 1.230) 9.78×10 − 5 12 Weighted median 1.110(1.006 to 1.224) 0.038 12 MR Egger 1.217(1.013 to 1.463) 0.063 12 Simple mode 1.121(0.950 to 1.322) 0.23 12 Weighted mode 1.116(0.965 to 1.291) 0.17 Left-hemisphere salience/ventral attention network to right-hemisphere control network white-matter structural connectivity 12 IVW 1.141(1.064 to 1.224) 0.00023 12 Weighted median 1.116(1.014 to 1.228) 0.025 12 MR Egger 1.262(1.047 to 1.521) 0.035 12 Simple mode 1.041(0.881 to 1.230) 0.65 12 Weighted mode 1.228(1.033 to 1.459) 0.04 Order Rhodospirillales Left-hemisphere visual network to hippocampus white-matter structural connectivity 13 IVW 0.887(0.834 to 0.942) 9.77×10 − 5 13 Weighted median 0.931(0.856 to 1.013) 0.099 13 MR Egger 0.822(0.611 to 1.105) 0.22 13 Simple mode 0.947(0.818 to 1.096) 0.48 13 Weighted mode 0.952(0.830 to 1.092) 0.5 Family Desulfovibrionaceae Left-hemisphere salience/ventral attention network to right-hemisphere control network white-matter structural connectivity 10 IVW 1.154(1.070 to 1.244) 0.00019 10 Weighted median 1.206(1.087 to 1.339) 0.00044 10 MR Egger 1.246(1.029 to 1.508) 0.054 10 Simple mode 1.236(1.026 to 1.490) 0.053 10 Weighted mode 1.233(1.052 to 1.446) 0.03 Family Rhodospirillaceae Left-hemisphere visual network to hippocampus white-matter structural connectivity 14 IVW 0.890(0.839 to 0.943) 8.55×10 − 5 14 Weighted median 0.929(0.857 to 1.007) 0.073 14 MR Egger 0.767(0.569 to 1.033) 0.11 14 Simple mode 0.943(0.825 to 1.077) 0.4 14 Weighted mode 0.946(0.823 to 1.088) 0.45 Genus Escherichia Shigella Left-hemisphere dorsal attention network to right-hemisphere limbic network white-matter structural connectivity 9 IVW 1.160(1.073 to 1.253) 0.00018 9 Weighted median 1.133(1.019 to 1.259) 0.021 9 MR Egger 1.251(1.015 to 1.541) 0.074 9 Simple mode 1.110(0.953 to 1.293) 0.22 9 Weighted mode 1.118(0.969 to 1.288) 0.16 Genus Howardella Left-hemisphere limbic network to left-hemisphere control network white-matter structural connectivity 10 IVW 0.913(0.874 to 0.953) 4.02×10 − 5 10 Weighted median 0.930(0.876 to 0.987) 0.017 10 MR Egger 0.971(0.798 to 1.180) 0.77 10 Simple mode 0.935(0.849 to 1.029) 0.2 10 Weighted mode 0.932(0.841 to 1.033) 0.21 Genus Ruminococcus gnavus group Left-hemisphere limbic network to right-hemisphere default mode network white-matter structural connectivity 12 IVW 0.894(0.849 to 0.941) 1.69×10 − 5 12 Weighted median 0.884(0.824 to 0.950) 0.00073 12 MR Egger 1.030(0.819 to 1.296) 0.8 12 Simple mode 0.887(0.788 to 0.999) 0.075 12 Weighted mode 0.901(0.801 to 1.013) 0.11 Genus Senegalimassilia Right-hemisphere somatomotor network to caudate white-matter structural connectivity 4 IVW 0.845(0.773 to 0.923) 0.0002 4 Weighted median 0.858(0.761 to 0.966) 0.012 4 MR Egger 0.966(0.727 to 1.285) 0.84 4 Simple mode 0.865(0.736 to 1.017) 0.18 4 Weighted mode 0.865(0.751 to 0.997) 0.14 Genus Veillonella Left-hemisphere dorsal attention network to right-hemisphere dorsal attention network white-matter structural connectivity 7 IVW 1.171(1.082 to 1.267) 9.12×10 − 5 7 Weighted median 1.153(1.041 to 1.277) 0.0063 7 MR Egger 1.064(0.575 to 1.970) 0.85 7 Simple mode 1.135(0.978 to 1.318) 0.15 7 Weighted mode 1.135(0.976 to 1.319) 0.15 Note : nSNP: Number of single-nucleotide polymorphisms; OR: Odds Ratio; CI: Confidence Interval; IVW: Inverse-variance weighted; Bacterial taxa at five levels (phylum, class, order, family, and genus). Sensitivity analysis Horizontal pleiotropy between the 11 pairs of gut microbiota and brain structural connectivity was examined by MR Egger intercept and the global test of MR-PRESSO. No pleiotropy was detected for any of the gut microbiota pairs ( Table S5 ). Furthermore, Heterogeneity was assessed using Cochran's Q test, and results indicated that none of the pairs exhibited heterogeneity ( Table S5 ). Scatterplots and funnel plots for each pair of associations are given in Figure S1-S2 , while leave-one-out sensitivity analysis for each pair of associations is shown in Figure S3. Additionally, MR-PRESSO analysis did not identify any significant outliers (global test P > 0.05, Tables S5 ). The causal effect of brain structural connectivity on gut microbiota Following the same analytical methods, we assessed the causal relationship between brain structural connectivity and gut microbiota. Results revealed that nine brain structure connectivity were positively associated with gut microbiota ( Table S6 ). Among these, the left-hemisphere control network to thalamus white-matter structural connectivity showed an increased abundance with three gut microbiotas, including phylum Firmicutes (OR: 1.22, 95%CI: 1.10–1.35, P-value = 1.06 \(\:\times\:\) 10 −4 ), class Clostridia (OR: 1.21, 95%CI: 1.09–1.34, P-value = 2.04 \(\:\times\:\) 10 −4 ), and order Clostridiales (OR: 1.21, 95%CI: 1.09–1.34, P-value = 2.12 \(\:\times\:\) 10 −4 ) ( Table S6 ). In addition, The Right-hemisphere default mode network to pallidum white-matter structural connectivity was significantly associated with genus Methanobrevibacter (OR: 1.94, 95%CI: 1.36–2.76, P-value = 2.39 \(\:\times\:\) 10 −4 ) ( Table S6 ). Detailed results are provided in Table S6. Sensitivity analysis confirmed no heterogeneity via Cochran's Q test, and no evidence of horizontal pleiotropy was detected by MR-Egger's intercept test and MR-PRESSO global test ( Table S7 ). Mediatory role of cytokine in gut-brain connectivity Effect of gut microbiota on inflammatory cytokines Using Mendelian Randomization with Bonferroni correction, our study found 26 significant associations between gut microbiota and inflammatory cytokines across various taxonomic levels i.e. 1 phylum, 1 class, 3 orders, 6 families, and 15 genera. Among them, the phylum Euryarchaeota was a risk factor for IL-2 (OR: 1.19, 95% CI: 1.09–1.31, P-value = 1.34 \(\:\times\:\) 10 −4 ). Additionally, class Verrucomicrobiae was a risk factor for PDGF-BB (OR: 1.23, 95% CI: 1.11–1.37, P-value = 1.09 \(\:\times\:\) 10 −4 ). Furthermore, among the orders, two were identified as risk factors i.e., Verrucomicrobiales for PDGF-BB (OR: 1.23, 95% CI: 1.11–1.37, P-value = 1.09 \(\:\times\:\) 10 −4 ), and Enterobacteriales for IL-1RA (OR: 1.55, 95% CI: 1.23–1.94, P-value = 1.84 \(\:\times\:\) 10 −4 ), whereas Lactobacillales exhibited a protective effect for B-NGF (OR. 0.71, 95% CI: 0.60–0.85, P-value = 1.18 \(\:\times\:\) 10 −4 ). In addition, four of the six family risk factors were identified, i.e., Porphyromonadaceae for VEGF (OR: 1.35, 95% CI: 1.16–1.58, P-value = 1.08 \(\:\times\:\) 10 −4 ), Verrucomicrobiaceae for PDGF-BB (OR: 1.23, 95% CI: 1.11–1.37, P-value = 1.10 \(\:\times\:\) 10 −4 ), Family XIII for M-CSF (OR: 1.70, 95% CI: 1.29–2.23, P-value = 1.37 \(\:\times\:\) 10 −4 ), Enterobacteriaceae for IL-1RA (OR: 1.55, 95% CI: 1.23–1.94, P-value = 1.84 \(\:\times\:\) 10 −4 ). Additionally, two families showed protective effects i.e., Defluviitaleaceae for PDGF-BB (OR: 0.82, 95% CI: 0.75–0.91, P-value = 1.15 \(\:\times\:\) 10 − 4 ) and Oxalobacteraceae for MIG (OR: 0.84, 95% CI: 0.77–0.92, P-value = 1.30 \(\:\times\:\) 10 −4 ). Finally, among the 15 genera, Enterorhabdus was identified as the most significant risk factor for MCP-3 (OR: 1.92, 95% CI: 1.38–2.67, P-value = 1.05 \(\:\times\:\) 10 −4 ). Additional risk factors included Butyricicoccus for TNF-B (OR: 2.23, 95% CI: 1.47–3.39, P-value = 1.66 \(\:\times\:\) 10 −4 ), Paraprevotella for TNF-B (OR. 1.38, 95% CI: 1.17–1.63, P-value = 1.33 \(\:\times\:\) 10 −4 ), Streptococcus for IL-7 (OR: 1.36, 95% CI: 1.16–1.59, P-value = 1.64 \(\:\times\:\) 10 −4 ), Ruminococcaceae UCG002 for MIG (OR: 1.27, 95% CI: 1.13–1.44, P-value = 1.26 \(\:\times\:\) 10 −4 ), and Akkermansia for PDGF-BB (OR: 1.23, 95% CI: 1.11–1.37, P-value = 1.09 \(\:\times\:\) 10 −4 ). Moreover, Protective factors included Parasutterella for IL-4 (OR: 0.85, 95% CI: 0.78–0.92, P-value = 1.44 \(\:\times\:\) 10 −4 ), Haemophilus for MCP-1/MCAF (OR: 0.84, 95% CI: 0.77–0.92, P-value = 1.70 \(\:\times\:\) 10 −4 ), Oscillibacter for TRAIL (OR: 0.83, 95% CI: 0.75–0.92, P-value = 1.96 \(\:\times\:\) 10 −4 ), Oxalobacter for IL-18 (OR: 0.83, 95% CI: 0.75–0.91, P-value = 1.32 \(\:\times\:\) 10 −4 ), Ruminiclostridium5 for IL-12_P70 (OR: 0.78, 95% CI: 0.69–0.89, P-value = 1.83 \(\:\times\:\) 10 −4 ), Ruminococcaceae UCG013 for MIP-1B (OR: 0.78, 95% CI: 0.68–0.89, P-value = 1.95 \(\:\times\:\) 10 −4 ), Ruminiclostridium9 for MCP-1/MCAF (OR: 0.75, 95% CI: 0.65–0.87, P-value = 1.07 \(\:\times\:\) 10 −4 ), Ruminiclostridium6 for B-NGF (OR: 0.74, 95% CI: 0.64–0.86, P-value = 1.34 \(\:\times\:\) 10 −4 ), and Eubacterium xylanophilum for RANTES (OR: 0.70, 95% CI: 0.59–0.85, P-value = 1.66 \(\:\times\:\) 10 −4 ). All results are given in Table S8 . Effect of inflammatory cytokines on brain structural connectivity After determined by the Bonferroni correction, a total of 14 significant causal associations between inflammatory cytokines and brain structural connectivity traits were identified. Among these, RANTES was identified as a risk factor for seven brain structural connectivity. The most significant association was found between left-hemisphere salience/ventral attention network and right-hemisphere visual network white-matter structural connectivity (OR: 1. 11, 95% CI: 1.07–1.16, P-value = 2. 58 \(\:\times\:\) 10 −7 ). Additional notable associations include the left-hemisphere dorsal attention network to left-hemisphere limbic network white-matter structural connectivity (OR: 1.08, 95% CI: 1.04-1. 13, P-value = 2. 14 \(\:\times\:\) 10 −4 ), the left hemisphere limbic network to hippocampal white matter structural connectivity (OR: 1. 09, 95% CI: 1. 04 − 1. 14, P-value = 2. 66 \(\:\times\:\) 10 −4 ), left hemisphere limbic network to caudate nucleus white matter structural connectivity (OR: 1. 07, 95% CI: 1. 03 − 1. 12, P-value = 5. 13 \(\:\times\:\) 10 −4 ), right hemisphere somatomotor network to right hemisphere salience/introspective attention network white matter structural connectivity (OR: 1.07, 95% CI: 1.03–1.11, P-value = 8. 31 \(\:\times\:\) 10 −4 ), left hemisphere visual network to right hemisphere somatomotor network white matter structural connectivity (OR: 1.08, 95% CI: 1.03–1.14, P-value = 1. 07 \(\:\times\:\) 10 −3 ), and left hemisphere default mode network with right hemisphere somatomotor network white matter structural connectivity (OR: 1.07, 95% CI: 1.03–1.11, P-value = 1. 14 \(\:\times\:\) 10 −3 ). Additionally, HGF was associated as a protective factor for five brain structure connectivity traits, notably between right hemisphere visual network and hippocampal white matter structure connectivity (OR: 0. 90, 95% CI: 0.85–0.95, P-value = 4. 23 \(\:\times\:\) 10 −4 ), left hemisphere visual network and hippocampal white matter structure connectivity (OR: 1.07, 95% CI: 1.03–1.11, P-value = 1. 14 \(\:\times\:\) 10 −3 ), left hemisphere visual network and hippocampus white matter structure connectivity (OR: 0. 90, 95% CI: 0.86–0.96, P-value = 4.66 \(\:\times\:\) 10 −4 ), left hemisphere limbic network to left hemisphere limbic network white matter structure connectivity (OR: 0. 91, 95% CI: 0.86–0.96, P-value = 7.88 \(\:\times\:\) 10 −4 ), and right hemisphere dorsal attention network to right hemisphere limbic network white matter structure connectivity (OR: 0. 91, 95% CI: 0.86–0.96, P-value = 1. 09 \(\:\times\:\) 10 −3 ). Furthermore, PDGF-BB served as a protective factor for white matter structural connectivity from the left hemisphere limbic network to the right hemisphere dorsal attention network (OR: 0.94, 95% CI: 0.91–0.97, P-value = 3. 18 \(\:\times\:\) 10 −4 ). Conversely, IL-13 was identified as a risk factor for left-hemisphere default mode network to caudate white-matter structural connectivity (OR: 1.05, 95% CI: 1.02–1.07, P-value = 3. 60 \(\:\times\:\) 10 −4 ), and IL-12_P70 was identified as a risk factor for connectivity of the right hemisphere salience/ventral attention network with white matter structures in the amygdala (OR: 1.06, 95% CI: 1.02–1.09, P-value = 1.11 \(\:\times\:\) 10 −3 ). All MR results are summarized in Table S9. Mediation analysis of gut microbiota on brain structural connectivity By performing a MR mediation analysis, we examined the role of inflammatory cytokines as mediators between gut microbiota on brain structural connectivity and identified 10 inflammatory cytokines that mediate the causal relationship between gut microbiota and brain structural connectivity (Table 2 ). Concisely, the results revealed that inflammatory cytokines, including RANTES, HGF, and IL-13, play a significant mediatory role in the relationship between gut microbiota and white-matter structural connectivity in different brain regions (Table 2 ). For instance, the genus Blautia significantly influenced the connectivity from the right-hemisphere somatomotor to the limbic network with HGF as the mediator, contributing a 9.54% mediation proportion (Table 2 ). Similarly, the Lachnospiraceae NK4A136 group was linked to the connectivity between the left-hemisphere salience/ventral attention network and the right-hemisphere visual network, mediated by RANTES with a 30.18% effect (Table 2 ). The mediation effects varied across different brain regions, with RANTES frequently acting as the mediator, particularly in the interactions involving the left-hemisphere somatomotor network. Notably, cytokines such as TNF-β, G-CSF, and MIG were implicated in mediating the relationship between specific microbial taxa and brain structural connectivity, such as genus Ruminococcaceae UCG003 and genus Lachnospiraceae UCG010 , and white-matter connectivity in brain regions like the accumbens and amygdala (Table 2 ). The mediation effects were generally moderate, with the mediated proportion of total effects ranging from 10–30% (Table 2 ). These findings highlight the crucial role of inflammatory cytokines, such as RANTES, HGF, and IL-13, as intermediaries in the gut-brain axis, influencing the structural connectivity between different brain regions. Detailed results are given in Table S10 . Table 2 The mediation effect of gut microbiota on brain structural connectivity via inflammatory cytokines Exposure Mediator Outcome Total effect Direct effect A Direct effect B Mediation effect Mediated proportion (%) β(95%CI) β(95%CI) β(95%CI) β(95%CI) Family Lactobacillaceae G-CSF Left-hemisphere control network to accumbens white-matter structural connectivity 0.065 0.134 0.062 0.008 12.78 (0.007, 0.122) (0.048, 0.221) (0.018, 0.105) (0.000, 0.016) Genus Lachnospiraceae UCG010 0.102 0.21 0.062 0.013 12.63 (0.029, 0.175) (0.073, -0.346) (0.018, 0.105) (0.000, 0.025) Genus Desulfovibrio -0.122 -0.264 0.062 -0.016 13.36 (-0.229, -0.015) (-0.393, -0.135) (0.018, 0.105) (-0.030, -0.002) Family Victivallaceae HGF Left-hemisphere limbic network to left-hemisphere default mode network white-matter structural connectivity -0.042 0.096 -0.105 -0.01 23.83 (-0.080, -0.004) (0.037, 0.154) (-0179, -0.032) (-0.019, -0.001) Genus Blautia Right-hemisphere somatomotor network to right-hemisphere limbic network white-matter structural connectivity -0.397 0.538 -0.07 -0.038 9.54 (-0.763, -0.031) (0.219, 0.857) (-0.127, -0.014) (-0.076, -0.000) Family Clostridiales vadin BB60 group IL-13 Right-hemisphere somatomotor network to right-hemisphere default mode network white-matter structural connectivity -0.076 -0.221 0.033 -0.007 9.58 (-0.139, -0.013) (-0.346, -0.096) (0.009, 0.057) (-0.014, -0.001) Genus Paraprevotella Left-hemisphere somatomotor network to left-hemisphere default mode network white-matter structural connectivity 0.06 0.184 0.033 0.006 10.09 (0.012, 0.107) (0.075, 0.293) (0.009, 0.056) (0.000, 0.012) Genus Phascolarctobacterium Left-hemisphere salience/ventral attention network to caudate white-matter structural connectivity 0.098 0.264 0.036 0.01 9.74 (0.020, 0.177) (0.098, 0.431) (0.009, 0.064) (0.000, 0.019) Genus Oxalobacter IL-18 Left-hemisphere visual network to thalamus white-matter structural connectivity -0.047 -0.187 0.037 -0.007 14.51 (-0.093, -0.001) (-0.282, -0.091) (0.010, 0.063) (-0.013, -0.001) Genus Ruminiclostridium6 IP-10 Right-hemisphere somatomotor network to amygdala white-matter structural connectivity -0.066 -0.231 0.055 -0.013 19.17 (-0.130, -0.002) (-0.381, -0.082) (0.015, 0.095) (-0.025, 0.000) Genus Sellimonas Right-hemisphere default mode network to accumbens white-matter structural connectivity 0.07 0.184 0.051 0.009 13.41 (0.026, 0.114) (0.082, 0.286) (0.014, 0.088) (0.001, 0.018) Genus Ruminococcaceae UCG005 PDGF-BB Left-hemisphere limbic network to right-hemisphere dorsal attention network white-matter structural connectivity 0.070 -0.165 -0.063 0.010 14.73 (0.002, 0.139) (-0.264, -0.066) (-0.097, -0.029) (0.002, 0.019) Genus Sellimonas MIG Right-hemisphere somatomotor network to accumbens white-matter structural connectivity 0.048 0.147 0.051 0.008 15.55 (0.002, 0.094) (0.056, 0.238) (0.014, 0.089) (0.000, 0.015) Genus Ruminococcaceae UCG002 Left-hemisphere visual network to putamen white-matter structural connectivity 0.065 0.243 0.044 0.011 16.40 (0.006, 0.124) (0.119, 0.367) (0.013, 0.075) (0.001, 0.020) Family Defluviitaleaceae RANTES Right-hemisphere salience/ventral attention network to right-hemisphere control network white-matter structural connectivity -0.072 -0.265 0.051 -0.013 18.65 (-0.129, -0.015) (-0.416, -0.113) (0.012, 0.089) (-0.026, -0.001) Family Rhodospirillaceae Left-hemisphere visual network to right-hemisphere dorsal attention network white-matter structural connectivity -0.074 -0.21 0.058 -0.012 16.68 (-0.134, -0.013) (-0.352, -0.067) (0.018, 0.099) (-0.024, − 0.000) Genus Lachnospiraceae NK4A136 group Left-hemisphere default mode network to right-hemisphere visual network white-matter structural connectivity -0.084 -0.191 0.065 -0.012 14.67 (-0.147, -0.020) (-0.338, -0.044) (0.023, 0.106) (-0.025, 0.000) Genus Eubacterium xylanophilum group Left-hemisphere somatomotor network to left-hemisphere default mode network white-matter structural connectivity -0.112 -0.351 0.05 -0.017 15.6 (-0.222, -0.000) (-0.533, -0.168) (0.011, 0.088) (-0.034, -0.001) Genus Lachnospiraceae NK4A136 group Left-hemisphere salience/ventral attention network to right-hemisphere visual network white-matter structural connectivity -0.068 -0.191 0.108 -0.021 30.18 (-0.135, -0.001) (-0.338, -0.044) (0.067, 0.149) (-0.0383, -0.003) Genus Lachnospiraceae NK4A136 group Left-hemisphere somatomotor network to right-hemisphere default mode network white-matter structural connectivity -0.086 -0.191 0.062 -0.012 13.79 (-0.150, -0.022) (-0.338, -0.044) (0.023, 0.101) (-0.024, − 0.000) Genus Lachnospiraceae NK4A136 group Left-hemisphere salience/ventral attention network to right-hemisphere control network white-matter structural connectivity -0.062 -0.191 0.06 -0.011 18.46 (-0.124, -0.001) (-0.338, -0.044) (0.023, 0.098) (-0.023, 0.000) Genus Ruminococcaceae UCG003 TNF-B Right-hemisphere somatomotor network to caudate white-matter structural connectivity -0.116 -0.461 0.04 -0.018 15.66 (-0.190, -0.043) (-0.800, -0.122) (0.015, 0.064) (-0.036, -0.001) Family Victivallaceae TRAIL Right-hemisphere limbic network to amygdala white-matter structural connectivity 0.052 0.095 0.04 0.004 7.34 (0.004, 0.101) (0.036, 0.154) (0.010, 0.071) (0.000, 0.008) Note : ‘Total effect’ indicates the effect of gut microbiota on brain structural connectivity, ‘Direct effect A’ indicates the effect of gut microbiota on inflammatory cytokines, ‘Direct effect B' indicates the effect of inflammatory cytokines on brain structural connectivity and ‘Mediation effect’ indicates the effect of gut microbiota on brain structural connectivity via inflammatory cytokines. Total effect, Direct effect A and Direct effect B were derived by IVW; Mediation effect was derived by using the delta method. All statistical tests were two-sided P < 0.05 was considered significant. GO, KEGG, Reactome analysis of inflammatory cytokines GO, KEGG, and Reactome pathway enrichment analyses were performed to elucidate the potential mechanisms linking gut microbiota-mediated inflammatory cytokines' effect on brain structure connectivity. GO analysis revealed significant enrichment in pathways related to cytokine activity, receptor-ligand interactions, and cytokine-mediated signaling, emphasizing the critical role of cytokine-receptor interactions in mediating downstream signaling processes (Fig. 3 a). KEGG pathway analysis identified key signaling cascades, including the JAK-STAT and IL-17 pathways, highlighting their involvement in inflammatory diseases like inflammatory bowel disease (IBD) (Fig. 3 b). The reactome analysis further pinpointed pathways, including interleukin signaling, chemokine receptor binding, and MAPK signaling, suggesting that these inflammatory factors influence neuronal activity and connectivity through these pathways (Fig. 3 c). These findings underscore the pivotal role of inflammatory signaling networks in mediating the gut-brain axis and offer valuable insights into the molecular mechanisms underlying brain structural connectivity changes driven by gut microbiota dysbiosis. The complete results of GO, KEGG, and analysis are respectively presented in Table S11 , Table S12 , and Table S13 . Discussion Given the significant impact of the microbiota-gut-brain axis, it is crucial to systematically investigate the causal relationship between gut microbiota and brain structural connectivity. To the best of our knowledge, this is the first study to comprehensively evaluate the causal associations between gut microbiota, inflammatory cytokines, and brain structural connectivity. In this study, we employed bidirectional two-sample MR analysis to rigorously examine the causal links between gut microbiota and brain structural alterations. Additionally, our findings provide a systematic investigation of the mediating effects of inflammatory cytokines in the relationship between gut microbiota and brain structural connectivity. After applying Bonferroni correction, we identified 11 gut microbiota taxa with significant causal relationships to brain structural connectivity, highlighting their potential roles within the microbiota-gut-brain axis. This research substantially enhances our understanding of the microbiota-gut-brain axis. In previous studies, Hu et al. (Hu et al., 2024 ) and Huang et al. (Huang et al., 2023 ). performed MR analysis to investigate the effects of gut flora on subcortical brain structures and the rate of change of brain structures, respectively. Our study differs from theirs in two significant aspects. Firstly, while the studies by Hu et al. and Huang et al (Hu et al., 2024 ; Huang et al., 2023 ) focused on specific brain structures, our study utilizes MRI-based GWAS data encompassing neural connectivity across the entire brain. MRI-based neural connectivity data provides a comprehensive view of connectivity within the whole brain network, offering a more comprehensive understanding of brain activity compared to focusing solely on subcortical structures or their rate of change. These data not only capture structural connectivity but also reflect functional connectivity, enabling a more thorough analysis of the relationship between functional and structural connectivity patterns and the interactions between gut flora and various brain regions. Secondly, our MR analysis was more comprehensive, incorporating both two-sample MR and additional sensitivity analyses, such as horizontal pleiotropy assessment and reverse MR analysis. Furthermore, we performed a mediation analysis to investigate the role of inflammatory cytokines, allowing us to systematically explore the interactions between gut flora and neural connectivity of the brain. In summary, our study offers a more detailed and thorough exploration of the relationship between gut flora and neural connectivity, employing advanced methodologies to understand the complex interactions within the brain's entire connectivity network. Our findings reveal patterns and mechanisms of gut flora-induced brain alterations analyzed through a large population of GWAS data. The family Desulfovibrionaceae and order Desulfovibrionales , which cognate with the genus Desulfovibrio , can lead to alterations in the white-matter structural connectivity responsible for connections with attentions and somatomotor (Table 1 and Fig. 2 ). Additionally, our study underscored the importance of mediators such as RANTES and G-CSF in modulating gut-brain interactions, emphasizing their potential to influence brain structural connectivity, especially through the genus Desulfovibrio and family Desulfovibrionaceae (Table 4). Notably, previous studies have reported increased levels of genus Desulfovibrio in patients with Parkinson's Disease (Nie et al., 2023 ) compared to healthy controls, correlating positively with disease severity, suggesting a potential pathogenic role. Furthermore, significant correlations between the order Desulfovibrionales and clinical stroke scales (Li, T. et al., 2022 ) reinforce the clinical relevance of gut-brain interactions. Additionally, an inverse correlation between global brain amyloid-beta (Aβ) load and the abundance of the family Desulfovibrionaceae suggests that lower bacterial levels may protect against Alzheimer's disease progression (Sheng et al., 2022 ). Research indicates that Desulfovibrio ’s production of excess hydrogen sulfide in the gastrointestinal tract may induce brain damage and neurological symptoms, with elevated plasma sulfide levels being associated with brain atrophy and reduced white matter integrity, potentially leading to neurologically related diseases (Haouzi et al., 2020 ; Panthi et al., 2018 ). Furthermore, G-CSF, a cytokine that plays a key role in the production and activation of granulocytes, has been shown to offer long-term neuroprotection by preventing brain atrophy and inducing somatic development, as demonstrated in numerous ischemic rodent models (Modi et al., 2020 ). G-CSF also has potential therapeutic applications in neurodegenerative disorders such as Parkinson’s disease (PD), intracerebral hemorrhage, experimental allergic encephalomyelitis, cerebral ischemia, spinal cord injury, and Alzheimer’s disease (AD) (Rahi et al., 2021 ). The gut microbiota can activate immune cells through the gut-immune axis further linking immune modulation with changes in brain structural connectivity (Forrest et al., 1994 ). Therefore, we hypothesize that excessive Desulfovibrio in the gut contributes to the development of neurological disease through the production of hydrogen sulfide, which reduces the systemic inflammatory response and may inhibit G-CSF production in immune cells. This, in turn, could lead to neurodegenerative disorders, brain atrophy, and reduced white matter integrity (Reekes et al., 2023 ). Our study also reveals that Ruminococcus gnavus acts as a protective factor for white matter connectivity between the limbic network and the default mode network (Fig. 2 ). Abnormal connectivity between these networks can lead to impaired attention, control, and emotional regulation, potentially contributing to conditions such as attention deficit hyperactivity disorder (ADHD) (Wan et al., 2020 ) and mood disorders (Tomasi and Volkow, 2012 ). Ruminococcus gnavus , a member of the phylum Firmicutes , is a common human gut symbiont (Crost et al., 2023 ). Furthermore, studies suggest that Ruminococcus gnavus alters the gut environment by reducing sialic acid derivatives, which may influence brain function via the gut-brain axis (Marizzoni et al., 2023 ). This bacterium might also enhance synaptic plasticity, thereby improving cognitive functions such as learning and memory. Additionally, Ruminococcus gnavus secretes short-chain fatty acids (Crost et al., 2023 ), which have neuroprotective effects on neurons (Hamamah et al., 2022 ) and can reduce anxiety and depression (Feng et al., 2023 ). Collectively, these findings highlight the multifaceted influence of Ruminococcus gnavus on brain function, underscoring its potential role in neurological disorders. However, the precise relationship between Ruminococcus gnavus and brain structural connectivity requires further investigation. Through reverse Mendelian randomization analysis, we uncovered the potential regulatory role of various brain structural connectivity in shaping gut microbiota, particularly the influence of thalamic, fornix, and amygdala white matter on enhancing the abundance of Firmicutes and its class Clostridia . This finding broadens our understanding of the gut-brain interaction and highlights the critical role of neuro-gut pathways in regulating gut microbial composition. The thalamus, fornix white matter, and amygdala white matter play key roles in neural regulation and emotion processing. Their complex neural circuits and interactions may influence gut microbiota composition by regulating the endocrine system, autonomic nervous system, and emotional regulation functions. Previous research, including studies by Labus et al. (Labus et al., 2019 ), has examined the interactions among gut microbial abundance, gastrointestinal sensorimotor function, and brain functional network indicators in irritable bowel syndrome. Their findings revealed that connectivity between subcortical (thalamus, caudate nucleus, and putamen) and cortical (primary and secondary somatosensory cortex) regions mediated the subnetwork links of Clostridium cluster XIVa ( Clostridium coccoides ) and genus Coprococcus . However, the direct impact of brain structural connectivity on gut microbiota remains unclear, necessitating further in-depth research. Our enrichment analyses highlighted the pivotal role of cytokine-mediated pathways, such as JAK-STAT, IL-17 pathways, and MAPK signaling, in connecting gut microbiota dysbiosis with alterations in brain connectivity. These findings support previous research that highlights the involvement of inflammatory pathways in modulating neuroinflammatory responses and neural plasticity. For example, the JAK-STAT pathway has been shown to play a key role in microglial activation and neurodegenerative processes (Panda et al., 2024 ). Moreover, the MAPK pathway is involved in regulating synaptic signaling and neuronal survival (Thomas and Huganir, 2004 ). The enrichment of interleukin and chemokine-related pathways further suggests that inflammatory factors, particularly cytokines and chemokines, act as essential mediators in the gut-brain axis, potentially influencing neurogenesis, synaptic connectivity, and even cognitive functions. While our study offers preliminary evidence supporting the hypothesis of brain-gut interaction, several limitations persist. Firstly, although Mendelian randomization is a powerful tool for reducing the influence of confounding variables, it cannot entirely rule out the presence of other potential confounders. Second, MR analysis helps assess causal relationships between exposure and outcome, but it cannot replace clinical trials, especially in objective domains. Therefore, further research is needed to confirm the proposed association between gut flora and neural connectivity in the brain. Third, our study has not yet explored the molecular mechanisms underlying the neural-gut interaction, which remains an important area for future research. Lastly, a deeper understanding of how genetic and environmental factors influence this interaction is essential for advancing our knowledge. Conclusion This study uncovers significant causal associations between gut microbiota and brain structural connectivity, highlighting the intricate role of inflammatory cytokines in mediating these relationships. We identified 11 causal relationships between the gut microbiota and brain structural connectivity, with 10 inflammatory cytokines mediating the relationship, suggesting a complex interaction between the two. In addition, nine reverse causal relationships were identified linking brain structural connectivity to alterations in the composition of the gut microbiota. Mediation analyses revealed that RANTES receptor levels mediated up to 30.18% of the relationship between the genus Lachnospiraceae NK4A136 group and left-hemisphere salience/ventral attention network and the right-hemisphere visual network. These findings not only deepen our understanding of the mechanisms of the gut-brain axis, but also provide new insights into the treatment of neurological disorders and promote innovative approaches to health promotion and disease management. Declarations Conflict of Interests The authors declare that they have no competing interests. Funding This work was supported by the Special Education Development Funding of Guangdong Medical University (4SG24186G). Author contributions Conceptualization and Design: QG, DY and ZH; Data curation and Formal Analysis: QG, DY, and JY; Visualization: QG, DY and ZT; Validation: AF and YW; Writing – original draft: QG, DY, and JY; Writing – review & editing: ZH, AF, and KZ; Supervision and Funding acquisition: ZH. All authors contributed to the article and approved the submitted version. Availability of data and materials This study analyzed publicly available datasets. 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Transl Neurodegener 7:3 Qi X, Guan F, Cheng S, Wen Y, Liu L, Ma M, Cheng B, Liang C, Zhang L, Liang X, Li P, Chu X, Ye J, Yao Y, Zhang F (2021) Sex specific effect of gut microbiota on the risk of psychiatric disorders: A Mendelian randomisation study and PRS analysis using UK Biobank cohort. World J Biol Psychiatry 22(7):495–504 Rahi V, Jamwal S, Kumar P (2021) Neuroprotection through G-CSF: recent advances and future viewpoints. Pharmacol Rep 73(2):372–385 Reekes TH, Ledbetter CR, Alexander JS, Stokes KY, Pardue S, Bhuiyan MAN, Patterson JC, Lofton KT, Kevil CG, Disbrow EA (2023) Elevated plasma sulfides are associated with cognitive dysfunction and brain atrophy in human Alzheimer's disease and related dementias. Redox Biol 62:102633 Relton CL, Davey Smith G (2012) Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol 41(1):161–176 Sampson TR, Debelius JW, Thron T, Janssen S, Shastri GG, Ilhan ZE, Challis C, Schretter CE, Rocha S, Gradinaru V, Chesselet MF, Keshavarzian A, Shannon KM, Krajmalnik-Brown R, Wittung-Stafshede P, Knight R, Mazmanian SK (2016) Gut Microbiota Regulate Motor Deficits and Neuroinflammation in a Model of Parkinson's Disease. Cell 167(6):1469–1480e1412 Sheng C, Yang K, He B, Du W, Cai Y, Han Y (2022) Combination of gut microbiota and plasma amyloid-β as a potential index for identifying preclinical Alzheimer's disease: a cross-sectional analysis from the SILCODE study. Alzheimers Res Ther 14(1):35 Smith JA, Das A, Ray SK, Banik NL (2012) Role of pro-inflammatory cytokines released from microglia in neurodegenerative diseases. Brain Res Bull 87(1):10–20 Sochocka M, Donskow-Łysoniewska K, Diniz BS, Kurpas D, Brzozowska E, Leszek J (2019) The Gut Microbiome Alterations and Inflammation-Driven Pathogenesis of Alzheimer's Disease-a Critical Review. Mol Neurobiol 56(3):1841–1851 Thomas GM, Huganir RL (2004) MAPK cascade signalling and synaptic plasticity. Nat Rev Neurosci 5(3):173–183 Tomasi D, Volkow ND (2012) Abnormal functional connectivity in children with attention-deficit/hyperactivity disorder. Biol Psychiatry 71(5):443–450 Vanderweele TJ (2015) Mediation Analysis: A Practitioner's Guide. Annu Rev Public Health 37(37):17 VanderWeele TJ (2016) Mediation Analysis: A Practitioner's Guide. Annu Rev Public Health 37:17–32 Verbanck M, Chen CY, Neale B, Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50(5):693–698 Wainberg M, Forde NJ, Mansour S, Kerrebijn I, Medland SE, Hawco C, Tripathy SJ (2024) Genetic architecture of the structural connectome. Nat Commun 15(1), 1962 Wan L, Ge WR, Zhang S, Sun YL, Wang B, Yang G (2020) Case-Control Study of the Effects of Gut Microbiota Composition on Neurotransmitter Metabolic Pathways in Children With Attention Deficit Hyperactivity Disorder. Front Neurosci 14:127 Williams JA, Burgess S, Suckling J, Lalousis PA, Batool F, Griffiths SL, Palmer E, Karwath A, Barsky A, Gkoutos GV, Wood S, Barnes NM, David AS, Donohoe G, Neill JC, Deakin B, Khandaker GM, Upthegrove R (2022) Inflammation and Brain Structure in Schizophrenia and Other Neuropsychiatric Disorders: A Mendelian Randomization Study. JAMA Psychiatry 79(5):498–507 Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, Roffman JL, Smoller JW, Zöllei L, Polimeni JR, Fischl B, Liu H, Buckner RL (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106(3):1125–1165 Zhao Y, Raichle ME, Wen J, Benzinger TL, Fagan AM, Hassenstab J, Vlassenko AG, Luo J, Cairns NJ, Christensen JJ, Morris JC, Yablonskiy DA (2017) In vivo detection of microstructural correlates of brain pathology in preclinical and early Alzheimer Disease with magnetic resonance imaging. NeuroImage 148:296–304 Zheng P, Zeng B, Zhou C, Liu M, Fang Z, Xu X, Zeng L, Chen J, Fan S, Du X, Zhang X, Yang D, Yang Y, Meng H, Li W, Melgiri ND, Licinio J, Wei H, Xie P (2016) Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host's metabolism. Mol Psychiatry 21(6):786–796 Zheng S, Liu L, Liang K, Yan J, Meng D, Liu Z, Tian S, Shan Y (2024) Multi-omics insight into the metabolic and cellular characteristics in the pathogenesis of hypothyroidism. Commun Biol 7(1):990 Additional Declarations The authors declare no competing interests. Supplementary Files FigureS1S3.docx SupplementaryTableS1S13.xlsb Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6197499","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":426836153,"identity":"cfd038d0-cd3c-4595-bb0f-7531b85a7065","order_by":0,"name":"Qianling Guo","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qianling","middleName":"","lastName":"Guo","suffix":""},{"id":426836154,"identity":"d908479a-2a05-495e-811e-287415daf91f","order_by":1,"name":"Dongli Yang","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dongli","middleName":"","lastName":"Yang","suffix":""},{"id":426836155,"identity":"024e9540-e475-4948-b79e-666fa4e5127b","order_by":2,"name":"Aamir Fahira","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Aamir","middleName":"","lastName":"Fahira","suffix":""},{"id":426836156,"identity":"abc695a4-e2a6-4bd4-b4ea-e0ae7456bd83","order_by":3,"name":"Jiahao Yang","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Yang","suffix":""},{"id":426836157,"identity":"94aea172-9dcf-40f4-a08f-c882ba9ec237","order_by":4,"name":"Kai Zhuang","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zhuang","suffix":""},{"id":426836158,"identity":"be62add9-7492-48d2-a3b9-2d751ff1864a","order_by":5,"name":"Ying Wen","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wen","suffix":""},{"id":426836159,"identity":"6d026a35-fa85-427b-b45f-9ba99e953dae","order_by":6,"name":"Zhuolun Tang","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhuolun","middleName":"","lastName":"Tang","suffix":""},{"id":426837302,"identity":"c8430b7d-c71a-4c53-abb0-26d14ce55d16","order_by":7,"name":"Zunnan Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYHACNjDJz8BgAKIZG4jWItlAshaDA8RqkZ/dwPaYp+aO3ebzi7du5mGwkd1wgPnZA3xaGOccYDfmOfYseduNZ2W3eRjSjDccYDM3wKeFWSKBTZqH7XCy2Y0zZkAthxM3HOBhk8DrEbCWf4eTjWeAtfwnrIUHpIW37bCdAX8PSMsBwlokgFok5/YdTpC4wVZ2c45BsvHMw2xmeLXIz0hgk3jz7bA9f//hbTfeVNjJ9h1vfoZXCzDaP4DIxAaJBAZIAmDGrx4O7Bn4DxCpdBSMglEwCkYcAAArkEcTjwt8swAAAABJRU5ErkJggg==","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zunnan","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-03-10 17:23:43","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6197499/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6197499/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78634291,"identity":"6d8c32ea-a63e-45c3-8413-819b38b26a54","added_by":"auto","created_at":"2025-03-17 04:45:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":327041,"visible":true,"origin":"","legend":"\u003cp\u003eA Schematic diagram of MR Analysis. \u003cstrong\u003eNote:\u003c/strong\u003e GWAS: Genome-wide association studies; SNP: single-nucleotide polymorphisms. (Figure created with biorender.com).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6197499/v1/338c0e0d17a746a681cf60da.png"},{"id":78634290,"identity":"353902e3-e020-440f-bef1-03b5f07e87d6","added_by":"auto","created_at":"2025-03-17 04:45:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112809,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot depicts the significant association between gut microbiota and brain structural connectivity through different approaches.\u003cstrong\u003e Note: \u003c/strong\u003eCI: Confidence Interval; OR: Odds Ratio; LH-SMN to RH-DAN: Left-hemisphere somatomotor network to right-hemisphere dorsal attention network white-matter structural connectivity; LH-LN to RH-DMN: Left-hemisphere limbic network to right-hemisphere default mode network white-matter structural connectivity ; LH-LN to LH-CN: Left-hemisphere limbic network to left-hemisphere control network white-matter structural connectivity ; LH-VN to HC: Left-hemisphere visual network to hippocampus white-matter structural connectivity; LH-DAN to RH-DAN: Left-hemisphere dorsal attention network to right-hemisphere dorsal attention network white-matter structural connectivity; LH-SMN to RH-SMN: Left-hemisphere somatomotor network to right-hemisphere somatomotor network white-matter structural connectivity; LH-DAN to RH-LN: Left-hemisphere dorsal attention network to right-hemisphere limbic network white-matter structural connectivity; LH-S/VANN to RH-CN: Left-hemisphere salience/ventral attention network to right-hemisphere control network white-matter structural connectivity; RH-SMN to CA: Right-hemisphere somatomotor network to caudate white-matter structural connectivity.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6197499/v1/54e773f60b37fe6df2f157b6.png"},{"id":78635319,"identity":"b2dc9480-2ad0-4984-9f99-1e20ced84108","added_by":"auto","created_at":"2025-03-17 05:01:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1190670,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis for inflammatory cytokines. (a) GO analysis, (b) KEGG analysis, (c) Reactome analysis\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6197499/v1/2d87cf008b4cf04068315d46.png"},{"id":78635839,"identity":"7489d882-dcf2-40ec-9dd0-32409b4e4744","added_by":"auto","created_at":"2025-03-17 05:09:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2887002,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6197499/v1/5a05476c-e125-452c-aec6-37b6ab7ddd3f.pdf"},{"id":78634292,"identity":"6bb29f0c-10cd-4006-a379-a2a7ee5132cb","added_by":"auto","created_at":"2025-03-17 04:45:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1709012,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1S3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6197499/v1/87235a0b7842b6427d8eccec.docx"},{"id":78634304,"identity":"4791fb9b-7e29-41e8-9c59-d0799a1f0a4b","added_by":"auto","created_at":"2025-03-17 04:45:02","extension":"xlsb","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":43488841,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1S13.xlsb","url":"https://assets-eu.researchsquare.com/files/rs-6197499/v1/53cfbd49c44db216088358b4.xlsb"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDissecting Causal Relationships Between gut microbiota imbalance, inflammatory cytokines, and structural connectivity in the brain: A Mendelian Randomization Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe gut microbiota, a complex microbial ecosystem, influences brain development and behavioral performance through various mechanisms. This microbial community communicates extensively with the central nervous system via the microbiota-gut-brain axis, which involves neural, endocrine, and immune pathways (Cryan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Studies have indicated that specific microbiota can indirectly influence brain structure and function by affecting the barrier function of the gut epithelium and producing metabolic products such as short-chain fatty acids and bile acids (Caspani and Swann, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Observational studies have previously identified an association between gut microbiota imbalance and structural changes in the brain related to various neurodegenerative and psychiatric disorders including Alzheimer\u0026rsquo;s disease (AD) (Sochocka et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Parkinson\u0026rsquo;s disease (PD) (Sampson et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), autism spectrum disorder (Fetissov et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), epilepsy (Dahlin and Prast-Nielsen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and major depressive disorder (Zheng et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Concisely, patients with Alzheimer's disease often exhibit significant differences in gut microbiota (Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), accompanied by structural changes such as hippocampal atrophy (Zhao et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite these observational studies providing preliminary evidence of gut-brain interactions, they are often confounded by various factors, making it challenging to establish causal relationships. Therefore, further investigation is needed to elucidate the causal relationship between gut microbiota and changes in brain structure.\u003c/p\u003e \u003cp\u003eInflammation plays a pivotal role in shaping both brain structure and function, with inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) serving as key mediators (Bagyinszky et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Inflammation is implicated in structural brain changes underlying neuropsychiatric disorders via microglia and astrocytic function, leading to disordered synaptic pruning and the subsequent effects on gray matter volume (GMV) (Khandaker et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The gut microbiota and its metabolites can regulate the function of local immune cells in the brain, thereby influencing neural responses and altering brain structure. Briefly, short-chain fatty acids (SCFAs), can modulate cytokine production to regulate blood-brain barrier permeability and modulate microglial activity, playing a critical role in maintaining brain health (Agirman et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recent Mendelian randomization (MR) analyses have provided evidence suggesting a potential causal relationship between inflammation and changes in brain structures (Williams et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The intricate interplay between the gut microbiota and brain structural connectivity, mediated by inflammation, is crucial for advancing our understanding of immunomodulatory mechanisms in gastrointestinal and neurological disorders. However, the specific role of the gut microbiota in modulating brain structure through inflammatory mediators remains unexplored. This study aims to address this gap by investigating these relationships through Mendelian randomization and mediation analysis.\u003c/p\u003e \u003cp\u003eMendelian randomization has emerged as a powerful tool for uncovering potential biological causal relationships. This technique relies on genetic variation as a naturally randomized instrumental variable, using the correlation of these variations with exposure to assess potential causal effects (Davey Smith and Hemani, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Recently, Mendelian randomization has been widely applied to investigate the causal relationship between gut microbiota and various diseases. Concisely, this method has been used to study the relationship between gut microbiota and neurological diseases, with results supporting the notion that gut microbiota imbalance may increase the risk of depression (Qi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, despite extensive research on the causal links between gut microbiota and systemic health conditions, no studies have yet explored the direct causal relationship between gut microbiota and brain structural connectivity using Mendelian randomization. Given the complexity of the microbiota-gut-brain axis and its potential role in neurodegenerative and mental health diseases, investigating how gut microbiota influences brain structural connectivity will fill an important knowledge gap and may provide a foundation for developing new therapeutic strategies.\u003c/p\u003e \u003cp\u003eThis study investigates the causal relationship between gut microbiota and brain structural connectivity using a two-sample Mendelian randomization approach. By elucidating these causal links, the findings contribute to a deeper understanding of the microbiota-gut-brain axis and its role in brain health. Moreover, this research may pave the way for novel diagnostic and therapeutic strategies targeting the gut microbiota, offering potential interventions for neurodegenerative diseases and other related neurological disorders\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThe flowchart of the study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The data analyzed in this study were publicly available from existing, published genome-wide association studies (GWAS, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Therefore, all original research was ethically approved, and informed consent was obtained. The study utilized 206 brain structural connectivity phenotypes and 211 taxonomic units of aggregated gut microbiome data from GWAS. Before MR analysis, the instrumental variables (IVs) were rigorously screened. Three core assumptions must be met for MR which comprise i) The assumption of correlation, i.e., there is a strong genetic correlation between the IVs and the exposure; ii) The assumption of independence, i.e., the IVs are not associated with any confounding factors influencing the exposure and the outcome; and iii) The assumption of exclusion restriction, i.e., the IVs influence the outcome variable only through the exposure and not through other pathways (Davey Smith and Hemani, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We then performed bidirectional two-sample MR analyses as well as sensitivity analyses. Furthermore, a two-step Mendelian Randomization approach was employed to screen for potential mediators among 41 inflammatory cytokines and to quantify their mediating effects in the causal associations between gut microbiota and brain structural connectivity. These comprehensive analyses not only ensure the validity of the causal inferences drawn from the MR approach but also provide a deeper understanding of the underlying mechanisms, particularly by identifying potential mediators and quantifying their effects. This thorough investigation enhances the reliability of the findings and offers new insights into the causal pathways linking gut microbiota and brain structural connectivity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData sources for gut microbiota\u003c/h3\u003e\n\u003cp\u003eSingle nucleotide polymorphisms (SNPs) associated with the composition of the human gut microbiome were selected as IVs from the GWAS dataset of the MiBioGen international consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mibiogen.gcc.rug.nl/\u003c/span\u003e\u003cspan address=\"https://mibiogen.gcc.rug.nl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This dataset encompasses 18,340 European participants from 24 independent cohorts. Microbial composition was analyzed by targeting three different variable regions of the 16S rRNA gene, which resulted in an estimated 5,717,754 SNPs. Two hundred and eleven taxa (9 phyla, 16 orders, 20 orders, 35 families, 131 genera) that fit the mbQTL (microbial quantitative trait loci) mapping analysis were included in this study (Kurilshikov et al., 2021).\u003c/p\u003e\n\u003ch3\u003eData sources for brain structural connectivity features\u003c/h3\u003e\n\u003cp\u003eThis study utilized structural brain connectivity data sourced from the UK Biobank encompassing 26,333 individuals of European genetic ancestry (Wainberg et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Briefly, Wainberg et al (Wainberg et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) conducted Magnetic resonance imaging (MRI) scans, incorporating both T1-weighted and diffusion-weighted sequences. T1 scans were employed for structural imaging, providing surface model files and additional structural segmentation. Conversely, diffusion-weighted MRI scans were utilized to capture white matter structural connections. Furthermore, quality control metrics such as signal-to-noise ratio, contrast-to-noise ratio, and assessment of head motion were employed to ensure data reliability. Only scans that met these quality control criteria were included in subsequent analyses, thereby ensuring the integrity of the imaging data used to characterize brain connectivity. The dataset encompasses three categories of structural brain connectivity measures, totaling 206 in all. These include hemisphere-level cortical-to-cortical connectivity (3 measures), network-level cortical-to-cortical connectivity within and between each of the 14 hemisphere-specific \"Yeo 7\" (Yeo et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) networks (105 measures), and cortical-to-subcortical connectivity between each \"Yeo 7\" network and 7 subcortical structures (98 measures) (Wainberg et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Statistical data are publicly available in the GWAS catalog under accession numbers GCST90302648 through GCST90302853.\u003c/p\u003e\n\u003ch3\u003eData sources for inflammatory cytokines\u003c/h3\u003e\n\u003cp\u003eThe GWAS conducted by Ahola-Olli et al. (Ahola-Olli et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) provided data on circulating cytokines and growth factors. This study uses a dataset containing the genome-wide meta-analysis summary statistics data of 41 inflammatory cytokines, conducted within three Finnish cohorts (YFS and FINRISK 1997 and 2002), encompassing 8,293 individuals with European ancestry (Corbin and Timpson, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The dataset provides comprehensive genetic mapping of cytokines implicated in inflammatory processes, offering valuable insights into their regulatory mechanisms. This dataset was used in mediation analyses to explore the relationships between gut microbiota, inflammatory cytokines, and brain structural connectivity, aiming to uncover the pathways linking the gut microbiota to changes in brain structure.\u003c/p\u003e\n\u003ch3\u003eSelection of instrumental variables\u003c/h3\u003e\n\u003cp\u003eTo delve into the relationship between the gut microbiome and brain structural connectivity, we expanded the gut microbiome significance threshold to \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e as the literature (Li, P. et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported. In addition, for brain structural connectivity and inflammatory cytokines, we used SNPs with a significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e (Zheng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) as genetic tools.\u003c/p\u003e \u003cp\u003eTo ensure the selection of independent SNP locus, we performed linkage disequilibrium (LD) analysis using the \u0026ldquo;clump_data\u0026rdquo; function of the R package \"TwoSampleMR\" (Hemani et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The screening criteria for the gut microbiota were set at r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0. 1 and kb\u0026thinsp;=\u0026thinsp;500, SNPs with r\u003csup\u003e2\u003c/sup\u003e greater than 0.1 to the most significant SNP in the range of 500 kb were excluded (Hemani et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For brain structural connectivity and inflammatory cytokines, the screening criteria were set at r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.01 and kb\u0026thinsp;=\u0026thinsp;10,000. The R package \"TwoSampleMR\u0026rdquo; was used to analyze the association between the exposure factors and the outcome phenotypes. Consistent analysis of the effect alleles of SNPs associated with both exposure and outcome phenotypes was conducted to ensure consistent effect alleles and to exclude SNPs with palindromic structures. Furthermore, to assess the strength of selected SNPs, the following formula (Levin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Palmer et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) was used to compute the R\u003csup\u003e2\u003c/sup\u003e and F-statistic corresponding to each SNP, and SNPs with an F-statistic less than 10 were excluded to avoid the introduction of a weak instrumental variable bias \u003cb\u003eTable S1-Table S3\u003c/b\u003e.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{F}\\:=\\:\\frac{{\\text{R}}^{2}\\times\\:(\\text{N}\\:-2)}{1-{\\text{R}}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e denotes the IV explanation of exposure, also known as PVE (phenotypic variance explained), and N denotes the sample size.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMR analysis\u003c/h2\u003e \u003cp\u003eThe primary analysis of this study uses the inverse-variance weighted method (IVW) (Burgess et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) to assess the causal relationship between gut microbiota and brain structural connectivity. For each association, the odds ratio (OR) and 95% confidence intervals (CI) were then calculated. Specifically, effect sizes and standard errors for both exposure and outcome were obtained for each genetic variant. A weighted sum of the effects, represented by the genetic instruments, is computed to determine the overall effect size. In addition, multiple tests were conducted, such as MR-PRESSO (Verbanck et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), weighted median (Bowden et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), weighted mode (Hartwig et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and MR-Egger (Burgess and Thompson, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, a bi-directional MR analysis (Hemani et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was conducted to investigate the presence of reverse causal relationships.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMediation MR analysis\u003c/h3\u003e\n\u003cp\u003eA two-step MR analysis (Relton and Davey Smith, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) was performed to explore the potential mediating role of inflammatory cytokines in the association between the gut microbiota and brain structural connectivity. In the first step, univariable MR (UVMR) was employed to assess the causal effect of the genetically determined gut microbiota and inflammatory cytokines (β1). The second step involved estimating the causal impact of each inflammatory cytokine as a mediator on brain structural connectivity (β2), assuming that the mediator is causally linked to the UVMR outcome. The mediation proportion of each mediator in the association between the gut microbiota and brain structural connectivity was calculated by the following formula (VanderWeele, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e):\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{\\text{M}\\text{P}}_{\\text{n}}=\\frac{{{\\beta\\:}}_{\\text{n}1}\\:\\times\\:\\:{{\\beta\\:}}_{\\text{n}2}}{{{\\beta\\:}}_{\\text{n}\\text{t}\\text{o}\\text{t}\\text{a}\\text{l}}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere β\u003csub\u003en1\u003c/sub\u003e represents the causal effect for each gut microbiota-inflammatory cytokines pair, β\u003csub\u003en2\u003c/sub\u003e represents the causal effect for each inflammatory cytokines/brain structural connectivity, β\u003csub\u003entotal\u003c/sub\u003e represents the total causal effect for each gut microbiota/brain structural connectivity pair, and MP\u003csub\u003en\u003c/sub\u003e represents the mediation proportion for each pair (Vanderweele, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Confidence intervals were estimated using the delta method (Kendall et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eEnrichment analysis\u003c/h3\u003e\n\u003cp\u003eFunctional enrichment analysis of 41 cell cycle factors was performed using the Metascape database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape.org/\u003c/span\u003e\u003cspan address=\"https://metascape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), focusing on Gene Ontology (GO), KEGG, and Reactome pathways. To visualize the results, a circular plot was employed, effectively highlighting the enriched terms. Significant terms were further subjected to hierarchical clustering based on Kappa-statistical similarities (Cohen and J., 1960) among their associated gene sets. Using a Kappa score threshold of 0.3, the hierarchical tree was partitioned into distinct term clusters, facilitating the identification of functional modules. The \u003cem\u003eP\u003c/em\u003e-value cutoff was set at 0.01 to ensure statistical significance, and the minimum enrichment threshold was defined as 1 to include all relevant terms.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMR sensitivity analysis\u003c/h2\u003e \u003cp\u003eTo assess the robustness of the results, a series of sensitivity analyses were conducted. Concisely, Heterogeneity was evaluated using Cochran's Q test (Greco et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), with a \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating the presence of heterogeneity. MR pleiotropy residual sum and outlier (MR-PRESSO) (Verbanck et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) were performed to further explore the stability of the results. When the global test \u003cem\u003eP\u003c/em\u003e-values in the MR-PRESSO analysis were less than 0.05, the estimates were adjusted for outliers. The MR-Egger intercept test and MR-PRESSO global test were utilized to detect the influence of pleiotropy on causal association estimates, with a \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating the presence of horizontal pleiotropy (Bowden et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The reliability of the association was assessed through leave-one-out analysis, funnel plots, and scatter plots. Briefly, a leave-one-out analysis (Burgess et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) was performed to ensure that no bias was caused by a specific SNP. Scatter plots demonstrate that the results are not influenced by outliers. Funnel plots (Burgess et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) are utilized to evaluate the reliability of the association. Furthermore, to minimize the risk of false positives, the Bonferroni correction was applied. This adjustment accounted for the number of bacterial taxa in the gut microbiome, setting a more stringent significance threshold: 2.37 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e (0.05/211) for forward analysis and gut microbiota to mediation in mediation analysis. Reverse causality analyses were then performed to investigate whether brain structural connectivity could influence the gut microbiota, with a Bonferroni correction of 2.43 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e (0.05/206) applied for reverse analysis. Additionally, MR analysis was conducted between mediation and brain structural connectivity, with a Bonferroni-corrected significance threshold of 0.0012 (0.05/41).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed in R software version 4.3.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The IVW, weighted median, MR-PRESSO, MR-Egger, and sensitivity analyses were conducted using the \u0026ldquo;TwoSampleMR\u0026rdquo; package (version 0.5.7) and \u0026ldquo;MR-PRESSO package (version 1.0)\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eThe causal effect of gut microbiota on brain structural connectivity\u003c/h2\u003e \u003cp\u003eA total of 11 gut microbiota were found to be significantly associated with brain structural connectivity, as determined by the Bonferroni correction with the IVW method as the primary analytical approach. Concisely, four taxa comprising order \u003cem\u003eDesulfovibrionales\u003c/em\u003e, family \u003cem\u003eDesulfovibrionaceae\u003c/em\u003e, genus \u003cem\u003eEscherichia Shigella\u003c/em\u003e, and genus \u003cem\u003eVeillonella\u003c/em\u003e were positively associated with brain structural connectivity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the order \u003cem\u003eDesulfovibrionales\u003c/em\u003e demonstrated a strong positive effect on the connectivity between the left-hemisphere somatomotor network and the right-hemisphere dorsal attention network (OR:1.17, 95%CI:1.09\u0026ndash;1.26, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.33\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;5\u003c/sup\u003e), This result was corroborated by additional MR methods (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, MR analysis indicated that five gut microbiota taxa comprising genus \u003cem\u003eSenegalimassilia\u003c/em\u003e, order \u003cem\u003eRhodospirillales\u003c/em\u003e, family \u003cem\u003eRhodospirillaceae\u003c/em\u003e, genus \u003cem\u003eRuminococcus gnavus\u003c/em\u003e, and genus \u003cem\u003eHowardella\u003c/em\u003e were associated with decreased risk development of brain structural connectivity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among these, the genus \u003cem\u003eRuminococcus gnavus\u003c/em\u003e was the most notably associated with reduced connectivity between the left-hemisphere limbic network and the right-hemisphere default mode network (OR:0.89, 95%CI:0.85\u0026ndash;0.94, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.69\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;5\u003c/sup\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Comprehensive details on the associations between gut microbiotas and brain structural connectivity are presented in \u003cb\u003eTable S4\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificant associations of gut microbiota with the brain structural connectivity: Findings from Mendelian randomization analyses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMethods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003eOrder \u003cem\u003eDesulfovibrionales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLeft-hemisphere somatomotor network to right-hemisphere dorsal attention network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.175(1.093 to 1.263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.164(1.051 to 1.290)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.199(0.989 to 1.454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.226(1.047 to 1.435)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.176(1.017 to 1.360)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLeft-hemisphere somatomotor network to right-hemisphere somatomotor network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.147(1.071 to 1.230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.78\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.110(1.006 to 1.224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.217(1.013 to 1.463)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.121(0.950 to 1.322)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.116(0.965 to 1.291)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLeft-hemisphere salience/ventral attention network to right-hemisphere control network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.141(1.064 to 1.224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.116(1.014 to 1.228)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.262(1.047 to 1.521)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.041(0.881 to 1.230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.228(1.033 to 1.459)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eOrder \u003cem\u003eRhodospirillales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLeft-hemisphere visual network to hippocampus white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.887(0.834 to 0.942)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.77\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.931(0.856 to 1.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.822(0.611 to 1.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.947(0.818 to 1.096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.952(0.830 to 1.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eFamily \u003cem\u003eDesulfovibrionaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLeft-hemisphere salience/ventral attention network to right-hemisphere control network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.154(1.070 to 1.244)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.206(1.087 to 1.339)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.246(1.029 to 1.508)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.236(1.026 to 1.490)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.233(1.052 to 1.446)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eFamily \u003cem\u003eRhodospirillaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLeft-hemisphere visual network to hippocampus white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.890(0.839 to 0.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.55\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.929(0.857 to 1.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.767(0.569 to 1.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.943(0.825 to 1.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.946(0.823 to 1.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGenus \u003cem\u003eEscherichia Shigella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLeft-hemisphere dorsal attention network to right-hemisphere limbic network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.160(1.073 to 1.253)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.133(1.019 to 1.259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.251(1.015 to 1.541)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.110(0.953 to 1.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.118(0.969 to 1.288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGenus \u003cem\u003eHowardella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLeft-hemisphere limbic network to left-hemisphere control network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.913(0.874 to 0.953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.02\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.930(0.876 to 0.987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.971(0.798 to 1.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.935(0.849 to 1.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.932(0.841 to 1.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGenus \u003cem\u003eRuminococcus gnavus group\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLeft-hemisphere limbic network to right-hemisphere default mode network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.894(0.849 to 0.941)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.69\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.884(0.824 to 0.950)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.030(0.819 to 1.296)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.887(0.788 to 0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.901(0.801 to 1.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGenus \u003cem\u003eSenegalimassilia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eRight-hemisphere somatomotor network to caudate white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.845(0.773 to 0.923)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.858(0.761 to 0.966)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.966(0.727 to 1.285)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.865(0.736 to 1.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.865(0.751 to 0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGenus \u003cem\u003eVeillonella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLeft-hemisphere dorsal attention network to right-hemisphere dorsal attention network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.171(1.082 to 1.267)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.12\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.153(1.041 to 1.277)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.064(0.575 to 1.970)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.135(0.978 to 1.318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.135(0.976 to 1.319)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: nSNP: Number of single-nucleotide polymorphisms; OR: Odds Ratio; CI: Confidence Interval; IVW: Inverse-variance weighted; Bacterial taxa at five levels (phylum, class, order, family, and genus).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eHorizontal pleiotropy between the 11 pairs of gut microbiota and brain structural connectivity was examined by MR Egger intercept and the global test of MR-PRESSO. No pleiotropy was detected for any of the gut microbiota pairs (\u003cb\u003eTable S5\u003c/b\u003e). Furthermore, Heterogeneity was assessed using Cochran's Q test, and results indicated that none of the pairs exhibited heterogeneity (\u003cb\u003eTable S5\u003c/b\u003e). Scatterplots and funnel plots for each pair of associations are given in \u003cb\u003eFigure S1-S2\u003c/b\u003e, while leave-one-out sensitivity analysis for each pair of associations is shown in \u003cb\u003eFigure S3.\u003c/b\u003e Additionally, MR-PRESSO analysis did not identify any significant outliers (global test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, \u003cb\u003eTables S5\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eThe causal effect of brain structural connectivity on gut microbiota\u003c/h2\u003e \u003cp\u003eFollowing the same analytical methods, we assessed the causal relationship between brain structural connectivity and gut microbiota. Results revealed that nine brain structure connectivity were positively associated with gut microbiota (\u003cb\u003eTable S6\u003c/b\u003e). Among these, the left-hemisphere control network to thalamus white-matter structural connectivity showed an increased abundance with three gut microbiotas, including phylum \u003cem\u003eFirmicutes\u003c/em\u003e (OR: 1.22, 95%CI: 1.10\u0026ndash;1.35, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.06\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), class \u003cem\u003eClostridia\u003c/em\u003e (OR: 1.21, 95%CI: 1.09\u0026ndash;1.34, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.04\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), and order \u003cem\u003eClostridiales\u003c/em\u003e (OR: 1.21, 95%CI: 1.09\u0026ndash;1.34, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.12\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e) (\u003cb\u003eTable S6\u003c/b\u003e). In addition, The Right-hemisphere default mode network to pallidum white-matter structural connectivity was significantly associated with genus \u003cem\u003eMethanobrevibacter\u003c/em\u003e (OR: 1.94, 95%CI: 1.36\u0026ndash;2.76, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.39\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e) (\u003cb\u003eTable S6\u003c/b\u003e). Detailed results are provided in \u003cb\u003eTable S6.\u003c/b\u003e Sensitivity analysis confirmed no heterogeneity via Cochran's Q test, and no evidence of horizontal pleiotropy was detected by MR-Egger's intercept test and MR-PRESSO global test (\u003cb\u003eTable S7\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMediatory role of cytokine in gut-brain connectivity\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eEffect of gut microbiota on inflammatory cytokines\u003c/h2\u003e \u003cp\u003eUsing Mendelian Randomization with Bonferroni correction, our study found 26 significant associations between gut microbiota and inflammatory cytokines across various taxonomic levels i.e. 1 phylum, 1 class, 3 orders, 6 families, and 15 genera. Among them, the phylum \u003cem\u003eEuryarchaeota\u003c/em\u003e was a risk factor for IL-2 (OR: 1.19, 95% CI: 1.09\u0026ndash;1.31, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.34\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e). Additionally, class \u003cem\u003eVerrucomicrobiae\u003c/em\u003e was a risk factor for PDGF-BB (OR: 1.23, 95% CI: 1.11\u0026ndash;1.37, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.09\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e). Furthermore, among the orders, two were identified as risk factors i.e., Verrucomicrobiales for PDGF-BB (OR: 1.23, 95% CI: 1.11\u0026ndash;1.37, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.09\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), and Enterobacteriales for IL-1RA (OR: 1.55, 95% CI: 1.23\u0026ndash;1.94, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.84\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), whereas Lactobacillales exhibited a protective effect for B-NGF (OR. 0.71, 95% CI: 0.60\u0026ndash;0.85, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.18\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e). In addition, four of the six family risk factors were identified, i.e., \u003cem\u003ePorphyromonadaceae\u003c/em\u003e for VEGF (OR: 1.35, 95% CI: 1.16\u0026ndash;1.58, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.08\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), \u003cem\u003eVerrucomicrobiaceae\u003c/em\u003e for PDGF-BB (OR: 1.23, 95% CI: 1.11\u0026ndash;1.37, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.10\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), \u003cem\u003eFamily XIII\u003c/em\u003e for M-CSF (OR: 1.70, 95% CI: 1.29\u0026ndash;2.23, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.37\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), \u003cem\u003eEnterobacteriaceae\u003c/em\u003e for IL-1RA (OR: 1.55, 95% CI: 1.23\u0026ndash;1.94, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.84\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e). Additionally, two families showed protective effects i.e., \u003cem\u003eDefluviitaleaceae\u003c/em\u003e for PDGF-BB (OR: 0.82, 95% CI: 0.75\u0026ndash;0.91, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.15\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10 \u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) and \u003cem\u003eOxalobacteraceae\u003c/em\u003e for MIG (OR: 0.84, 95% CI: 0.77\u0026ndash;0.92, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.30\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e). Finally, among the 15 genera, \u003cem\u003eEnterorhabdus\u003c/em\u003e was identified as the most significant risk factor for MCP-3 (OR: 1.92, 95% CI: 1.38\u0026ndash;2.67, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.05\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e). Additional risk factors included \u003cem\u003eButyricicoccus\u003c/em\u003e for TNF-B (OR: 2.23, 95% CI: 1.47\u0026ndash;3.39, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.66\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), \u003cem\u003eParaprevotella\u003c/em\u003e for TNF-B (OR. 1.38, 95% CI: 1.17\u0026ndash;1.63, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.33\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), \u003cem\u003eStreptococcus\u003c/em\u003e for IL-7 (OR: 1.36, 95% CI: 1.16\u0026ndash;1.59, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.64\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), \u003cem\u003eRuminococcaceae\u003c/em\u003e UCG002 for MIG (OR: 1.27, 95% CI: 1.13\u0026ndash;1.44, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.26\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), and \u003cem\u003eAkkermansia\u003c/em\u003e for PDGF-BB (OR: 1.23, 95% CI: 1.11\u0026ndash;1.37, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.09\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e). Moreover, Protective factors included \u003cem\u003eParasutterella\u003c/em\u003e for IL-4 (OR: 0.85, 95% CI: 0.78\u0026ndash;0.92, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.44\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), \u003cem\u003eHaemophilus\u003c/em\u003e for MCP-1/MCAF (OR: 0.84, 95% CI: 0.77\u0026ndash;0.92, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.70\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), \u003cem\u003eOscillibacter\u003c/em\u003e for TRAIL (OR: 0.83, 95% CI: 0.75\u0026ndash;0.92, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.96\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), \u003cem\u003eOxalobacter\u003c/em\u003e for IL-18 (OR: 0.83, 95% CI: 0.75\u0026ndash;0.91, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.32\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), \u003cem\u003eRuminiclostridium5\u003c/em\u003e for IL-12_P70 (OR: 0.78, 95% CI: 0.69\u0026ndash;0.89, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.83\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), \u003cem\u003eRuminococcaceae UCG013\u003c/em\u003e for MIP-1B (OR: 0.78, 95% CI: 0.68\u0026ndash;0.89, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.95\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), \u003cem\u003eRuminiclostridium9\u003c/em\u003e for MCP-1/MCAF (OR: 0.75, 95% CI: 0.65\u0026ndash;0.87, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.07\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), \u003cem\u003eRuminiclostridium6\u003c/em\u003e for B-NGF (OR: 0.74, 95% CI: 0.64\u0026ndash;0.86, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.34\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), and \u003cem\u003eEubacterium xylanophilum\u003c/em\u003e for RANTES (OR: 0.70, 95% CI: 0.59\u0026ndash;0.85, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.66\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e). All results are given in \u003cb\u003eTable S8\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEffect of inflammatory cytokines on brain structural connectivity\u003c/h2\u003e \u003cp\u003eAfter determined by the Bonferroni correction, a total of 14 significant causal associations between inflammatory cytokines and brain structural connectivity traits were identified. Among these, RANTES was identified as a risk factor for seven brain structural connectivity. The most significant association was found between left-hemisphere salience/ventral attention network and right-hemisphere visual network white-matter structural connectivity (OR: 1. 11, 95% CI: 1.07\u0026ndash;1.16, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2. 58\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;7\u003c/sup\u003e). Additional notable associations include the left-hemisphere dorsal attention network to left-hemisphere limbic network white-matter structural connectivity (OR: 1.08, 95% CI: 1.04-1. 13, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2. 14\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), the left hemisphere limbic network to hippocampal white matter structural connectivity (OR: 1. 09, 95% CI: 1. 04\u0026thinsp;\u0026minus;\u0026thinsp;1. 14, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2. 66\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), left hemisphere limbic network to caudate nucleus white matter structural connectivity (OR: 1. 07, 95% CI: 1. 03\u0026thinsp;\u0026minus;\u0026thinsp;1. 12, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5. 13\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), right hemisphere somatomotor network to right hemisphere salience/introspective attention network white matter structural connectivity (OR: 1.07, 95% CI: 1.03\u0026ndash;1.11, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8. 31\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), left hemisphere visual network to right hemisphere somatomotor network white matter structural connectivity (OR: 1.08, 95% CI: 1.03\u0026ndash;1.14, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1. 07\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;3\u003c/sup\u003e), and left hemisphere default mode network with right hemisphere somatomotor network white matter structural connectivity (OR: 1.07, 95% CI: 1.03\u0026ndash;1.11, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1. 14\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;3\u003c/sup\u003e). Additionally, HGF was associated as a protective factor for five brain structure connectivity traits, notably between right hemisphere visual network and hippocampal white matter structure connectivity (OR: 0. 90, 95% CI: 0.85\u0026ndash;0.95, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4. 23\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), left hemisphere visual network and hippocampal white matter structure connectivity (OR: 1.07, 95% CI: 1.03\u0026ndash;1.11, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1. 14\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;3\u003c/sup\u003e), left hemisphere visual network and hippocampus white matter structure connectivity (OR: 0. 90, 95% CI: 0.86\u0026ndash;0.96, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.66\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), left hemisphere limbic network to left hemisphere limbic network white matter structure connectivity (OR: 0. 91, 95% CI: 0.86\u0026ndash;0.96, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.88\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), and right hemisphere dorsal attention network to right hemisphere limbic network white matter structure connectivity (OR: 0. 91, 95% CI: 0.86\u0026ndash;0.96, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1. 09\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;3\u003c/sup\u003e). Furthermore, PDGF-BB served as a protective factor for white matter structural connectivity from the left hemisphere limbic network to the right hemisphere dorsal attention network (OR: 0.94, 95% CI: 0.91\u0026ndash;0.97, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3. 18\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e). Conversely, IL-13 was identified as a risk factor for left-hemisphere default mode network to caudate white-matter structural connectivity (OR: 1.05, 95% CI: 1.02\u0026ndash;1.07, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3. 60\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e), and IL-12_P70 was identified as a risk factor for connectivity of the right hemisphere salience/ventral attention network with white matter structures in the amygdala (OR: 1.06, 95% CI: 1.02\u0026ndash;1.09, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.11\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;3\u003c/sup\u003e). All MR results are summarized in \u003cb\u003eTable S9.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMediation analysis of gut microbiota on brain structural connectivity\u003c/h2\u003e \u003cp\u003eBy performing a MR mediation analysis, we examined the role of inflammatory cytokines as mediators between gut microbiota on brain structural connectivity and identified 10 inflammatory cytokines that mediate the causal relationship between gut microbiota and brain structural connectivity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Concisely, the results revealed that inflammatory cytokines, including RANTES, HGF, and IL-13, play a significant mediatory role in the relationship between gut microbiota and white-matter structural connectivity in different brain regions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For instance, the genus \u003cem\u003eBlautia\u003c/em\u003e significantly influenced the connectivity from the right-hemisphere somatomotor to the limbic network with HGF as the mediator, contributing a 9.54% mediation proportion (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, the Lachnospiraceae NK4A136 group was linked to the connectivity between the left-hemisphere salience/ventral attention network and the right-hemisphere visual network, mediated by RANTES with a 30.18% effect (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mediation effects varied across different brain regions, with RANTES frequently acting as the mediator, particularly in the interactions involving the left-hemisphere somatomotor network. Notably, cytokines such as TNF-β, G-CSF, and MIG were implicated in mediating the relationship between specific microbial taxa and brain structural connectivity, such as genus \u003cem\u003eRuminococcaceae UCG003\u003c/em\u003e and genus \u003cem\u003eLachnospiraceae UCG010\u003c/em\u003e, and white-matter connectivity in brain regions like the accumbens and amygdala (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The mediation effects were generally moderate, with the mediated proportion of total effects ranging from 10\u0026ndash;30% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings highlight the crucial role of inflammatory cytokines, such as RANTES, HGF, and IL-13, as intermediaries in the gut-brain axis, influencing the structural connectivity between different brain regions. Detailed results are given in \u003cb\u003eTable S10\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe mediation effect of gut microbiota on brain structural connectivity via inflammatory cytokines\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMediator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDirect effect A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDirect effect B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMediation effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMediated proportion (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ(95%CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily \u003cem\u003eLactobacillaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eG-CSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eLeft-hemisphere control network to accumbens white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e12.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.007, 0.122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.048, 0.221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.018, 0.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000, 0.016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eLachnospiraceae UCG010\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e12.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.029, 0.175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.073, -0.346)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.018, 0.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000, 0.025)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eDesulfovibrio\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e13.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.229, -0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.393, -0.135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.018, 0.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.030, -0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily \u003cem\u003eVictivallaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeft-hemisphere limbic network to left-hemisphere default mode network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e23.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.080, -0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.037, 0.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0179, -0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.019, -0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eBlautia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRight-hemisphere somatomotor network to right-hemisphere limbic network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.763, -0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.219, 0.857)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.127, -0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.076, -0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily \u003cem\u003eClostridiales vadin BB60 group\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eIL-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRight-hemisphere somatomotor network to right-hemisphere default mode network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.139, -0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.346, -0.096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.009, 0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.014, -0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eParaprevotella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeft-hemisphere somatomotor network to left-hemisphere default mode network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.012, 0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.075, 0.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.009, 0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000, 0.012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003ePhascolarctobacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeft-hemisphere salience/ventral attention network to caudate white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.020, 0.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.098, 0.431)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.009, 0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000, 0.019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eOxalobacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIL-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeft-hemisphere visual network to thalamus white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.093, -0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.282, -0.091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.010, 0.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.013, -0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eRuminiclostridium6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIP-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRight-hemisphere somatomotor network to amygdala white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e19.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.130, -0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.381, -0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.015, 0.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.025, 0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eSellimonas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRight-hemisphere default mode network to accumbens white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e13.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.026, 0.114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.082, 0.286)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.014, 0.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.001, 0.018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eRuminococcaceae UCG005\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePDGF-BB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeft-hemisphere limbic network to right-hemisphere dorsal attention network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.002, 0.139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.264, -0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.097, -0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.002, 0.019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eSellimonas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRight-hemisphere somatomotor network to accumbens white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e15.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.002, 0.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.056, 0.238)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.014, 0.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000, 0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eRuminococcaceae UCG002\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeft-hemisphere visual network to putamen white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e16.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.006, 0.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.119, 0.367)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.013, 0.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.001, 0.020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily \u003cem\u003eDefluviitaleaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003eRANTES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRight-hemisphere salience/ventral attention network to right-hemisphere control network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e18.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.129, -0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.416, -0.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.012, 0.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.026, -0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily \u003cem\u003eRhodospirillaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeft-hemisphere visual network to right-hemisphere dorsal attention network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e16.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.134, -0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.352, -0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.018, 0.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.024, \u0026minus;\u0026thinsp;0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eLachnospiraceae NK4A136 group\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeft-hemisphere default mode network to right-hemisphere visual network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.147, -0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.338, -0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.023, 0.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.025, 0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eEubacterium xylanophilum group\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeft-hemisphere somatomotor network to left-hemisphere default mode network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.222, -0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.533, -0.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.011, 0.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.034, -0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eLachnospiraceae NK4A136 group\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeft-hemisphere salience/ventral attention network to right-hemisphere visual network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e30.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.135, -0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.338, -0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.067, 0.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.0383, -0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eLachnospiraceae NK4A136 group\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeft-hemisphere somatomotor network to right-hemisphere default mode network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e13.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.150, -0.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.338, -0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.023, 0.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.024, \u0026minus;\u0026thinsp;0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eLachnospiraceae NK4A136 group\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeft-hemisphere salience/ventral attention network to right-hemisphere control network white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e18.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.124, -0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.338, -0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.023, 0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.023, 0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus \u003cem\u003eRuminococcaceae UCG003\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTNF-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRight-hemisphere somatomotor network to caudate white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e15.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.190, -0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.800, -0.122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.015, 0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.036, -0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily \u003cem\u003eVictivallaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTRAIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRight-hemisphere limbic network to amygdala white-matter structural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.004, 0.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.036, 0.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.010, 0.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000, 0.008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u0026lsquo;Total effect\u0026rsquo; indicates the effect of gut microbiota on brain structural connectivity, \u0026lsquo;Direct effect A\u0026rsquo; indicates the effect of gut microbiota on inflammatory cytokines, \u0026lsquo;Direct effect B' indicates the effect of inflammatory cytokines on brain structural connectivity and \u0026lsquo;Mediation effect\u0026rsquo; indicates the effect of gut microbiota on brain structural connectivity via inflammatory cytokines. Total effect, Direct effect A and Direct effect B were derived by IVW; Mediation effect was derived by using the delta method. All statistical tests were two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eGO, KEGG, Reactome analysis of inflammatory cytokines\u003c/h2\u003e \u003cp\u003eGO, KEGG, and Reactome pathway enrichment analyses were performed to elucidate the potential mechanisms linking gut microbiota-mediated inflammatory cytokines' effect on brain structure connectivity. GO analysis revealed significant enrichment in pathways related to cytokine activity, receptor-ligand interactions, and cytokine-mediated signaling, emphasizing the critical role of cytokine-receptor interactions in mediating downstream signaling processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). KEGG pathway analysis identified key signaling cascades, including the JAK-STAT and IL-17 pathways, highlighting their involvement in inflammatory diseases like inflammatory bowel disease (IBD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The reactome analysis further pinpointed pathways, including interleukin signaling, chemokine receptor binding, and MAPK signaling, suggesting that these inflammatory factors influence neuronal activity and connectivity through these pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). These findings underscore the pivotal role of inflammatory signaling networks in mediating the gut-brain axis and offer valuable insights into the molecular mechanisms underlying brain structural connectivity changes driven by gut microbiota dysbiosis. The complete results of GO, KEGG, and analysis are respectively presented in \u003cb\u003eTable S11\u003c/b\u003e, \u003cb\u003eTable S12\u003c/b\u003e, and \u003cb\u003eTable S13\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGiven the significant impact of the microbiota-gut-brain axis, it is crucial to systematically investigate the causal relationship between gut microbiota and brain structural connectivity. To the best of our knowledge, this is the first study to comprehensively evaluate the causal associations between gut microbiota, inflammatory cytokines, and brain structural connectivity. In this study, we employed bidirectional two-sample MR analysis to rigorously examine the causal links between gut microbiota and brain structural alterations. Additionally, our findings provide a systematic investigation of the mediating effects of inflammatory cytokines in the relationship between gut microbiota and brain structural connectivity. After applying Bonferroni correction, we identified 11 gut microbiota taxa with significant causal relationships to brain structural connectivity, highlighting their potential roles within the microbiota-gut-brain axis. This research substantially enhances our understanding of the microbiota-gut-brain axis.\u003c/p\u003e \u003cp\u003eIn previous studies, Hu et al. (Hu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Huang et al. (Huang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). performed MR analysis to investigate the effects of gut flora on subcortical brain structures and the rate of change of brain structures, respectively. Our study differs from theirs in two significant aspects. Firstly, while the studies by Hu et al. and Huang et al (Hu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) focused on specific brain structures, our study utilizes MRI-based GWAS data encompassing neural connectivity across the entire brain. MRI-based neural connectivity data provides a comprehensive view of connectivity within the whole brain network, offering a more comprehensive understanding of brain activity compared to focusing solely on subcortical structures or their rate of change. These data not only capture structural connectivity but also reflect functional connectivity, enabling a more thorough analysis of the relationship between functional and structural connectivity patterns and the interactions between gut flora and various brain regions. Secondly, our MR analysis was more comprehensive, incorporating both two-sample MR and additional sensitivity analyses, such as horizontal pleiotropy assessment and reverse MR analysis. Furthermore, we performed a mediation analysis to investigate the role of inflammatory cytokines, allowing us to systematically explore the interactions between gut flora and neural connectivity of the brain. In summary, our study offers a more detailed and thorough exploration of the relationship between gut flora and neural connectivity, employing advanced methodologies to understand the complex interactions within the brain's entire connectivity network.\u003c/p\u003e \u003cp\u003eOur findings reveal patterns and mechanisms of gut flora-induced brain alterations analyzed through a large population of GWAS data. The family \u003cem\u003eDesulfovibrionaceae\u003c/em\u003e and order \u003cem\u003eDesulfovibrionales\u003c/em\u003e, which cognate with the genus \u003cem\u003eDesulfovibrio\u003c/em\u003e, can lead to alterations in the white-matter structural connectivity responsible for connections with attentions and somatomotor (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, our study underscored the importance of mediators such as RANTES and G-CSF in modulating gut-brain interactions, emphasizing their potential to influence brain structural connectivity, especially through the genus \u003cem\u003eDesulfovibrio\u003c/em\u003e and family \u003cem\u003eDesulfovibrionaceae\u003c/em\u003e (Table\u0026nbsp;4). Notably, previous studies have reported increased levels of genus \u003cem\u003eDesulfovibrio\u003c/em\u003e in patients with Parkinson's Disease (Nie et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) compared to healthy controls, correlating positively with disease severity, suggesting a potential pathogenic role. Furthermore, significant correlations between the order \u003cem\u003eDesulfovibrionales\u003c/em\u003e and clinical stroke scales (Li, T. et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reinforce the clinical relevance of gut-brain interactions. Additionally, an inverse correlation between global brain amyloid-beta (Aβ) load and the abundance of the family \u003cem\u003eDesulfovibrionaceae\u003c/em\u003e suggests that lower bacterial levels may protect against Alzheimer's disease progression (Sheng et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Research indicates that \u003cem\u003eDesulfovibrio\u003c/em\u003e\u0026rsquo;s production of excess hydrogen sulfide in the gastrointestinal tract may induce brain damage and neurological symptoms, with elevated plasma sulfide levels being associated with brain atrophy and reduced white matter integrity, potentially leading to neurologically related diseases (Haouzi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Panthi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, G-CSF, a cytokine that plays a key role in the production and activation of granulocytes, has been shown to offer long-term neuroprotection by preventing brain atrophy and inducing somatic development, as demonstrated in numerous ischemic rodent models (Modi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). G-CSF also has potential therapeutic applications in neurodegenerative disorders such as Parkinson\u0026rsquo;s disease (PD), intracerebral hemorrhage, experimental allergic encephalomyelitis, cerebral ischemia, spinal cord injury, and Alzheimer\u0026rsquo;s disease (AD) (Rahi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The gut microbiota can activate immune cells through the gut-immune axis further linking immune modulation with changes in brain structural connectivity (Forrest et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Therefore, we hypothesize that excessive \u003cem\u003eDesulfovibrio\u003c/em\u003e in the gut contributes to the development of neurological disease through the production of hydrogen sulfide, which reduces the systemic inflammatory response and may inhibit G-CSF production in immune cells. This, in turn, could lead to neurodegenerative disorders, brain atrophy, and reduced white matter integrity (Reekes et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study also reveals that \u003cem\u003eRuminococcus gnavus\u003c/em\u003e acts as a protective factor for white matter connectivity between the limbic network and the default mode network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Abnormal connectivity between these networks can lead to impaired attention, control, and emotional regulation, potentially contributing to conditions such as attention deficit hyperactivity disorder (ADHD) (Wan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and mood disorders (Tomasi and Volkow, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). \u003cem\u003eRuminococcus gnavus\u003c/em\u003e, a member of the phylum \u003cem\u003eFirmicutes\u003c/em\u003e, is a common human gut symbiont (Crost et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, studies suggest that \u003cem\u003eRuminococcus gnavus\u003c/em\u003e alters the gut environment by reducing sialic acid derivatives, which may influence brain function via the gut-brain axis (Marizzoni et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This bacterium might also enhance synaptic plasticity, thereby improving cognitive functions such as learning and memory. Additionally, \u003cem\u003eRuminococcus gnavus\u003c/em\u003e secretes short-chain fatty acids (Crost et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which have neuroprotective effects on neurons (Hamamah et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and can reduce anxiety and depression (Feng et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Collectively, these findings highlight the multifaceted influence of \u003cem\u003eRuminococcus gnavus\u003c/em\u003e on brain function, underscoring its potential role in neurological disorders. However, the precise relationship between \u003cem\u003eRuminococcus gnavus\u003c/em\u003e and brain structural connectivity requires further investigation.\u003c/p\u003e \u003cp\u003eThrough reverse Mendelian randomization analysis, we uncovered the potential regulatory role of various brain structural connectivity in shaping gut microbiota, particularly the influence of thalamic, fornix, and amygdala white matter on enhancing the abundance of \u003cem\u003eFirmicutes\u003c/em\u003e and its class \u003cem\u003eClostridia\u003c/em\u003e. This finding broadens our understanding of the gut-brain interaction and highlights the critical role of neuro-gut pathways in regulating gut microbial composition. The thalamus, fornix white matter, and amygdala white matter play key roles in neural regulation and emotion processing. Their complex neural circuits and interactions may influence gut microbiota composition by regulating the endocrine system, autonomic nervous system, and emotional regulation functions. Previous research, including studies by Labus et al. (Labus et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), has examined the interactions among gut microbial abundance, gastrointestinal sensorimotor function, and brain functional network indicators in irritable bowel syndrome. Their findings revealed that connectivity between subcortical (thalamus, caudate nucleus, and putamen) and cortical (primary and secondary somatosensory cortex) regions mediated the subnetwork links of \u003cem\u003eClostridium\u003c/em\u003e cluster XIVa (\u003cem\u003eClostridium coccoides\u003c/em\u003e) and genus \u003cem\u003eCoprococcus\u003c/em\u003e. However, the direct impact of brain structural connectivity on gut microbiota remains unclear, necessitating further in-depth research.\u003c/p\u003e \u003cp\u003eOur enrichment analyses highlighted the pivotal role of cytokine-mediated pathways, such as JAK-STAT, IL-17 pathways, and MAPK signaling, in connecting gut microbiota dysbiosis with alterations in brain connectivity. These findings support previous research that highlights the involvement of inflammatory pathways in modulating neuroinflammatory responses and neural plasticity. For example, the JAK-STAT pathway has been shown to play a key role in microglial activation and neurodegenerative processes (Panda et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, the MAPK pathway is involved in regulating synaptic signaling and neuronal survival (Thomas and Huganir, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The enrichment of interleukin and chemokine-related pathways further suggests that inflammatory factors, particularly cytokines and chemokines, act as essential mediators in the gut-brain axis, potentially influencing neurogenesis, synaptic connectivity, and even cognitive functions.\u003c/p\u003e \u003cp\u003eWhile our study offers preliminary evidence supporting the hypothesis of brain-gut interaction, several limitations persist. Firstly, although Mendelian randomization is a powerful tool for reducing the influence of confounding variables, it cannot entirely rule out the presence of other potential confounders. Second, MR analysis helps assess causal relationships between exposure and outcome, but it cannot replace clinical trials, especially in objective domains. Therefore, further research is needed to confirm the proposed association between gut flora and neural connectivity in the brain. Third, our study has not yet explored the molecular mechanisms underlying the neural-gut interaction, which remains an important area for future research. Lastly, a deeper understanding of how genetic and environmental factors influence this interaction is essential for advancing our knowledge.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study uncovers significant causal associations between gut microbiota and brain structural connectivity, highlighting the intricate role of inflammatory cytokines in mediating these relationships. We identified 11 causal relationships between the gut microbiota and brain structural connectivity, with 10 inflammatory cytokines mediating the relationship, suggesting a complex interaction between the two. In addition, nine reverse causal relationships were identified linking brain structural connectivity to alterations in the composition of the gut microbiota. Mediation analyses revealed that RANTES receptor levels mediated up to 30.18% of the relationship between the genus \u003cem\u003eLachnospiraceae\u003c/em\u003e NK4A136 group and left-hemisphere salience/ventral attention network and the right-hemisphere visual network. These findings not only deepen our understanding of the mechanisms of the gut-brain axis, but also provide new insights into the treatment of neurological disorders and promote innovative approaches to health promotion and disease management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Special Education Development Funding of Guangdong Medical University (4SG24186G).\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eConceptualization and Design: QG, DY and ZH; Data curation and Formal Analysis: QG, DY, and JY; Visualization: QG, DY and ZT; Validation: AF and YW; Writing \u0026ndash; original draft: QG, DY, and JY; Writing \u0026ndash; review \u0026amp; editing: ZH, AF, and KZ; Supervision and Funding acquisition: ZH. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThis study analyzed publicly available datasets. The GWAS summary statistics for brain structural connectivity are accessible in the GWAS Catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Summary statistics for gut microbiota can be found at the MiBioGen consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mibiogen.gcc.rug.nl/\u003c/span\u003e\u003cspan address=\"https://mibiogen.gcc.rug.nl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GWAS summary statistical data for Inflammatory cytokines are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.bris.ac.uk/data/dataset/\u003c/span\u003e\u003cspan address=\"https://data.bris.ac.uk/data/dataset/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgirman G, Yu KB, Hsiao EY (2021) Signaling inflammation across the gut-brain axis. 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Commun Biol 7(1):990\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Guangdong Medical University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Microbiota-Gut-Brain Axis, Gut Microbiota, Brain Structural Connectivity, Mendelian Randomization, Magnetic Resonance Imaging","lastPublishedDoi":"10.21203/rs.3.rs-6197499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6197499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eGrowing evidence indicates that the imbalances in gut microbiota influence brain structural connectivity, a key component of the microbiota-gut-brain axis. However, a deeper understanding of this complex bidirectional relationship remains elusive. This study aims to deepen our understanding of this bidirectional relationship by examining the underlying causal relationship and the mediating role of inflammatory cytokines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThis study analyzed GWAS data from 18,340 participants for gut microbiota composition and MRI data from 82,382 participants for brain structural connectivity. We conducted a bidirectional two-sample Mendelian randomization (MR) to explore potential causal relationships between 211 gut microbiota taxa and 206 brain connectivity features. A two-step mediation analysis involving 41 inflammatory cytokines was performed, using the inverse variance weighted (IVW) method as the main analytical approach, supplemented by sensitivity analyses and reverse MR to check for robustness, reverse causation, heterogeneity, and horizontal pleiotropy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eAfter Bonferroni correction, MR analysis identified significant correlations between 11 pairs of gut microbiota taxa and brain connectivity traits, with 6 positive and 5 negative associations. Reverse MR confirmed positive associations in nine pairs. Sensitivity analyses found no evidence of horizontal pleiotropy, heterogeneity, or reverse causality. Inflammatory cytokines, such as RANTES, HGF, and IL-13, mediated 10–30% of these relationships, mainly through JAK-STAT, IL-17, and MAPK pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThis research establishes potential causal links between gut microbiota and brain structural connectivity, bridging a crucial gap in the microbiota-gut-brain axis research. These findings enhance our understanding of the axis and suggest new therapeutic targets for neurological disorders.\u003c/p\u003e","manuscriptTitle":"Dissecting Causal Relationships Between gut microbiota imbalance, inflammatory cytokines, and structural connectivity in the brain: A Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 04:44:57","doi":"10.21203/rs.3.rs-6197499/v1","editorialEvents":[{"type":"communityComments","content":2}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"16af76df-f6e2-4b6e-905a-b114babee8fd","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45479019,"name":"Population Genetics"},{"id":45479020,"name":"Molecular Genetics"},{"id":45479021,"name":"Molecular Epidemiology"},{"id":45479022,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2025-03-17T04:44:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-17 04:44:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6197499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6197499","identity":"rs-6197499","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}