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Microbiome data are subject to experimental bias that is caused by DNA extraction, PCR amplification among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis and Callahan (2019) proposed a model for how such bias affects the observed taxonomic profiles, which assumes main effects of bias without taxon-taxon interactions. Our newly developed method, LOCOM (logistic regression for compositional analysis) for testing differential abundance of taxa, is the first method that accounted for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicated that LOCOM remained robust to a reasonable range of interaction biases. The other methods tended to have inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods cannot control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

Recent advancements of multimodal neuroimaging such as functional MRI (fMRI) and diffusion MRI (dMRI) offers unprecedented opportunities to understand brain development. Most existing neurodevelopmental studies focus on using a single imaging modality to study microstructure or neural activations in localized brain regions. The developmental changes of brain network architecture in childhood and adolescence are not well understood. Our study made use of dMRI and resting-state fMRI imaging data sets from Philadelphia Neurodevelopmental Cohort (PNC) study to characterize developmental changes in both structural as well as functional brain connectomes. A multimodal multilevel model (MMM) is developed and implemented in PNC study to investigate brain maturation in both white matter structural connection and intrinsic functional connection. MMM addresses several major challenges in multimodal connectivity analysis. First, by using a first-level data generative model for observed measures and a second-level latent network modeling, MMM effectively infers underlying connection states from noisy imaging-based connectivity measurements. Secondly, MMM models the interplay between the structural and functional connections to capture the relationship between different brain connectomes. Thirdly, MMM incorporates covariate effects in the network modeling to investigate network heterogeneity across subpopoulations. Finally, by using a module-wise parameterization based on brain network topology, MMM is scalable to whole-brain connectomics. MMM analysis of the PNC study generates new insights in neurodevelopment during adolescence including revealing the majority of the white fiber connectivity growth are related to the cognitive networks where the most significant increase is found between the default mode and the executive control network with a 15% increase in the probability of structural connections. We also uncover functional connectome development mainly derived from global functional integration rather than direct anatomical connections. To the best of our knowledge, these findings have not been reported in the literature using multimodal connectomics. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Background: Chronic kidney disease (CKD) is characterized by overactivation of the sympathetic nervous system (SNS) that leads to increased cardiovascular disease risk. Despite the deleterious consequences of SNS overactivity, there are very few therapeutic options available to combat sympathetic overactivity., Aim: To evaluate the effects of Mindfulness-Based Stress Reduction (MBSR) on SNS activity in CKD patients., Method: Participants with CKD stages III-IV were randomized to an 8-week MBSR program or Health Education Program (HEP; a structurally parallel, active control group). Primary outcomes were direct intraneural measures of SNS activity directed to muscle (MSNA) via microneurography at rest and during stress maneuvers., Results: 28 participants (63 ±9 years; 86% males) completed the intervention with 16 in MBSR and 12 in HEP. There was a significant Group (MBSR vs. HEP) by Time (baseline vs. post-intervention) interaction in the change in MSNA reactivity to mental stress (p=0.026), with a significant reduction in the mean change in MSNA over 3 minutes of mental arithmetic at post-intervention (10.6 ± 7.1 to 5.0 ± 5.7 bursts/min, p<0.001), while no change was observed within the HEP group (p=0.773)., Conclusions: In this randomized controlled trial, patients with CKD had an amelioration of sympathetic reactivity during mental stress following 8-weeks of MBSR but not after HEP. Our findings demonstrate that mindfulness training is feasible and may have clinically beneficial effects on autonomic function in CKD., Competing Interests: Conflict-of-Interest: The authors have declared that no conflict of interest exists. Conflict of Interest Disclosures: None reported.

Microbiome data are subject to experimental bias that is caused by DNA extraction, PCR amplification among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis and Callahan (2019) proposed a model for how such bias affects the observed taxonomic profiles, which assumes main effects of bias without taxon-taxon interactions. Our newly developed method, LOCOM (logistic regression for compositional analysis) for testing differential abundance of taxa, is the first method that accounted for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicated that LOCOM remained robust to a reasonable range of interaction biases. The other methods tended to have inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods cannot control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.