Biology subjects

Stoddard, J.

Publications and source records attributed to Stoddard, J..

3 recordsLinked to original sources

bioRxiv2021

Cell Stiffening Contributes to Complement-mediated Injury of Choroidal Endothelial Cells in Early AMD

Age-related macular degeneration (AMD) is the leading cause of blindness in the aging population. Yet, no therapies exist for [~]85% of all AMD patients who have the dry form that is marked by degeneration of the retinal pigment epithelium (RPE) and underlying choroidal vasculature. As the choroidal vessels are crucial for RPE development and maintenance, understanding how they degenerate may lead to effective therapies for dry AMD. One likely causative factor for choroidal vascular loss is the cytolytic membrane attack complex (MAC) of the complement pathway that is abundant on choroidal vessels of humans with early dry AMD. To examine this possibility, we studied the effect of complement activation on choroidal endothelial cells (ECs) isolated from a rhesus monkey model of early AMD that, we report, exhibits MAC deposition and choriocapillaris endothelial loss similar to that seen in human early AMD. Treatment of choroidal ECs from AMD eyes with complement-competent normal human serum caused extensive actin cytoskeletal injury that was significantly less pronounced in choroidal ECs from young normal monkey eyes. We further show that ECs from AMD eyes are significantly stiffer than their younger counterparts and exhibit peripheral actin organization that is distinct from the longitudinal stress fibers in young ECs. Finally, these differences in complement susceptibility and mechanostructural properties were found to be regulated by the differential activity of small GTPases Rac and Rho because Rac inhibition in AMD cells led to simultaneous reduction in stiffness and complement susceptibility while Rho inhibition in young cells exacerbated complement injury. Thus, by identifying cell stiffness and cytoskeletal regulators Rac and Rho as important determinants of complement susceptibility, the current findings offer a new mechanistic insight into choroidal vascular loss in early AMD that warrants further investigation for assessment of translational potential.

pathology↗

bioRxiv2021

Dichotomous thinking and informational waste in neuroimaging

Neuroimaging relies on separate statistical inferences at tens of thousands of spatial locations. Such massively univariate analysis typically requires an adjustment for multiple testing in an attempt to maintain the family-wise error rate at a nominal level of 5%. First, we examine three sources of substantial information loss that are associated with the common practice under the massively univariate framework: (a) the hierarchical data structures (spatial units and trials) are not well maintained in the modeling process; (b) the adjustment for multiple testing leads to an artificial step of strict thresholding; (c) information is excessively reduced during both modeling and result reporting. These sources of information loss have far-reaching impacts on result interpretability as well as reproducibility in neuroimaging. Second, to improve inference efficiency, predictive accuracy, and generalizability, we propose a Bayesian multilevel modeling framework that closely characterizes the data hierarchies across spatial units and experimental trials. Rather than analyzing the data in a way that first creates multiplicity and then resorts to a post hoc solution to address them, we suggest directly incorporating the cross-space information into one single model under the Bayesian framework (so there is no multiplicity issue). Third, regardless of the modeling framework one adopts, we make four actionable suggestions to alleviate information waste and to improve reproducibility: 1) abandon strict dichotomization, 2) report full results, 3) quantify effects, and 4) model data hierarchies. We provide examples for all of these points using both demo and real studies, including the recent NARPS investigation.

neuroscience↗

bioRxiv2017

Intraclass correlation: improved modeling approaches and applications for neuroimaging

Intraclass correlation (ICC) is a reliability metric that gauges similarity when, for example, entities are measured under similar, or even the same, well-controlled conditions, which in MRI applications include runs/sessions, twins, parent/child, scanners, sites, etc. The popular definition and interpretation of ICC are usually framed statistically under the conventional ANOVA platform. Here, we show that the ANOVA frame-work is often limited, rigid, and inflexible in modeling capabilities. Additionally we provide a comprehensive overview of ICC analysis in its prior usage in neuroimaging. These intrinsic limitations motivate several novel improvements. Specifically, we start with the conventional ICC model under the ANOVA platform, and extend it along two dimensions: first, fixing the failure in ICC estimation when negative values occur under degen-erative circumstance, and second, incorporating reliability information of effect estimates into the ICC model. These endeavors lead to four modeling strategies: linear mixed-effects (LME), Bayesian mixed-effects (BME), multilevel mixed-effects (MME), and Bayesian multilevel mixed-effects (BMME). Compared to ANOVA, each of these four models directly provides estimates for fixed effects as well as their statistical significances, in addition to the ICC estimate. These new modeling approaches can also accommodate missing data as well as fixed effects for confounding variables. More importantly, we show that the novel MME and BMME approaches offer more accurate characterization and decomposition among the variance components, leading to more robust ICC computation. Based on these theoretical considerations and model performance comparisons with a real experimental dataset, we offer the following general-purpose recommendations. First, ICC estimation through MME or BMME is preferable when precision information is available for the effect estimate; precision information provides weights that more accurately allocate the variances in the data. When precision information is unavailable, ICC estimation through LME or the novel BME is the preferred option. Second, even though the absolute agreement version, ICC(2,1), is presently more popular in the field, the consistency version, ICC(3,1), is a practical and informative choice for whole-brain ICC analysis that achieves a well-balanced compromise when all potential fixed effects are accounted for. Third, approaches for clear, meaningful, and useful result reporting in ICC analysis are discussed. All models, ICC formulations, and related statistical testing methods have been implemented in an open source program 3dICC, which is publicly available as part of the AFNI suite. Even though our work here focuses on the whole brain level, the modeling strategy and recommendations can be equivalently applied to other situations such as voxel, region, and network levels.

neuroscience↗