Accurate Confidence and Bayesian Interval Estimation for Non-centrality Parameters and Effect Size Indices

Study design features increase replicability in cross-sectional and longitudinal brain-wide association studies

Brain-wide association studies (BWAS) are a fundamental tool in discovering brain-behavior associations. Several recent studies showed that thousands of study participants are required for good replicability of BWAS because the standardized effect sizes (ESs) are much smaller than the reported standardized ESs in smaller studies. Here, we perform analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional magnetic resonance imaging studies from the Lifespan Brain Chart Consortium (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized ESs and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger variability in covariate have larger reported standardized ES. In addition, the longitudinal studies we examined reported systematically larger standardized ES than cross-sectional studies. Analyzing age effects on global and regional brain measures from the United Kingdom Biobank and the Alzheimer's Disease Neuroimaging Initiative, we show that modifying longitudinal study design through sampling schemes improves the standardized ESs and replicability. Sampling schemes that improve standardized ESs and replicability include increasing between-subject age variability in the sample and adding a single additional longitudinal measurement per subject. To ensure that our results are generalizable, we further evaluate these longitudinal sampling schemes on cognitive, psychopathology, and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset. We demonstrate that commonly used longitudinal models can, counterintuitively, reduce standardized ESs and replicability. The benefit of conducting longitudinal studies depends on the strengths of the between- versus within-subject associations of the brain and non-brain measures. Explicitly modeling between- versus within-subject effects avoids averaging the effects and allows optimizing the standardized ESs for each separately. Together, these results provide guidance for study designs that improve the replicability of BWAS.

Multivariate testing and effect size measures for batch effect evaluation in radiomic features

While precision medicine applications of radiomics analysis are promising, differences in image acquisition can cause "batch effects" that reduce reproducibility and affect downstream predictive analyses. Harmonization methods such as ComBat have been developed to correct these effects, but evaluation methods for quantifying batch effects are inconsistent. In this study, we propose the use of the multivariate statistical test PERMANOVA and the Robust Effect Size Index (RESI) to better quantify and characterize batch effects in radiomics data. We evaluate these methods in both simulated and real radiomics features extracted from full-field digital mammography (FFDM) data. PERMANOVA demonstrated higher power than standard univariate statistical testing, and RESI was able to interpretably quantify the effect size of site at extremely large sample sizes. These methods show promise as more powerful and interpretable methods for the detection and quantification of batch effects in radiomics studies.

GammaGateR: semi-automated marker gating for single-cell multiplexed imaging

MOTIVATION

Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data.

RESULTS

To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation.

AVAILABILITY AND IMPLEMENTATION

The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.

Canonical babbling trajectories across the first year of life in autism and typical development

LAY ABSTRACT

Our study examined how babies develop their ability to talk to help identify early signs of autism. We looked at babies' production of babbling with mature syllables across the first year of life. Babies usually start producing mature babbling at 7 months of age before they say their first words. Some studies have suggested that babies who are later diagnosed with autism produce this kind of babbling less frequently in their first year of life, but other studies have shown complicated outcomes. In this new study, we followed 44 autistic babies and compared them to 127 typically developing babies. We recorded the babies once every month, all day long, from the time that they were born until they were around 13 months old. Then, we studied their mature babbling from segments of these recordings. We found that the rate at which babies used mature babbling was lower in boys with autism, and higher in girls with autism, compared to babies without autism. This research helps us understand how babies with autism learn to talk. It also raises important questions about differences between boys and girls with autism. Our study can help us improve how scientists and clinicians can identify autism earlier, which could lead to better communication supports for autistic children and their families.

Enhanced Patient Portal Engagement Associated with Improved Weight Loss Outcomes in Post-Bariatric Surgery Patients

Background

Bariatric surgery is an effective intervention for obesity, but it requires comprehensive postoperative self-management to achieve optimal outcomes. While patient portals are generally seen as beneficial in engaging patients in health management, the link between their use and post-bariatric surgery weight loss remains unclear.

Objective

This study investigated the association between patient portal engagement and postoperative body mass index (BMI) reduction among bariatric surgery patients.

Methods

This retrospective longitudinal study included patients who underwent Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) at Vanderbilt University Medical Center (VUMC) between January 2018 and March 2021. Using generalized estimating equations, we estimated the association between active days of postoperative patient portal use and the reduction of BMI percentage (%BMI) at 3, 6, and 12 months post-surgery. Covariates included duration since surgery, the patient's age at the time of surgery, gender, race and ethnicity, type of bariatric surgery, severity of comorbid conditions, and socioeconomic disadvantage.

Results

The study included 1,415 patients, mostly female (80.9%), with diverse racial and ethnic backgrounds. 805 (56.9%) patients underwent RYGB and 610 (43.1%) underwent SG. By one-year post-surgery, the mean (SD) %BMI reduction was 31.1% (8.3%), and the mean (SD) number of patient portal active days was 61.0 (41.2). A significantly positive association was observed between patient portal engagement and %BMI reduction, with variations revealed over time. Each 10-day increment of active portal use was associated with a 0.57% ([95% CI: 0.42- 0.72], P < .001) and 0.35% ([95% CI: 0.22- 0.49], P < .001) %BMI reduction at 3 and 6 months postoperatively. The association was not statistically significant at 12 months postoperatively (β=-0.07, [95% CI: -0.24- 0.09], P = .54). Various portal functions, including messaging, visits, my record, medical tools, billing, resources, and others, were positively associated with %BMI reduction at 3- and 6-months follow-ups.

Conclusions

Greater patient portal engagement, which may represent stronger adherence to postoperative instructions, better self-management of health, and enhanced communication with care teams, was associated with improved postoperative weight loss. Future investigations are needed to identify important portal features that contribute to the long-term success of weight loss management.

GammaGateR: semi-automated marker gating for single-cell multiplexed imaging

Motivation

Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data.

Results

To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation.

Availability and Implementation

The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.