Dose of Product or Product Concentration: A Comparison of Change in Heart Rate by THC Concentration for Participants Using Cannabis Daily and Occasionally.

Introduction: Studies show that acute cannabis use significantly increases heart rate (HR) and mildly raises blood pressure in the minutes following smoked or inhaled use of cannabis. However, less is known about how the THC concentration of the product or an individual's frequency of use (i.e., tolerance) may affect the magnitude of the change in HR. It is also relatively unexamined how the physical effects of increased HR after acute cannabis use relate to self-reported drug effects or blood THC levels. Aims: To describe the relationship between THC concentration of product used, self-reported subjective intoxication, THC blood levels, and frequency of cannabis use with the change in HR after acute cannabis use. Materials and Methods: Participants (n = 140) were given 15 min to smoke self-supplied cannabis ad libitum, HR was measured at baseline and an average of 2 min post-cannabis smoking. The ARCI-Marijuana scale and Visual Analog Scales (VAS) were administered, and blood samples were taken at both time points. Participants were asked about their frequency of use. Information about the product used was recorded from the package. Linear regression was used to analyze the relationship between changes in HR (post-pre cannabis use) and post-cannabis use HR, blood THC concentration, THC product concentration, frequency of use, and self-reported drug effect. Results: There was a significantly higher HR among those who smoked cannabis compared to the controls (p < 0.001), which did not significantly differ by frequency of use (p = 0.18). Higher concentration THC (extract) products did not produce a significantly different HR than lower concentration (flower) products (p = 0.096). VAS score was associated with an increase in HR (p < 0.05). Overall, blood THC levels were not significantly related to the change in HR (p = 0.69); however, when probed, there was a slight positive association among the occasional use group only. Discussion: Cardiovascular effects of cannabis consumption may not be as subject to tolerance with daily cannabis use and do not directly increase with THC concentration of the product. This is a departure from other effects (i.e., cognitive, subjective drug effects) where tolerance is well established. These findings also suggest that, at least among those with daily use, higher concentration THC products (>60%) do not necessarily produce cardiovascular physiological effects that are significantly more robust than lower concentration (<20%) products.

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.

A Study of Pupil Response to Light as a Digital Biomarker of Recent Cannabis Use.

Introduction: Given the traffic safety and occupational injury prevention implications associated with cannabis impairment, there is a need for objective and validated measures of recent cannabis use. Pupillary light response may offer an approach for detection. Method: Eighty-four participants (mean age: 32, 42% female) with daily, occasional, and no-use cannabis use histories participated in pupillary light response tests before and after smoking cannabis ad libitum or relaxing for 15 min (no use). The impact of recent cannabis consumption on trajectories of the pupillary light response was modeled using functional data analysis tools. Logistic regression models for detecting recent cannabis use were compared, and average pupil trajectories across cannabis use groups and times since light test administration were estimated. Results: Models revealed small, significant differences in pupil response to light after cannabis use comparing the occasional use group to the no-use control group, and similar statistically significant differences in pupil response patterns comparing the daily use group to the no-use comparison group. Trajectories of pupillary light response estimated using functional data analysis found that acute cannabis smoking was associated with less initial and sustained pupil constriction compared to no cannabis smoking. Conclusion: These analyses show the promise of pairing pupillary light response and functional data analysis methods to assess recent cannabis use.

Effects of acute cannabis inhalation on reaction time, decision-making, and memory using a tablet-based application.

BACKGROUND: Acute cannabis use has been demonstrated to slow reaction time and affect decision-making and short-term memory. These effects may have utility in identifying impairment associated with recent use. However, these effects have not been widely investigated among individuals with a pattern of daily use, who may have acquired tolerance. The purpose of this study was to examine the impact of tolerance to cannabis on the acute effects as measured by reaction time, decision-making (gap acceptance), and short-term memory. METHODS: Participants (ages 25-45) completed a tablet-based (iPad) test battery before and approximately 60 min after smoking cannabis flower. The change in performance from before to after cannabis use was compared across three groups of cannabis users: (1) occasional use (n = 23); (2) daily use (n = 31); or (3) no current use (n = 32). Participants in the occasional and daily use group self-administered ad libitum, by smoking or vaping, self-supplied cannabis flower with a high concentration of total THC (15-30%). RESULTS: The occasional use group exhibited decrements in reaction time (slowed) and short-term memory (replicated fewer shapes) from before to after cannabis use, as compared to the no-use group. In the gap acceptance task, daily use participants took more time to complete the task post-smoking cannabis as compared to those with no use or occasional use; however, the level of accuracy did not significantly change. CONCLUSIONS: The findings are consistent with acquired tolerance to certain acute psychomotor effects with daily cannabis use. The finding from the gap acceptance task which showed a decline in speed but not accuracy may indicate a prioritization of accuracy over response time. Cognitive and psychomotor assessments may have utility for identifying impairment associated with recent cannabis use.

Accuracy and replicability of identifying eyelid tremor as an indicator of recent cannabis smoking.

INTRODUCTION: Cannabis intoxication may increase the risk of motor vehicle crashes. However, reliable methods of assessing cannabis intoxication are limited. The presence of eyelid tremors is among the signs of cannabis use identified under the Drug Evaluation and Classification Program of the International Association of Chiefs of Police. Our objectives were to assess the accuracy and replicability of identifying eyelid tremor as an indicator of recent cannabis smoking using a blinded, controlled study design. METHODS: Adult subjects (N = 103) were recruited into three groups based on their cannabis use history: daily, occasional, and no current cannabis use. Participants' closed eyelids were video recorded for 30 seconds by infrared videography goggles before and at a mean ± standard deviation time of 71.4 ± 4.6 minutes after the onset of a 15-minute interval of ad libitum cannabis flower smoking or vaping. Three observers with expertise in neuro-ophthalmology and medical toxicology were trained on exemplar videos of eyelids to reach a consensus on how to grade eyelid tremor. Without knowledge of subjects' cannabis use history or time point (pre- or post-smoking), observers reviewed each video for eyelid tremor graded as absent, slight, moderate, or severe. During subsequent data analysis, this score was further dichotomized as a consensus score of absent (absent/slight) or present (moderate/severe). RESULTS: Kappa and intraclass correlation coefficient statistics demonstrated moderate agreement among the coders, which ranged from 0.44-0.45 and 0.58-0.61, respectively. There was no significant association between recent cannabis use and the observers' consensus assessment that eyelid tremor was present, and cannabis users were less likely to have tremors (odds ratio: 0.75; 95 percent confidence interval: 0.25, 2.40). The assessment of eyelid tremor as an indicator of recent cannabis smoking had a sensitivity of 0.86, specificity of 0.18, and accuracy of 0.64. DISCUSSION: Eyelid tremor has fair sensitivity but poor specificity and accuracy for identification of recent cannabis use. Inter-rater reliability for assessment of eyelid tremor was moderate for the presence and degree of tremor. The weak association between recent cannabis use and eyelid tremor does not support its utility in identifying recent cannabis use. LIMITATIONS: Videos were recorded at only one time point after cannabis use. Adherence to abstinence could not be strictly supervised. Due to regulatory restrictions, we were unable to control the cannabis product used or administer a fixed Δ9-tetrahydrocannabinol dose. Participants were predominately non-Hispanic and White. CONCLUSIONS: In a cohort of participants with a range of cannabis use histories, acute cannabis smoking was not associated with the presence of eyelid tremor, regardless of cannabis use history, at 70 minutes post-smoking. Additional research is needed to identify the presence of eyelid tremor accurately, determine the relationship between cannabis dose and timeline in relation to last cannabis use to eyelid tremor, and determine how it should be, if at all, utilized for cannabis Drug Recognition Evaluator examinations.

Neural signatures of emotion regulation.

Emotional experience is central to a fulfilling life. Although exposure to negative experiences is inevitable, an individual's emotion regulation response may buffer against psychopathology. Identification of neural activation patterns associated with emotion regulation via an fMRI task is a promising and non-invasive means of furthering our understanding of the how the brain engages with negative experiences. Prior work has applied multivariate pattern analysis to identify signatures of response to negative emotion-inducing images; we adapt these techniques to establish novel neural signatures associated with conscious efforts to modulate emotional response. We model voxel-level activation via LASSO principal components regression and linear discriminant analysis to predict if a subject was engaged in emotion regulation and to identify brain regions which define this emotion regulation signature. We train our models using 82 participants and evaluate them on a holdout sample of 40 participants, demonstrating an accuracy up to 82.5% across three classes. Our results suggest that emotion regulation produces a unique signature that is differentiable from passive viewing of negative and neutral imagery.

Regression and Alignment for Functional Data and Network Topology.

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of pre-processing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

Statistical analysis of single-cell protein data.

Immune modulation is considered a hallmark of cancer initiation and progression, with immune cell density being consistently associated with clinical outcomes of individuals with cancer. Multiplex immunofluorescence (mIF) microscopy combined with automated image analysis is a novel and increasingly used technique that allows for the assessment and visualization of the tumor microenvironment (TME). Recently, application of this new technology to tissue microarrays (TMAs) or whole tissue sections from large cancer studies has been used to characterize different cell populations in the TME with enhanced reproducibility and accuracy. Generally, mIF data has been used to examine the presence and abundance of immune cells in the tumor and stroma compartments; however, this aggregate measure assumes uniform patterns of immune cells throughout the TME and overlooks spatial heterogeneity. Recently, the spatial contexture of the TME has been explored with a variety of statistical methods. In this PSB workshop, speakers will present some of the state-of-the-art statistical methods for assessing the TIME from mIF data.

Troponin and a Myopathy-Linked Mutation in TPM3 Inhibit Cofilin-2-Induced Thin Filament Depolymerization.

Uniform actin filament length is required for synchronized contraction of skeletal muscle. In myopathies linked to mutations in tropomyosin (Tpm) genes, irregular thin filaments are a common feature, which may result from defects in length maintenance mechanisms. The current work investigated the effects of the myopathy-causing p.R91C variant in Tpm3.12, a tropomyosin isoform expressed in slow-twitch muscle fibers, on the regulation of actin severing and depolymerization by cofilin-2. The affinity of cofilin-2 for F-actin was not significantly changed by either Tpm3.12 or Tpm3.12-R91C, though it increased two-fold in the presence of troponin (without Ca2+). Saturation of the filament with cofilin-2 removed both Tpm variants from the filament, although Tpm3.12-R91C was more resistant. In the presence of troponin (±Ca2+), Tpm remained on the filament, even at high cofilin-2 concentrations. Both Tpm3.12 variants inhibited filament severing and depolymerization by cofilin-2. However, the inhibition was more efficient in the presence of Tpm3.12-R91C, indicating that the pathogenic variant impaired cofilin-2-dependent actin filament turnover. Troponin (±Ca2+) further inhibited but did not completely stop cofilin-2-dependent actin severing and depolymerization.

A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.

OBJECTIVE: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). METHODS: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. RESULTS: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. CONCLUSION: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

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.

A platform-independent framework for phenotyping of multiplex tissue imaging data.

Multiplex imaging is a powerful tool to analyze the structural and functional states of cells in their morphological and pathological contexts. However, hypothesis testing with multiplex imaging data is a challenging task due to the extent and complexity of the information obtained. Various computational pipelines have been developed and validated to extract knowledge from specific imaging platforms. A common problem with customized pipelines is their reduced applicability across different imaging platforms: Every multiplex imaging technique exhibits platform-specific characteristics in terms of signal-to-noise ratio and acquisition artifacts that need to be accounted for to yield reliable and reproducible results. We propose a pixel classifier-based image preprocessing step that aims to minimize platform-dependency for all multiplex image analysis pipelines. Signal detection and noise reduction as well as artifact removal can be posed as a pixel classification problem in which all pixels in multiplex images can be assigned to two general classes of either I) signal of interest or II) artifacts and noise. The resulting feature representation maps contain pixel-scale representations of the input data, but exhibit significantly increased signal-to-noise ratios with normalized pixel values as output data. We demonstrate the validity of our proposed image preprocessing approach by comparing the results of two well-accepted and widely-used image analysis pipelines.

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures.

Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular patterns by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering the spatial contribution. As such, none of the existing approaches has fully accounted for the simultaneous heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular organizations. Functional principal component analysis (FPCA) is applied to estimate FPC scores which are then served as predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, potentially adjusting for other confounders. Using a non-small cell lung cancer dataset (n = 153) as a case study, we found that the spatial heterogeneity in the TME cellular composition of CD14+ cells, CD19+ B cells, CD4+ and CD8+ T cells, and CK+ tumor cells, had a significant non-zero effect on the overall survival (p = 0.027). Furthermore, using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset (n = 33), our proposed method identified a significant impact of cellular interactions between tumor and immune cells on the overall survival (p = 0.046). In simulation studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.

A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimation.

Objective: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). Methods: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. Results: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83% ± 27%. Conclusion: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

A Latent Variable Analysis of Psychomotor and Neurocognitive Performance After Acute Cannabis Smoking.

Objective: This paper evaluated a novel, tablet-based neurocognitive and psychomotor test battery for detecting impairment from acute cannabis smoking using advanced quantitative methods. The study was conducted in a state with legal, recreational cannabis use and included participants who use cannabis occasionally or daily, and a no use comparison group. Methods: Participants completed a tablet-based test assessing reaction time, decision making, working memory and spatial-motor performance. The test was completed before and after participants smoked cannabis (or after a rest period in the case of controls). An Exploratory Factor Analysis approach was implemented to reduce dimensionality and evaluate correlations across the four assessed domains. Linear regression models were utilized to quantify associations between factor scores and cannabis use groups (daily vs. occasional vs. no use). Results: Seven factors were identified explaining 56.7% of the variance among the 18 measures. Regression models of the change in factors after cannabis smoking indicated those who use cannabis daily demonstrated poorer performance on a latent factor termed Displaced and Delayed (standardized coefficient 0.567, 95% CI: 0.178, 0.955; P = 0.005) compared to those with no use. Those who use cannabis occasionally exhibited a decline in performance on a latent factor termed Recall and Reaction (standardized coefficient 0.714, 95% CI: 0.092, 1.336; P = 0.025) compared to no use. Conclusions: This analysis demonstrates an innovative, quantitative approach to study how cannabis consumption affects neurocognitive and psychomotor performance. Results demonstrated that acute cannabis use is associated with changes in neurocognitive and psychomotor performance, with differences based on the pattern of occasional or daily use.

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data.

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.

A Video Segmentation Pipeline for Assessing changes in Pupil Response to Light After Cannabis Consumption.

Due to long-standing federal restrictions on cannabis-related research, the implications of cannabis legalization on traffic and occupational safety are understudied. Accordingly, there is a need for objective and validated measures of acute cannabis impairment that may be applied in public safety and occupational settings. Pupillary response to light may offer an avenue for detection that outperforms typical sobriety tests and THC concentrations. We developed a video processing and analysis pipeline that extracts pupil sizes during a light stimulus test administered with goggles utilizing infrared videography. The analysis compared pupil size trajectories in response to a light for those with occasional, daily, and no cannabis use before and after smoking. Pupils were segmented using a combination of image pre-processing techniques and segmentation algorithms which were validated using manually segmented data and found to achieve 99% precision and 94% F-score. Features extracted from the pupil size trajectories captured pupil constriction and rebound dilation and were analyzed using generalized estimating equations. We find that acute cannabis use results in less pupil constriction and slower pupil rebound dilation in the light stimulus test.

Pattern-based clustering of daily weigh-in trajectories using dynamic time warping.

"Smart"-scales are a new tool for frequent monitoring of weight change as well as weigh-in behavior. These scales give researchers the opportunity to discover patterns in the frequency that individuals weigh themselves over time, and how these patterns are associated with overall weight loss. Our motivating data come from an 18-month behavioral weight loss study of 55 adults classified as overweight or obese who were instructed to weigh themselves daily. Adherence to daily weigh-in routines produces a binary times series for each subject, indicating whether a participant weighed in on a given day. To characterize weigh-in by time-invariant patterns rather than overall adherence, we propose using hierarchical clustering with dynamic time warping (DTW). We perform an extensive simulation study to evaluate the performance of DTW compared to Euclidean and Jaccard distances to recover underlying patterns in adherence time series. In addition, we compare cluster performance using cluster validation indices (CVIs) under the single, average, complete, and Ward linkages and evaluate how internal and external CVIs compare for clustering binary time series. We apply conclusions from the simulation to cluster our real data and summarize observed weigh-in patterns. Our analysis finds that the adherence trajectory pattern is significantly associated with weight loss.

mxnorm: An R Package to Normalize Multiplexed Imaging Data.

Multiplexed imaging is an emerging single-cell assay that can be used to understand and analyze complex processes in tissue-based cancers, autoimmune disorders, and more. These imaging technologies, which include co-detection by indexing (CODEX), multiplexed ion beam imaging (MIBI), and multiplexed immunofluorescence imaging (MxIF), provide detailed information about spatial interactions between cells (Angelo et al., 2014; Gerdes et al., 2013; Goltsev et al., 2018). Multiplexed imaging experiments generate data across hundreds of slides and images, often resulting in terabytes of complex data to analyze through imaging analysis pipelines. Methods are rapidly developing to improve particular parts of the pipeline, including software packages in R and Python like spatialTime, imcRtools, MCMICR0, and Squidpy (Creed et al., 2021; Palla et al., 2021; Schapiro et al., 2021; Windhager et al., 2021). An important, but understudied component of this pipeline is the analysis of technical variation within this complex data source - intensity normalization is one way to remove this technical variability. The combination of disparate pre-processing pipelines, imaging variables, optical effects, and within-slide dependencies create batch and slide effects that can be reduced via normalization methods. Current state-of-the-art methods vary heavily across research labs and image acquisition platforms, without one singular method that is uniformly robust - optimal statistical methods seek to improve similarity across images and slides by removing this technical variability while maintaining the underlying biological signal in the data. mxnorm is open-source software built with R and S3 methods that implements, evaluates, and visualizes normalization techniques for multiplexed imaging data. Extending methodology described in Harris et al. (2022), we intend to set a foundation for the evaluation of multiplexed imaging normalization methods in R. This easily allows users to extend normalization methods into the field, and provides a robust evaluation framework to measure both technical variability and the efficacy of various normalization methods. One key component of the R package is the ability to supply user-defined normalization methods and thresholding algorithms to assess normalization in multiplexed imaging data. Core features, usage details, and extensive tutorials are available in the package documentation and vignette on CRAN and the software repository.

Registration of 24-hour accelerometric rest-activity profiles and its application to human chronotypes.

By collecting data continuously over 24 hours, accelerometers and other wearable devices can provide novel insights into circadian rhythms and their relationship to human health. Existing approaches for analyzing diurnal patterns using these data, including the cosinor model and functional principal components analysis, have revealed and quantified population-level diurnal patterns, but considerable subject-level variability remained uncaptured in features such as wake/sleep times and activity intensity. This remaining informative variability could provide a better understanding of chronotypes, or behavioral manifestations of one's underlying 24-hour rhythm. Curve registration, or alignment, is a technique in functional data analysis that separates "vertical" variability in activity intensity from "horizontal" variability in time-dependent markers like wake and sleep times; this data-driven approach is well-suited to studying chronotypes using accelerometer data. We develop a parametric registration framework for 24-hour accelerometric rest-activity profiles represented as dichotomized into epoch-level states of activity or rest. Specifically, we estimate subject-specific piecewise linear time-warping functions parametrized with a small set of parameters. We apply this method to data from the Baltimore Longitudinal Study of Aging and illustrate how estimated parameters give a more flexible quantification of chronotypes compared to traditional approaches.