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.

Network enrichment significance testing in brain-phenotype association studies.

Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about spatial properties of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genetics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose network enrichment significance testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study enrichment of associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.

Use of ECT for Children With and Without Catatonia: A Single-Site Retrospective Analysis.

OBJECTIVES: The objective study was to investigate the safety and efficacy of electroconvulsive therapy (ECT) in a retrospective cohort of pediatric patients. METHODS: A single-site retrospective analysis was conducted of patients aged younger than 18 years who received ECT in a private university hospital from January 28, 2012 to April 8, 2023. Treatment efficacy and adverse events were determined retrospectively through review of the medical record. RESULTS: A total of 36 pediatric patients met the inclusion criteria. Catatonia was the most common presenting indication for ECT, followed by psychosis and suicidal ideation. For all patients, Clinical Global Impressions-Improvement scale scores indicated that it was very likely for a subject to experience at least "much improvement" with the estimated probability of receiving a Clinical Global Impressions-Improvement scale score better than 3 of 0.852 (t.s. = 16.3; P < 0.001; 95% confidence interval, 0.711-0.931). All patients with catatonia demonstrated a positive clinical response and experienced a statistically significant reduction in total Bush-Francis Catatonia Rating Scale scores observed (t = 11.9; df = 20; SD = 6.3; P < 0.001; 95% confidence interval, 12.6-17.9). No significant adverse events were reported for any patient in the cohort. However, 14 (38.9%) patients experienced prolonged seizures, all of which were terminated with propofol (mean, 49.7 mg). CONCLUSIONS: This study provides further data supporting the safe and effective use of pediatric ECT in the treatment of various psychiatric conditions. However, more research is needed to determine the risk factors associated with prolonged seizures and the optimal seizure parameters in young people.

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.

Spatial nonstationarity of image noise in widefield optical imaging and its effects on cluster-based inference for resting-state functional connectivity.

BACKGROUND: Resting-state functional connectivity (RSFC) analysis with widefield optical imaging (WOI) is a potentially powerful tool to develop imaging biomarkers in mouse models of disease before translating them to human neuroimaging with functional magnetic resonance imaging (fMRI). The delineation of such biomarkers depends on rigorous statistical analysis. However, statistical understanding of WOI data is limited. In particular, cluster-based analysis of neuroimaging data depends on assumptions of spatial stationarity (i.e., that the distribution of cluster sizes under the null is equal at all brain locations). Whether actual data deviate from this assumption has not previously been examined in WOI. NEW METHOD: In this manuscript, we characterize the effects of spatial nonstationarity in WOI RSFC data and adapt a "two-pass" technique from fMRI to correct cluster sizes and mitigate spatial bias, both parametrically and nonparametrically. These methods are tested on multi-institutional data. RESULTS AND COMPARISON WITH EXISTING METHODS: We find that spatial nonstationarity has a substantial effect on inference in WOI RSFC data with false positives much more likely at some brain regions than others. This pattern of bias varies between imaging systems, contrasts, and mouse ages, all of which could affect experimental reproducibility if not accounted for. CONCLUSIONS: Both parametric and nonparametric corrections for nonstationarity result in significant improvements in spatial bias. The proposed methods are simple to implement and will improve the robustness of inference in optical neuroimaging data.

Trajectory of depressive symptoms over adolescence in autistic and neurotypical youth.

BACKGROUND: Adolescence coincides with a dramatic rise in the onset of psychiatric conditions including depression. Depression symptoms may be particularly prevalent and impairing for youth with autism spectrum disorder (ASD). While prior research suggests adolescence is associated with worsening depression symptoms for typically developing (TD) and autistic youth, it is unclear if they follow a similar course. METHOD: The study examined the trajectory of depressive symptoms in autistic and neurotypical youth over a 4-year longitudinal study using linear and logistic mixed effects models. In youth with clinically relevant depressive scores (t-score > 65), moderating factors (i.e., diagnosis, age, puberty, sex) were explored. During Year 1, the sample included 244 youth 10-to-13 years: 140 in the ASD group (36 females) and 104 in the TD group (46 females). RESULTS: Autistic youth had elevated depression scores compared to TD peers (p < 0.001) and females were higher than males in both groups (p = 0.001). There was significant diagnosis by age (p < 0.001) and diagnosis by pubertal stage (p < 0.05) interactions. In the ASD group, elevated depressive scores presented in early adolescence and decreased during middle adolescence and puberty, whereas the TD group showed the opposite trend with an increase in depression symptoms with advancing development. LIMITATIONS: Limitations include an unequal sex distribution (fewer females), non-representative autistic sample (e.g., cognition and race/ethnicity), and potential confound of the COVID-19 pandemic. CONCLUSIONS: Autistic youth present with higher rates of depressive symptoms early in development; yet, approaching middle adolescence and puberty, the symptom trajectory in the autistic youth declines coinciding with an increase in the TD youth. While group trajectories are divergent, they lead to similar levels of depression in late adolescence with higher symptoms in females. Findings suggest a period of quiescence in depressive symptomology influenced by biopsychosocial factors impacting affective profiles.

Lifespan development of thalamic nuclei and characterizing thalamic nuclei abnormalities in schizophrenia using normative modeling.

Thalamic abnormalities have been repeatedly implicated in the pathophysiology of schizophrenia and other neurodevelopmental disorders. Uncovering the etiology of thalamic abnormalities and how they may contribute to illness phenotypes faces at least two obstacles. First, the typical developmental trajectories of thalamic nuclei and their association with cognition across the lifespan are largely unknown. Second, modest effect sizes indicate marked individual differences and pose a significant challenge to personalized medicine. To address these knowledge gaps, we characterized the development of thalamic nuclei volumes using normative models generated from the Human Connectome Project Lifespan datasets (5-100+ years), then applied them to an independent clinical cohort to determine the frequency of thalamic volume deviations in people with schizophrenia (17-61 years). Normative models revealed diverse non-linear age effects across the lifespan. Association nuclei exhibited negative age effects during youth but stabilized in adulthood until turning negative again with older age. Sensorimotor nuclei volumes remained relatively stable through youth and adulthood until also turning negative with older age. Up to 18% of individuals with schizophrenia exhibited abnormally small (i.e., below the 5th centile) mediodorsal and pulvinar volumes, and the degree of deviation, but not raw volumes, correlated with the severity of cognitive impairment. While case-control differences are robust, only a minority of patients demonstrate unusually small thalamic nuclei volumes. Normative modeling enables the identification of these individuals, which is a necessary step toward precision medicine.

Smaller anterior hippocampal subfields in the early stage of psychosis.

Hippocampal volume is smaller in schizophrenia, but it is unclear when in the illness the changes appear and whether specific regions (anterior, posterior) and subfields (CA1, CA2/3, dentate gyrus, subiculum) are affected. Here, we used a high-resolution T2-weighted sequence specialized for imaging hippocampal subfields to test the hypothesis that anterior CA1 volume is lower in early psychosis. We measured subfield volumes across hippocampal regions in a group of 90 individuals in the early stage of a non-affective psychotic disorder and 70 demographically similar healthy individuals. We observed smaller volume in the anterior CA1 and dentate gyrus subfields in the early psychosis group. Our findings support models that implicate anterior CA1 and dentate gyrus subfield deficits in the mechanism of psychosis.

Exploring the Trajectory of Catatonia in Neurodiverse and Neurotypical Pediatric Hospitalizations: A Multicenter Longitudinal Analysis.

Objective: Catatonia is a neuropsychiatric disorder that occurs in pediatric patients with a range of associated medical, psychiatric, and neurodevelopmental disorders (NDDs). This study describes hospital care of pediatric catatonia patients and compares treatments for neurotypical patients and those with NDDs. Methods: Retrospective cohort study from 1/1/2018 to 6/1/2023 of two academic medical centers of patients aged 18 and younger with catatonia. Patients were retrospectively assessed using the clinical global impressions-improvement (CGI-I) by two independent reviewers. Results: One hundred sixty-five patients were hospitalized for catatonia, of whom 50.3% had an NDD. Median age was 15. One hundred sixty-four patients were treated with a benzodiazepine, with a median maximum 24-hour dose of 6 mg lorazepam-equivalents, which did not differ for patients with and without NDDs. Electroconvulsive therapy (ECT) was utilized in 14.5% of patients. Median length of medical hospitalization was 5 days and hospitalizations were longer in neurotypical patients than in patients with NDDs. In an ordinal regression model, the probability of observing at least "much improvement" (CGI < 3) was 88.3% (95% CI: 82.4% to 92.3%), with NDD diagnosis associated with a lower odds of clinical response. Conclusions: The probability of patients achieving a CGI-I score indicating at least "much improvement" was 88.3%. Administered benzodiazepine dose and ECT treatment were similar for all patients, but neurotypical patients had longer hospitalizations than those with NDDs and had a higher odds of a more favorable clinical response. Research under controlled conditions is needed to optimize and endure equitable catatonia treatment in youth.

Gender diversity in autistic and neurotypical youth over adolescence and puberty: A longitudinal study.

Recent research in autism spectrum disorder (ASD) has suggested a higher prevalence of gender diversity in individuals diagnosed with ASD. Adolescence is a critical period for the consolidation of gender identity, yet the extent to which the experience of gender diversity is stable over adolescence and puberty in autistic youth is poorly understood. The aim of the study was to examine the consistency of gender diversity using the gender diversity screening questionnaire for self- and parent-report of youth (GDSQ-S, GDSQ-P) over a four-year longitudinal study of pubertal development in youth with ASD (N = 140, 36 assigned-female-at birth (AFAB)) and typical development (TD, N = 104, 58 assigned-male-at-birth [AMAB]) and their parents. The extent to which diagnosis (ASD vs. TD), assigned sex (AFAB vs. AMAB) and developmental level (age, puberty) predict GDSQ trajectory over time was explored. There was a significant diagnosis by sex-assigned-at-birth by age interaction for GDSQ-S Gender Diversity, p = 0.002, showing higher scores in autistic AFAB youth over adolescence, and TD AFAB showing initially lower, then increasing levels over adolescence. For GDSQ-P, Gender Incongruence was significantly different between the groups, p = 0.032, showing higher incongruence for autistic AFAB around age 10, decreasing between age 12-14 before increasing again, while TD AFAB evidence the inverse trend. AMAB trends were stable. The significant diagnostic, developmental and sex-based differences indicate AFAB youth experience greater gender diversity that evolves over development. Findings suggest gender identity formation is nuanced and may be influenced by pubertal progression, hormonal patterns, and psychosocial factors. Results underscore the need for enhanced understanding of the unique, dynamic profiles of females-assigned-at-birth.

Transcriptional cartography integrates multiscale biology of the human cortex.

The cerebral cortex underlies many of our unique strengths and vulnerabilities, but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate, and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization - ranging from protein-protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources, and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.

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 (ß=-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.

Biological variation in cervical spinal cord MRI morphometry in healthy individuals and people with multiple sclerosis.

BACKGROUND AND PURPOSE: Conclusions from prior literature regarding the impact of sex, age, and height on spinal cord (SC) MRI morphometrics are conflicting, while the effect of body weight on SC morphometrics has been found to be nonsignificant. The purpose of this case-control study is to assess the associations between cervical SC MRI morphometric parameters and age, sex, height, and weight to establish their potential role as confounding variables in a clinical study of people with multiple sclerosis (MS) compared to a cohort of healthy volunteers. METHODS: Sixty-nine healthy volunteers and 31 people with MS underwent cervical SC MRI at 3 Tesla field strength. Images were centered at the C3/C4 intervertebral disc and processed using Spinal Cord Toolbox v.4.0.2. Mixed-effects linear regression models were used to evaluate the effects of biological variables and disease status on morphometric parameters. RESULTS: Sex, age, and height had significant effects on cord and gray matter (GM) cross-sectional area (CSA) as well as the GM:cord CSA ratio. There were no significant effects of body weight on morphometric parameters. The effect of MS disease duration on cord CSA in the C4 level was significant when controlling for all other variables. CONCLUSIONS: Studies of disease-related changes in SC morphometry should control for sex, age, and height to account for physiological variation.

Mapping Individual Differences in Intermodal Coupling in Neurodevelopment.

Within-individual coupling between measures of brain structure and function evolves in development and may underlie differential risk for neuropsychiatric disorders. Despite increasing interest in the development of structure-function relationships, rigorous methods to quantify and test individual differences in coupling remain nascent. In this article, we explore and address gaps in approaches for testing and spatially localizing individual differences in intermodal coupling. We propose a new method, called CIDeR, which is designed to simultaneously perform hypothesis testing in a way that limits false positive results and improve detection of true positive results. Through a comparison across different approaches to testing individual differences in intermodal coupling, we delineate subtle differences in the hypotheses they test, which may ultimately lead researchers to arrive at different results. Finally, we illustrate the utility of CIDeR in two applications to brain development using data from the Philadelphia Neurodevelopmental Cohort.

Investigating Social Competence in a Pilot Randomized Clinical Trial of a Theatre-Based Intervention Enhanced for Adults with Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is characterized by challenges in social competence that persist in adulthood, yet few treatment options exist. A pilot randomized clinical trial (RCT) of a peer-mediated, theatre-based intervention with established efficacy in youth with ASD was examined in autistic adults. The final sample consisted of forty-seven 18-to-40-year-old participants randomized to the experimental (EXP N = 23) or waitlist control (WLC N = 24) condition. A multimodal, social interdependent model was employed to examine social competence changes in brain (incidental face memory (IFM) using event-related potentials), cognition (Wechsler Memory Scale-III), behavior (Contextual Assessment of Social Skills) and function (Social Responsiveness Scale (SRS); Adaptive Behavior Assessment Scale (ABAS) Social Composite). Using analysis of covariance in which pretest was controlled in the model, posttest between-group differences were observed on IFM (p = 0.016, η2 = 0.139, d = 0.79) and several social and adaptive functional (SRS, ABAS) outcomes in social communication and interaction (SCI) (p = 0.019, η2 = 0.121, d = -00.45), communication (p = 0.044 η2 = 0.09, d = -00.31), and motivation (p = 0.001, η2 = 0.229, d = -0.79) domains. At two-month follow-up, gains in social motivation remained (p = 0.041, η2 = 0.100, d = -0.77). The results offer preliminary support for a unique theatre-based social skills intervention for autistic adults who have few treatment options to enhance social competence. The trial was pre-registered with ClinicalTrials.gov (Identifier: NCT04349644).

Evaluation of resampling-based inference for topological features of neuroimages.

Many recent studies have demonstrated the inflated type 1 error rate of the original Gaussian random field (GRF) methods for inference of neuroimages and identified resampling (permutation and bootstrapping) methods that have better performance. There has been no evaluation of resampling procedures when using robust (sandwich) statistical images with different topological features (TF) used for neuroimaging inference. Here, we consider estimation of distributions TFs of a statistical image and evaluate resampling procedures that can be used when exchangeability is violated. We compare the methods using realistic simulations and study sex differences in life-span age-related changes in gray matter volume in the Nathan Kline Institute Rockland sample. We find that our proposed wild bootstrap and the commonly used permutation procedure perform well in sample sizes above 50 under realistic simulations with heteroskedasticity. The Rademacher wild bootstrap has fewer assumptions than the permutation and performs similarly in samples of 100 or more, so is valid in a broader range of conditions. We also evaluate the GRF-based pTFCE method and show that it has inflated error rates in samples less than 200. Our R package, pbj , is available on Github and allows the user to reproducibly implement various resampling-based group level neuroimage analyses.