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Training in Advanced Statistical Methods in Neuroimaging and Genetics

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National Institute of Neurological Disorders and Stroke (NINDS)

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This education project is a continuation of our current, national class, Training in Advanced Statistical Methods in Neuroimaging and Genetics. Over the past 15 year the National Institutes of Health has greatly increased funding of grants that utilized advanced neuroimaging methods, genetic methods, and advanced statistical methods. While introductory courses are offered, ours is the only advanced course offered in the United States that provides an intensive, hands-on (“doing”) learning opportunity to better prepare biomedical and clinical researchers in advanced statistical methods. In one decade the combined budgets that utilize these advanced analysis techniques from the National Institute of Neurological Disorders and Stroke, National Institute of Mental Health, National Institute on Aging, National Institute on Drug Abuse, and National Institute of Biomedical Imaging and Bioengineering grew 5-fold, and there continues to be a great need to provide an educational opportunity to ensure the workforce is well positioned to carry out important work that has been identified by these and other institutes. Our program will continue to meet this need. We bring together a group of diverse world-class scientists and educators in a two-week intensive format to provide theoretical lectures paired with hands-on computer tutorials. Our course has served 103 students (55 total in 2021-2022 via Zoom due to COVID-19), and in 2023 (our 1st year of in-person) we taught 20 students, and 28 students in 2024 (in-person). We will enroll 26-30 students in April 2025 session. In our competitive renewal we will continue to enroll 26-30 students per year. With this being an advanced course, we ensure that the students accepted are a good education-level match for the content. We also implement mechanisms to maximize diverse perspective in our students and our teaching faculty. These students are accepted from across the United States, with attention to attracting a diverse student cohort. This education program will continue to distribute Tuition Awards based on financial need. We have evolved our course based on feedback from our current course alumni. In our class, over two weeks, students learn and put into practice methods such as: hierarchical statistical models, Bayesian statistics, network science, functional and structural connectomics, disease driven degeneration of the brain, and methods for analysis of genetics data such as polygenic risk scoring and structural equation modeling. The course concludes with lectures and labs on multi-modal analysis (imaging and imaging-genetics), and classification methods for biomarker development. Our course now includes 5 guest lecturers and team building activities outside of the classroom. To ensure students apply the acquired knowledge and skills to their independent research projects back at their home institutes, we supplement the course with our innovative continuing education: zoom-based sessions with the faculty for 8-months post formal course and students having near-real-time access regarding technical implementation questions through the Slack. This continued education portion greatly increases success utilizing their new practical skills in their own research.

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National Institute of Biomedical Imaging and Bioengineering

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National Institute of Neurological Disorders and Stroke

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United States National Institutes of Health

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Methods for quantifying selection and predicting evolutionary dynamics

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National Institute of General Medical Sciences (NIGMS)

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Natural selection is central to many challenges in biology and medicine, from the emergence of drug resistance in pathogens to cancer evolution. Understanding selection can also aid in protein engineering and help identify clinically relevant mutations in human disease genes. Temporal genetic data — sequences and phenotypes sampled over time — can be an especially powerful tool for understanding selection because it allows us to observe evolutionary dynamics directly. But while temporal data from sources like pathogen surveillance, ancient DNA, and experimental evolution have grown tremendously in recent years, statistical analyses of these data remain challenging. My lab will continue to pioneer the development of new computational methods to learn from temporal genetic data, revealing variants and phenotypes under selection and harnessing this information for predictive models of evolution. Over the past five years, we have developed several approaches to quantify selection from temporal data. Thanks to the use of mathematical methods from statistical physics, our methods are fast and accurate despite the inclusion of complex features such as linkage disequilibrium, epistasis, and time-varying selection. We demonstrated the power of these approaches through studies of HIV-1 immune escape and SARS-CoV-2 adaptation during the COVID-19 pandemic, where our analysis identified key mutations affecting viral transmission even before their importance was validated experimentally. Building on this foundation, we will pursue three synergistic research directions: First, we will develop new methods to jointly analyze selection on both individual mutations and phenotypic traits, fusing concepts from population genetics, quantitative genetics, and machine learning. Second, we will apply these methods to study rapid evolution in viral pathogens. Phenotypic models will help us to understand how immune pressure drives antigenic change in respiratory viruses and to compare evolutionary constraints on pathogens across host species. As an ambitious new direction, we will leverage these insights to develop predictive models of pathogen evolution, with influenza as a first target. Our research will systematically identify the features with the greatest power to predict evolution and characterize how and why predictive power may decline over time. Finally, we will extend our approaches to improve the interpretation of high-throughput mutagenesis experiments that measure the effects of thousands of mutations simultaneously. The proposed research will transform our understanding of how selection guides evolution across biological scales, from individual mutations to complex phenotypes, with applications ranging from predicting viral evolution to protein engineering. These advances could ultimately improve our ability to anticipate and control evolutionary processes across a wide range of biological contexts.

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Correlated factor models for exploratory analysis of complex multimodal study designs

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National Institute of General Medical Sciences (NIGMS)

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Most human diseases are complex, manifesting from an interplay between genes and environment over the lifespan that involve myriad biological processes. Genome-wide association studies have primarily implicated non-coding variation that is thought to lead to disease via disruption of complex, multi-level biological systems. Thus, improvements in our understanding of these fundamental processes underlying disease necessitates studying the relationship between multiple omics (multi-omic) modalities, both longitudinally and in conjunction with non-omic data. While recent years have seen an explosion of studies collecting multi-omic data in human populations, analysis of these data remains challenging both statistically and computationally. Here, I propose several new methods based on correlated latent factor models that will extend the capabilities of multi-omic inference methods to more complex study designs. I will develop model-based imputation methods that allow robust handling of missing data, enabling larger-scale studies of multi-omic biological contexts, and allowing researchers to design targeted multi-omic panels to extract the maximum amount of clinically-relevant information. I will develop multi-omic analysis methods that integrate across tissues and time points, enabling the study of dynamic molecular process and detection of systems-level impacts of intervention or disease onset. Finally, I will develop integration methods based on non-linear representation learning. This will enable detection of complex relationships between omics methods and integration with structured non-omics data such as doctor’s notes and radiographic images. To demonstrate the broad utility of the proposed methods, I will conduct collaborative analyses of varied cohorts. These include a population of individuals with subclinical atherosclerosis (MESA), a study anlyzing the relationship between microbiome features and immune health in the context of the COVID-19 pandemic (ImmunoMicrobiome), and a study of the impact of Alzheimer’s disease on neuroimaging and spinal uid biomarkers (ADNI). Completion of this research program will provide new insights into the fundamental biological processes underlying a host of common conditions, while bootstrapping the larger multi-omics research community by providing new tools that can handle complex study designs and integration tasks.

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Longitudinal MRI Measures of Cerebrovascular Injury and AD Atrophy in a Study of Latinos (SOL-INCA-MRI Long)

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Latinos constitute a heterogeneous population which accounted for slightly more than 50% of the United States (US) population growth for 2010 to 2020. Latinos are also becoming a larger proportion of older individuals in the US. Despite this, biomarker studies of normal aging and cognitive impairment remain limited, and the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) constitutes the only known representative sample. Moreover, epidemiological data indicate that Latinos have a higher prevalence of vascular risk factors, lower cardiovascular health metrics, and a greater likelihood of having mild cognitive impairment (MCI) or dementia due non-Alzheimer’s disease processes that differ by heritage. Consequently, diagnosis and treatment of Latino persons with cognitive impairment may be more challenging, but more amenable to prevention through reduction of vascular risk factors than non-Hispanic White persons where vascular risk and disease is less prevalent. The first cycle of “MRI Measures of Cerebrovascular Injury and Alzheimer’s disease Atrophy in a Study of Latinos (RF1 AG054548; AKA SOL-INCA-MRI)” was designed to identify biological underpinnings of normal cognitive aging, MCI and Alzheimer’s disease and related dementias (ADRD) in a representative subgroup of the HCHS/SOL 62 + 9 years of age on average. Despite restrictions imposed by the COVID pandemic, our investigators successfully obtained brain MRI from 2668 individuals or >95% of the proposed study cohort. From these data we have published on 1) differences in brain structure from ages 35-85; 2) the impact of vascular risk and sleep on brain structure; 4) the association of cognition with subsequent MRI measures; and 4) genetic influences on select brain measures. These early results, while of scientific value, are cross- sectional and do not reflect ongoing degeneration or incident vascular injury, limiting inferential power that might extend scientific knowledge of brain aging and ADRD in this unique cohort. For this application, we propose to extend our work to include longitudinal MRI analysis, leveraging longitudinal biomarker and clinical data from 3 visits, spanning approximately 12 years of HCHS/SOL and its cognitive ancillary study (SOL-INCA- AD; R01 AG075758, Gonzalez, DeCarli Co-PIs) on a deeply characterized and diverse Hispanic/Latino cohort. Adding longitudinal image analysis in combination with longitudinal lifestyle, medical risk factors, plasma ATN biomarkers, genetics and cognitive assessment in this Latino cohort will address multiple ADRD research milestones and priorities while enabling stronger statistical inference of risk and resilience factors amongst representative, yet relatively young-old members of diverse Latino communities, creating the opportunity to identify modifiable risk factors, potentially reducing societal burden due to later-life ADRD in this rapidly growing portion of the older US population.

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Hispanic Community Health Study/Study of Latinos

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United States National Institutes of Health

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The Pathogenic Role of IL-33 in the SARS-CoV-2-Infected Lung

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National Heart Lung and Blood Institute (NHLBI)

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), remains a significant threat to global public health. Pulmonary immunopathological damage plays a role in driving pneumonia, acute respiratory distress syndrome (ARDS), and multiorgan failure in severe COVID-19. Therefore, dissecting the pulmonary immune response to SARS-CoV-2 infection is critical to under- stand disease pathogenesis and develop more effective therapeutics. Targeting of type 2 immune pathways is a potential avenue of therapeutic intervention in severe COVID-19. In particular, research has demonstrated a link between the type 2 cytokine IL-13 and COVID-19 severity. This proposal builds on the preliminary experi- ments demonstrating that blockade of the alarmin cytokine IL-33 confers protection in a mouse model of COVID- 19. IL-33 is a potent inducer of type 2 immunity in the lung, as its receptor ST2 is constitutively expressed by type 2 cells including type 2 innate lymphoid cells (ILC2s). It is hypothesized that IL-33/ST2 signaling induces a pathogenic inflammatory environment in the acutely infected lung by activating IL-13-secreting ILC2s and that IL-33-mediated inflammation enhances disruption of the respiratory epithelial barrier. To test this hypothesis, the impact of IL-33/ST2 signaling axis blockade on the inflammatory environment in the acutely infected lung will be described through assessment of the immune cell populations and the transcriptional profile of the respiratory epithelium (Aim 1). Considering that SARS-CoV-2-mediated ARDS is characterized by pulmonary inflammation and respiratory epithelial barrier disruption, these descriptive studies will elucidate pathways through which IL- 33 may drive pathogenesis. Further, the mechanism through which the IL-33/ST2 signaling axis drives patho- genesis will be directly tested (Aim 2). Mouse models of ILC2 depletion, selective ST2 knockout in ILC2s, and adoptive ILC2 transfer will be used to test whether IL-33-activated ILC2s drive pulmonary pathogenesis in the context of acute SARS-CoV-2 infection. Collectively, the proposed experiments will determine the mechanism underlying IL-33-mediated pathogenesis. This research is significant because it will further our understanding of how modulation of type 2 immunity can serve as a novel and promising therapeutic strategy in the treatment of respiratory viral infection-induced pulmonary pathology.

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Sensory and molecular studies of human taste dysfunction

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National Institute on Deafness and Other Communication Disorders (NIDCD)

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Project 3. Sensory and molecular studies of human taste dysfunction Taste dysfunction is a cardinal feature of COVID. Project 3 of this P50 Clinical Research Center (CRC) proposal focuses on (1) molecular description of taste tissue from people with sustained COVID-19-associated taste dysfunction, compared to people with no current taste problems (regardless of infection history) (Aim 3.1), and (2) mechanistic interrogation of COVID-associated taste dysfunction using taste organoids (Aim 3.2). We will test the hypothesis that people with COVID taste dysfunction have fewer taste receptor cells, reduced expression of taste-relevant genes, and immune cell infiltration due to sustained inflammation. In Aim 3.1, we will sample taste tissue from people with and without sustained COVID-19-associated taste dysfunction to measure taste receptor cell number and gene expression of inflammatory (e.g., cytokines and chemokines) and other molecules with single-cell RNA sequencing (scRNA-seq) methods. In Aim 3.2, we will use taste organoids derived from wild-type mice, humanized-ACE2 mice, and humans to examine SARS-CoV-2 tropism in taste tissue to determine if taste tissue homeostasis is altered by (a) SARS-CoV-2 infection or (b) inflammatory molecules identified in Aim 3.1 and/or known to be elevated in COVID. Project 3 of this CRC proposal is supported by Project 1 and the Chemosensory Clinical Services Core, which will perform and support remote and in-house sensory screening of all participants in this research program. The investigators here are experts in their fields, particularly in single-cell biology, genetics, and stem cell biology of taste tissue. We have engaged consultants who are inflammation, infection, and immunology experts. Several types of pilot data support this application, including scRNA-seq data from human fungiform tissue and taste organoid data after treatment with inflammatory molecules. Institutional support for this project is outlined in a Letter of Support from administrative officials, and the Monell Chemical Senses Center is well suited to complete this project because of its cross-disciplinary focus on chemosensory biology and its connection with an experienced coronavirus expert at the nearby University of Pennsylvania. This project is part of a larger program to understand and treat people with communication disorders of taste and smell dysfunction due to COVID. We anticipate our data will answer key unsolved questions regarding taste dysfunction and point to potential avenues of treatment for this debilitating condition.

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United States National Institutes of Health

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Elucidating kinetics and thermodynamics of RNA-ligand interactions using single molecule approaches

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National Institute of General Medical Sciences (NIGMS)

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BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)

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Project summary/abstract: Current understanding of and predictive models for folded RNA structures lag far behind our advances in protein folding. Yet recent work reveals the central role of RNA folds in diseases like viral infection, cancer and neurodegeneration. The potential to develop drugs against RNA targets causing illness is impeded by critical knowledge gaps in our understanding of the sequence-structure-function relationships of RNA polymers. Beyond structure, the role of fast fluctuations between the various conformations of RNA is being recognized as playing a bigger role in RNA than in amino acid function. Quantifying both shape and kinetics of RNA requires single molecule tools that can achieve millisecond time and nanometer distance resolution. Through this Maximizing Investigator Research Award (MIRA), the Kamenetska Lab will be supported in their continued efforts to develop such single molecule biophysical tools combined with machine learning approaches in order to expand our knowledge and understanding of the structural and kinetic properties of RNA. These optical tweezer force spectroscopy tools are uniquely suited to quantifying the full energy landscape profile of RNA structures that governs the dynamics of these molecules. Here I propose to use these methods, based on published results from my laboratory, to fill three critical knowledge gaps. First, I will investigate the effects on RNA mechanics and dynamics of non-specific interactions between nucleic acids, including RNA, with small molecules and ions present in mammalian cells. Second, I will build on our work on synthetic and modified RNA structures to systematically quantify the relationship between structure and folding energetics, generating data for training predictive models of RNA folding not currently available. Finally, I will build analytic methods and pursue structural and kinetic characterization of complex RNA tertiary structures with multiple conformations. My targets include SARS-CoV-2 viral genomic RNA implicated in viral gene regulation, telomeric and 5’ untranslated regions (5’ UTR) structures associated with cancer phenotypes.

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Understanding trajectories of cannabis use frequency across the lifespan based on routine screening in a large outpatient population

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Cannabis use is prevalent and increasing in the US, with growth in older adult use outpacing increases in all other age groups. Cannabis use increases risk of cannabis use disorder (CUD) and other adverse health outcomes, with up to 33% of people who use cannabis having a CUD. In the context of legalized, more frequent and higher potency cannabis use, longitudinal studies examining the long-term risks of cannabis use are critically needed. Latent class trajectory modeling is an established approach to modeling cannabis use patterns among adolescent and young adult research participants. Few studies have included middle-aged or older adults, for whom cannabis morbidity may be most concerning due to risks of drug interactions, diminished health, falls and injuries, and impaired cognition. The proposed study responds to NIDA’s call for research on the health effects of cannabis, particularly among older adults, and trajectories of substance use and adverse health outcomes. This study will use 10 years of electronic health record data from a large health system that screens patients annually with a valid practical, single-item cannabis screen. The sample includes more than 331,000 adult patients (≥ 18 years)—including large samples of middle aged and older adults—who have completed the cannabis screen on 3 or more occasions as part of routine clinical care (2016 – 2025). The screen asks about the frequency of cannabis use (none to daily), with frequency of use being the most important predictor of CUD and adverse health conditions, even when accounting for heterogeneity of cannabis products. Specific aims are to conduct developmental research to inform a future R01 that will assess the extent to which different longitudinal cannabis use patterns predict subsequent adverse health outcomes. Aim 1 is to conduct preliminary analyses to understand sample biases, cohort effects (e.g., COVID- 19) and data missing. Aim 2 is to apply multistep trajectory modeling to identify groups of patients, separately for 4 age groups (18-34, 35-49, 50-64, ≥ 65), who follow similar trajectories of cannabis use and characterize patients in each trajectory group by demographics, health conditions, medication use, health care utilization and diagnosed CUD. Aim 3 is to describe, for each age-based trajectory group, the year-by-year prevalence of concurrent adverse health outcomes associate with cannabis use (i.e., depression, psychotic disorder, chronic pain, polysubstance use, cognitive impairment, diagnosed CUD) over the study period. Secondarily, by age group, we will repeat Aims 2 and 3 separately in women and men and in 4 subgroups defined by race and ethnicity: Black, Hispanic, Asian and White. Public Health Impact: More than 59 million US adults use cannabis, yet little is known about the long-term patterns of cannabis use and associated adverse health outcomes, particularly for middle-aged and older adults. Results will have direct clinical implications for care of patients whose cannabis use is associated with adverse outcomes and build the foundation for future research to predict subsequent adverse health outcomes by trajectory group.

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Effect of paid family care (vs aides) on Medicaid waiver participants w/ IDD across the lifespan

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Home and community-based services are the most common support for people with Intellectual and/or Developmental Disability (I/DD) who otherwise would require facility-level care. Many state Medicaid waivers allow participants to choose to hire family as direct support personnel to meet their care needs at home and in the community rather than a professional aide. Ability to hire family increased during the Covid-19 pandemic through state policy changes, and yet we do not know how involving paid family affects the ability for people with I/DD to remain at home. The objectives of this study are to use North Carolina (NC) as a case to elucidate the experiences of waiver participants with I/DD (Innovations Waiver participants). With no national data fields systematically identifying self-direction status or who is paid for personal care, foundational state-level work is required to understand people with I/DD’s experiences with self-direction. First, we will describe prevalence and dynamics of paid family care using Medicaid administrative data over the past 9+ years, including patterns by self-direction or not, by individual and geographic factors and by era (pre-post Covid-19 (Aim 1). Second, qualitative approaches will center the voices of people with I/DD and their families to obtain their perspectives on what is gained and what is lost from self-directed care including paid family care (Aim 2). Specifically, photo elicitation, case study, and focus group interviews will examine the lived experience of accessing and receiving care through the Innovations Waiver according to 1) individuals with I/DD, 2) their parent/partner/guardian/unpaid family caregiver, 3) their paid family caregivers, 4) their paid aide, and 5) Innovations Waiver experts. Third, we will estimate the comparative effectiveness of paid family care versus paid aide care only on person-centered outcomes (e.g., home time, preventive care) and on potential harms (potentially harmful medications, injurious falls, mistreatment) (Aim 3). We hypothesize that waiver participants with paid family care will have better person-centered outcomes and no increase in harms compared to those with paid aides alone. Effects of self- direction will also be explored. By using a convergent parallel mixed-methods process we will integrate results to paint a full picture of the comparative effectiveness of paid family care and self-direction from childhood to older adulthood, including identification of any harms. The results of this 5-year R01 study will be immediately applicable to state Medicaid office benefit design and inform strategies to optimize quality of care and life for people living with I/DD from childhood throughout the lifespan. Results from the North Carolina case will also position us to pursue a national study, given knowledge gained along with emerging efforts to identify “self- direction” in national CMS data sets. Examining paid family care and self-direction’s effects across the lifespan aligns with NIA’s strategic goal to improve the health, well-being, and independence of adults as they age.

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Low-dose naltrexone (LDN) for the treatment of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

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National Institute of Neurological Disorders and Stroke (NINDS)

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In this UG3/UH3 Exploratory Clinical Trial, we will test low-dose naltrexone (LDN) as a treatment for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). ME/CFS affects approximately 1 million people in the United States, with incidence rates increasing with the SARS-CoV-2 pandemic. ME/CFS is characterized by profound fatigue, cognitive issues, pain sensitivity, and post-exertional malaise (PEM). Several studies support the hypothesis that ME/CFS involves chronic inflammatory activity in the central nervous system (CNS) that is driven by hyperactive microglia. More than 35 years since recognizing ME/CFS as a distinct medical condition, there is still no FDA-approved medications and no consensus on optimal treatment of the disorder. There is an urgent need to identify treatments that are safe and effective in reducing the severity of ME/CFS. Low dose naltrexone (LDN) involves daily doses of naltrexone in the 0.5mg to 6.0mg range. LDN crosses the blood-brain barrier, pushes microglia from an inflammatory to a resting state, and reduces the production of pro-inflammatory chemicals in the brain. LDN reduces fatigue severity in conditions such as ME/CFS, fibromyalgia, and Long-COVID. LDN is an ideal first treatment for ME/CFS because it is generically available, inexpensive, safe, and well-tolerated. LDN also has no abuse potential. In this Phase II trial, several questions will be answered to optimize a future Phase III efficacy trial of LDN for ME/CFS. This trial uses a remote design where individuals can enroll from anywhere in the United States and can complete all study tasks from their home. This approach allows individuals who are homebound or bedbound to participate in the clinical trial. Study 1 is a dose-finding study where 75 ME/CFS participants will receive LDN at 1.5mg/day, 3.0mg/day, 4.5mg/day, and 6.0mg/day for 2 months each, in blinded order. This study will be used to determine the best dose of LDN to be used in future trials. Study 2 is a randomized controlled trial (RCT) in 150 individuals with ME/CFS. Participants will be randomized to receive LDN or placebo. This study will be used to test safety and tolerability, determine the likely side-effects, determine the best measure to use as a primary outcome, identify predictors of a positive LDN response, and preliminarily measure the strength of the LDN effect. A subgroup of participants (25 LDN and 25 placebo) will be recruited close to the University of Alabama at Birmingham (UAB) to complete advanced neuroimaging and blood tests of neuroinflammation, neurodegeneration, and oxidate stress. These tests may yield biomarkers of LDN response for predicting who is a good LDN candidate, or for tracking improvement with the treatment. Neuroimaging will focus on brain lactate and temperature, two measures of brain inflammation. Study 3 is an extended-duration study where participants may be switched between placebo and LDN, in order to collect additional safety, tolerability, efficacy, and durability information. Ultimately, we hope this study will lead to the first widely accepted pharmaceutical treatment for ME/CFS.