Kilian M. Pohl - US grants

DESCRIPTION (provided by applicant): The National Consortium on Alcohol and NeuroDevelopment in Adolescence (N-CANDA) proposes a longitudinal study of developmental trajectories of brain structure, function, and physiology and their alteration by adolescent alcoho exposure. The prospective design will assess young people before the start of any significant substance use, thereby facilitating identification of antecedent conditions influencing the development of Alcohol Use Disorder (AUD) and the effects thereafter. A synergistic program to assess dependent-variable domains (clinical, neuropsychological, physiological, brain structural and functional), quantified and tracked longitudinally will be combined across 4 collection sites to create a large and rich body of data. An accelerated longitudinal design allows for construction of trajectories over the entire adolescent age range and encompasses the period of brain structural and functional development during the vulnerable time of AUD emergence. The investigators of the N- CANDA Data Analysis, Integration, and Informatics Component will provide fundamental neuroimaging science support for MRI pulse sequence development, acquisition, and analysis protocols. This component will also provide the infrastructure for deposition, organization, storage, archiving, retrieval, and initial analysis of neuroimaging, neuropsychological, and clinical data collected at all four N- CANDA acquisition sites. The Specific Aims of the Data Analysis, Integration, and Informatics Component are to: Aim 1. Develop procedures for collection of neuroimaging, neuropsychological, and clinical assessment data harmonized with existing large-scale neurodevelopmental research efforts. Aim 2. Ensure that quality control measures are in place at each site for the acquisition of imaging data. Aim 3. Develop the informatics infrastructure for data submission, database construction, data analysis, integration, and distribution for all N-CANDA sites through a query system that allows exploration of the multiple domains of the database. Aim 4. Provide a pipeline for macrostructural, microstructural and functional neuroimage data processing and analysis. Aim 5. Develop informatics processes to coordinate the combining of data and the analysis of results across and within research components for a priori hypothesis testing and association discovery and atheoretical data mining in search of relations not previously considered. !

? DESCRIPTION (provided by applicant): Cardiac dyssynchrony deteriorates cardiac function and often cannot be treated effectively. The goal of this proposal is to develop and provide a new analysis technique for understanding the complex cardiac motion patterns (in time and space) of patients with cardiac dyssynchrony, with the hope of improving its treatment outcomes, specifically with respect to cardiac resynchronization therapy (CRT). CRT, the most effective treatment for dyssynchrony with worsening heart failure, significantly improves outcome in only ~66% of heart failure patients selected for the treatment, which is based on ECG criteria. Selection criteria based on imaging are essential to improving the success rate. Unfortunately, response rates are not higher with selection criteria based on echocardiography, the most popular cardiac imaging technique. Cine cardiovascular magnetic resonance (CMR) has the potential to better characterize dyssynchrony, as it shows cardiac mechanics and intramural wall motion with much higher spatial resolution than echocardiography. However, quantitative assessments of CMR have been mostly limited to global volumetric measures, which ignore most of the motion information captured by the images. For example, studies of a number of distinctive motion features of dyssynchrony (such as septal flash and apical rocking) have been confined to qualitative assessment, limiting inference of their potential utility for improving CRT treatment. To accurately quantify cardiac function through CMR, we have developed biomechanical models for describing cardiac function and machine learning technology for identifying morphological and functional patterns atypical for healthy hearts. We propose to combine these two technologies to accurately quantify cardiac dyssynchrony within the Left Ventricle (LV). Specifically, our methods will extract a rich description of LV motion and strain from the CMRs of a set of retrospectively selected subjects with synchronous or dyssynchronous LV motion. We will then use machine learning methods to identify local and global motion patterns specific to dyssynchrony. Finally, we will correlate these patterns to already existing clinical scores to find potentially predictive markers with respect to CRT outcome. We hypothesize that these markers will have a higher correlation to CRT outcome than current clinical markers alone. Identifying these markers will have the potential to further stratify the disease with respect to the expected outcome of CRT, which then can be used to derive new selection criteria that lead to higher success rates. The project will also disseminate our novel, data-driven methodology for quantifying that motion. Other research groups can apply our tools to specifically study dyssynchrony, as well as other cardiac diseases impacting LV motion in general.

The CDC estimated that one-quarter of Americans living with HIV were over the age of 55 in 2012. By next year, they will be over age 60, entering into the age demographic where Alzheimer's disease (AD) becomes a distinct differential for clinicians. Because up to one-half of people living with HIV experience cognitive impairment from HIV or related factors along, the likelihood for delayed diagnosis of early AD is substantial. Differentiating HIV-associated Neurocognitive Disorder (HAND) from the Mild Cognitive Impairment stage of AD (MCI-AD) is one of the most pressing issues in geriatric neuroHIV. Current HAND nosology does not provide guidance on this issue. Published work suggests the likelihood for distinct phenotypes that would facilitate diagnostic sorting with commonly available inputs from neuropsychological testing and structural imaging. In this application, we will use a new approach that leverages computational machine learning with inputs from structural imaging, neuropsychological testing, motor examination and affective/behavioral assessments to determine the factors that most accurately discriminate HAND from MCI-AD. Our preliminary examinations using this novel technique demonstrate a likelihood that this approach will provide diagnostic sorting that exceeds 90% accuracy. We will examine tightly characterized phenotypes using HIV tests to exclude HAND and PET amyloid scanning to exclude AD among 75 HIV+/amyloid marker negative participants with HAND to 50 HIV-negative/amyloid+ cases with MCI (MCI-AD group), all age, sex and disease severity matched and all over age 60, the population of interest due to dual risk. Our methodology will iterate the most distinctive aspects of each disease's phenotype to inform sorting and subsequently, guidelines. We will validate the identified inputs that most clearly contribute to the algorithm though clinical correlations and through the ability of the determined clusters (e.g. diagnostic group) to predict the meaningful outcomes of disease progression. The long-term goal of this work is to inform clinical guidelines, thus, the modalities examined are readily available in clinical care. This work will also extend our understanding of neuropathology in older HIV patients and may identify factors that shift paradigms because our novel approach does not rely on a priori assumptions to inform neuropsychological abnormalities and brain structural alterations linked to HAND in older age. In an exploratory aim, we extend this examination of HAND neuropathogenesis with the added examination of diffusion tensor imaging (DTI) and a monocyte associated inflammatory marker, soluable CD163 (sCD163), two measures tightly linked to HAND in published work among virally suppressed patients in the current era.

PROJECT SUMMARY / ABSTRACT During young adulthood, drinking dramatically increases, with binge-level drinking peaking at age 22 and nearly half of individuals who drink report binge-level alcohol use. Frequent binge alcohol use during the protracted neuromaturation extending into the mid 20s may result in greater brain and cognitive effects than similar alcohol use in later adulthood. This application is in response to RFA-AA-17-005, Continuation of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) Data Analysis Resource (U24) to determine the predictors and effects of accelerated alcohol use in adolescence and young adulthood. NCANDA-2 will follow the initial cohort of 831 participants (ages 12-21 at first visit) from 5 collection sites and acquire the necessary data to advance our understanding of adolescent development and the effects of alcohol use on the adolescent and young adult brain using multimodal neuroimaging, cognitive testing, and behavioral assessment. The examination of alcohol consequences will focus on structural and functional maturation of brain areas that actively develop during adolescence, are involved in psychological regulation, respond to rewards, and appear vulnerable to deleterious effects of alcohol. With the additional longitudinal data provided by this renewal, we will determine the effects of alcohol exposure on the developmental trajectory of the adolescent human brain and identify preexisting psychobiological vulnerabilities that may put an adolescent or young adult at elevated risk for an alcohol use disorder. The Data Analysis Resource (DAR) will 1) oversee data collection with standardization and comparability across sites; 2) perform data analysis for core measures collected at each site; 3) facilitate across-site pooling and centralized data storage; 4) create a database across assessment modalities for efficient retrieval; 5) coordinate data and resource sharing; 6) harmonize data across sites; and 7) create and implement novel, multimodality analyses using machine learning to engage broad spectrum data. The DAR has 5 Specific Aims: Aim 1. Maintain standardized procedures for collection of neuroimaging, neuropsychological, and clinical assessment data and harmonize with existing large-scale neurodevelopmental research efforts. Aim 2. Ensure across-site quality control for imaging and neuropsychological data acquisition. Aim 3. Maintain and advance informatics infrastructure for data submission, analysis, and distribution. a) Maintain a database integrating the diverse and comprehensive data from all NCANDA sites. b) Provide data to consortium PIs for hypothesis testing within and across experimental domains. Aim 4. Provide macrostructural, microstructural, and fMRI neuroimage processing and analysis. Aim 5. Develop and maintain a data sharing and distribution system for the scientific public.

PROJECT SUMMARY / ABSTRACT A significant outcome of prospective studies using noninvasive neuroimaging with Magnetic Resoance Imaging (MRI) is the recognition that brain structural anomalies occur even in individuals apparently free of neurological disorders. Recently, we found that 0.6% of 833 adolescent and young adult participants in a large, NIAAA- funded project (NCANDA) had anomalies identified as gray matter heterotopias. These isolated clumps of gray matter neurons located in the wrong part of the brain are thought to be associated with seizures. This incidence of heterotopias was high given that participants with a history of a seizure disorder were excluded from study. We pursued this finding by investigating heterotopia incidence in another larger NIAAA-funded project (ABCD). In a computerized search of 7,863 neuroradiological readings, 84 (1.07%) participants were identified with gray matter heterotopias. Given that heterotopias have been reported in animal models of fetal alcohol exposure, we sought to examine the relation between hetertopias and prenatal alcohol exposure in the ABCD data. Retrospective questioning of mothers of ABCD participants indicated a high incidence of heterotopias in offspring of mothers who answered in the affirmative that they had drunk alcohol while but before finding out they were pregnant. These findings raise the possibility that the incidence of heterotopias in a population enriched with fetal alcohol exposure (FAE) and fetal alcohol syndrome (FAS) should be higher than in unaffected controls and would support the inference that prenatal alcohol exposure causes heterotopias in the cortex of exposed individuals. Accordingly, in collaboration with a clinical neuroradiologist, we will oversee blind clinical readings of the MRIs of 60 FAE, 60 FAS, and 60 control subjects to seek gray matter heterotopias, in addition to other structural anomalies, in these subjects with known outcomes of prenatal alcohol exposure. Such a finding would be novel and clinically significant, serving as a warning to health-care providers of the potential of seizures and as further reason to refrain from drinking alcohol when intending to become pregnant.