Martin A. Styner, PhD - US grants

The primary objective of the Neuroimaging Core is to serve the clinical projects utilizing image acquisition and processing technology (Project 1, Project 2, Project 3) for MRI imaging and for quantitative measurements of structural MRI (sMRI) and MR Diffusion Tensor Imaging (DTI) and to prepare the quantitative results for analysis by the Biostatistics Core. The core will provide state-of-the-art high-field scanner MRI technology including optimized pulse sequences for imaging of neonates (3T Siemens Allegra head-only), adults (3T Siemens Trio) and animals (Bruker 9.4T high-field system). The core will provide well established and validated image analysis methods and also introduce novel methods dedicated to the needs of this project. This core is particularly important because of the need to provide imaging and quantitative methods of neuroimaging to serve several of the projects, the great track record of collaboration between Drs Gerig and Styner, as well as the excellent, well established collaboration between Radiology MRI Research and small animal imaging, Psychiatry, Biostatistics, the Neurodevelopmental Disorders Research Center and the Neuroimaging Analysis Laboratory, making it possible to pool the excellent complementary expertise of all groups. The Neuroimaging Core processing will be shared between UNC, Utah and Yale using new capabilities of networked computing infrastructure and secure data transfer. The Core has profound experience with multi-center studies and processing data from other sites. The planned activities can substantially benefit from the strong expertise in neonatal MRI and image analysis, a special field where UNC has become a leading institution with several funded projects. The small animal imaging and image analysis activities will be supported by the expertise of three consultants and leaders in this field (Susumu Mori, John Sled, Michael Tyszka). Given the specialized expertise of our multi-disciplinary group and the close collaboration of our researchers in other large national programs of crucial importance for this project (BIRN, NA-MIC, NLM ITK), we expect to see a similar rapid development of animal imaging and related image analysis which is well documented by the preliminary data, leveraging our experience with development of novel image analysis methodology driven by challenging applications. The service of the Imaging Core will include selection and development of optimal acquisition protocols, data transfer of image data to the image analysis lab, storing and archiving of clinical and animal study image data, 3D segmentation and quantitative analysis to obtain measurements of brain structures and pathways, rigorous validation and quality control, and preparation of resulting data for statistical analysis.

DESCRIPTION (provided by applicant): In the last decade, MRI studies of human brain morphometry have been used to investigate a multitude of pathologies and drug-related effects in psychiatric research. Better understanding of normal neural development is essential to gain a better understanding of the underlying pathology of neurodevelopmental disorders such as autism or schizophrenia, as the timing of an insult into the developing brain is often critical in defining the resulting disorder. Nonhuman primates, and in particular macaque monkeys, have been widely used in animal models to investigate the neural substrates of human development in higher cognitive functions and complex social interactions. However, we lack crucial, detailed information of normal brain maturation in macaques, especially in the rapidly changing early stages. Matching maturational periods in humans with those in macaques aids us in establishing nonhuman primate translational models of developmental neuropathology. This project will provide three results: a) a developmental macaque brain MR database, b) the corresponding computational toolbox for cross-sectional and longitudinal atlas building and c) a comparative study contrasting human and macaque brain development and maturation patterns in both genders based on the former. The computational atlas building toolbox will be of translational nature as it is applied to the existing human NIH MRI database of normal brain development, as well as the proposed macaque brain development database. This longitudinal database will be acquired from a cohort of healthy macaque monkeys ranging from a few week olds up to 3-year-old adolescents. This grant is thus translational from a viewpoint of both the study of brain development as well as method and tool development. This creates an immensely valuable resource for primate researcher, as well as for the general neuroimaging field. The comparative study will allow us to characterize normal brain development in the rhesus macaque and compare it to human brain development. We expect that the efficient and cost-effective creation and dissemination of this unique database, the computational toolbox, along with the results from the developmental study will lead to the creation of many new translational primate models of developmental neuropathology worldwide.

DESCRIPTION (provided by applicant): The gut microbiota of the infant, acquired by exposure to the mother and the early rearing environment, plays a critical role in establishing a functional gastrointestinal tract and promoting health via stimulating the maturation of the immune, endocrine, and enteric nervous systems. Our project uses a nonhuman primate model to specifically determine the mechanistic pathways through which the infant's gut microbiome affects brain development, as well as emotional and behavioral responsiveness during the first year of life. The mother's microbiome will be experimentally controlled during the pregnancy and nursing periods, enabling us to generate infants with two distinctive microbiome profiles. Microbial community analyses will be run on Illumina MiSeq to acquire high quality 16S rRNA data in order to track the development of the infant's microbiota while it is with the mother, and subsequently after weaning when the gut bacterial community is reorganized, due to the consumption of solid foods and exposure to peers. Based on preliminary results, we focus on two sets of signaling mediators: 1) the production of neurochemicals by gut bacteria, including monoamine neurotransmitters and GABA, and 2) inflammatory cytokine activity in the blood and intrathecal compartments. At one year of age, state-of-the-art neuroimaging will be used for structural analyses of cortical brain regions (gray and white matter volumes), and for quantifying the pace of myelination with diffusion tensor imaging. During the initial R21 phase, we will verify that 20 infant monkeys with distinct microbiome profiles can be reliably generated, and that population differences persist, allowing us to create peer social groups comprised of infants with comparable microbiota. During the R33 phase, a larger cohort of 40 infants will be generated, enabling us to also use fecal transplant methods to systematically change the composition of the gut microbiota and to test the hypothesis that behavioral and neural trajectories will shift to that of the donor animal. This project takes advantage of the unique resources of a major primate breeding facility, permitting us to evaluate a large number of infant monkeys under controlled environmental conditions, and the multi-disciplinary expertise and resources of the collaborating research team at 4 institutions. Innovative methods are employed, including the biopsy collection of gut tissue specimens to validate sequencing and neurochemical conclusions from directed metabolomic panels derived from the more routinely collected rectal swabs. Our project directly addresses a central tenet of the RFA, which is to investigate the influence of the maternal and infant microbiome on behavioral and brain maturation, and to determine if a dysregulated microbiome is associated with atypical development. Multi-tiered, molecular biology and developmental neuroscience approaches are employed. This research has a clear translational relevance for child health, contributing to extant findings that indicate an abnormal microbiome is evident in several pediatric disorders. We are poised to validate new biomarkers for tracking the influence of the gut microbiota on systemic physiology and to make novel discoveries on how the gut and enteric nervous system interface with the microbiome to influence the normal functioning and development of the central nervous system.

ABSTRACT The 25rd biennial International Conference on Information Processing in Medical Imaging (IPMI 2017) will be held on the campus of Appalachian State University, Boone, North Carolina, on June 25-30, 2017. The objective of IPMI 2017 is to provide an open and informal atmosphere for unfettered discussion of the latest developments in information processing applied to biological and biomedical/clinical imaging. Topics include physical, biological and statistical modeling of biological and anatomical structure and function, computational and statistical image analysis. During the last four decades, IPMI has evolved with the medical imaging community it serves. Today IPMI is recognized as one of the preeminent international forums for presentation of leading-edge methodological research in the medical imaging field. The conference employs a single track format and is of intermediate scale (about 120 participants). Noteworthy is its focus on extensive discussions of the presented material with an allocated question and discussion time of 25minutes per presentation. The central mission of IPMI to disseminate the latest and most exciting results in biomedical imaging and to educate students and young professionals in the field and to involve them in strengthening their careers and give them exposure and contact with leaders in the field.

DESCRIPTION (provided by applicant): The gut microbiota of the infant, acquired by exposure to the mother and the early rearing environment, plays a critical role in establishing a functional gastrointestinal tract and promoting health via stimulating the maturation of the immune, endocrine, and enteric nervous systems. Our project uses a nonhuman primate model to specifically determine the mechanistic pathways through which the infant's gut microbiome affects brain development, as well as emotional and behavioral responsiveness during the first year of life. The mother's microbiome will be experimentally controlled during the pregnancy and nursing periods, enabling us to generate infants with two distinctive microbiome profiles. Microbial community analyses will be run on Illumina MiSeq to acquire high quality 16S rRNA data in order to track the development of the infant's microbiota while it is with the mother, and subsequently after weaning when the gut bacterial community is reorganized, due to the consumption of solid foods and exposure to peers. Based on preliminary results, we focus on two sets of signaling mediators: 1) the production of neurochemicals by gut bacteria, including monoamine neurotransmitters and GABA, and 2) inflammatory cytokine activity in the blood and intrathecal compartments. At one year of age, state-of-the-art neuroimaging will be used for structural analyses of cortical brain regions (gray and white matter volumes), and for quantifying the pace of myelination with diffusion tensor imaging. During the initial R21 phase, we will verify that 20 infant monkeys with distinct microbiome profiles can be reliably generated, and that population differences persist, allowing us to create peer social groups comprised of infants with comparable microbiota. During the R33 phase, a larger cohort of 40 infants will be generated, enabling us to also use fecal transplant methods to systematically change the composition of the gut microbiota and to test the hypothesis that behavioral and neural trajectories will shift to that of the donor animal. This project takes advantage of the unique resources of a major primate breeding facility, permitting us to evaluate a large number of infant monkeys under controlled environmental conditions, and the multi-disciplinary expertise and resources of the collaborating research team at 4 institutions. Innovative methods are employed, including the biopsy collection of gut tissue specimens to validate sequencing and neurochemical conclusions from directed metabolomic panels derived from the more routinely collected rectal swabs. Our project directly addresses a central tenet of the RFA, which is to investigate the influence of the maternal and infant microbiome on behavioral and brain maturation, and to determine if a dysregulated microbiome is associated with atypical development. Multi-tiered, molecular biology and developmental neuroscience approaches are employed. This research has a clear translational relevance for child health, contributing to extant findings that indicate an abnormal microbiome is evident in several pediatric disorders. We are poised to validate new biomarkers for tracking the influence of the gut microbiota on systemic physiology and to make novel discoveries on how the gut and enteric nervous system interface with the microbiome to influence the normal functioning and development of the central nervous system.

DESCRIPTION (provided by applicant): Studies in rodents show that the gut microbiome influences neurodevelopment and subsequent anxiety-related behaviors which are relevant to a wide range of psychiatric illnesses. However, there is a fundamental gap in translating animal data into the clinic: no study has directly tested whether differences in microbial colonization impact anxiety-related behavior in humans. Furthermore, the mechanisms and pathways by which microbiota alter brain development are poorly understood. Our long-term goal is to determine how colonization of the gut microbiome impacts human brain development and later risk for psychiatric illness. The objective of this application is to determine how microbial colonization impacts anxious behavior at 1 year of age and to identify signaling mechanisms and neural circuits mediating this relationship using high resolution magnetic resonance imaging (MRI), diffusion tensor imaging (DTI) and resting state fMRI (rfcMRI). The rationale for the proposed research is that modulation of the gut microbiota could normalize neurodevelopmental trajectories early in the disease process, ultimately preventing the onset of psychiatric illness o reducing its severity. We will achieve our objective through 5 specific aims. In the R21 phase we will: 1) Confirm that sufficient bacterial diversity is present in fecal samples at 2 weeks and 1 year of age to test relationships with anxious behavior, brain development, and hypothesized signaling mechanisms; and 2) Confirm that hypothesized signaling mechanisms can be successfully probed at 2 weeks and 1 year of age. In the R33 phase we will: 3) Determine how patterns of microbial colonization in infancy relate to anxious behavior at 1 year of age; 4) Identify neural circuits which mediate associations between gut microbiota and anxious behavior in human infants; and 5) Determine the signaling mechanisms by which microbiota affect neurodevelopment and anxious behavior in human infants. Our central hypothesis is that anxiety-related behaviors will differ between infants with different patterns of bacterial colonization and this relationship will be mediated by changes in the amygdala, hippocampus, and medial prefrontal cortex. We further hypothesize that microbiota will impact neurodevelopment by altering pro-inflammatory cytokines and cortisol reactivity, potentially through synergistic effects on the kynurenine arm of the tryptophan metabolic pathway. The application is innovative in that it will be the first study to test if and how microbial compositin relates to anxious behavior in a human cohort. The proposed research is significant in that it is an essential first-step in developing novel interventions to promote a healthy microbiome and reduce risk for psychiatric illness.

Abstract A plethora of neuroscience studies has found that Alzheimer?s disease (AD) can be understood as a dysfunction syndrome where the structural and functional connectivity of the large-scale network are progressively disrupted by molecular pathomechanism that is not fully understood. The disruptions to the network exhibit dynamic patterns at different stages of AD, which holds valuable clues to understand AD progression. Current network computational tools are designed for cross-sectional data only, which is insufficient to maintain temporal consistency in investigating longitudinal network changes. To address this problem, we will develop the first extensive computational tool for longitudinal network analysis. Specifically, we will propose a learning-based approach to precisely quantify the evolution of brain network from noisy imaging data (Aim 1). Sparse representation and tensor analysis technique will be integrated to seek for the consistent longitudinal brain networks. We will apply our longitudinal network analysis tool to the series diffusion-weighted imaging (DWI) data from ADNI database to investigate how Alzheimer?s disease attacks human brain network by inspecting the dynamic interactions between the hub and non-hub nodes in AD progression (Aim 2). The outcome of this project will be the first longitudinal brain network analysis tool in computational neuroscience and neuroimaging fields. We will release the software (both binary program and source code), to facilitate the network studies in other neuro diseases that show brain network dysfunction.

Rescuing Missed Longitudinal MRI visits in the UNC Early Brain Development Studies Database PROJECT ABSTRACT In our ongoing R01 (MH123747-01A1) ?The Development of Individual Differences in Adolescent Brain Structure and Risk?) project, we aim to characterize the portion of individual differences in brain structure in the early adolescent brain is already present in the earlier years of life. Early adolescence and puberty is a major period of postnatal brain development, characterized by dynamic structural and functional brain maturation and reorganization, and emerging risk for psychiatric disorders, though it is not known how this period of development contributes to individual differences in brain structure and risk. The UNC Early Brain Development Study (EBDS) is a unique and innovative longitudinal study that has followed children, enrolled prenatally, with imaging and cognitive/behavioral assessments at birth, 1, 2, 4, 6, 8, and 10 years. 482 children from this cohort are now reaching adolescence, and we are following these children at 12, 14, and 16 years of age via MRI, cognitive and behavioral assessments, with a focus on the phenotypes of executive function, attention, and anxiety, consistent with RDoC constructs important for psychiatric disorder risk. One particular aim is to investigate the use of machine learning (ML) for the predictive analysis of early brain development to cognitive and behavioral outcomes in adolescence and to risk for subsequent psychiatric disorders. Yet, most machine learning (ML) algorithms applied to longitudinal data do not perform well (or at all) when data points are missing, as ML methods need both complete data and large sample sizes. As longitudinal studies suffer commonly from significant missing data at different time points due to acquisition failure as well as participant attrition, even a rich database like the UNC EBDS is reduced to a significantly lower sample size by selecting only complete datasets to apply predictive ML (less than a third of the datasets of EBDS data from age 1 ? 10 years is complete). Here, we propose to rescue missing EBDS timepoints (at ages 1 - 10 yrs) of structural MR image data via multi-modal, multi-timepoints image predictions. This image data imputation includes cross-modality image generation (generating missing MRI data from existing MRI data at the same time), where available, as well as multi-timepoints imputation of longitudinal data (generating missing MRI data from existing MRI data at different time points). We will then apply our out-of-distribution model to provide additional information on the appropriateness of the imputed data. Subsequently, the same image processing that was applied to the original EBDS MRI data will be applied to the imputed/generated MRI data to compute missing information of morphometric measures (regional volumes, cortical thickness, surface area, and white matter fiber tract properties). This imputed data will be a highly significant resource for longitudinal ML/AI studies of brain development performed on the EBDS dataset, as it would allow for an increase in training data of over 200%. The original MR images, the imputed MR images, and the morphometric measures will all be shared via NDA, alongside the trained imputation network for use by others.