Sylvain Bouix - US grants

DESCRIPTION (provided by applicant): This R01 application is for five years of funding to develop, evaluate and apply novel computational tools for the purpose of understanding morphometric changes in neuroanatomical structures related to schizophrenia. Shape measures are of interest in schizophrenia because this disorder is viewed by some as a neurodevelopmental in origin and because there is evidence to suggest that during morphogenesis of the brain, abnormal pressures and/or tissue formations likely change the shape of brain structures, particularly those in the midline of the brain, as well as influencing folding patterns of the neocortex. We believe that computational morphometry tools are critical to characterize and to quantify shape changes accurately. In fact, neuroscience research as a whole has shown a growing interest in computer assisted shape studies for numerous conditions including, but not limited to, normal neurodevelopment, Alzheimer's disease, schizophrenia and schizotypal personality disorder (SPD), bipolar disorder, psychotic affective disorder, and fetal alcohol exposure. Our primary objective is thus to develop further new image analysis techniques to enable the detection and localization of shape differences between populations. We will apply this new technology to selected brain structures in first episode schizophrenic subjects compared to first episode affective subjects (mainly manic), and normal controls. Our secondary objective is to quantitatively evaluate and compare current state-of-the-art shape analysis tools, including ours, as few algorithms have been validated. Accordingly, a synthetic data set with known shape modifications will be created to use in a control experiment. In addition, we will evaluate all shape techniques on real data with previously observed shape changes (i.e., caudate nucleus in SPDs and amygdala-hippocampus in patients with schizophrenia). Finally, in an effort to promote open science, we will make all results, data, parameters and algorithms publicly available to the scientific community. By characterizing and delineating shape abnormalities in schizophrenia, we will understand further the role of brain morphometry abnormalities in schizophrenia, a disorder that is a major public health problem, affecting close to 1% of the general population. PUBLIC HEALTH RELEVANCE: A well accepted hypothesis is that some brain disorders are neurodevelopmental in origin and that during morphogenesis of the brain, abnormal pressures or tissue formations likely impact the proper development of neuroanatomical structures. The goal of this project is to design computational tools to analyze the morphometry of brain structures in the context of schizophrenia and related disorders. We believe that our proposed study, to design and to evaluate computational morphometric tools, will provide us with invaluable information about normal and abnormal neurodevelopment and its correlation with mental illnesses.

While mild traumatic brain injury (mTBI) has become the focus of many neuroimaging studies, the understanding of mTBI, particularly in patients who evince no radiological evidence of injury and yet experience clinical and cognitive symptoms, has remained a complex challenge. Sophisticated imaging tools are needed to delineate the kind of subtle brain injury that is extant in these patients, as existing tools are often ill-suited for the diagnosis of mTBI. For example, conventional magnetic resonance imaging (MRI) studies have focused on seeking a spatially consistent pattern of abnormal signal using statistical analyses that compare average differences between groups, i.e., separating mTBI from healthy controls. While these methods are successful in many diseases, they are not as useful in mTBI, where brain injuries are spatially heterogeneous. The goal of this proposal is to develop a robust framework to perform subject-specific neuroimaging analyses of Diffusion MRI (dMRI), as this modality has shown excellent sensitivity to brain injuries and can locate subtle brain abnormalities that are not detected using routine clinical neuroradiological readings. New algorithms will be developed to create Individualized Brain Abnormality (IBA) maps that will have a number of clinical and research applications. In this proposal, this technology will be used to analyze a previously acquired dataset from the INTRuST Clinical Consortium, a multi-center effort to study subjects with Post- Traumatic Stress Disorder (PTSD) and mTBI. Neuroimaging abnormality measures will be linked to clinical and neuropsychological assessments. This technique will allow us to tease apart neuroimaging differences between PTSD and mTBI and to establish baseline relationships between neuroimaging markers, and clinical and cognitive measures. Upon completion of this project, a set of tools, which have the potential to establish radiological evidence of brain injury in mTBI, will have been designed and evaluated, thereby enhancing both the diagnosis and monitoring of progression/recovery of injury, as well assessing the efficacy of therapies on the injured brain. RELEVANCE (See instructions): One major limitation to standard clinical care is that imaging methods used routinely for diagnosis are not sensitive enough to detect the subtle pathologies of mild Traumatic Brain Injury (mTBI). The overarching goal of this proposal is to design tools to create individualized brain injury maps from diffusion MRI that can detect these subtle abnormalities, and help establish a link between imaging and symptomatology in mTBI.

Project Summary The past decade has brought an explosion in the development of precise acquisition and analytic tools (both mathematical and statistical) that allow for investigating in-vivo brain imaging data in the context of interconnected networks, or connectomes. This rapid advancement, fueled by additional funds from Congress (BRAIN initiative), however, is not mirrored by the development of neuroanatomy atlases and ontology tools that would make in-vivo connectivity analysis more accurate. For one, the advent of the Human Connectome Project, generating large high-resolution datasets, is creating a need for development of corresponding, high definition atlases. This, however, requires time, and interdisciplinary anatomical knowledge. In addition, the atlases that exist are rarely portable, flexible, or easily transferable between image analysis tools, and do not follow a common standard of neuroanatomy. The overall aim of this project is to generate and disseminate state-of-the-art, high-resolution full brain MR atlases, as the extension of the previous version of the Harvard-Oxford Atlas (HOA), a popular and widely available atlas through FMRIB Software Library (FSL) atlas, developed in our labs over a decade ago. As part of this project, we will: 1. Consolidate expert neuroanatomical knowledge into a single ontology. This will include the development of a graphical representation of regions' structural relationships, both hierarchical (lobes, lobules, gyri and subcortical structures) and network-based; 2. Manually parcellate (using developed ontologies) 200 MR datasets provided as part of the publicly available Human Connectome Project dataset; and 3. Refine a software platform for storing, editing and disseminating the atlases that will include version control and the ability for the neuroanatomical community to contribute new atlases or modifications to this 2nd Generation HOA atlas. Upon the successful completion of this project, we will have provided the neuroscientific community with an unprecedented data set of 200 high definition MR data parcellated into 392 gray and white matter PUs using structural MRI and 189 white matter fascicles using diffusion MRI. All structures will be described in a comprehensive taxonomy based on decades of neuroanatomical expertise. Importantly, the data set will be freely available to the community and distributed in an atlas-tailored revision control system, that will track changes in atlas image data and metadata and will integrate with tools for atlas release and distribution.