Marek R. Kubicki, MD, PhD - US grants

Although considerable progress has been made in delineating MRI gray matter abnormalities in schizophrenia, relatively little progress has been made in evaluating white matter abnormalities, or the white matter fiber tract connections between gray matter regions, particularly those that connect the frontal and temporal lobes. These tracts that have long been thought to be abnormal in schizophrenia. Many subsequent investigators have hypothesized some type of connectivity deficit or disconnection model of schizophrenia. We plan to explicitly investigate such anomalies in this proposal. The overarching aim of this three-year proposal is to investigate abnormalities of connectivity at an anatomic and functional level in schizophrenia. Accordingly, we plan to employ a novel and integrative approach to investigating abnormal brain connectivity in schizophrenia, including an analysis of white matter fiber tract abnormalities, as well as the network nodes that these pathways interconnect. As a Driving Biological Project (DBP), this integrative aim will require the highly advanced computational strategies and robust software implementation provided by other subprojects in this grant. This project will emphasize MR Diffusion Tensor Imaging (DTI), a relatively new MR imaging technique that affords a unique opportunity to investigate and quantify white matter abnormalities in the brain. It will also include functional MRI (fMRI) probes of working, episodic, and semantic memory systems known to be abnormal in schizophrenia. Our use of fMRI will focus specifically on connectivity among regions implicated in the pathophysiology of schizophrenia using memory activation tasks to bring out the hypothesized abnormalities. An event related verbal episodic encoding and recognition memory task will target prefrontal and medial temporal sites. This task was developed based on our prior fMRI experience in normals and in patients with known seizure foci undergoing surgical treatment. Earlier evidence using electrophysiological methods suggested sensitivity of similar experiments to frontal vs. temporal lobe lesion location and numerous PET and fMRI studies have implicated these regions in schizophrenia. We will investigate the working memory system with fMRI using the n-back paradigm which has been shown by our group to robustly activate the prefrontal and parietal cortex in neurological patients and controls. Similar results in healthy controls have been widely reported in the PET and fMRI literature. In the present study, this task will afford us an opportunity to examine fronto-parietal connectivity and basal ganglia sites of interest that are also often activated. Finally, an event-related semantic memory task patterned after our earlier blocked design experiments will be used to activate left inferior prefrontal and left superior temporal regions of interest (ROIs). The broader PET and fMRI literature typically implicates these sites for language and semantic memory retrieval. These leading edge technologies will be integrated with volumetric and shape analyses of implicated gray matter structures as well as clinical and neurocognitive assessments to fully characterize the sample. In addition, although not the focus of this project, we will also collect blood from all subjects for genetic analysis.

DESCRIPTION (provided by applicant): The ability to identify and follow structural brain changes during brain maturation and aging is both fundamental to our understanding of brain function, and crucial in clinical studies. While post-mortem studies of human brain can provide data on local histological changes, they are only cross sectional, brain samples are not optimal, and sample sizes are small. In contrast, non-invasive in vivo imaging can provide powerful longitudinal data for large populations. Moreover, post-processing techniques make it possible to analyze the entire brain, providing anatomically specific data that allows for investigating relationships between imaging and function. Recent developments in diffusion MRI and fiber tractography have revealed correlations between imaging changes and cognitive aging in both monkeys (Makris et al, 2007), and humans (Voineskos et al., 2012). Unfortunately, the biological underpinnings of such imaging changes are largely speculative (Paus 2010) and hence the specificity of imaging measures for histological features is unknown. The lack of such validation is largely due to the inability to conduct well-controlled studies of both brain tissu and imaging in humans. In this application we propose a multidisciplinary study using the rhesus monkey model of normal aging. This is enabled by a collaboration of three PIs, with unique and complementary expertise in MRI imaging, morphometry, neuroanatomy and cognitive aging. We have available a cohort of over 50 normal aging rhesus monkeys of both sexes, ranging in age from 5 (young adults) to over 30 (oldest of the old) years of age. Most important is the availability of cognitive and DTI data that can be used for histopathological validation of archived, cryoprotected, unstained tissue from all of these monkeys. The main aims of this proposal are: 1). To establish sensitivity of individual diffusion tensor imaging (DTI) metrics to cognitive maturation and aging, 2). To investigate biological underpinnings of diffusion changes during cognitive maturation and aging, and 3). To develop free imaging tools necessary to accomplish the goals of this proposal. The results of this proposed study will greatly impact our understanding of aging processes and their mechanisms and provide tissue validated understanding of imaging measures that can be applied to studies of normal human aging as well as many other clinical populations including traumatic brain injury, multiple sclerosis, and schizophrenia.

DESCRIPTION (provided by applicant): Project Summary/Abstract White matter plays a critical role in brain communication and connectivity. While schizophrenia has been hypothesized to be a dis-connection syndrome, the exact biological nature of white matter abnormalities in this disease is still unknown. Furthermore, while multiple publications report white matter abnormalities in schizophrenia, those findings to date have not lead to further understanding of schizophrenia etiology or new developments in pharmacological treatment. This is primarily because imaging measures remain nonspecific to the underlying microstructural pathology, and changes observed with MRI have been viewed as an ongoing consequence of gray matter pathological processes, thus not worth pursuing the possibility of white matter involvement further. In this application we propose to go beyond measuring white matter integrity. We hypothesize that white matter in schizophrenia may be compromised by several, distinct pathological processes that can be observed at different stages of the disease (some even before the presence of clinical symptoms), and thus could potentially constitute biomarkers of risk, onset, and outcome of schizophrenia. The overall goal of this proposal is to use Diffusion MRI, along with the newest MRI acquisition and analysis methods, and to apply them to distinct schizophrenia and schizophrenia related populations (both retrospectively as well as prospectively). With these tools and measures, we intend to test three related, complementary theories: A) Schizophrenics as well as subjects at risk for developing schizophrenia share white matter signatures that may be related to neurodevelopmental disruptions of white matter geometry; B) acute psychosis is likely associated with a pathology (neuroinflammation) affecting the extracellular volume; and C) chronic schizophrenia is likely associated with increasing cellular (myelin) pathology. The assembled team of computer scientists and clinical neuroscience researchers will work to localize and to characterize the neurobiology that underlies white matter changes which are frequently reported along the time course of schizophrenia. The results of this study will have an important impact on our understanding of mechanisms underlying schizophrenia, and provide imaging biomarkers of vulnerability, psychosis, outcome, and measures that can be used to monitor the efficacy of medical treatment. What makes this application unique is the assembly of tools that go beyond standard DTI, and access a number of schizophrenia populations, where these tools can be all applied.

Abstract I am a clinical radiologist and neuroscientist by training. I have devoted my research career to understand mechanisms, nature and time-course of white matter pathology in schizophrenia. I am an Associate Professor in Departments of Radiology and Psychiatry at Brigham and Women?s Hospital, Harvard Medical School. I also serve as Associate Director of Psychiatry Neuroimaging Laboratory, Department of Psychiatry at BWH, and as a co-Director of Center for Morphometric Analysis, Department of Psychiatry, and Massachusetts General Hospital, where I have a secondary appointment as research scientist. I have been conducting an NIH sponsored research since 2003, when I received my first R03 award. I am currently PI on an NIMH R01 award, focusing on Development of New Neuroimaging Biomarkers of Risk, Onset and Outcome of Schizophrenia. I am also a PI on NIA funded R01 on understanding mechanisms of brain development, maturation and aging through a set of neuroimaging tools and their validation in rhesus monkeys. Those two grants together have the potential of delivering validated neuroimaging biomarkers, which could be used to diagnose and monitor neurobiological changes due to myelin loss and neuroinflammation in schizophrenia, aging, and a whole list of other neuropsychiatric diseases where those changes are involved. Besides my research, I am also involved in mentoring Harvard, MIT and BU undergraduate and graduate thesis students, postdoctoral fellows (including T32 and K23 awardees), foreign fellows, and summer students. I am also involved in administrative work, as head of two large laboratories, at BWH and at MGH, and various local and regional committees. Finally, I am getting involved in nonclinical and non-POR research (through my ongoing and pending animal projects), and that takes away from both my mentoring and my POR research. The K24 would protect time and help me to focus on most important for my career development- mentoring (30%), further training and new POR (20%), while devoting remaining 50% to ongoing neuroimaging research in biomarkers of white matter changes in aging and schizophrenia.

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.