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Neurobiology

Each of the CCB Driving Biological Projects (DBP) addresses heterogeneous aspects of computational biology. Their breadth and diversity is necessary for the joint efforts that develop the computational atlas and shape representation infrastructure within the Center. The NIH Roadmap was established in part to facilitate the building of this kind of computational biology infrastructure via collective, multidisciplinary and biomedically driven endeavors. The section below includes details of the current and graduating CCB DBP projects.

The following CCB Driving Biological Project report shows our 2004 – 2007 progress in developing new computational methodologies and applying these to various biological systems. The specific biological research endeavors we completed in 2007 included Mapping Language Development Longitudinally, Mapping Brain Changes in Alzheimer’s and Those at Risk, Mechanisms Underlying the Clinical Progression of MS and EAE, and Genetic Influences on Brain Structure in Schizophrenia. View the Report

CCB DBP RFA

Applications due December 10, 2009.

CCB/NCBC Collaborations
Currently, there are three official CCB/NCBC collaborative projects. These are:

  • The Cognitive Atlas Project (http://www.cognitiveatlas.org) aims to develop a knowledge base (or ontology) of mental processes, mental tasks, and brain systems. The project is led by Russell Poldrack of the UCLA Departments of Psychology and Psychiatry & Biobehavioral Sciences. This research will develop a system for collaborative editing of the knowledge base. This will facilitate more efficient collaborations of researchers from the cognitive science and neuroscience communities.
  • The Cardiac Atlas Project (http://www.cardiacatlas.org) is led by Alistair Young of the Department of Anatomy with Radiology of the University of Auckland, New Zealand. The goal of this project is to establish a web-accessible structural and functional atlas of the normal and pathological heart for clinical, research, and educational purposes. The Cardiac Atlas Project (CAP) will provide the infrastructure for managing substantial number of Cardiac Magnetic Resonance (CMR) scans, derived functional analyses, and associated clinical information. The CAP database will be extendible to allow inclusion of data from coronary angiography, ECG, histology, blood proteins, peptides and other serological data, and genomic information in the longer term.
  • The Diffeomorphic Neuroanatomical Registration Framework (http://www.picsl.upenn.edu/) is a project lead by James Gee at the University of Pennsylvania. This project incorporates specific anatomical constraints with public-domain deformable image registration methods. It also develops a discrete domain diffeomorphic image registration methodology which unifies common a priori constraints in a single, flexible image normalization framework. This framework allows neuroscientists to explore all ranges of diffeomorphic transformation models, including matching brain sulci, white matter surfaces, image volumes or some combination thereof. Regularization models include new, elastic diffeomorphisms, viscoelastic diffeomorphisms and the extremely powerful group of general diffeomorphisms.
  • The Reconstruction and Mapping of Human Brain Vasculature project, investigates the cause of death and long term disability of cerebrovascular diseases. The project gained knowledge of the human brain’s vascular architecture in relating hemodynamics to physiopathology, which facilitates the diagnosis and treatment of cerebrovascular disease. The project is conducted at George Mason University by Dr. Juan Cebral. The 3 aims of the project include: Aim1 – Digital reconstruction of a normative set of MRA: a) Build 3D vascular reconstructions from MRA data of normal subjects. b) Evaluate and verify the vascular reconstructions using a battery of reliability tests; Aim2 – Statistical morphometric analysis: a) Characterize the geometry and branching topology of the arterial structures of the brain. b) Conduct statistical analysis to establish inter-subject variability of the vascular structural characteristics and test for differences between males and females, and left/right hemispheres; Aim3 – Probabilistic angiographic atlas: Construct a brain vascular atlas using the CCB registration pipeline and integrate with the other modalities of the CCB brain atlas.

New DBPs (2007 – 2009/2010)

In 2006 – 2007, we conducted an extensive search for new Driving Biological Projects that will stimulate the development of new computational models and software tools within the CCB for the next three years (2007 – 2010). A number of applications were received from UCLA and outside institutions. These applications were reviewed by the CCB Committee on Collaborations, the CCB Science Advisory Board, and the CCB Science and Program officers at the NIH. Four outstanding applications were selected to be funded for the next three years. Two of the project PIs are within UCLA and two are from outside institutions (Florida State University and Virginia Tech).

Identifying Age Related Atrophy Using Levelset Registration of Embedded Maps (PI Christopher Wyatt)
Structural brain changes that occur during the process of normal aging are of great interest to neuroscientists investigating age-related changes in cognitive function. A variety of techniques currently exist to identify group differences in brain anatomy with the greatest focus having been placed on cortical anatomy. Although cortical anatomy is of considerable significance, the underlying white matter tracts are at great risk in the aged brain. The goal of this Driving Biologic Project (DBP) is to evaluate the relationship between changes in cognitive function and cerebral white matter in older adults. More

Developmental Origin of Phenotypic Variation in Drosophila Melanogaster

This project’s overall goal is to help close the gap between the increasingly rapid ability to sequence the DNA (genotype) of an organism and the relative lack of techniques for measuring the even more complex physical form (phenotype) of the organism. The measurement of complex, high-dimensional phenotypes is an essential complement to the genomic-level data. We call the integration of such phenotypic data with genetics “phenomics”. This project exploits an existing system for automated wing measurement, and will develop techniques for the rapid measurement of other phenotypes, including whole flies. More

Mapping Brain Changes in HIV/AIDS

This 3-year project will provide enormous insight into HIV/AIDS. Knowledge of how HIV affects the brain is sorely lacking. Working with members of the UCLA Center for Computational Biology (CCB), we will develop a multidimensional, computational atlas of HIV/AIDS, including the most powerful tools to track HIV disease progression in the brain. We will investigate how specific factors affect the rate of brain degeneration, such as anti-retroviral therapy, duration of illness, age and gender, and viral load. We will relate brain changes over time to immune system measures, such as CD4+ T-cell counts. This effort will create the computational tools to study how deficits in HIV patients emerge and spread in the living brain. More

Vervet Genetics and Brain Morphology

Using MRI, many aspects of normal and abnormal human brain morphology have been demonstrated to be genetically mediated, and in some instances, the responsible genes have been identified. While studies of crosses between inbred mouse strains have demonstrated that Quantitative Trait Loci (QTLs) responsible for normal variations in brain morphology can be identified, the vast structural and behavioral differences between mice and humans render these murine QTLs less likely to be relevant to the human. The vervet monkey is an Old World primate that is ideal to serve as an intermediate animal model for the investigation of the genetic underpinnings of brain morphology as well as for more general investigations of genetic/morphologic/biochemical/behavioral relationships. We propose here to perform MRI of all 433 adolescent and adult members of the UCLA Vervet Research Colony (VRC). The MRI scans will be used to define brain phenotypes including brain volume, cerebellar volume, corpus callosal area and hippocampal volume. The heritability of these phenotypes will be estimated, and QTLs associated with the heritable phenotypes will be identified using more than 350 polymorphic markers already identified in the vervet genome. Enhanced with the MRI phenotypes from this project, the VRC will be an extraordinary resource for the study of relationships between genes, brain, and behavior in the living primate. More

Graduating DBPs

Mapping Language Development Longitudinally

Principal Investigators: Elizabeth Sowell & Mirella Dapretto

The overarching goal of the proposed studies is to longitudinally investigate the functional and behavioral significance of structural change recently observed in classical brain language regions during the process of normal development. By delineating the relationship among linguistic and communicative developments, brain function, and brain structure, this study will bridge the informational gap on the neural basis of language in the normally developing brain. We will use functional and structural magnetic resonance imaging and detailed neurocognitive assessments to assess the following specific aims: 1) To quantify change in functional and structural signal within children studied longitudinally; 2) To investigate whether change in functional signal across time is related to change in the underlying brain structure; and 3) To relate behavioral language change to changes in brain structure and function.

Mapping Brain Changes In Alzheimer’s & Those at Risk

Principal Investigator: Paul Thompson

New strategies to map the impact of Alzheimer’s disease on the human brain are vital to detect the disease earlier, understand how its spreads, and track how drug treatments combat it. Without a cure, the number of AD victims will rise from 2 to 3.5 million now to an estimated 10-14 million by the year 2030. Several promising treatments for AD are being developed, but most therapeutic trials rely on clinical symptoms or cognitive testing as the endpoint to determine efficacy. Imaging methods to quantify the emergence of AD in the brain before clinical symptoms appear, and chart its progression in the brain, would be of tremendous value in combating dementia. As new treatments are developed, there is an urgent need to develop imaging measures that can be readily applied to large numbers of patients over time. Dynamic brain mapping strategies, proposed here, will help evaluate therapies by providing powerful biological markers for the disease process, including new techniques to map how it spreads in the brain, and detect small changes in rates of progression. These maps and metrics will track, in exquisite detail, how the disease spreads in patients and those at risk. We will also study how this spread is modified by effective interventions.

Mechanisms Underlying the Clinical Progression of MS and EAE

Principal Investigator: Rhonda Voskuhl

The neuroimaging and neuropathology of the clinical progression in disability in Multiple Sclerosis (MS) and Experimental Autoimmune Encephalomyelitis (EAE) will be examined for causal associations using longitudinal studies. Focus will be placed on abnormalities in white and gray matter anterograde and retrograde to inflammatory lesions. In essence, we will focus “beyond the lesion”. Firstly, we will determine the rate of structural change in white and gray matter as measured by MRI in RRMS patients either unlikely (early RRMS) or likely (late RRMS) to transition to SPMS. Secondly, we will determine the rate of structural change in white and gray matter as measured by MRI in mice during early, middle and late stages of EAE. Thirdly, we will determine the anterograde and retrograde effects of EAE induced axonal injury during early, middle and late stages of EAE. Finally, we will then integrate data from all categories.

Genetic Influences on Brain Structure in Schizophrenia

Principal Investigator: Tyrone Cannon

This project focuses on the development of new computational tools at the intersection of neuroimaging and genetics and the application of these tools to the inheritance and expression of a complexly-determined neuropsychiatric phenotype, schizophrenia. The project incorporates the 3-dimensional probabilistic brain atlas framework fundamental to all of the computational neuroscience applications at the Center for Computational Biology, but extends this backbone in novel ways to incorporate modeling of the genetic and non-genetic determinants of variability in regional brain structure and function, as well as the contributions of specific DNA polymorphisms to these traits, on a voxel basis. In integrating this study, we aim both to drive the development of new computational algorithms for testing genetic hypotheses in relation to neuroimaging-based phenotypes, and to leverage these new tools into novel discoveries concerning the genetic architecture and pathophysiology of schizophrenia.