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CCB (U54) Grant Interactions – LONI Resource (P41)


Follow this link to view the Grants Interaction Chart for all LONI Grants

CCB (U54) Synopsis

An interdisciplinary effort for developing exotic mathematical techniques and engineering new computational tools targeting specific DBP challenges, yet applicable beyond their scope.

  • Development of novel mathematical and computational algorithms and techniques for:
    • representation of biological shape, form and size
    • modeling of biological shape, form and size
  • Design, implementation and validation of efficient and robust computational tools for:
    • management of complex biomedical data
    • integration of imaging and non-imaging biomedical data
    • analysis of biomedical data
    • visualization of biomedical data

Overview LONIR (P41) Synopsis

A collaborative resource for managing and dissemination tools for neuroimaging and brain mapping research.

  • Atlasing
  • Database
  • Visualization
  • Pipeline
  • Infrastructure and Service
  • Cores

CCB (2004-2009) symbiosis / LONIR P41 Continuation (2007-2012)

  • CCB Core Research Aims
    • Core 1: Computational Science
      Aims: level sets methods for nonlinear image registration; modeling, analysis and visualization of biological shape; conformal mapping; level-sets and PDE-based data segmentation
    • Core 2: Computational ToolsAims: biosequence analysis; alternative splicing annotation tools; integration and interoperability of various computational biology resources and tools for data mediation, parsing and interpretation
    • Core 3:Aims: Mapping of quantitative trait loci associated with variations of brain morphology in primates; HIV/AIDS induced dementia; shape analysis of phenotypic variation in Drosophila melanogaster; deformable registration methods for studying age-related white matter changes
  • LONIR P41 Projects and Aims
    • Project 1: Brain Image Processing & Segmentation
      Aims: improving BrainSuite masking, morphology-based segmentation and performance; automatic labeling of cortical anatomy and combined surface and volumetric registration
    • Project 2: Deformation Morphometry & RegistrationAims: fluid registration using mutual information; Tensor-based morphometry; Statistics for tensor morphometry; Fluid registration of DTI and HARDI
    • Project 3: Brain AtlasingAims: construction of optimal brain atlases; multivariate analysis statistical toolkit; Toolkit for model selection
    • Project 4: NeuroinformaticsAims: brain visualization; image de-identification and brain imaging data mining; neuroinformatics pipeline and data provenance
  • Interactions
    • LONIR is a resource for collaborative neuroimaging research, CCB is a more general computational biology center.
    • LONIR designs and disseminates advanced tools for brain mapping (e.g., AIR, Pipeline), CCB develops new mathematical and computational techniques for studying broad range of problems related to biological shape, form and size (e.g., ShapeLibrary, Statistical Learning Toolkit, iTools).
    • LONIR’s domain of application is the brain, CCB’s scope is more diverse.
    • Data and neuroscientific expertise from LONIR and used to develop and validate new CCB mathematical algorithms and their computer implementations.
    • Novel scientific techniques developed by CCB fuels the applied neuroimaging research conducted within LONIR
    • LONIR tools are applied in nature and employed mainly for neuroimaging studies. CCB tools are based on new theoretical models and are typically more applicable for a wider range of biological problems.

Research, Science and Tools Interactions

  • Science
    • LONIR is a resource for neuroscience collaborations. The algorithms developed as part of LONIR consist solely of classical or more widely accepted techniques (Euler-Lagrange optimization, random processes, continuum mechanics, signal processing).
    • Whereas the mathematical methods employed in CCB research are more 21st century and exotic (level sets, harmonic analysis, conformal parameterization, Monte-Carlo Markov Chains, genetic additive common environment (ACE) modeling, ReML, etc.)
  • Tools
    • CCB introduces algorithms and efficient software products that are widely applicable for variety of biological systems, application domains and covers the range of laboratory to clinical research.
    • LONIR designs and distributes software tools for cutting edge brain mapping and neuroimaging studies.
  • Symbiosis The LONIR and CCB research efforts have symbiotic relations.
    • CCB work produces more broadly applicable biomedical research methods;
    • LONIR is focused on the cutting edge of brain mapping and neuroimaging. In other words, LONIR is applied brain mapping research. CCB is basic computational science with matching biomedical tools.
    • New abstract mathematical models developed by CCB are transformed and fine-tuned as sensitive methodologies in the neuroscience research undertaken at the LONIR.
    • Conversely, many of the data modeling and analysis challenges of LONIR serve as a driving force for the mathematical developments in CCB.
  • Specifically
    • CCB research focuses on:
      • Representation, Analysis and Visualization of Biological Shape, Form and Size
      • Develop novel mathematical computationally efficient algorithms for general biomedical research
      • Expand the current computational framework using Grid and Network computing
      • Efficient modeling and analysis of data obtained from various imaging, genetics, biological and other sources.
    • LONIR efforts are on:
      • Generate brain mapping specific algorithms and validate them on multimodal multidimensional imaging data
      • Increase the amount and quality of the collaborative projects and tools we distribute to the community
      • Atlas development