Colin Holmes - US grants
Affiliations: | 1987-1993 | Neurological Sciences | McGill University, Montreal, QC, Canada |
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The funding information displayed below comes from the NIH Research Portfolio Online Reporting Tools and the NSF Award Database.The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
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High-probability grants
According to our matching algorithm, Colin Holmes is the likely recipient of the following grants.| Years | Recipients | Code | Title / Keywords | Matching score |
|---|---|---|---|---|
| 1997 — 1999 | Holmes, Colin Toga, Arthur [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Shared Instrumentation For Computational Neuroanatomy @ University of California-Los Angeles Recent enhancements in the resolution of primary data and the complexity of algorithms have resulted in significant increases in the computational load for the neuroimaging, `rain mapping and biomedical imaging communities. In response to these increased demands, a group of neuro- and biomedical scientists with common interests and computational needs have come together to seek funding for a shared graphics supercomputer. This group has a critical need for potent visualization hardware, extremely fast, parallelizable CPU architectures and considerable amounts of data storage and transfer capabilities. This equipment is required to satisfy the demands generated by three dimensional volume viewing, intricate surface extraction, nonlinear warping and the volumetric blending of complex polygonal objects with both raw data and the results of statistical analysis. Housed at the Laboratory of Neuroimaging at UCLA, the device will be available on a shared basis for interactive use locally and batch processing remotely. After evaluation of several vendors of graphics platforms, the equipment selected was an Onyx graphics supercomputer manufactured by Silicon Graphics Inc. Simulations and benchmarking confirmed the ability of this instrumentation to fulfill the needs of the participants. An administrative plan was created to manage the equipment equitably and to optimize its utility for the participating research projects. The ongoing research in nine local and four remote projects will directly and immediately benefit by using existing software that is capable of taking advantage of the features of the instrument. Programmers and other experts in this architecture are already members of the staff. Technical and management personnel are also among the funded group of participants. The existing collaborations of the participants and the common algorithmic requirements will enable sharing of computer code, analytic procedures and computational strategies. The availability of suc h a machine will enhance the productivity of ongoing funded research and foster the use of leading edge technology for all participants. |
0.961 |
| 1998 | Holmes, Colin J | P41Activity Code Description: Undocumented code - click on the grant title for more information. |
Neuroimaging Modeling Resource: Anatomical Fundamentals @ University of California Los Angeles The anatomic analysis of neuroimaging data sets has traditionally focused on the development of tools to extract or quantify features within the data that are obvious to casual visual inspection. This has resulted in an emphasis on percellating the brain on the basis of large scale macroscopic features. Such features are typically either bounded by abrupt changes in signal intensity (e.g., identification of the thalamus as distinct from the surrounding! white matter. or more generally. segmentation of tissue into gray matter, white matter and CSF), or are defined in terms of their gross morphometric characteristics (e.g., delineation of the boundaries of specific sulci or gyri). The importance and utility of tools to facilitate the analysis of macroscopic anatomic-dc features in neuroimaging data is clear, and the tools for such analyses are so mature that their existence and use is implicit throughout this proposal. The emphasis of this section will be the development of new tools tha t will allow us and our collaborators to push beyond the limits of macroscopic anatomy into the realm of features traditionally associated with microscopic anatomy. Although not necessarily obvious on casual visual inspection of the images, the latest generation of imaging technology has crossed resolution thresholds that allow brain images to be anatomically subdivided on the basis of features that are rooted in the microscopic realm. Tools that are based on a clear understanding of the associated microscopic anatomic fundamentals will allow optimal detection and analysis of these features. assuring that we will be able to take full advantage of each future incremental increase in image quality. Specific Aims 1. Develop methods for enhancing the laminar organization of the cortex so that it can be detected, quantified, and compared in MRI data sets both within and across subjects and during development or aging. Specific Aims Specific Aims 2. Develop general tools that can incorporate contextual information (e.g., texture or local orientation), prior anatomic expectations. or expert neuroanatomic guidance, to generate signals that are optimally sensitive to microscopic anatomic features. Specific Aims 3. S systematically ) explore anatomic resources to identify microscopic features expected to produce characteristic MRI signatures. Specific Aims 4. Anatomically validate the tools developed in this project using unique data sets including post-mortem cryornacrotome data of subjects who underwent antemortem MRI scanning. |
0.919 |