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Colin J. Holmes

Researcher at University of California, Los Angeles

Publications -  31
Citations -  12972

Colin J. Holmes is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Population & Raphe nuclei. The author has an hindex of 22, co-authored 31 publications receiving 12277 citations. Previous affiliations of Colin J. Holmes include Montreal Neurological Institute and Hospital & McGill University.

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A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM)

TL;DR: The ability to quantify the variance of the human brain as a function of age in a large population of subjects for whom data is also available about their genetic composition and behaviour will allow for the first assessment of cerebral genotype-phenotype-behavioural correlations in humans to take place in a population this large.
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Design and construction of a realistic digital brain phantom

TL;DR: The authors present a realistic, high-resolution, digital, volumetric phantom of the human brain, which can be used to simulate tomographic images of the head and is the ideal tool to test intermodality registration algorithms.
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Automated Image Registration: I. General Methods and Intrasubject, Intramodality Validation

TL;DR: The registration algorithm described is a robust and flexible tool that can be used to address a variety of image registration problems and can be tailored to meet different needs by optimizing tradeoffs between speed and accuracy.
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In vivo evidence for post-adolescent brain maturation in frontal and striatal regions.

TL;DR: The pattern of brain maturation during these years was distinct from earlier development, and was localized to large regions of dorsal, medial and orbital frontal cortex and lenticular nuclei, with relatively little change in any other location.
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Enhancement of MR images using registration for signal averaging

TL;DR: The usefulness of intrasubject registration for post hoc MR signal averaging for in vivo human neuroanatomy was investigated and the enhanced signal in the averaged images resulted in higher quality anatomical images, which lent themselves to several postprocessing techniques.