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Oliver C. Lyttelton

Researcher at Montreal Neurological Institute and Hospital

Publications -  9
Citations -  1659

Oliver C. Lyttelton is an academic researcher from Montreal Neurological Institute and Hospital. The author has contributed to research in topics: Brain morphometry & Planum temporale. The author has an hindex of 8, co-authored 9 publications receiving 1504 citations. Previous affiliations of Oliver C. Lyttelton include McGill University.

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An unbiased iterative group registration template for cortical surface analysis.

TL;DR: This paper presents a methodology for developing unbiased, high resolution iterative registration templates from a group of 222 subject hemispheres and shows that the resulting template provides better alignment of a separate set of test data than single-subject templates.
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Brain Size and Cortical Structure in the Adult Human Brain

TL;DR: It is suggested that sex effects are explained by brain size effects in cortical structure at a macroscopic and lobar regional level, and that it is necessary to consider true relationships between cortical measures and brain size due to the limitations of linear stereotaxic normalization.
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Multi-level bootstrap analysis of stable clusters in resting-state fMRI.

TL;DR: A generic statistical framework to quantify the stability of such resting-state networks (RSNs), which was implemented with k-means clustering and demonstrated the capacity of BASC to establish successful correspondences between these two levels of analysis and at the same time retain some interesting subject-specific characteristics.
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SurfStat: A Matlab toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory

TL;DR: SurfStat: A Matlab toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory is presented.
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Positional and surface area asymmetry of the human cerebral cortex.

TL;DR: This study uses fully automated, surface-based techniques to analyse position and surface area asymmetry for the mid-surfaces of 112 right-handed subjects' cortical hemispheres from a cohort of young adults to resolve the confound of these variables inherent in VBM studies.