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David H. Laidlaw
Researcher at Brown University
Publications - 248
Citations - 10822
David H. Laidlaw is an academic researcher from Brown University. The author has contributed to research in topics: Visualization & Diffusion MRI. The author has an hindex of 49, co-authored 246 publications receiving 9917 citations. Previous affiliations of David H. Laidlaw include California Institute of Technology & University of Miami.
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Journal ArticleDOI
Permutation and parametric tests for effect sizes in voxel-based morphometry of gray matter volume in brain structural MRI.
David Alexander Dickie,Shadia Mikhael,Dominic Job,Joanna M. Wardlaw,David H. Laidlaw,Mark E. Bastin +5 more
TL;DR: Permutation testing inference may provide a more sensitive method than traditional parametric inference for identifying age-related differences ingray matter proportion and should be used in future univariate VBM studies investigating age related changes in gray matter to avoid potential false findings.
Journal ArticleDOI
In vivo recruitment patterns in the anterior oblique and dorsoradial ligaments of the first carpometacarpal joint
Eni Halilaj,Michael J. Rainbow,Douglas C. Moore,David H. Laidlaw,Arnold-Peter C. Weiss,Amy L. Ladd,Joseph J. Crisco +6 more
TL;DR: Findings of mean ligament recruitments across the CMC range of motion indicate that the AOL is likely slack during most physiological positions, whereas the DRL may be taut and therefore support the joint in positions of CMC joint abduction and flexion.
Journal ArticleDOI
A Comparative evaluation of voxel-based spatial mapping in diffusion tensor imaging.
TL;DR: Results show that reliability depended greatly on the method used for spatial mapping, but less on skeletonization or template type, and that large deformations between them may be related to observed differences in patterns of significant voxels.
Journal ArticleDOI
Loose, artistic "textures" for visualization
TL;DR: While this approach is more open-ended than a perceptual psychology approach, both approaches are worthy of pursuit, and the potential benefits of using the less structured approach outweigh any risk of failure.
Proceedings ArticleDOI
A Kinematics-Based Method For Generating Cartilage Maps and Deformations in the Multi-Articulating Wrist Joint From CT Images
TL;DR: This proposed cartilage model, a meshless incompressible height-field captures the physical properties important for estimating the shape, contact area, and deformation magnitude of cartilage at each articulation and can serve as an effective building block for a future forward-dynamic predictive model of the human wrist.