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Variable clustering reveals associations between subcortical brain volume and cognitive changes in pediatric traumatic brain injury

, , , , , , , , , and . 12th International Symposium on Medical Information Processing and Analysis, SPIE, (January 2017)
DOI: 10.1117/12.2256977

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Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3., , , , , , , , , and . Frontiers Neuroinformatics, (2019)Diffusion tensor distribution function metrics boost power to detect deficits in Alzheimer's disease., , , , , , , , , and . ISBI, page 1088-1092. IEEE, (2016)Pipeline for Analyzing Lesions After Stroke (PALS)., , , , and . Frontiers Neuroinformatics, (2018)Deep Learning for Quality Control of Subcortical Brain 3D Shape Models., , , , , , , , , and 3 other author(s). ShapeMI@MICCAI, volume 11167 of Lecture Notes in Computer Science, page 268-276. Springer, (2018)Variable clustering reveals associations between subcortical brain volume and cognitive changes in pediatric traumatic brain injury, , , , , , , , , and . 12th International Symposium on Medical Information Processing and Analysis, SPIE, (January 2017)Feature selection improves the accuracy of classifying Alzheimer disease using diffusion tensor images., , , , , , , and . ISBI, page 126-130. IEEE, (2015)Multi-Shell Diffusion MRI Measures of Brain Aging: A Preliminary Comparison From ADNI3., , , , , , , , , and 2 other author(s). ISBI, page 173-177. IEEE, (2019)Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging., , , , , , , , , and 2 other author(s). MLMI@MICCAI, volume 10541 of Lecture Notes in Computer Science, page 371-378. Springer, (2017)