TY - GEN
T1 - Mapping lifetime brain volumetry with covariate-adjusted restricted cubic spline regression from cross-sectional multi-site MRI
AU - Huo, Yuankai
AU - Aboud, Katherine
AU - Kang, Hakmook
AU - Cutting, Laurie E.
AU - Landman, Bennett A.
PY - 2016
Y1 - 2016
N2 - Understanding brain volumetry is essential to understand neurodevelopment and disease. Historically,age-related changes have been studied in detail for specific age ranges (e.g.,early childhood,teen,young adults,elderly,etc.) or more sparsely sampled for wider considerations of lifetime aging. Recent advancements in data sharing and robust processing have made available considerable quantities of brain images from normal,healthy volunteers. However,existing analysis approaches have had difficulty addressing (1) complex volumetric developments on the large cohort across the life time (e.g.,beyond cubic age trends),(2) accounting for confound effects,and (3) maintaining an analysis framework consistent with the general linear model (GLM) approach pervasive in neuroscience. To address these challenges,we propose to use covariateadjusted restricted cubic spline (C-RCS) regression within a multi-site crosssectional framework. This model allows for flexible consideration of nonlinear age-associated patterns while accounting for traditional covariates and interaction effects. As a demonstration of this approach on lifetime brain aging,we derive normative volumetric trajectories and 95 % confidence intervals from 5111 healthy patients from 64 sites while accounting for confounding sex,intracranial volume and field strength effects. The volumetric results are shown to be consistent with traditional studies that have explored more limited age ranges using single-site analyses. This work represents the first integration of C-RCS with neuroimaging and the derivation of structural covariance networks (SCNs) from a large study of multi-site,cross-sectional data.
AB - Understanding brain volumetry is essential to understand neurodevelopment and disease. Historically,age-related changes have been studied in detail for specific age ranges (e.g.,early childhood,teen,young adults,elderly,etc.) or more sparsely sampled for wider considerations of lifetime aging. Recent advancements in data sharing and robust processing have made available considerable quantities of brain images from normal,healthy volunteers. However,existing analysis approaches have had difficulty addressing (1) complex volumetric developments on the large cohort across the life time (e.g.,beyond cubic age trends),(2) accounting for confound effects,and (3) maintaining an analysis framework consistent with the general linear model (GLM) approach pervasive in neuroscience. To address these challenges,we propose to use covariateadjusted restricted cubic spline (C-RCS) regression within a multi-site crosssectional framework. This model allows for flexible consideration of nonlinear age-associated patterns while accounting for traditional covariates and interaction effects. As a demonstration of this approach on lifetime brain aging,we derive normative volumetric trajectories and 95 % confidence intervals from 5111 healthy patients from 64 sites while accounting for confounding sex,intracranial volume and field strength effects. The volumetric results are shown to be consistent with traditional studies that have explored more limited age ranges using single-site analyses. This work represents the first integration of C-RCS with neuroimaging and the derivation of structural covariance networks (SCNs) from a large study of multi-site,cross-sectional data.
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U2 - 10.1007/978-3-319-46720-7_10
DO - 10.1007/978-3-319-46720-7_10
M3 - Conference contribution
C2 - 28191550
AN - SCOPUS:84996478382
SN - 9783319467191
VL - 9900 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 81
EP - 88
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PB - Springer Verlag
T2 - 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Y2 - 21 October 2016 through 21 October 2016
ER -