TY - GEN
T1 - Multivariate nonlinear mixed model to analyze longitudinal image data
T2 - 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
AU - Xu, Shun
AU - Styner, Martin
AU - Gilmore, John
AU - Piven, Joseph
AU - Gerig, Guido
PY - 2008
Y1 - 2008
N2 - With great potential in studying neuro-development, neuro-degeneration, and the aging process, longitudinal image data is gaining increasing interest and attention in the neuroimaging community. In this paper, we present a parametric nonlinear model to statistically study multivariate longitudinal data with asymptotic properties. We demonstrate our preliminary results in a combined study of two longitudinal neuroimaging data sets of early brain development to cover a wider time span and to gain a larger sample size. Such combined analysis of multiple longitudinal image data sets has not been conducted before and presents a challenge for traditional analysis methods. To our knowledge, this is the first multivariate nonlinear longitudinal analysis to study early brain development. Our methodology is generic in nature and can be applied to any longitudinal data with nonlinear growth patterns that can not easily be modeled by linear methods.
AB - With great potential in studying neuro-development, neuro-degeneration, and the aging process, longitudinal image data is gaining increasing interest and attention in the neuroimaging community. In this paper, we present a parametric nonlinear model to statistically study multivariate longitudinal data with asymptotic properties. We demonstrate our preliminary results in a combined study of two longitudinal neuroimaging data sets of early brain development to cover a wider time span and to gain a larger sample size. Such combined analysis of multiple longitudinal image data sets has not been conducted before and presents a challenge for traditional analysis methods. To our knowledge, this is the first multivariate nonlinear longitudinal analysis to study early brain development. Our methodology is generic in nature and can be applied to any longitudinal data with nonlinear growth patterns that can not easily be modeled by linear methods.
UR - http://www.scopus.com/inward/record.url?scp=51849104121&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51849104121&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2008.4563011
DO - 10.1109/CVPRW.2008.4563011
M3 - Conference contribution
AN - SCOPUS:51849104121
SN - 9781424423408
T3 - 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
BT - 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
Y2 - 23 June 2008 through 28 June 2008
ER -