TY - GEN
T1 - Tailor the longitudinal anaysis for NIH longitudinal normal brain developmental study
AU - Chen, Yasheng
AU - An, Hongyu
AU - Shen, Dinggang
AU - Zhu, Hongtu
AU - Lin, Weili
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/7/29
Y1 - 2014/7/29
N2 - There are imminent needs for longitudinal analysis to make physiological inferences on NIH MRI study of normal brain development. But up to date, two critical aspects for longitudinal analysis, namely the selections of mean and covariance structures have not been addressed by the neuroimaging community. For the mean structure, we employed a linear free-knot B-spline regression in combination with quasi-least square estimating equations to approximate a nonlinear growth trajectory with piecewise linear segments for a friendly physiological interpretation. For covariance structure selection, we have proposed a novel time varying correlation structure considering not only the time separation between the repeated measures but also when these acquisitions occurred. We have demonstrated that the proposed covariance structure has a lower Akaike information criterion value than the commonly used Markov correlation structure.
AB - There are imminent needs for longitudinal analysis to make physiological inferences on NIH MRI study of normal brain development. But up to date, two critical aspects for longitudinal analysis, namely the selections of mean and covariance structures have not been addressed by the neuroimaging community. For the mean structure, we employed a linear free-knot B-spline regression in combination with quasi-least square estimating equations to approximate a nonlinear growth trajectory with piecewise linear segments for a friendly physiological interpretation. For covariance structure selection, we have proposed a novel time varying correlation structure considering not only the time separation between the repeated measures but also when these acquisitions occurred. We have demonstrated that the proposed covariance structure has a lower Akaike information criterion value than the commonly used Markov correlation structure.
KW - Covariance structure selection
KW - Free-knot B-spline
KW - Linear mixed ef-fects model
KW - Longitudinal analysis
KW - Nonlinear regression
UR - http://www.scopus.com/inward/record.url?scp=84927914970&partnerID=8YFLogxK
U2 - 10.1109/isbi.2014.6868092
DO - 10.1109/isbi.2014.6868092
M3 - Conference contribution
AN - SCOPUS:84927914970
T3 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
SP - 1206
EP - 1209
BT - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Y2 - 29 April 2014 through 2 May 2014
ER -