Tailor the longitudinal anaysis for NIH longitudinal normal brain developmental study

Yasheng Chen, Hongyu An, Dinggang Shen, Hongtu Zhu, Weili Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1206-1209
Number of pages4
ISBN (Electronic)9781467319591
DOIs
StatePublished - Jul 29 2014
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: Apr 29 2014May 2 2014

Publication series

Name2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Other

Other2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Country/TerritoryChina
CityBeijing
Period4/29/145/2/14

Keywords

  • Covariance structure selection
  • Free-knot B-spline
  • Linear mixed ef-fects model
  • Longitudinal analysis
  • Nonlinear regression

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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