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Conference Paper: A longitudinal functional analysis framework for analysis of white matter tract statistics

TitleA longitudinal functional analysis framework for analysis of white matter tract statistics
Authors
KeywordsCoefficient functions
Diffusion properties
Finite sample performance
Longitudinal imaging
Mixed effects models
Spatial-temporal correlation
Varying coefficients
White-matter fiber tracts
Issue Date2013
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 23rd International Conference on Information Processing in Medical Imaging (IPMI 2013), Asilomar, CA., 28 June-3 July 2013. In Lecture Notes in Computer Science, 2013, v. 7917, p. 220-231 How to Cite?
AbstractMany longitudinal imaging studies have been/are being widely conducted to use diffusion tensor imaging (DTI) to better understand white matter maturation in normal controls and diseased subjects. There is an urgent demand for the development of statistical methods for analyzing diffusion properties along major fiber tracts obtained from longitudinal DTI studies. Jointly analyzing fiber-tract diffusion properties and covariates from longitudinal studies raises several major challenges including (i) infinite-dimensional functional response data, (ii) complex spatial-temporal correlation structure, and (iii) complex spatial smoothness. To address these challenges, this article is to develop a longitudinal functional analysis framework (LFAF) to delineate the dynamic changes of diffusion properties along major fiber tracts and their association with a set of covariates of interest (e.g., age and group status) and the structure of the variability of these white matter tract properties in various longitudinal studies. Our LFAF consists of a functional mixed effects model for addressing all three challenges, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial-temporal correlation structure, a test procedure with a global test statistic for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of LFAF and to demonstrate that LFAF significantly outperforms a voxel-wise mixed model method. We apply LFAF to study the spatial-temporal dynamics of white-matter fiber tracts in a clinical study of neurodevelopment. © 2013 Springer-Verlag.
DescriptionLNCS v. 7917 entitled: Information processing in medical imaging : 23rd international conference, IPMI 2013 ... proceedings
Persistent Identifierhttp://hdl.handle.net/10722/191132
ISBN
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorYuan, Yen_US
dc.contributor.authorGilmore, JHen_US
dc.contributor.authorGeng, Xen_US
dc.contributor.authorStyner, MAen_US
dc.contributor.authorChen, Ken_US
dc.contributor.authorWang, JLen_US
dc.contributor.authorZhu, Hen_US
dc.date.accessioned2013-09-17T16:17:16Z-
dc.date.available2013-09-17T16:17:16Z-
dc.date.issued2013en_US
dc.identifier.citationThe 23rd International Conference on Information Processing in Medical Imaging (IPMI 2013), Asilomar, CA., 28 June-3 July 2013. In Lecture Notes in Computer Science, 2013, v. 7917, p. 220-231en_US
dc.identifier.isbn978-364238867-5-
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10722/191132-
dc.descriptionLNCS v. 7917 entitled: Information processing in medical imaging : 23rd international conference, IPMI 2013 ... proceedings-
dc.description.abstractMany longitudinal imaging studies have been/are being widely conducted to use diffusion tensor imaging (DTI) to better understand white matter maturation in normal controls and diseased subjects. There is an urgent demand for the development of statistical methods for analyzing diffusion properties along major fiber tracts obtained from longitudinal DTI studies. Jointly analyzing fiber-tract diffusion properties and covariates from longitudinal studies raises several major challenges including (i) infinite-dimensional functional response data, (ii) complex spatial-temporal correlation structure, and (iii) complex spatial smoothness. To address these challenges, this article is to develop a longitudinal functional analysis framework (LFAF) to delineate the dynamic changes of diffusion properties along major fiber tracts and their association with a set of covariates of interest (e.g., age and group status) and the structure of the variability of these white matter tract properties in various longitudinal studies. Our LFAF consists of a functional mixed effects model for addressing all three challenges, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial-temporal correlation structure, a test procedure with a global test statistic for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of LFAF and to demonstrate that LFAF significantly outperforms a voxel-wise mixed model method. We apply LFAF to study the spatial-temporal dynamics of white-matter fiber tracts in a clinical study of neurodevelopment. © 2013 Springer-Verlag.-
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.rightsThe original publication is available at www.springerlink.com-
dc.subjectCoefficient functions-
dc.subjectDiffusion properties-
dc.subjectFinite sample performance-
dc.subjectLongitudinal imaging-
dc.subjectMixed effects models-
dc.subjectSpatial-temporal correlation-
dc.subjectVarying coefficients-
dc.subjectWhite-matter fiber tracts-
dc.titleA longitudinal functional analysis framework for analysis of white matter tract statisticsen_US
dc.typeConference_Paperen_US
dc.identifier.emailGeng, X: gengx@hku.hken_US
dc.identifier.authorityGeng, X=rp01678en_US
dc.identifier.doi10.1007/978-3-642-38868-2_19-
dc.identifier.scopuseid_2-s2.0-84879852389-
dc.identifier.hkuros223163en_US
dc.identifier.volume7917en_US
dc.identifier.spage220en_US
dc.identifier.epage231en_US
dc.publisher.placeGermany-
dc.customcontrol.immutablesml 131024-

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