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A Longitudinal Functional Analysis Framework for Analysis of White Matter Tract Statistics

Institution:
1Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN, USA.
2Department of Psychiatry, and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
3Department of Statistics, University of Pittsburgh, PA, USA.
4Department of Statistics, University of California at Davis, CA, USA.
5Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Publisher:
Inf Process Med Imaging IPMI 2013
Publication Date:
Jun-2013
Volume Number:
23
Pages:
220-31
Citation:
Inf Process Med Imaging. 2013 Jun; 23:220-31.
PubMed ID:
24683971
PMCID:
PMC3974206
Appears in Collections:
NA-MIC
Sponsors:
R01 ES017240/ES/NIEHS NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
P30 HD003110/HD/NICHD NIH HHS/United States
UL1 RR025747/RR/NCRR NIH HHS/United States
P01 CA142538/CA/NCI NIH HHS/United States
R01 MH086633/MH/NIMH NIH HHS/United States
P50 MH064065/MH/NIMH NIH HHS/United States
R01 HD053000/HD/NICHD NIH HHS/United States
R01 MH070890/MH/NIMH NIH HHS/United States
NSF DMS 12-28369, 09-06813
Generated Citation:
Yuan Y., Gilmore J., Geng X., Styner M., Chen K., Wang J-l., Zhu H. A Longitudinal Functional Analysis Framework for Analysis of White Matter Tract Statistics. Inf Process Med Imaging. 2013 Jun; 23:220-31. PMID: 24683971. PMCID: PMC3974206.
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Many 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.

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