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Multiscale Adaptive Generalized Estimating Equations for Longitudinal Neuroimaging Data

Institution:
Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA.
Publication Date:
May-2013
Journal:
Neuroimage
Volume Number:
72
Pages:
91-105
Citation:
Neuroimage. 2013 May 15;72:91-105.
PubMed ID:
23357075
PMCID:
PMC3621129
Appears in Collections:
NA-MIC
Sponsors:
R01 MH060352/MH/NIMH NIH HHS/United States
P41 RR005959/RR/NCRR NIH HHS/United States
R42 NS059095/NS/NINDS NIH HHS/United States
P01 DA022446/DA/NIDA NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
R41 NS059095/NS/NINDS NIH HHS/United States
R01 MH086633/MH/NIMH NIH HHS/United States
P50 MH064065/MH/NIMH NIH HHS/United States
R01 NS054079/NS/NINDS NIH HHS/United States
R01 ES017240/ES/NIEHS NIH HHS/United States
P30 HD003110/HD/NICHD NIH HHS/United States
P41 RR013642/RR/NCRR NIH HHS/United States
R01 MH070890/MH/NIMH NIH HHS/United States
R01 HD053000/HD/NICHD NIH HHS/United States
R01 NS037312/NS/NINDS NIH HHS/United States
U01 MH070890/MH/NIMH NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
UL1 RR025747/RR/NCRR NIH HHS/United States
P01 CA142538/CA/NCI NIH HHS/United States
R21 AG033387/AG/NIA NIH HHS/United States
T32 MH019111/MH/NIMH NIH HHS/United States
Generated Citation:
Li Y., Gilmore J.H., Shen D., Styner M., Lin W., Zhu H. Multiscale Adaptive Generalized Estimating Equations for Longitudinal Neuroimaging Data. Neuroimage. 2013 May 15;72:91-105. PMID: 23357075. PMCID: PMC3621129.
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Many large-scale longitudinal imaging studies have been or are being widely conducted to better understand the progress of neuropsychiatric and neurodegenerative disorders and normal brain development. The goal of this article is to develop a multiscale adaptive generalized estimation equation (MAGEE) method for spatial and adaptive analysis of neuroimaging data from longitudinal studies. MAGEE is applicable to making statistical inference on regression coefficients in both balanced and unbalanced longitudinal designs and even in twin and familial studies, whereas standard software platforms have several major limitations in handling these complex studies. Specifically, conventional voxel-based analyses in these software platforms involve Gaussian smoothing imaging data and then independently fitting a statistical model at each voxel. However, the conventional smoothing methods suffer from the lack of spatial adaptivity to the shape and spatial extent of region of interest and the arbitrary choice of smoothing extent, while independently fitting statistical models across voxels does not account for the spatial properties of imaging observations and noise distribution. To address such drawbacks, we adapt a powerful propagation-separation (PS) procedure to sequentially incorporate the neighboring information of each voxel and develop a new novel strategy to solely update a set of parameters of interest, while fixing other nuisance parameters at their initial estimators. Simulation studies and real data analysis show that MAGEE significantly outperforms voxel-based analysis.

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