Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data

被引：100

作者：

Bernal-Rusiel, Jorge L. ^{[1
]}

Reuter, Martin ^{[1
,2
]}

Greve, Douglas N. ^{[1
]}

Fischl, Bruce ^{[1
,3
]}

Sabuncu, Mert R. ^{[1
,3
]}

机构：

[1] Harvard Univ, Athinoula A Martinos Ctr Biomed Imaging, Sch Med, Massachusetts Gen Hosp, Charlestown, MA USA

[2] MIT, Dept Mech Engn, Cambridge, MA 02139 USA

[3] MIT, Comp Sci & Artificial Intelligence Lab, Cambridge, MA 02139 USA

来源：

NEUROIMAGE | 2013年 / 81卷

基金：

美国国家卫生研究院;

关键词：

Longitudinal studies; Linear Mixed Effects models; Statistical analysis; Mass-univariate analysis; MILD COGNITIVE IMPAIRMENT; SURFACE-BASED ANALYSIS; OPEN ACCESS SERIES; CORTICAL THICKNESS; BRAIN ATROPHY; ALZHEIMERS-DISEASE; HIPPOCAMPAL ATROPHY; BAYESIAN-INFERENCE; MRI DATA; VOLUME;

D O I：

10.1016/j.neuroimage.2013.05.049

中图分类号：

Q189 [神经科学];

学科分类号：

071006 [神经生物学];

摘要：

We present an extension of the Linear Mixed Effects (LME) modeling approach to be applied to the mass-univariate analysis of longitudinal neuroimaging (LNI) data. The proposed method, called spatiotemporal LME or ST-LME, builds on the flexible LME framework and exploits the spatial structure in image data. We instantiated ST-LME for the analysis of cortical surface measurements (e.g. thickness) computed by FreeSurfer, a widely-used brain Magnetic Resonance Image (MRI) analysis software package. We validate the proposed ST-LME method and provide a quantitative and objective empirical comparison with two popular alternative methods, using two brain MRI datasets obtained from the Alzheimer's disease neuroimaging initiative (ADNI) and Open Access Series of Imaging Studies (OASIS). Our experiments revealed that ST-LME offers a dramatic gain in statistical power and repeatability of findings, while providing good control of the false positive rate. (C) 2013 Elsevier Inc. All rights reserved.

引用

页码：358 / 370

页数：13

共 91 条

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