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

Jorge L Bernal-Rusiel; Martin Reuter; Douglas N Greve; Bruce Fischl; Mert R Sabuncu; Alzheimer's Disease Neuroimaging Initiative

doi:10.1016/j.neuroimage.2013.05.049

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

Neuroimage. 2013 Nov 1:81:358-370. doi: 10.1016/j.neuroimage.2013.05.049. Epub 2013 May 20.

Authors

Jorge L Bernal-Rusiel¹, Martin Reuter², Douglas N Greve¹, Bruce Fischl³, Mert R Sabuncu⁴; Alzheimer's Disease Neuroimaging Initiative

Affiliations

¹ Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA.
² Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
³ Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁴ Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: msabuncu@nmr.mgh.harvard.edu.

Abstract

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.

Keywords: Linear Mixed Effects models; Longitudinal studies; Mass-univariate analysis; Statistical analysis.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Brain / pathology*
Brain / physiology
Humans
Image Interpretation, Computer-Assisted / methods*
Linear Models
Magnetic Resonance Imaging
Models, Neurological*
Software*

Abstract

Publication types

MeSH terms

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