Cited by 125Functional Brain Connectivity StudiesStatistical Methods and Bayesian InferenceDementia and Cognitive Impairment Research

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

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Bernal-Rusiel, Jorge L., Reuter, Martin, Greve, Douglas N., Fischl, Bruce, Sabuncu, Mert R.·2013·NeuroImage

DOI 10.1016/j.neuroimage.2013.05.049PMC PMC3816382

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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.

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Authors: Bernal-Rusiel, J. L., Reuter, M., Greve, D. N., Fischl, B., & Sabuncu, M. R.
Affiliations: Harvard University (US)
Corresponding author: Sabuncu, Mert R.
ORCID: Reuter, Martin
Greve, Douglas N.
Fischl, Bruce
Sabuncu, Mert R.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2013-11
Volume: 81
Pages: 358-370
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2013.05.049
PMC: PMC3816382
OpenAlex: W2132114367
Cited by: 125
References: 93
Citations by year: 2026: 2 · 2025: 8 · 2024: 7 · 2023: 9 · 2022: 13 · 2021: 11 · 2020: 16 · 2019: 6 · 2018: 10 · 2017: 12 · 2016: 13 · 2015: 11 · 2014: 6
FWCI: 5.46
Type: journal article
Language: en
Update notices: erratum (2015-03-01)
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institutes of Health
National Institute on Aging (AG022381)
National Institute of Neurological Disorders and Stroke (1R01NS070963, 1R21NS072652-01, 2R01NS042861-06A1, 5P01NS058793-03, R01 NS052585-01)
BrightFocus Foundation (AHAF-A2012333)
National Center for Research Resources (P41-RR14075)
NIH Blueprint for Neuroscience Research (5U01-MH093765)
Ellison Medical Foundation
National Institute of Biomedical Imaging and Bioengineering (1K25EB013649-01, R01EB006758)
National Institute of Child Health and Human Development (1S10RR019307, 1S10RR023043, 1S10RR023401, R01-HD071664)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Keywords: longitudinal neuroimaging, mass univariate analysis, cortical thickness, FreeSurfer, Alzheimer's disease, statistical power, false positive rate control, brain MRI
MeSH terms: Brain, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Software, Linear Models

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

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