Cited by 360Statistical Methods and Bayesian InferenceDementia and Cognitive Impairment ResearchFunctional Brain Connectivity Studies

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Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models

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

DOI 10.1016/j.neuroimage.2012.10.065PMC PMC3586747

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Abstract

Longitudinal neuroimaging (LNI) studies are rapidly becoming more prevalent and growing in size. Today, no standardized computational tools exist for the analysis of LNI data and widely used methods are sub-optimal for the types of data encountered in real-life studies. Linear Mixed Effects (LME) modeling, a mature approach well known in the statistics community, offers a powerful and versatile framework for analyzing real-life LNI data. This article presents the theory behind LME models, contrasts it with other popular approaches in the context of LNI, and is accompanied with an array of computational tools that will be made freely available through FreeSurfer - a popular Magnetic Resonance Image (MRI) analysis software package. Our core contribution is to provide a quantitative empirical evaluation of the performance of LME and competing alternatives popularly used in prior longitudinal structural MRI studies, namely repeated measures ANOVA and the analysis of annualized longitudinal change measures (e.g. atrophy rate). In our experiments, we analyzed MRI-derived longitudinal hippocampal volume and entorhinal cortex thickness measurements from a public dataset consisting of Alzheimer's patients, subjects with mild cognitive impairment and healthy controls. Our results suggest that the LME approach offers superior statistical power in detecting longitudinal group differences.

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Authors: Bernal-Rusiel, J. L., Greve, D. N., Reuter, M., Fischl, B., & Sabuncu, M. R.
Affiliations: Harvard University (US)
Corresponding author: Sabuncu, Mert R.
ORCID: Greve, Douglas N.
Reuter, Martin
Fischl, Bruce
Sabuncu, Mert R.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2013-02
Volume: 66
Pages: 249-260
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2012.10.065
PMC: PMC3586747
OpenAlex: W2142864721
Cited by: 360
References: 78
Citations by year: 2026: 12 · 2025: 30 · 2024: 23 · 2023: 25 · 2022: 32 · 2021: 33 · 2020: 29 · 2019: 27 · 2018: 33 · 2017: 24 · 2016: 36 · 2015: 28 · 2014: 16 · 2013: 9
FWCI: 16.20
Type: journal article
Language: en
Update notices: erratum (2015-03-01)
Open access: green
europepmc.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute of Neurological Disorders and Stroke (1R21NS072652-01, R01 NS052585-01, 2R01NS042861-06A1, 1R01NS070963, 5P01NS058793-03)
Harvard University (AHAF A2012333)
NIH Blueprint for Neuroscience Research (5U01-MH093765)
Ellison Medical Foundation
National Institute of Child Health and Human Development (R01-HD071664, 1S10RR019307, 1S10RR023401, 1S10RR023043)
Harvard Catalyst (1KL2RR025757-01)
National Center for Research Resources (P41-RR14075)
National Institute on Aging (AG022381)
National Institute of Biomedical Imaging and Bioengineering (R01EB006758, 1K25EB013649-01)
National Institutes of Health (5R01AG008122-22)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: longitudinal neuroimaging, linear mixed models, structural MRI, hippocampal volume, entorhinal cortex thickness, Alzheimer's disease, mild cognitive impairment, atrophy rate, repeated measures ANOVA, statistical power, FreeSurfer
MeSH terms: Aged, Aged, 80 and over, Algorithms, Alzheimer Disease, Brain, Female, Humans, Image Processing, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Linear Models, Cognitive Dysfunction

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Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models

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