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Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models
Open accessAbstract
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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“Multivariate LME models can increased statistical power and reliability compared to univariate methods30,31.”
From Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study (2018) · cites this paper as [31]
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“Longitudinal LME modelling has been demonstrated to be a be superior technique to more common alternatives such as repeated measure ANOVA or simple paired T-test in that it provides more statistical power and is robust in the face of missing data (Bernal-Rusiel et al., 2013).”
From Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise (2016) · cites this paper as Bernal-Rusiel et al., 2013
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Metadata
- Authors
- Bernal-Rusiel, J. L., Greve, D. N., Reuter, M., Fischl, B., & Sabuncu, M. R.
- Affiliations
- Harvard University (US)
- Corresponding author
- Sabuncu, Mert R.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 66
- Pages
- 249-260
- ISSN
- 1053-8119 (ISSN-L)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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