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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

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Related Experiment Video

Updated: May 17, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models.

Jorge L Bernal-Rusiel1, Douglas N Greve1, Martin Reuter2

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA.

Neuroimage
|November 6, 2012
PubMed
Summary

Linear Mixed Effects (LME) modeling provides a powerful framework for analyzing longitudinal neuroimaging data. This statistical approach offers superior power for detecting group differences in brain changes over time compared to traditional methods.

Keywords:
Linear Mixed Effects modelsLongitudinal studiesStatistical analysis

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Area of Science:

  • Neuroimaging and Computational Neuroscience
  • Statistical Modeling in Medical Research

Background:

  • Longitudinal neuroimaging (LNI) studies are increasing in size and prevalence.
  • Current computational tools for LNI data analysis are not standardized and often sub-optimal.
  • Existing methods may lack the power to detect subtle changes in real-world LNI datasets.

Purpose of the Study:

  • To introduce Linear Mixed Effects (LME) modeling as a robust framework for LNI data analysis.
  • To provide a quantitative comparison of LME against commonly used methods in longitudinal neuroimaging.
  • To make computational tools for LME analysis freely available within the FreeSurfer software package.

Main Methods:

  • Theoretical overview of LME models and their application to LNI.
  • Empirical evaluation comparing LME with repeated measures ANOVA and annualized change measures.
  • Analysis of longitudinal MRI data (hippocampal volume, entorhinal cortex thickness) from a public dataset including Alzheimer's patients, MCI subjects, and healthy controls.

Main Results:

  • Linear Mixed Effects (LME) modeling demonstrated superior statistical power in detecting longitudinal group differences.
  • LME effectively analyzes complex, real-world LNI data, outperforming traditional statistical approaches.
  • The proposed computational tools integrated into FreeSurfer facilitate the application of LME.

Conclusions:

  • LME modeling is a highly effective and statistically powerful method for analyzing longitudinal neuroimaging data.
  • Adoption of LME can enhance the sensitivity of LNI studies in detecting disease-related brain changes.
  • The freely available LME tools in FreeSurfer will support broader adoption and improve LNI data analysis.