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Multicompartment Models: Overview01:14

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Basics of Multivariate Analysis in Neuroimaging Data
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A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imaging.

Murat Bilgel1, Jerry L Prince2, Dean F Wong3

  • 1Image Analysis and Communications Laboratory, Johns Hopkins University School of Engineering, Baltimore, MD, USA; Dept. of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.

Neuroimage
|April 21, 2016
PubMed
Summary

We developed a new model to track neurodegenerative disease progression using brain imaging. This method identifies early changes in Alzheimer's disease, pinpointing specific brain regions affected first by amyloid buildup.

Keywords:
Amyloid imagingLongitudinal image analysisProgression score

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Area of Science:

  • Neuroimaging and computational neuroscience
  • Biomarker discovery and analysis
  • Disease progression modeling

Background:

  • Characterizing temporal biomarker trajectories is crucial for monitoring neurodegenerative diseases and intervening early.
  • Longitudinal neuroimaging provides rich voxelwise data but standard analyses often overlook individual differences in disease stage and progression rate.
  • Existing methods typically analyze each brain region or biomarker independently, limiting comprehensive trajectory analysis.

Purpose of the Study:

  • To propose a novel multivariate nonlinear mixed effects model for estimating voxelwise neuroimaging biomarker trajectories from longitudinal data.
  • To account for individual variability in disease progression and analyze voxelwise measures collectively, considering spatial correlations.
  • To enable the construction of individualized disease progression trajectories and identify early biomarkers.

Main Methods:

  • Developed a multivariate nonlinear mixed effects model incorporating an expectation-maximization framework.
  • Predicted individual progression scores by collectively analyzing voxelwise biomarker data, handling large datasets and variable visits.
  • Accounted for spatial correlations among voxels to improve trajectory estimation.

Main Results:

  • Applied the method to longitudinal positron emission tomography data of cortical beta-amyloid deposition in 104 individuals.
  • Identified the precuneus as the earliest cortical region for amyloid accumulation, followed by cingulate, frontal, and lateral parietal cortices.
  • Progression scores correlated with global brain amyloid levels, validating the model's ability to capture disease trajectory.

Conclusions:

  • The proposed model effectively estimates individualized disease progression trajectories from longitudinal neuroimaging data.
  • It accurately captures early changes in Alzheimer's disease biomarkers, specifically cortical amyloid deposition.
  • This method is broadly applicable to various longitudinal imaging modalities for disease monitoring and biomarker discovery.