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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Estimating anatomical trajectories with Bayesian mixed-effects modeling.

G Ziegler1, W D Penny2, G R Ridgway3

  • 1Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK; Dementia Research Centre, Institute of Neurology, University College London, UK.

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Summary
This summary is machine-generated.

This study presents a new Bayesian framework to analyze brain structure changes over time. It models individual and group differences in brain development and aging, aiding in understanding neurodegenerative diseases.

Keywords:
Bayesian inferenceBrain morphologyDementiaLifespan brain agingLongitudinal analysisMulti-level models

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Area of Science:

  • Neuroimaging
  • Biostatistics
  • Developmental Neuroscience

Background:

  • Longitudinal studies are crucial for understanding brain development and aging.
  • Voxel-Based Morphometry (VBM) is a key technique for analyzing structural brain changes.
  • Characterizing heterogeneous structural trajectories requires advanced statistical modeling.

Purpose of the Study:

  • To introduce a mass-univariate Bayesian framework for analyzing whole-brain structural trajectories.
  • To characterize heterogeneous structural growth and decline in developmental and aging studies.
  • To model individual and group differences in brain structure changes over time.

Main Methods:

  • Utilizing longitudinal Voxel-Based Morphometry (VBM) data.
  • Employing a probabilistic generative model with linear mixed-effects models.
  • Applying Expectation Maximization (EM) for model inversion and Posterior Probability Maps (PPM) for Bayesian inference.

Main Results:

  • The framework successfully models individual and ensemble average changes in brain structure.
  • Voxelwise priors on individual differences were estimated from data.
  • Model evidence allows for comparisons of different model orders.

Conclusions:

  • The developed framework provides a robust method for analyzing longitudinal brain structure.
  • It can identify subject-specific characteristics contributing to volume changes in healthy and diseased states.
  • This approach aids in understanding neurodevelopment, aging, and neurodegenerative conditions like Alzheimer's Disease.