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Comparisons of neurodegeneration over time between healthy ageing and Alzheimer's disease cohorts via Bayesian inference
  1. Marcela I Cespedes1,
  2. Jurgen Fripp2,
  3. James M McGree1,
  4. Christopher C Drovandi1,
  5. Kerrie Mengersen1,
  6. James D Doecke2
  1. 1School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
  2. 2CSIRO Digital Productivity and Services, Australia E-Health Research Centre, Herston, Queensland, Australia
  1. Correspondence to Marcela I Cespedes; Marcela.Cespedes{at}


Objectives In recent years, large-scale longitudinal neuroimaging studies have improved our understanding of healthy ageing and pathologies including Alzheimer's disease (AD). A particular focus of these studies is group differences and identification of participants at risk of deteriorating to a worse diagnosis. For this, statistical analysis using linear mixed-effects (LME) models are used to account for correlated observations from individuals measured over time. A Bayesian framework for LME models in AD is introduced in this paper to provide additional insight often not found in current LME volumetric analyses.

Setting and participants Longitudinal neuroimaging case study of ageing was analysed in this research on 260 participants diagnosed as either healthy controls (HC), mild cognitive impaired (MCI) or AD. Bayesian LME models for the ventricle and hippocampus regions were used to: (1) estimate how the volumes of these regions change over time by diagnosis, (2) identify high-risk non-AD individuals with AD like degeneration and (3) determine probabilistic trajectories of diagnosis groups over age.

Results We observed (1) large differences in the average rate of change of volume for the ventricle and hippocampus regions between diagnosis groups, (2) high-risk individuals who had progressed from HC to MCI and displayed similar rates of deterioration as AD counterparts, and (3) critical time points which indicate where deterioration of regions begins to diverge between the diagnosis groups.

Conclusions To the best of our knowledge, this is the first application of Bayesian LME models to neuroimaging data which provides inference on a population and individual level in the AD field. The application of a Bayesian LME framework allows for additional information to be extracted from longitudinal studies. This provides health professionals with valuable information of neurodegeneration stages, and a potential to provide a better understanding of disease pathology.

  • Bayesian inference
  • mixed effects models
  • neurodegeneration
  • Alzheimer's disease
  • longitudinal neuroimaging study

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See:

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  • Contributors KM and MIC conceived and designed the research concept, CCD provided additional suggestions. Statistical analysis and manuscript drafting was performed by MIC. JDD and JF were responsible for the acquisition and interpretation of the data. MIC, JF, JMM, CCD, KM and JDD participated in a critical revision of the manuscript and approved the final manuscript. MIC is responsible for the overall content as the corresponding author.

  • Funding This research was jointly funded by an Australian Postgraduate Award (APA), Commonwealth Scientific and Industrial Research Organisation (CSIRO) Digital Productivity and Services Division, and the ARC Centre of Excellence for Mathematical and Statistical Frontiers.

  • Competing interests None declared.

  • Ethics approval St Vincent’s Hospital, Melbourne, Austin Health, Edith Cowan University and Hollywood Private Hospital Human Research Committees.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement Data are available from the Australian Imaging, Biomarkers and Lifestyle longitudinal study of ageing (AIBL). This study is funded by the CSIRO and partners. Access to the data is conditional on approval from the AIBL management committee; for guidelines, refer to All R code and simulated data for this manuscript are available at

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