Comparisons of neurodegeneration over time between healthy ageing and Alzheimer's disease cohorts via Bayesian inference.
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.



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.



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.



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.
School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
Marcela I
0000-0002-0997-1941
MI
Cespedes
CSIRO Digital Productivity and Services, Australia E-Health Research Centre, Herston, Queensland, Australia.
Jurgen
0000-0001-9705-0079
J
Fripp
School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
James M
0000-0003-2997-8929
JM
McGree
School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
Christopher C
0000-0001-9222-8763
CC
Drovandi
School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
Kerrie
K
Mengersen
CSIRO Digital Productivity and Services, Australia E-Health Research Centre, Herston, Queensland, Australia.
James D
0000-0003-2863-0293
JD
Doecke
D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000000981:diagnostic imaging*; D001499:Bayes Theorem; D001921:Brain / Q000000981:diagnostic imaging / Q000473:pathology; D016022:Case-Control Studies; D002552:Cerebral Ventricles / Q000000981:diagnostic imaging* / Q000473:pathology; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging*; D005260:Female; D000073656:Healthy Aging; D006624:Hippocampus / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D019636:Neurodegenerative Diseases / Q000000981:diagnostic imaging; D009929:Organ Size; D013997:Time Factors
Alzheimer's disease; Bayesian inference; longitudinal neuroimaging study; mixed effects models; neurodegeneration