Advanced analytics for better understanding the spread of Alzheimer's diseaseAnalytics for a better world project
This project connects to SDG 3, Good Health and Well-Being.
- M. Dyrba (German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany)
- R. Mohammadi (University of Amsterdam, The Netherlands)
Advanced analytics for better understanding the spread of Alzheimer’s disease
Alzheimer’s disease (AD) is characterized by a cascade of pathological processes that can be assessed in vivo using different neuroimaging methods. Recent research suggests a systematic sequence of pathogenic events on a global biomarker level, but little is known about the associations and dependencies of distinct lesion patterns on a regional level. Markov random fields are a probabilistic graphical modeling approach that represent the interaction between individual random variables by an undirected graph. We propose the novel application of this approach to study the interregional associations and dependencies between multimodal imaging markers of AD pathology and to compare different hypotheses regarding the spread of the disease. We retrieved multimodal imaging data from 577 subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative. Mean amyloid load (AV45-PET), glucose metabolism (FDG-PET), and gray matter volume (MRI) were calculated for the six principle nodes of the default mode network— a functional network of brain regions that appears to be preferentially targeted by AD. Multimodal Markov random field models were developed for three different hypotheses regarding the spread of the disease: the “intraregional evolution model”, the “trans-neuronal spread” hypothesis, and the “wear-and-tear” hypothesis. The model likelihood to reflect the given data was evaluated using tenfold cross-validation with 1,000 repetitions. The most likely graph structure contained the posterior cingulate cortex as main hub region with edges to various other regions, in accordance with the “wear-and-tear” hypothesis of disease vulnerability. Probabilistic graphical models facilitate the analysis of interactions between several variables in a network model and therefore afford great potential to complement traditional multiple regression analyses in multimodal neuroimaging research.
1- Dyrba, M., Mohammadi, R., Grothe, M.J., Kirste, T., and Teipel, S.J. (2020) “Assessing inter-modal and inter-regional dependencies in prodromal Alzheimer’s disease using multimodal MRI/PET and Gaussian graphical models”, Frontiers in Aging Neuroscience, 12:99.
2- Dyrba, M., Grothea, M.J., Mohammadi, A., Binderiii, H., Kirsteiv, T., and Teipel, S.J. (2018) “Comparison of different hypotheses regarding the spread of Alzheimer’s disease using Markov random fields and multimodal imaging”, Journal of Alzheimer Disease, 65(3), 731-746
Those publications are based on the freely available online Software “BDgraph” (an R package) developed by R. Mohammadi (University of Amsterdam); Link: https://CRAN.R-project.org/package=BDgraph
Paper related to this software:
– Mohammadi, R. and Wit, E. (2019) “BDgraph: An R Package for Bayesian Structure Learning in Graphical Models”, Journal of Statistical Software, 89(3):1- 30.