Comparison of Different Hypotheses Regarding the Spread of Alzheimer’s Disease Using Markov Random Fields and Multimodal Imaging

Article type: Research Article

Authors: Dyrba, Martin^{a; *} | Grothe, Michel J.^a | Mohammadi, Abdolreza^b | Binder, Harald^c | Kirste, Thomas^d | Teipel, Stefan J.^{a; e} | for the Alzheimer’s Disease Neuroimaging Initiative¹

Affiliations: [a] German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Rostock, Germany | [b] Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands | [c] Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center, Johannes Gutenberg University, Mainz, Germany | [d] Mobile Multimedia Information Systems Group (MMIS), University of Rostock, Rostock, Germany | [e] Clinic for Psychosomatic and Psychotherapeutic Medicine, University Medical Center Rostock, Rostock, Germany

Correspondence: [*] Correspondence to: Martin Dyrba, German Center for Neurodegenerative Diseases (DZNE), c/o Zentrum für Nervenheilkunde, Gehlsheimer Str. 20, D-18147 Rostock, Germany. Tel.: +49 381 494 9482; Fax: +49 381 494 9472; E-mail: martin.dyrba@dzne.de.

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNIand provided data but did not participate in analysis or in the writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: 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.

Keywords: Alzheimer’s disease, AV45-PET, FDG-PET, Markov random field, mild cognitive impairment, multimodal imaging, probabilistic graphical model

DOI: 10.3233/JAD-161197

Journal: Journal of Alzheimer's Disease, vol. 65, no. 3, pp. 731-746, 2018