Affiliations: [a] Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| [b] Faculty of Computer Science, Heilbronn University of Applied Sciences, Heilbronn, Germany
| [c] Center for Machine Learning, Heilbronn University of Applied Sciences, Heilbronn, Germany
| [d] GECKO Institute for Medicine, Informatics and Economics, Heilbronn University of Applied Sciences, Heilbronn, Germany
Correspondence:
[*]
Correspondence to: Alexandra Reichenbach, Max Planck Str. 39, 74081 Heilbronn, Germany. Tel.: +49 7131 504397; E-mail: [email protected].
Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuro-imaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or 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: Background:Alzheimer’s disease (AD) is a neurodegenerative disorder with homogenous disease patterns. Neuropathological changes precede symptoms by up to two decades making neuroimaging biomarkers a prime candidate for early diagnosis, prognosis, and patient stratification. Objective:The goal of the study was to discern intermediate AD stages and their precursors based on neuroanatomical features for stratifying patients on their progression through different stages. Methods:Data include grey matter features from 14 brain regions extracted from longitudinal structural MRI and cognitive data obtained from 1,017 healthy controls and AD patients of ADNI. AD progression was modeled with a Hidden Markov Model, whose hidden states signify disease stages derived from the neuroanatomical data. To tie the progression in brain atrophy to a behavioral marker, we analyzed the ADAS-cog sub-scores in the stages. Results:The optimal model consists of eight states with differentiable neuroanatomical features, forming two routes crossing once at a very early point and merging at the final state. The cortical route is characterized by early and sustained atrophy in cortical regions. The limbic route is characterized by early decrease in limbic regions. Cognitive differences between the two routes are most noticeable in the memory domain with subjects from the limbic route experiencing stronger memory impairments. Conclusion:Our findings corroborate that more than one pattern of grey matter deterioration with several discernable stages can be identified in the progression of AD. These neuroanatomical subtypes are behaviorally meaningful and provide a door into early diagnosis of AD and prognosis of the disease’s progression.
