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Review
. 2017 Aug;30(4):371-379.
doi: 10.1097/WCO.0000000000000460.

Imaging plus X: multimodal models of neurodegenerative disease

Neil P Oxtoby  1 , Daniel C Alexander; EuroPOND consortium
Affiliations
Review

Imaging plus X: multimodal models of neurodegenerative disease

Neil P Oxtoby et al. Curr Opin Neurol. 2017 Aug.

Abstract

Purpose of review: This article argues that the time is approaching for data-driven disease modelling to take centre stage in the study and management of neurodegenerative disease. The snowstorm of data now available to the clinician defies qualitative evaluation; the heterogeneity of data types complicates integration through traditional statistical methods; and the large datasets becoming available remain far from the big-data sizes necessary for fully data-driven machine-learning approaches. The recent emergence of data-driven disease progression models provides a balance between imposed knowledge of disease features and patterns learned from data. The resulting models are both predictive of disease progression in individual patients and informative in terms of revealing underlying biological patterns.

Recent findings: Largely inspired by observational models, data-driven disease progression models have emerged in the last few years as a feasible means for understanding the development of neurodegenerative diseases. These models have revealed insights into frontotemporal dementia, Huntington's disease, multiple sclerosis, Parkinson's disease and other conditions. For example, event-based models have revealed finer graded understanding of progression patterns; self-modelling regression and differential equation models have provided data-driven biomarker trajectories; spatiotemporal models have shown that brain shape changes, for example of the hippocampus, can occur before detectable neurodegeneration; and network models have provided some support for prion-like mechanistic hypotheses of disease propagation. The most mature results are in sporadic Alzheimer's disease, in large part because of the availability of the Alzheimer's disease neuroimaging initiative dataset. Results generally support the prevailing amyloid-led hypothetical model of Alzheimer's disease, while revealing finer detail and insight into disease progression.

Summary: The emerging field of disease progression modelling provides a natural mechanism to integrate different kinds of information, for example from imaging, serum and cerebrospinal fluid markers and cognitive tests, to obtain new insights into progressive diseases. Such insights include fine-grained longitudinal patterns of neurodegeneration, from early stages, and the heterogeneity of these trajectories over the population. More pragmatically, such models enable finer precision in patient staging and stratification, prediction of progression rates and earlier and better identification of at-risk individuals. We argue that this will make disease progression modelling invaluable for recruitment and end-points in future clinical trials, potentially ameliorating the high failure rate in trials of, e.g., Alzheimer's disease therapies. We review the state of the art in these techniques and discuss the future steps required to translate the ideas to front-line application.

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Figures

Box 1
Box 1
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FIGURE 1
FIGURE 1
Old paradigm disease progression models. (a) It shows the hypothetical model of [36], which illustrates qualitative sigmoid evolution in AD of scalar biomarkers such as CSF Aβ level, cognitive test scores and hippocampal volume or atrophy. The lack of quantitative information prevents direct diagnostic usage. (b) It shows a traditional longitudinal model of AD atrophy [39] by binning individuals a-priori into ‘mild’, ‘moderate’ and ‘severe’ classes based on cognitive test scores. The model can potentially match new individuals to the same stages using imaging data, but must exclude cognitive scores to avoid circularity. AD, Alzheimer's disease.
FIGURE 2
FIGURE 2
Event-based models of disease progression. (a) An EBM of familial AD from [49]. The set of biomarkers (vertical axis) includes atrophy rates in each cortical region. The positional variance diagram is a visualization of the uncertainty in the ordering: each row is a grayscale histogram of probabilities that the event occurs in each position of the ordered sequence. Groups of interchangeable events appear as large blocks (e.g. later in the progression, lower right), whereas strong confidence in the ordering of events appears as a thin diagonal (e.g. earlier in the progression, upper left). (b) An EBM of sporadic AD learned from the ADNI data set in [50] with more diverse biomarkers: CSF tau and Aβ; cognitive test scores; regional atrophy and volumes from MRI. The model defines a staging system based on the set of biomarkers showing abnormality. (c) Stage assignments of cognitively normal, mild cognitive impaired (MCI) and AD individuals showing classification accuracy similar to state-of-the-art pattern recognition approaches [44], but with an explicit generative model. AD, Alzheimer's disease; ADNI, Alzheimer's disease neuroimaging initiative; CSF, cerebrospinal fluid; EBM, event-based model.
FIGURE 3
FIGURE 3
The temporally continuous self-modelling regression approach of [55]. The model shows the characteristic trajectories of a diverse set of biomarkers against a common continuous disease stage variable learned from the ADNI and PAQUID (Personnes Agées Quid) data sets. The model can potentially estimate the disease stage of a new patient by identifying the position along the trajectory set that best matches their data. ADNI, Alzheimer's disease neuroimaging initiative.
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