Alexandra L. Young

A data-driven model of biomarker changes in sporadic Alzheimer's disease

Young et al. reformulate an event-based model for the progression of Alzheimers disease to make it applicable to a heterogeneous sporadic disease population. The enhanced model predicts the ordering of biomarker abnormality in sporadic Alzheimers disease independently of clinical diagnoses or biomarker cut-points, and shows state-of-the-art diagnostic classification performance.

Multiple Orderings of Events in Disease Progression

The event-based model constructs a discrete picture of disease progression from cross-sectional data sets, with each event corresponding to a new biomarker becoming abnormal. However, it relies on the assumption that all subjects follow a single event sequence. This is a major simplification for sporadic disease data sets, which are highly heterogeneous, include distinct subgroups, and contain significant proportions of outliers. In this work we relax this assumption by considering two extensions to the event-based model: a generalised Mallows model, which allows subjects to deviate from the main event sequence, and a Dirichlet process mixture of generalised Mallows models, which models clusters of subjects that follow different event sequences, each of which has a corresponding variance. We develop a Gibbs sampling technique to infer the parameters of the two models from multi-modal biomarker data sets. We apply our technique to data from the Alzheimers Disease Neuroimaging Initiative to determine the sequence in which brain regions become abnormal in sporadic Alzheimers disease, as well as the heterogeneity of that sequence in the cohort. We find that the generalised Mallows model estimates a larger variation in the event sequence across subjects than the original event-based model. Fitting a Dirichlet process model detects three subgroups of the population with different event sequences. The Gibbs sampler additionally provides an estimate of the uncertainty in each of the model parameters, for example an individuals latent disease stage and cluster assignment. The distributions and mixtures of sequences that this new family of models introduces offer better characterisation of disease progression of heterogeneous populations, new insight into disease mechanisms, and have the potential for enhanced disease stratification and differential diagnosis.

Data-driven models of dominantly-inherited Alzheimer's disease progression

See Li and Donohue (doi:10.1093/brain/awy089) for a scientific commentary on this article. The key to developing interventions for Alzheimer’s disease may lie within the less common familial form, which has a predictable presymptomatic phase. Oxtoby et al. use modern computational techniques to characterize familial Alzheimer’s disease progression. The resulting data-driven models allow fine-grained patient staging, with potential utility in clinical trials.

Progression of regional grey matter atrophy in multiple sclerosis

See Stankoff and Louapre (doi:10.1093/brain/awy114) for a scientific commentary on this article. Grey matter atrophy in multiple sclerosis affects certain areas preferentially. Eshaghi et al. use a data-driven computational model to predict the order in which regions atrophy, and use this sequence to stage patients. Atrophy begins in deep grey matter nuclei and posterior cortical regions, before spreading to other cortical areas.

An image‐based model of brain volume biomarker changes in Huntington's disease

Determining the sequence in which Huntingtons disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine‐grained model of temporal progression of Huntingtons disease from premanifest through to manifest stages.

A Vertex Clustering Model for Disease Progression: Application to Cortical Thickness Images

We present a disease progression model with single vertex resolution that we apply to cortical thickness data. Our model works by clustering together vertices on the cortex that have similar temporal dynamics and building a common trajectory for vertices in the same cluster. The model estimates optimal stages and progression speeds for every subject. Simulated data show that it is able to accurately recover the vertex clusters and the underlying parameters. Moreover, our clustering model finds similar patterns of atrophy for typical Alzheimer’s disease (tAD) subjects on two independent datasets: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and a cohort from the Dementia Research Centre (DRC), UK. Using a separate set of subjects with Posterior Cortical Atrophy (PCA) from the DRC dataset, we also show that the model finds different patterns of atrophy in PCA compared to tAD. Finally, our model provides a novel way to parcellate the brain based on disease dynamics.

A simulation system for biomarker evolution in neurodegenerative disease.

We present a framework for simulating cross-sectional or longitudinal biomarker data sets from neurodegenerative disease cohorts that reflect the temporal evolution of the disease and population diversity. The simulation system provides a mechanism for evaluating the performance of data-driven models of disease progression, which bring together biomarker measurements from large cross-sectional (or short term longitudinal) cohorts to recover the average population-wide dynamics. We demonstrate the use of the simulation framework in two different ways. First, to evaluate the performance of the Event Based Model (EBM) for recovering biomarker abnormality orderings from cross-sectional datasets. Second, to evaluate the performance of a differential equation model (DEM) for recovering biomarker abnormality trajectories from short-term longitudinal datasets. Results highlight several important considerations when applying data-driven models to sporadic disease datasets as well as key areas for future work. The system reveals several important insights into the behaviour of each model. For example, the EBM is robust to noise on the underlying biomarker trajectory parameters, under-sampling of the underlying disease time course and outliers who follow alternative event sequences. However, the EBM is sensitive to accurate estimation of the distribution of normal and abnormal biomarker measurements. In contrast, we find that the DEM is sensitive to noise on the biomarker trajectory parameters, resulting in an over estimation of the time taken for biomarker trajectories to go from normal to abnormal. This over estimate is approximately twice as long as the actual transition time of the trajectory for the expected noise level in neurodegenerative disease datasets. This simulation framework is equally applicable to a range of other models and longitudinal analysis techniques.

Learning Imaging Biomarker Trajectories from Noisy Alzheimer’s Disease Data Using a Bayesian Multilevel Model

Characterising the time course of a disease with a protracted incubation period ultimately requires dense longitudinal studies, which can be prohibitively long and expensive. Considering what can be learned in the absence of such data, we estimate cohort-level biomarker trajectories by fitting cross-sectional data to a differential equation model, then integrating the fit. These fits inform our new stochastic differential equation model for synthesising individual-level biomarker trajectories for prognosis support. Our Bayesian multilevel regression model explicitly includes measurement noise estimation to avoid regression dilution bias. Applicable to any disease, here we perform experiments on Alzheimer’s disease imaging biomarker data — volumes of regions of interest within the brain. We find that Alzheimer’s disease imaging biomarkers are dynamic over timescales from a few years to a few decades.

A DATA-DRIVEN MODEL OF BIOMARKER CHANGES IN SPORADIC ALZHEIMER'S DISEASE

Background: Determining the sequence in which Alzheimer’s disease (AD) biomarkers become abnormal would provide important insights into disease biology and a mechanism for disease staging. Here we implement a probabilistic event-based model (EBM) (Fonteijn et al. 2012) to determine the sequence of biomarker abnormality in sporadic AD, characterise the uncertainty in the ordering, and provide a natural patient staging system. Unlike previous attempts to construct such a model, our method does not rely on a-priori clinical diagnostic information, or explicit biomarker cut-points, and it allows for a proportion of cases and controls to be misdiagnosed. Methods: We included 285 ADNI subjects (92 cognitively normal (CN), 129 mild cognitive impairment (MCI), 64 AD) with measurements of 14 AD-related biomarkers including CSF A b 1-42, p-tau, t-tau, whole brain

Non-Parametric Mixture Modelling and its Application to Disease Progression Modelling

Dementia is characterised by its progressive degeneration of cognitive abilities. In research cohorts, detailed neuropsychological test batteries are often administered to better understand how cognition changes over time. Understanding cognitive changes in dementia is of great importance, particularly in determining how structural changes in the brain may affect cognition and in facilitating earlier detection of symptomatic changes. Disease progression models are often applied to these data to understand how a disease changes over time from cross-sectional data or to disease trajectories from large numbers of individuals. Previous disease progression models used to build longitudinal models from cross-sectional data have focused on brain imaging data; however, these models are not directly applicable to cognitive data. Here we use the novel, non-parametric, Kernel Density Estimation Mixture Modelling (KDEMM) approach and demonstrate accurate modelling of the progression of cognitive test data. We found that using KDEMM resulted in more accurate models of disease progression in simulated data compared to Gaussian Mixture Models (GMMs) for the majority of parameters used to simulate the data. When comparing KDEMM and GMM to cognitive data collected in different Alzheimers Disease subtypes, we found the KDEMM resulted in a model much more in line with clinical phenotype. We anticipate that the KDEMM will be used to integrate cognitive test data, and other non-normally distributed datasets into complex disease progression models.