Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative condition that results in impaired performance in multiple cognitive domains. Preclinical changes in eye movements and language can occur with the disease, and progress alongside worsening cognition. In this article, we present the results from a machine learning analysis of a novel multimodal dataset for AD classification. The cohort includes data from two novel tasks not previously assessed in classification models for AD (pupil fixation and description of a pleasant past experience), as well as two established tasks (picture description and paragraph reading). Our dataset includes language and eye movement data from 79 memory clinic patients with diagnoses of mild-moderate AD, mild cognitive impairment (MCI), or subjective memory complaints (SMC), and 83 older adult controls. The analysis of the individual novel tasks showed similar classification accuracy when compared to established tasks, demonstrating their discriminative ability for memory clinic patients. Fusing the multimodal data across tasks yielded the highest overall AUC of 0.83 ± 0.01, indicating that the data from novel tasks are complementary to established tasks.
Highlights
Dementia affects approximately 47 million individuals globally and is considered to be one of the costliest diseases in developed countries (El-Hayek et al, 2019)
We found that the novel task results are lower than established task results with the exception of the memory task Gaussian Naïve Bayes (GNB) model (p > 0.1)
We did not find a significant difference when comparing language-alone models for the established tasks against the best memory task model (p > 0.1), with the exception of the picture description Random Forest (RF) model, which was significantly outperformed by the memory GNB model (p ≤ 0.001)
Summary
Dementia affects approximately 47 million individuals globally and is considered to be one of the costliest diseases in developed countries (El-Hayek et al, 2019). Successful disease-modifying therapies for AD are most likely to be effective in individuals without advanced neurodegenerative changes (Sperling et al, 2014; Reiman et al, 2016). These individuals, as well as individuals with pre-clinical or very early stage disease, are of particular interest for disease-modifying drug trials for dementia, as preventing decline appears to be more promising than reversing it (Trempe and Lewis, 2018). Current evidence suggests that pre-clinical pathological hallmarks of AD are present years before overt clinical symptoms occur (Vickers et al, 2016) and that both dementia and cognitive impairment can often go undetected (Lang et al, 2017)
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