Use of machine learning to quantify cognitive function in AD, FTD, and DLB

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Project Abstract / Summary Cognitive assessment is a key element of the diagnostic evaluation of patients suspected of having early symptomatic stages of neurodegenerative brain diseases, including Alzheimer’s disease (AD), Frontotemporal Lobar Degeneration (FTLD), and Lewy Body Disease (LBD). As biomarkers mature, the field is separating clinical syndromes arising from these diseases from the neuropathologic changes themselves, leading to concerns about classification systems for the illnesses these diseases produce. Many tests typically employed in cognitive assessment are verbal, often implemented by an examiner asking the patient a question and the patient answering. These tests are typically scored by hand, with the examiner counting correct or incorrect answers and a simple score being generated against normative scores. Many of the tests still in use were developed 30+ years ago. An exciting array of recent advances in artificial intelligence methods has begun to enable the measurement and classification of language and other cognitive characteristics captured in audio recordings. Here we introduce a new approach to accomplishing both of these possibilities. Recent developments in Natural Language Processing (NLP) and Machine Learning (ML) have now made possible the automated discovery and classification of features measurable in speech samples. Once established, these feature sets can be connected to distributions of cortical atrophy, thus enabling links between specific cognitive abnormalities and underlying neural networks. This approach to the analysis of clinical dementia syndromes can be achieved through a sufficiently large number of cognitive test measures recorded as speech samples. In addition, such analyses require the use of the latest generation of artificial intelligence models, called transformer-networks, to be able to learn the unique ways in which individuals with cognitive impairment or dementia respond to questions requiring memory, executive function, emotion, or language. In Aim 1, we will investigate the validity of an unsupervised artificial intelligence model for measuring cognitive abnormalities in patients with AD, FTLD, or DLB against traditional clinical measures and against MRI measures of regional brain atrophy. In Aim 2, we will investigate the performance of an unsupervised artificial intelligence model for classifying cognitive abnormalities in AD, FTLD, and DLB patients into clusters. In Aim 3, we will evaluate the reliability of these automated measures of cognitive abnormalities in AD, FTLD, and DLB. Through a finer-grained analysis of cognition in people with AD, FTLD, or DLB, it should be possible to develop better understanding of the overlapping and dissociable features of these dementias, aiming for improved diagnostic classification and better monitoring.