Multi-site longitudinal Integrated Neurocognitive and Sleep-Behavior Profiler for the Endophenotypic Classification of Dementia Subtypes (INSPECDS)

It is estimated that Alzheimer’s and other neurodegenerative diseases causing dementia will surpass cancer as the second leading cause of death by the year 2040. Alzheimer’s disease (AD) is the leading cause of dementia, followed by synucleinopathies, including dementia with Lewy bodies (DLB), Parkinson’s disease with dementia (PDD), and Fronto-temporal dementia. There is an urgent, unmet need for effective tools to aid in the classification of dementia subtypes, in the earliest detectable stages of the pathophysiological process. To address this, Advanced Brain Monitoring (ABM) is leveraging day/night assessment technologies to create the Integrated Neurocognitive and Sleep-Behavior Profiler for the Endophenotypic Classification of Dementia Subtypes (INSPECDS) to profile Alzheimer’s and other dementias. The components of the platform are the Alertness and Memory Profiler (AMP), the Sleep Profiler (SP), and integrated machine-learning, classification algorithms, hosted on a secure cloud-based, infrastructure for automated data processing, analysis, & reporting. AMP is unique among neurocognitive testing platforms in that it is the only one that integrates advanced electrophysiological measures (e.g., 24-channel wireless EEG/ECG) during the performance of computerized neurocognitive tasks and has proven effective in characterizing cognitive decline in Alzheimer’s disease. This capability permits researchers to explore real-time relations between fluctuations in alertness, discrete cognitive functions, and specific neural processes believed to subserve observed performance deficits in Alzheimer’s disease and other dementias. The SP is FDA-cleared, easily applied, wireless-EEG device that was developed and validated to measure sleep architecture for in-home sleep studies with submental EMG and wireless accelerometers to monitor head and limb movements to quantify the characteristics of REM-sleep behavior disorder, considered to be a prodromal expression of synucleinopathy. The application of machine-learning, classification algorithms streamlines the processing and analyses of these data to derive statistical probabilities of Alzheimer’s disease and other dementia subtypes. The overarching goal of the current submission is to finalize implementation of a secure, cloud-based infrastructure to compile the data obtained from the AMP and SP, train classification algorithms to discriminate among Alzheimer’s disease and other dementia subtypes, validate diagnostic accuracy, and integrate optimized classifiers within the cloud-based architecture. The INSPECDS system is the first clinical research tool of its kind with application in both university-based research settings and pharmaceutical clinical trials to aid in the endophenotypic stratification of Alzheimer’s disease and other dementias.