It is estimated that neurodegenerative diseases causing dementia will surpass cancer as the leading cause of death by the year 2040. Alzheimer's disease (AD) is the leading cause of dementia, followed by synucleinopathies, such as dementia with Lewy bodies (DLB) and Parkinson's disease with dementia (PDD). Among clinical researchers focused on investigating the varying etiologies, genetic associations, biomarkers, and treatment options for these neurodengenerative diseases, there is an urgent need for effective tools to aid in the classification of dementia subtypes, in the earliest detectable stages of the pathophysiological process. To address this unmet need Advanced Brain Monitoring (ABM) proposes to leverage two previously developed technologies to create an Integrated Neurocognitive and Sleep-Behavior Profiler for the Endophenotypic Classification of Dementia Subtypes (INSPECDS). The core components of the INSPECDS platform will be a previously developed Alertness and Memory Profiler (AMP), a recently developed Sleep Profiler, and integrated machine-learning, classification algorithms, hosted on a secure, cloud-based, infrastructure for automated data processing, analysis, and reporting. The AMP was developed and validated during a previous NIH-funded SBIR Phase I/II project for the purpose of detecting the neurocognitive effects of sleep deprivation in adults diagnosed with obstructive sleep apnea. The AMP is truly unique among neurocognitive testing platforms in that it is the only one which integrates advanced, electrophysiological measures (e.g., 24-channel, wireless EEG and ECG) during the performance of computerized neurocognitive tasks. This advanced 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. The Sleep Profiler is an FDA-cleared, easily applied, wireless-EEG device that was developed and validated to measure sleep architecture for in-home sleep studies. With integrated measures of submental (chin) EMG and wireless accelerometers to monitor head and limb movements, the Sleep Profiler is an ideal device for quantifying the characteristics of REM-sleep behavior disorder (RBD), which is considered to be a prodromal expression of synucleinopathy. Furthermore, the application of sophisticated, machine-learning, classification algorithms will streamline the processing and analyses of these data to derive statistical probabilities of various dementia subtypes. The overarching goal of the current, Direct-to-Phase II, SBIR project is to develop a secure, cloud-based infrastructure to compile the data obtained from the AMP and Sleep Profiler, train classification algorithms to discriminate among dementia subtypes, validate diagnostic accuracy, and integrate optimized classifiers within the cloud-based architecture. Once completed, the INSPECDS system will be the first clinical research tool of its kind and find immediate application in both university-based research settings and pharmaceutical industry clinical trials to aid in the endophenotypic stratification of research participants.
Public Health Relevance Statement: Public Health Relevance: Among clinical researchers focused on investigating the varying etiologies, genetic associations, clinical course, and treatment options for neurodengenerative diseases, there is an urgent need for effective tools to aid in the classification of dementia subtypes, in the earliest detectable stages of the pathophysiological process. To address this unmet need, Advanced Brain Monitoring (Carlsbad, CA) is developing Integrated Neurocognitive and Sleep-Behavior Profilers for the Endophenotypic Classification of Dementia Subtypes (INSPECDS), which will provide an inexpensive, non-invasive solution combining neurocognitive, electrophysiological (EEG, ECG, EMG), and sleep-behavior assessment into a single, integrated system featuring automated scoring and classification algorithms. Once completed, the INSPECDS system will be the first clinical research tool of its kind and find immediate application in university-based research settings and pharmaceutical industry clinical trials to aid in the endophenotypic stratification of research participants.
Project Terms: Accelerometer; Address; Adult; alertness; Algorithms; Alzheimer's Disease; Architecture; Automatic Data Processing; base; Behavior; Behavior assessment; Biological Markers; Blinded; Brain; brain behavior; California; Cardiovascular Diseases; Cause of Death; Characteristics; Chin; Classification; Clinic; Clinical; Clinical Research; Clinical Trials; cloud based; cognitive function; cohort; computerized; Data; data acquisition; Data Analyses; Databases; Dementia; Depressed mood; Devices; Diagnosis; diagnostic accuracy; Disease; Disease Progression; Drug Industry; Economic Burden; Elderly; Electrocardiogram; Electrophysiology (science); Enrollment; Etiology; FDA approved; Funding; General Hospitals; genetic association; Goals; Head Movements; Home environment; human subject; Individual; Lewy Body Dementia; limb movement; Machine Learning; Malignant Neoplasms; Massachusetts; Measures; Memory; mild cognitive impairment; Minor; Modification; Monitor; Neurocognitive; neurocognitive test; Neurodegenerative Disorders; Neuropsychological Tests; Obstructive Sleep Apnea; Parkinson Disease; Parkinson's Dementia; Participant; Patients; Performance; Phase; Probability; Process; public health relevance; relating to nervous system; REM Sleep Behavior Disorder; Reporting; Research; Research Infrastructure; Research Personnel; Sampling; Secure; Sleep; Sleep Architecture; Sleep Deprivation; Small Business Innovation Research Grant; Staging; Stratification; stroke; synucleinopathy; System; Technology; Time; tool; Training; United States National Institutes of Health; Universities; Wireless Technology
