(300 WORDS MAX.) Since its establishment in 2006, ElMindA has developed BNA™ (Brain Network Activation) technology. BNA enables the visualization and quantification of discrete neural networks and the quantitative assessment of interconnectivity of these systems that are associated with brain function and dysfunction. In the current proposal, we propose a new study in partnership with the Brain Health Laboratory at Glasgow University that collected EEG data from mild Alzheimer's patients and healthy controls (100 datasets each). EEG was recorded while participants engaged in a computerized memory task ("ADEPT") that was designed to repeatedly elicit brain activity in the networks most at risk, in particular brain activity associated with episodic memory. Clinical trial studies using this task revealed an overall classification accuracy of 85.7%. To enter the clinical market however, electrophysiology technologies often face the fundamental barrier of high noise-to-signal ratio and lack of standardized pre-processing practice. BNA offers the unique capacity of automatic data processing, enabling scalability and repeatability. ElMindA's BNA is thus singularly well-suited to automatically detect the signature of AD within the electrophysiological signal. The primary goal of the proposed study is to determine whether an integrated ADEPT/BNA approach can serve as a sensitive, automated and scalable diagnostic tool for AD patients. The project will utilize a second generation algorithms that were recently developed and validatd by ElMindA for more robust and sensitive outcomes. A successful outcome will position this collaborative project to obtain regulatory and marketing clearance, and lead to market launch of a new tool to aid the diagnosis of AD.