MRI: Development of an Instrument for a Comprehensive Study of Alzheimer's Disease: Multimodal Imaging, Visualization, Machine Learning and Therapeutic Brain Stimulation

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This project develops an instrument to establish an integrated infrastructure not only in terms of multiple recording modalities, but also in terms of collection and management of structural, functional, and metabolic brain data. As an application it utilizes the comprehensive study of Alzheimer's Disease (AD),notably neuropsychological testing, genetics, and demographic factors, all linked to a database with common evaluations and standardized measures, thus setting up an environment most suitable for multi-site studies and the merging of data across sites. This instrument is expected to create an appropriate environment for seeking the gold standards of machine/deep learning (i.e., stability, sparsity, interpretability, accuracy, and ability to handle missing data inherent to clinical studies and to confront multicollinearity--an intrinsic characteristic from the repeated measures design in longitudinal studies). This work integrates a Neuroimaging Web-Services Interface, Multimodal Neuroimaging Platform, and a Computational Platform. The instrument is created to overcome the opacity of ensemble methods such as ANNs (Artificial Neural Networks) through sparse, and yet highly interpretable, decision trees and processes. The deliberation process for a diagnosis or prognosis is likely to be enhanced by learning what significant features are essential to lead to a given classification and/or prediction outcome. The modular structure of the design allows extensions to other application domains that involve other neurological disorders where brain imaging is part of any subject critical care. Utilizing the instrument through the web interface as a cyber-physical system may enable the research community to apply new data mining concepts or to execute novel classification and prediction algorithms on a multimodal and computationally-effective neuroimaging platform, thus opening the field for multi-site studies, as well as extensive data sharing. This sophisticated instrument allows for training new graduates who combine engineering and computing know-how into the fields of bioscience, computing, and medicine. It significantly promotes science and national health by eliciting new understanding of a disease that according to the data reported in 2017 by the Alzheimer's Association, affects 10% of the population over 65 (with growing costs estimated above $259 billion).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.