3D Reconstruction and Analysis of Alzheimer's Patient Biopsy Samples to Map and Quantify Hallmarks of Pathogenesis and Vulnerability

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PROJECT SUMMARY/ABSTRACT This competitive supplement application requests funds to advance tools and methodology for the normalization of large-scale, 3D EM image volumes as a means to enhance the performance, reusability, and repeatability of high throughput artificial intelligence and machine learning (AI/ML) algorithms for automatic volume segmentation of brain cellular and subcellular ultrastructure. This work will be conducted in the context of an active research project that is advancing the acquisition, processing/refinement, and dissemination of large-scale 3D EM reference data derived from a remarkable collection of legacy biopsy brain samples from patients suffering from Alzheimer’s Disease (AD) (5R01AG065549). This active project is deeply rooted in the use of advance AI/ML technologies for delineating key ultrastructural constituents of neurons and glia exhibiting hallmarks of the progression of AD. It is organized to comprehensively target areas associated with plaques, tangles and brain vasculature, attending to locations where existing findings suggest cell and network vulnerability and contain molecular interactions suspected by some to underlie the initiation and progression of AD. Through this work, we are advancing the development and dissemination of fully trained neural-network models for volume segmentation to simplify (and reduce the costs associated with) community efforts to extract their own 3D geometries and associated morphometrics from this collection of AD reference data and similar repositories of neuronal 3D EM data. With this supplemental effort, we will develop, refine and disseminate a set of tools which allow for direct feedback and standardization of primary image quality, whereby benchmarks can be established so as to optimize the entire process holistically, giving a more rigorously defined target for image characteristics at time of image acquisition. The outcome of this work is to advance the use of transfer learning methods, facilitating repeatability and reuse of trained neural network models for scalable EM image segmentation.