Mattie, David (2025) Development and applications of a fault-tolerant neuroscience platform: from understanding healthy brain development to Alzheimer’s disease progression. Doctoral (PhD) thesis, Memorial University of Newfoundland.
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Abstract
Many neuroscientific studies transform Magnetic Resonance Imaging (MRI) data using an intricate cadence of data processing, extraction, and featureengineering steps to derive the datasets necessary to accomplish a given research objective. These processing steps may require multiple software systems across different software modalities leading to a fragile and difficultto- replicate task-specific pipeline. Thus, there is a need in neuroscience research for a pipeline management platform that is extensible, vendor-neutral, facilitates interdisciplinary collaboration, supports reproducible research, and can simplify the complex orchestration of measurement extraction from raw brain data. In this study, a pipeline orchestration software was developed and its use and extensibility were demonstrated across three novel and distinct neuroscience-related research studies. The first study introduces our software in the context of its ability to execute pipelines on a large dataset and produce insights expected of neurotypical participants. The second research study identifies novel biomarkers of early-stage Alzheimer’s disease and compares their predictive performance against known biomarkers. The third study investigates the typical neural development of cognitive abilities in schoolaged children, focusing on language acquisition, and examines potential associations with environmental and developmental factors which support the theory of interactive specialization. Our three studies demonstrate the wide applicability of our pipeline orchestration software to address neuroscience research questions, and its support to reproducible research. Our software (AirCRUSH) is available at https://github.com/dmattie/aircrush, https://github.com/dmattie/aircrush-core-operators
| Item Type: | Thesis (Doctoral (PhD)) |
|---|---|
| URI: | http://research.library.mun.ca/id/eprint/16891 |
| Item ID: | 16891 |
| Additional Information: | Includes bibliographical references (pages 151-177) -- Restricted until January 1, 2026 |
| Keywords: | neuroscience, fault tolerance, MRI, Alzheimers, language acquisition |
| Department(s): | Science, Faculty of > Computer Science |
| Date: | May 2025 |
| Date Type: | Submission |
| Library of Congress Subject Heading: | Neurosciences--Research; Fault tolerance (Engineering); Magnetic resonance imaging; Alzheimer's disease--Diagnosis; Data processing--Automation; Open source software; Language acquisition--Research |
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