Alzheimer's Disease (AD) is a neurodegenerative disorder affecting over 35 million people. Early diagnosis and intervention are crucial for improving outcomes. Digital Cognitive Biomarkers (DCBs) offer a promising approach for early detection and disease management, quantifying cognitive processes of encoding and retrieval through a hierarchical Bayesian cognitive processing model using wordlist memory tests. We hypothesize that DCBs will correlate with classic AD cerebrospinal fluid (CSF) biomarkers (Aβ42, T-tau, p-tau) in patients with varying cognitive decline levels compared to healthy elderly controls. Using Alzheimer's Disease Neuroimaging Initiative (ADNI) data and paired Pearson correlation coefficient analysis, our results support the hypothesis, indicating that DCBs correlate with CSF biomarkers and demonstrating their potential as a noninvasive diagnostic tool for AD. Furthermore, DCBs exhibited improved diagnostic accuracy compared to classic AD CSF biomarkers, as indicated by the area under the Receiver Operating Characteristic curve analysis. DCBs hold promise for monitoring disease progression, response to therapeutics, and identifying patients at earlier disease stages. Future research should validate these findings in diverse populations and conduct longitudinal studies to assess DCBs' potential in tracking disease progression and treatment response. Integrating DCBs with other diagnostic approaches, such as neuroimaging, could enhance overall AD diagnosis accuracy and provide a comprehensive understanding of an individual's cognitive health. In conclusion, DCBs may offer a valuable, noninvasive tool for early diagnosis and management of Alzheimer's Disease, supporting the initial hypothesis.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:honorstheses-2514 |
| Date | 01 January 2023 |
| Creators | Corripio, Kasey |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Honors Undergraduate Theses |
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