| Title: |
MRI Distance Measures as a Predictor of Subsequent Clinical Status During the Preclinical Phase of Alzheimer's Disease and Related Disorders |
| Authors: |
Zhang, X; Caffo, BS; Soldan, A; Pettigrew, C; Guray, E; Davatzikos, C; Morris, JC; Benzinger, TLS; Johnson, SC; Masters, CL; Fripp, J; Resnick, SM; Bilgel, M; Kukull, WA; Albert, MS; Wang, Z |
| Publisher Information: |
Wiley |
| Publication Year: |
2025 |
| Collection: |
The University of Melbourne: Digital Repository |
| Description: |
Brain atrophy over time, as measured by magnetic resonance imaging (MRI), has been shown to predict subsequent cognitive impairment among individuals who were cognitively normal when first evaluated, indicating that subtle brain atrophy associated with Alzheimer's disease (AD) may begin years before clinical symptoms appear. Traditionally, atrophy has been quantified by differences in brain volume or thickness over a specified timeframe. Research indicates that the rate of atrophy varies across different brain regions, which themselves exhibit complex spatial and hierarchical organizations. These characteristics collectively emphasize the need for diverse summary measures that can effectively capture the multidimensional nature of degeneration. In this study, we explore the use of distance measurements to quantify brain volumetric changes using processed MRI data from the Preclinical Alzheimer's Disease Consortium (PAC). We conducted a series of analyses to predict future diagnostic status by modeling MRI trajectories for participants who were cognitively normal at baseline and either remained cognitively normal or progressed to mild cognitive impairment (MCI) over time, with adjustments for age, sex, education, and APOE genotype. We consider multiple distance measures and brain regions through a two-step approach. First, we build base models by fitting individual mixed-effect models for each distance metric and brain region pairing, using follow-up diagnosis (normal vs. MCI) as the outcome and volumetric changes from the baseline, as summarized by a given distance measure, as predictors. The second step aggregates these individual region-distance base models to derive an overall estimate of diagnostic status. Our analyses showed that the distance measures approach consistently outperformed the traditional direct volumetric approach in terms of predictive accuracy, both in individual base models and the aggregated models. This work highlights the potential advantage of using distance measures over the ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| ISSN: |
1065-9471 |
| Relation: |
https://hdl.handle.net/11343/360671 |
| Availability: |
https://hdl.handle.net/11343/360671 |
| Rights: |
https://creativecommons.org/licenses/by-nc-nd/4.0 ; CC BY-NC-ND |
| Accession Number: |
edsbas.16B7FBAF |
| Database: |
BASE |