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Activity Number:
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382
- Imaging and Clinical Biomarkers in Neurodegenerative Disease
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #322267
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Title:
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Nonlinear Z-Score Estimation for Establishing Cognitive Norms
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Author(s):
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John Kornak* and Jingxuan Wang and Fei Jiang and Julie Fields and Adam Staffaroni and Howard Rosen and Adam Boxer and Bradley Boeve and Consortium ALLFTD
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Companies:
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University of California, San Francisco and University of California, San Francisco and University of California, San Francisco and Mayo Clinic Rochester and University of California, San Francisco and University of California, San Francisco and University of California, San Francisco and Mayo Clinic Rochester and National Institutes of Health
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Keywords:
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cognitive scores;
nonlinear modeling;
truncation effects;
frontotemporal dementia
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Abstract:
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A common approach for detecting individuals with cognitive deficiency based on neuropsychological test scores, is to compare Z-scores to that of cognitively normal individuals after linear correction for age, sex, and education. Extreme negative Z-scores relative to the control normative distribution indicate cognitive deficiency. Here the linear correction approach is extended to consider non-linear relationships (using shape-constrained additive models) between predictors (age, sex, and education) and cognitive score, as well as accounting for heterogeneous standard deviation of the cognitive scores with respect to age. Finally, we extend this modeling approach to also incorporate ceiling and floor effects in cognitive outcomes.
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Authors who are presenting talks have a * after their name.
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