Laurance Lau

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2024-25

April 30, 2024

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-25.pdf

A growing body of evidence suggests that iron deposition in the brain contributes to neurodegeneration. We use this as motivation to analyze the dietary and brain imaging data of UK Biobank and explore their connections to Alzheimer's disease (AD). We use decision tree and logistic regression models to predict AD incidence and describe a pipeline for analyzing brain imaging data, using quantitative susceptibility mapping (QSM) to infer iron concentrations in the brain. We find no evidence linking diet to AD incidence and find significantly higher QSM of the deep gray matter regions overall in participants with AD.

Advisors: Chunlei Liu

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BibTeX citation:

@mastersthesis{Lau:EECS-2024-25,
    Author= {Lau, Laurance},
    Title= {Iron in Alzheimer’s Disease: Analysis of the UK Biobank Dataset},
    School= {EECS Department, University of California, Berkeley},
    Year= {2024},
    Month= {Apr},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-25.html},
    Number= {UCB/EECS-2024-25},
    Abstract= {A growing body of evidence suggests that iron deposition in the brain contributes to neurodegeneration. We use this as motivation to analyze the dietary and brain imaging data of UK Biobank and explore their connections to Alzheimer's disease (AD). We use decision tree and logistic regression models to predict AD incidence and describe a pipeline for analyzing brain imaging data, using quantitative susceptibility mapping (QSM) to infer iron concentrations in the brain. We find no evidence linking diet to AD incidence and find significantly higher QSM of the deep gray matter regions overall in participants with AD.},
}

EndNote citation:

%0 Thesis
%A Lau, Laurance 
%T Iron in Alzheimer’s Disease: Analysis of the UK Biobank Dataset
%I EECS Department, University of California, Berkeley
%D 2024
%8 April 30
%@ UCB/EECS-2024-25
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-25.html
%F Lau:EECS-2024-25