Abstract Number:
2796
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Zhou Lan (1), Lauren O'Donnell (1)
Institutions:
(1) Brigham and Women's Hospital, Harvard Medical School, N/A
Co-Author:
First Author:
Zhou Lan
Brigham and Women's Hospital, Harvard Medical School
Presenting Author:
Zhou Lan
Brigham and Women's Hospital, Harvard Medical School
Abstract Text:
In vivo fiber tractography is a 3D reconstruction technique to assess neural tracts using data collected by dMRI. The fiber tract obtained from the technique can be used for studying the brain's anatomy and its associations to function of interest covariates. Recent machine learning methods can efficiently identify subject-level white matter tracts. However, analyzing the scalar clinical/psychological factors (e.g., cognitive score) and fiber tracts is difficult. The current methods use high-level summary statistics of fiber tract; thus, the relationship investigation is based on traditional regression models. In this paper, we find the FA quantiles over the points of a fiber tract (Microstructural Quantile Profile) can be used to differentiate the effect of function of interest covariates. We adopted and further developed the quantile regression methodology with clustered data to infer the relationship between Microstructural Quantile Profile and scalar clinical/psychological factors. Insightful spatial findings were provided via our new approach. The method is more robust in identifying the relationship between fiber tract and scalar clinical/psychological factors.
Keywords:
fiber tractography|diffusion MRI|quantile regression|microstructural quantile profile| |
Sponsors:
Section on Statistics in Imaging
Tracks:
Brain Imaging
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