3:30pm - 4:30pm | Biostat Seminar: Statistical Approaches for Integrative Learning for Neuroimaging Data

Date: 
Wednesday, October 27, 2021

-- DATE & TIME --
Wednesday, October 27 2021
3:30pm, Online via Zoom

 

-- ZOOM LINK --
https://ucla.zoom.us/j/92676785924?pwd=US9WSHpyZlMxdUJtWVE1TmJMYWxFUT09
Meeting ID: 926 7678 5924 | Password: 507253

 

-- SPEAKER --
Suprateek Kundu, Ph.D.
Associate Professor Department of Biostatistics
University of Texas MD Anderson Cancer Center

 

-- ABSTRACT --
Motivated by a recent interest in data fusion methods in medical imaging, we discuss novel approaches for joint analysis of multiple neuroimaging datasets. In the first part of the talk, I discuss our recently developed approach for integrative Bayesian learning of multiple brain networks using functional magnetic resonance imaging data. We illustrate that joint network learning results in biologically interpretable and reproducible results compared to single network analysis. In the second part of the talk, we propose a novel approach for joint estimation of multiple scalar-on-image regression models involving high-dimensional noisy images. Standard scalar- on-image regression models that fit each dataset separately are not equipped to leverage information across inter-related images, and existing multi-task learning approaches are compromised by the inability to account for the noise that is often observed in images.  Under both convex and non-convex grouped penalties that are designed to pool information across inter-related images for joint learning, we are able to explicitly account for noise in high-dimensional images via a projection-based approach. In the presence of non-convexity arising due to noisy images, we derive non-asymptotic error bounds under non-convex as well as convex grouped penalties, even when the number of voxels increases exponentially with sample size. A projected gradient descent algorithm is used for computation, which is shown to approximate the optimal solution via well-defined non-asymptotic optimization error bounds under noisy images. Extensive simulations and application to a motivating longitudinal Alzheimer’s disease study illustrate significantly improved predictive ability and greater power to detect true signals, that are simply missed by existing methods without noise correction due to the attenuation to null phenomenon.

 

-- FLYER --
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