Thursday, May 26, 2022 • 2:00–3:15 pm (CT) • Light Hall, Room 512
Organizer: Lily Wang, George Mason University
Chair: Andrew Brown, Clemson University
Individualized treatment regimes incorporating imaging features
Xinyi Li, Clemson University
Michael Kosorok, University of North Carolina at Chapel Hill
Precision medicine seeks to discover an optimal personalized treatment plan and thereby provide informed and principled decision support, based on the characteristics of individual patients. With recent advancements in medical imaging, it is crucial to incorporate patient-specific imaging features in the study of individualized treatment regimes. We propose a novel, data-driven method to construct interpretable image features which can be incorporated, along with other features, to guide optimal treatment regimes. The proposed method treats imaging information as a realization of a stochastic process, and employs smoothing techniques in estimation. We show that the proposed estimators are consistent under mild conditions. The proposed method is applied to a dataset provided by the Alzheimer's Disease Neuroimaging Initiative.
Big imaging data learning: A parallel solution
Shan Yu, University of Virginia
Lily Wang, George Mason University
GuanNan Wang, College of William and Mary
Nowadays, we are living in the era of "Big Data.'' Big imaging data captured through advanced technologies emphasizes the need for developing new and computationally efficient methods and credible theoretical support tailored for analyzing such large-scale data. Parallel statistical computing has proved to be a handy tool when dealing with big data. In general, it uses multiple processing elements simultaneously to solve a problem. However, it is hard to execute the conventional spline regressions in parallel. We develop a novel parallel smoothing technique for image-on-scalar regression using different hardware parallelism levels. Moreover, conflated with concurrent computing, the proposed method can be easily extended to the distributed system. Regarding the theoretical support of estimators from the proposed parallel algorithm, we show that the spline estimators reach the same convergence rate as the global spline estimators. The newly developed method is evaluated through several simulation studies and an analysis of the ADNI data.
Model-based segmentation for improved activation detection in single-subject functional magnetic resonance imaging studies
Ranjan Maitra, Iowa State University
Wei-Chen Chen, Food and Drug Administration, Center for Devices and Radiological Health
Functional Magnetic Resonance Imaging (fMRI) maps cerebral activation in response to stimuli but this activation is often difficult to detect, especially in low-signal contexts and single-subject studies. Accurate activation detection can be guided by the fact that very few voxels are, in reality, truly activated and that these voxels are spatially localized, but it is challenging to incorporate both these facts. We address these twin challenges to single-subject and low-signal fMRI by developing a computationally feasible and methodologically sound model-based approach, implemented in the R package MixfMRI, that bounds the a priori expected proportion of activated voxels while also incorporating spatial context. An added benefit of our methodology is the ability to distinguish the different kinds of activation. The value of our suggested approach in low-signal and single-subject fMRI studies is illustrated on a sports imagination experiment that is often used to detect awareness and improve treatment in patients in a persistent vegetative state (PVS). Our ability to reliably distinguish activation in this experiment potentially opens the door to the adoption of fMRI as a clinical tool for the improved treatment and therapy of PVS survivors and other patients.
