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Activity Number: 415
Type: Topic Contributed
Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #315694
Title: Spatially Weighted Reduced-Rank Framework for Neuroimaging Data with Application to Alzheimer's Disease
Author(s): Mihye Ahn* and Haipeng Shen and Chao Huang and Yong Fan and Hongtu Zhu
Companies: The University of Nevada, Reno and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and University of Pennsylvania and The University of North Carolina at Chapel Hill
Keywords:
Abstract:

Recently, much attention has been received on the analysis of functional imaging data to delineate the intrinsic functional connectivity pattern among different brain regions within each subject. However, only few approaches for integrating functional connectivity pattern from multiple subjects have been proposed. The goal of this paper is to develop a reduced-rank model framework for analyzing the whole-brain voxel-wise functional images across multiple subjects in the frequency domain. Considering the neighboring voxels with different weights, the frequency and spatial factors can be extracted. Imposing sparsity on the frequency factors enables us to identify the dominant frequencies. In addition, the spatial maps can be used for detecting group difference, when the comparison between different groups is of specific interest. Simulation study shows that the proposed method achieves less spatial variability and better estimates of frequency and spatial factors, compared to some existing methods. Finally, we apply the proposed method to ADNI data.


Authors who are presenting talks have a * after their name.

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