Online Program Home
My Program

Abstract Details

Activity Number: 77
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #318719
Title: Wavelet Analysis of Large-P-Small-N Cross-Correlation Matrices in fMRI Studies of Neuroplasticity
Author(s): Jiayi Wu* and Sam Efromovich
Companies: and The University of Texas at Dallas
Keywords: neuroplasticity ; nonparametric wavelet regression ; large-p-small-n

To understand how human brain functions and to develop possible treatments for brain diseases, it is important to explore the neuroplasticity, which is the ability of the brain to reorganize neural connectivity based on the new experiences and learning. An functional magnetic resonance imaging (fMRI) experiment has been performed on 24 participants by the UT Southwestern Medical Center. The conventional method is to calculate a single Pearson Correlation coefficient of the spatially averaged time series for each participant. The proposed method, where a voxel-wise fMRI time series is treated as an equidistance nonparametric wavelet regression, makes it possible to analyze the neural connectivity on a voxel-to-voxel level, which therefore implies the analysis of the large-p-small-n cross-correlation matrices. Adaptive thresholding estimates are developed to solve the large-p-small-n problem. The statistical analysis and the obtained results will be shown for all participants.

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

Back to the full JSM 2016 program

Copyright © American Statistical Association