B1380
Title: Inter-subject correlation analysis for heterogeneous functional data
Authors: Hongnan Wang - Meta (United States)
Ping-Shou Zhong - University of Illinois at Chicago (United States) [presenting]
Abstract: A focus of the inter-subject correlation (ISC) analysis is to understand the correlation among individuals' brain activities to identify the brain regions that respond similarly to the same real-life stimuli. It plays an important role in neuroscience research. The aim is to develop a consistent test for the ISC analysis with fMRI data. We explore the benefit of using nonparametric smoothing in the ISC test and propose a nonparametric test procedure for testing the existence of the inter-subject correlation. More specifically, testing whether the covariance matrix among subjects is diagonal. Our proposed test is applicable under subject heteroscedasticity and temporal heteroscedasticity. We establish the asymptotic distributions of the proposed test statistics under the null hypothesis and a series of local alternative hypotheses. Numerical studies show that the proposed test procedure performs better than the commonly used methods in the ISC studies and cross-sectional dependence tests, including the adjusted Lagrange multiplier test, Pesarans cross-sectional dependence (CD) test, and the adjusted Pesarans CD test.
