New blockwise permutation tests preserving exchangeability in functional neuroimaging.

In this paper, we present a new blockwise permutation test approach based on the moments of the test statistic. The method is of importance to functional neuroimaging studies. In order to preserve the exchangeability condition required in permutation, we divide the time series into certain exchangeability blocks.

On clustering fMRI using Potts and mixture regression models.

In this paper, we propose a model based clustering method for functional magnetic resonance imaging (fMRI) data to detect the functional connectivity network. The Potts model, which represents spatial interactions of neighboring voxels, is introduced to integrate the temporal mixture regression modeling into one single unified model. The estimation of the parameters is achieved through a restoration maximization (RM) algorithm for computation efficiency and accuracy.

Unified framework for robust estimation of brain networks from FMRI using temporal and spatial correlation analyses.

There is a rapidly growing interest in the neuroimaging field to use functional magnetic resonance imaging (fMRI) to explore brain networks, i.e., how regions of the brain communicate with one another.

Efficient Moments-based Permutation Tests.

In this paper, we develop an efficient moments-based permutation test approach to improve the test's computational efficiency by approximating the permutation distribution of the test statistic with Pearson distribution series. This approach involves the calculation of the first four moments of the permutation distribution. We propose a novel recursive method to derive these moments theoretically and analytically without any permutation.

Functional interactivity in fMRI using multiple seeds' correlation analyses--novel methods and comparisons.

This paper presents novel statistical methods for estimating brain networks from fMRI data. Functional interactions are detected by simultaneously examining multi-seed correlations via multiple correlation coefficients. Spatially structured noise in fMRI is also taken into account during the identification of functional interconnection networks through non-central F hypothesis tests.

Nonlinear estimation and modeling of fMRI data using spatio-temporal support vector regression.

This paper presents a new and general nonlinear framework for fMRI data analysis based on statistical learning methodology: support vector machines. Unlike most current methods which assume a linear model for simplicity, the estimation and analysis of fMRI signal within the proposed framework is nonlinear, which matches recent findings on the dynamics underlying neural activity and hemodynamic physiology. The approach utilizes spatio-temporal support vector regression (SVR), within which the intrinsic spatio-temporal autocorrelations in fMRI data are reflected.