Temporal Dynamics in Spatial Random Field Theory: A Methodological Advance in fMRI Data Analysis

Oregon Convention Center 

This research enhances fMRI data analysis by integrating temporal dynamics into spatial random field theory. We developed a new test statistic,, within the time-adaptive Scale Space Gaussian Random Field Model, focusing on signal detection in fMRI data. It captures the global maximum across spatial and temporal dimensions.
Our methodology, employing the Functional Autoregressive (FAR (1)) model, focuses on temporal dependencies and spatial arrangements in data, significantly contributing to neuroimaging studies. We used a simulation approach to estimate the p-value for testing the signal using X_max and understand its advantages in analyzing spatial-temporal patterns in fMRI data.

Time-Adaptive Scale Space

Gaussian Random Field Model

fMRI Data Analysis

Functional Autoregressive Model

Statistical Methodology 

Section on Statistics in Imaging