Temporal Dynamics in Spatial Random Field Theory: A Methodological Advance in fMRI Data Analysis
Shafie Khalil
Co-Author
Department of Applied Statistics & Research Methods, University of Northern Colorado, United States
Monday, Aug 5: 9:35 AM - 9:50 AM
3309
Contributed Papers
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
Main Sponsor
Section on Statistics in Imaging
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