Improving the accuracy of brain activation maps in the group-level analysis of fMRI data utilizing spatiotemporal Gaussian process model

Accuracy and precision of the statistical analysis methods used for brain activation maps are essential. Adjusting models to consider spatiotemporal correlation embedded in fMRI data may increase their accuracy, but it also introduces a high computational cost. The present study aimed apply assess Gaussian process (STGP) model improve accuracy reduce We applied both simulated experimental memory tfMRI compared findings with fast, fully Bayesian, General Linear Models (GLM). To precision, were fitted (1000 voxels,100 times point 50 people), an average indexes 100 repetitions was computed. Functional all calculated analysis. STGP resulted higher Z-score whole brain, 1000 most activated voxels, frontal lobe as approved area. Based on data, showed more than other two models. However, its time GLM, price correction, much less that Bayesian model. Spatiotemporal further improved GLM This can result accurate maps. Moreover, model’s speed appears be reasonable application.