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In this paper, we present a framework for neural activity detection using fMRI data, based on both statistical data analysis (data-driven) and graphical information modeling (model-based). The data-driven approaches do rough prediction when an extraordinary amount of neural activities arise. By proper exploration of spatial, temporal, inter-subject correlations, the model-based approaches can provide more insights to details, and physiological meaning from high data volume, low signal-to-noise ratio (SNR) fMRI measurements. Through temporal cluster analysis (TCA), matched filtering, linear predictive coding (LPC), and variational Bayesian Gaussian mixture modeling (VBGMM), the temporal fMRI signals are converted into event prototypes associated with three neural statuses: activation, deactivation, and normality. As a result, the high volume fMRI data generated from multiple subjects can be statistically …