A hybrid approach for compressive neural activity detection with functional MR images

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 modeled as coupled finite-state sequences. Based on the graphical-model representation, the neural activities captured through fMRI can be classified and detected at reduced computational cost. The whole framework consists of three components: 1) image enhancement, event prediction and capture; 2) event feature extraction and modeling; and 3) graphical model based Bayesian inference. The experiment results demonstrate the advantages of the proposed hybrid, compressive signal processing approach in terms of computational cost and robustness against inter-subject variability as well as various artifacts.