Compressive neural activity detection with fMR images using Graphical Model Inference Online publication date: Tue, 11-Jan-2011
by Chuan Li, Qi Hao, Weihong Guo, Fei Hu
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 3, No. 3, 2010
Abstract: In this paper, a neural activity detection framework using fMRI data is proposed. The framework consists of three stages: Prediction: Predicting regions of interest associated with an extraordinary amount of neural activities through Temporal Clustering Analysis (TCA). Modelling: Categorising fMRI signals related to neural activities into event prototypes through Linear Predictive Coding (LPC). Inference: Determining the types of neural activities in terms of activation, deactivation and normality through graphical model based Bayesian inference. The experiment results demonstrate the advantages of this approach in terms of computational cost and robustness against artefacts.
Online publication date: Tue, 11-Jan-2011
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