title: Compressive neural activity detection with fMR images using Graphical Model Inference.
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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA. li005@crimson.ua.edu
authors.forename: Chuan
authors.initials: C
authors.lastname: Li
authors.forename: Qi
authors.initials: Q
authors.lastname: Hao
authors.forename: Weihong
authors.initials: W
authors.lastname: Guo
authors.forename: Fei
authors.initials: F
authors.lastname: Hu
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D016011:Normal Distribution