Integrating and classifying parametric features from fMRI data for brain function characterization

Recent advances in functional magnetic resonance imaging (fMRI) provide an unparalleled opportunity for measuring and characterizing brain function in humans. However, the typically small signal change is very noisy and susceptible to various artifacts, such as those caused by scanner drift, head motion, and cardio-respiratory effects. This paper presents an integrated and exploratory approach to characterize brain function from fMRI data by providing techniques for both functional segregation and integration without any prior knowledge of the experimental paradigm. We demonstrate that principal component analysis (PCA) can be used for temporal shape modeling and shape feature extraction, shedding lights from a different perspective for the application of PCA in fMRI analysis. Appropriate feature screening is also performed to eliminate the parameters corresponding to data noise or artifacts. The extracted and screened shape parameters are revealed to be effective and efficient representations of the true fMRI time series. We then propose a novel strategy which classifies the fMRI data into distinct activation regions based on the selected temporal shape features. Furthermore, we propose to infer functional connectivity of the identified patterns by the distance measures in this parametric shape feature space. Validation for accuracy, sensitivity, and efficiency of the method and comparison with existing fMRI analysis techniques are performed using both simulated and real fMRI data.