Paper
20 March 2006 Integrating and classifying parametric features from fMRI data for brain function characterization
Yongmei Michelle Wang, Chunxiao Zhou
Author Affiliations +
Abstract
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.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yongmei Michelle Wang and Chunxiao Zhou "Integrating and classifying parametric features from fMRI data for brain function characterization", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61446V (20 March 2006); https://doi.org/10.1117/12.653646
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Functional magnetic resonance imaging

Brain

Principal component analysis

Data modeling

Feature extraction

Shape analysis

Statistical modeling

Back to Top