FMRI brain activity and underlying hemodynamics estimation in a new Bayesian framework

The emerging functional MRI (Magnetic Resonance Imaging), fMRI, imaging modality was developed to obtain non-invasive information regarding the neural processes behind pre-determined task. The data is gathered in such a way that the extraction certainty of the desired information is maximized. Still this is a difficult task due to low Signal-to-Noise Ratio (SNR), corrupting noise and artifacts from several sources. The most prevalent method, here called SPM-GLM [1] uses a conventional statistical inference methodology based on the t-statistics, where it assumes a rather rigid shape on the BOLD Hemodynamic Response Function (HRF), constant for the whole region of interest (ROI). A new algorithm, designed in a Bayesian framework, is presented in this paper, called SPM-MAP. The algorithm jointly detects the brain activated regions and the underlying HRF in an adaptative and local basis. This approach presents two main advantages: 1) the activity detection benefits from the method's ...

• Type: Conference
• Year: 2008
• Where: ISBI
• Authors: David M. Afonso, João M. Sanches, Martin H. Lauterbach
• Keywords: BOLD Hemodynamic Response, HRF Shape, ISBI 2008, Magnetic Resonance Imaging, Medical Imaging