Bayesian analysis of MEG visual evoked responses

David M. Schmidt; John S. George; C. C. Wood

doi:10.1117/12.349576

20 May 1999 Bayesian analysis of MEG visual evoked responses

David M. Schmidt, John S. George, C. C. Wood

Author Affiliations +

Proceedings Volume 3660, Medical Imaging 1999: Physiology and Function from Multidimensional Images; (1999) https://doi.org/10.1117/12.349576
Event: Medical Imaging '99, 1999, San Diego, CA, United States

Abstract

We have developed a method for analyzing neural electromagnetic data that allows probabilistic inferences to be drawn about regions of activation. The method involves the generation of a large number of possible solutions which both fit the data and prior expectations about the nature of probable solutions made explicit by a Bayesian formalism. In addition, we have introduced a model for the current distributions that produce MEG (and EEG) data that allows extended regions of activity, and can easily incorporate prior information such as anatomical constraints from MRI. To evaluate the feasibility and utility of the Bayesian approach with actual data, we analyzed MEG data from a visual evoked response experiment. We compared Bayesian analyses of MEG responses to visual stimuli in the left and right visual fields, in order to examine the sensitivity of the method to detect known features of human visual cortex organization. We also examined the changing pattern of cortical activation as a function of time.

Citation Download Citation

David M. Schmidt, John S. George, and C. C. Wood "Bayesian analysis of MEG visual evoked responses", Proc. SPIE 3660, Medical Imaging 1999: Physiology and Function from Multidimensional Images, (20 May 1999); https://doi.org/10.1117/12.349576

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