Inclusão de MRI e informação multigrid a priori para inferência bayesiana de fontes de M/EEG / MRI image and multigrid a priori information for bayesian M/EEG source localization

AUTOR(ES)
FONTE

IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia

DATA DE PUBLICAÇÃO

28/04/2011

RESUMO

Functional Neuroimaging has evolved in the last few decades with the introduction of techniques such as Positron Emission Tomography or PET and Functional Magnetic Ressonance Image or fMRI [Belliveau et al., 1991]. These allow observing brain activity with a resolution of a few millimeters and, due to the nature of the signal, a time resolution of the order of 5 seconds [Kim et al., 1997]. M/EEG, on the other hand, have a millisecond time resolution, since the signal is produced by the transport of ions through cell membranes [Nunez and Srinivasan, 2006]. However their space resolution is much lower since these are typically ill posed problems with many more unknowns than data points. A high resolution M/EEG has of the order of O(200) data channels, which allow measuring the magnetic or electric field at O(200) positions around the head. For a resolution scale of order l there are O(L l )3 variables, where L = 15cm. In this work we aim at studying methods to increase the spatial resolution of EEG techniques, since functional mapping of the human brain is intimately related to the localization of the activity in space as well as in time [Friston, 2009] (often relative to the time of external stimuli). Any advance in the inverse problem of source localization for EEG can rather easily be extended to deal with MEG. Bayesian methods are the natural setting to deal with ill posed problems [Wipf and Nagarajan, 2009]. There are essentially two directions in which Bayesian algorithms can be improved, by building a better likelihood or a prior distribution. While we recognize that important advances can be done in the former direction we here concentrate in the latter. In this work we introduce a multiscale method to build an improved prior distribution. A similar idea has been studied within an easier context of fMRI [Amaral et al., 2004]. Several new problems appear in dealing with the vectorial character of EEG. The most important, is the construction of a set of renormalized lattices that approximate the cortex region where the source activity is located and the related problem of de ning the relevant variables in coarser scale representation of the cortex. Validation of a new algorithm is always an essential problem. We present results which suggest on simulated data, that our method might be a valid alternative to current algorithms, judged both by the rate of errors in source localization as well as by the time it takes to converge.

ASSUNTO(S)

física médica magnetoencephalography medical physics electroencephalography eletroencefalografia magnetoencefalografia

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