Abstract
Electromagnetic source imaging (ESI) techniques have become one of the most common alternatives for understanding cognitive processes in the human brain and for guiding possible therapies for neurological diseases. However, ESI accuracy strongly depends on the forward model capabilities to accurately describe the subject's head anatomy from the available structural data. Attempting to improve the ESI performance, we enhance the brain structure model within the individual-defined forward problem formulation, combining the head geometry complexity of the modeled tissue compartments and the prior knowledge of the brain tissue morphology. We validate the proposed methodology using 25 subjects, from which a set of magnetic-resonance imaging scans is acquired, extracting the anatomical priors and an electroencephalography signal set needed for validating the ESI scenarios. Obtained results confirm that incorporating patient-specific head models enhances the performed accuracy and improves the localization of focal and deep sources.
Highlights
Electroencephalography (EEG) and Magnetoencephalography (MEG) recordings are widely used as noninvasive neuroimaging techniques to describe the dynamics of brain activity, driving to a better understanding of cognitive processes and neurological diseases in the human brain
Signals are directly influenced by the conductivity patterns of each head tissue, many studies concentrated on the effects of realistic head modeling on the EEG modality since MEG is assumed to be less affected by uncertainties inherent to the experimentally determined conductivity values of the different conductive compartments [2]
The following techniques are worth mentioning in electromagnetic studies: removing or minimizing the unwanted nonbrain signals or artifacts [3, 4], rereferencing of acquired EEG data [5], and enhancing the source space analyses or source estimation models (termed electromagnetic source imaging (ESI)) that are employed to estimate the synchronously active neural sources, which generate the electrical potentials measured over the scalp [6]
Summary
Electroencephalography (EEG) and Magnetoencephalography (MEG) recordings are widely used as noninvasive neuroimaging techniques to describe the dynamics of brain activity, driving to a better understanding of cognitive processes and neurological diseases in the human brain. Recorded EEG/MEG signals from each scalp electrode are affected by the volume-conducted activity coming from multiple sources spatially dispersed in the brain cortex [1]. Signals are directly influenced by the conductivity patterns of each head tissue, many studies concentrated on the effects of realistic head modeling on the EEG modality since MEG is assumed to be less affected by uncertainties inherent to the experimentally determined conductivity values of the different conductive compartments [2]. The following techniques are worth mentioning in electromagnetic studies: removing or minimizing the unwanted nonbrain signals or artifacts [3, 4], rereferencing of acquired EEG data [5], and enhancing the source space analyses or source estimation models (termed electromagnetic source imaging (ESI)) that are employed to estimate the synchronously active neural sources, which generate the electrical potentials measured over the scalp [6]. To increase the precision of estimated brain sources and to provide target-specific stimulation, a personalized pipeline is required for an accurate head model generation as realistic as possible [9], which is much more complex and must consider tissue conductivities and the individual shape of the compartments with different electrical conductivity
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