Abstract — In electromagnetic source analysis it is necessary to determine how many sources are required to describe the EEG or MEG adequately. Model selection procedures (MSP’s, or goodness of fit procedures) give an estimate of the required number of sources. Existing and new MSP’s are evaluated in different source and noise settings: two sources which are close or distant, and noise which is uncorrelated or correlated. The commonly used MSP residual variance is seen to be ineffective, that is it often selects too many sources. Alternatives like the adjusted Hotelling’s test, Bayes information criterion, and the Wald test on source amplitudes are seen to be effective. The adjusted Hotelling’s test is recommended if a conservative approach is taken, and MSP’s such as Bayes information criterion or the Wald test on source amplitudes are recommended if a more liberal approach is desirable. The MSP’s are applied to empirical data (visual evoked fields). I.

After the introduction of digital imaging devices in medicine computerized tissue recognition and classification (i.e., segmentation) have become important in research and clinical applications. Segmented data can be applied among numerous research fields including volumetric analysis of particular tissues and structures, construction of anatomical models, three-dimensional (3D) visualization, and multimodal visualization, hence making segmentation essential in modern image analysis. In this research project several PC based software were developed in order to segment medical images, to visualize raw and segmented images in 3D, and to produce EEG brain maps in which MR images and EEG signals were integrated. The software package was tested and validated in numerous clinical research projects in hospital environment. 1.

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this report. Thus, in what follows, only a brief overview is given and a few details are spotlighted. Additional material (including the original data sets) are available on the Internet [11].

This paper presents a multimodality approach for the localization of epileptic foci using PET, MRI and EEG combined without the need of external markers. Mutual Information algorithm is used for MRI-PET registration. Dipole coordinates (provided by BESA software) are projected onto the MRI using a specifically developed algorithm. The four anatomical references used for electrode positioning (nasion, inion and two preauricular points) are located on the MRI using a triplanar viewer combined with a surface-rendering tool. Geometric transformation using deformation of the ideal sphere used for dipole calculations is then applied to match the patient’s brain size and shape. Eight treatment-refractory epileptic patients have been studied. The combination of the anatomical information from the MRI, hipoperfusion areas in PET and dipole position and orientation helped the physician in the diagnosis of epileptic focus location. Neurosurgery was not indicated for patients where PET and dipole results were inconsistent; in two cases it was clinically indicated despite the mismatch, showing a negative follow up. The multimodality approach presented does not require external markers for dipole projection onto the MRI, this being the main difference with previous methods. The proposed method may play an important role in the indication of surgery for treatment-refractory epileptic patients.

Reward-related mesolimbic dopamine steers animal behavior, creating automatic approach toward reward-associated objects and avoidance of objects unlikely to be beneficial. Theories of dopamine suggest that this reflects underlying biases in perception and attention, with reward enhancing the representation of reward-associated stimuli such that attention is more likely to be deployed to the location of these objects. Using measures of behavior and brain electricity in male and female humans, we demonstrate this to be the case. Sensory and perceptual processing of reward-associated visual features is facilitated such that attention is deployed to objects characterized by these features in subsequent experimental trials. This is the case even when participants know that a strategic decision to attend to reward-associated features will be counterproductive and result in suboptimal performance. Other results show that the magnitude of visual bias created by reward is predicted by the response to reward feedback in anterior cingulate cortex, an area with strong connections to dopaminergic structures in the midbrain. These results demonstrate that reward has an impact on vision that is independent of its role in the strategic establishment of endogenous attention. We suggest that reward acts to change visual salience and thus plays an important and undervalued role in attentional control.