The activation of G-protein coupled receptors by agonist compounds results in diverse biological responses in cells, such as the endocytosis process consisting in the translocation of receptors from the plasma membrane to the cytoplasm within internalizing vesicles or endosomes. In order to functionally evaluate endocytosis events resulted from pharmacological responses, we have developed an image analysis method –the Q-Endosomes algorithm– that specifically discriminates the fluorescent signal originated at endosomes from that one observed at the plasma membrane in images obtained from living cells by fluorescence microscopy. Mu opioid (MOP) receptor tagged at the carboxy-terminus with yellow fluorescent protein (YFP) and permanently expressed in HEK293 cells was used as experimental model to validate this methodology. Time-course experiments performed with several agonists resulted in different sigmoid curves depending on the drug used to initiate MOP receptor endocytosis. Thus, endocytosis resulting from the simultaneous activation of co-expressed MOP and serotonin 5-HT2C receptors by morphine plus serotonin was significantly different, in kinetics as well as in maximal response parameters, from the one caused by DAMGO, sufentanyl or methadone. Therefore, this analytical tool permits the pharmacological characterization of receptor endocytosis in living cells with functional and temporal resolution.

The large range of potential applications, not only for patients but also for healthy people, that could be achieved by affective BCI (aBCI) makes more latent the necessity of finding a commonly accepted protocol for real-time EEG-based emotion recognition. Based on wavelet package for spectral feature extraction, attending to the nature of the EEG signal, we have specified some of the main parameters needed for the implementation of robust positive and negative emotion classification. 12 seconds has resulted as the most appropriate sliding window size; from that, a set of 20 target frequencylocation variables have been proposed as the most relevant features that carry the emotional information. Lastly, QDA and KNN classifiers and population rating criterion for stimuli labeling have been suggested as the most suitable approaches for EEG-base emotion recognition. The proposed model reached a mean accuracy of 98% (s.d. 1.4) and 98.96% (s.d. 1.28) in a subject-dependent approach for QDA and KNN classifier, respectively. This new model represents a step forward towards real-time classification. Although results were not conclusive, new insights regarding subjectindependent approximation have been discussed. (J.C. Fernandez-Troyano), mval33@alumno.uned.es (Mikel Val-Calvo), jm.ferrandez@upct.es (J.M. Ferrández), e.fernandez@umh.es (Eduardo Fernandez)

Understanding the neurophysiology of emotions, the neuronal structures involved in processing emotional information and the circuits by which they act, is key to designing applications in the field of affective neuroscience, to advance both new treatments and applications of brain–computer interactions. However, efforts have focused on developing computational models capable of emotion classification instead of on studying the neural substrates involved in the emotional process. In this context, we have carried out a study of cortical asymmetries and functional cortical connectivity based on the electroencephalographic signal of 24 subjects stimulated with videos of positive and negative emotional content to bring some light to the neurobiology behind emotional processes. Our results show opposite interhemispheric asymmetry patterns throughout the cortex for both emotional categories and specific connectivity patterns regarding each of the studied emotional categories. However, in general, the same key areas, such as the right hemisphere and more anterior cortical regions, presented higher levels of activity during the processing of both valence emotional categories. These results suggest a common neural pathway for processing positive and negative emotions, but with different activation patterns. These preliminary results are encouraging for elucidating the neuronal circuits of the emotional valence dimension.

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