Decision, Sensation, and Habituation: A Multi-Layer Dynamic Field Model for Inhibition of Return

Inhibition of Return (IOR) is one of the most consistent and widely studied effects in experimental psychology. The effect refers to a delayed response to visual stimuli in a cued location after initial priming at that location. This article presents a dynamic field model for IOR. The model describes the evolution of three coupled activation fields. The decision field, inspired by the intermediate layer of the superior colliculus, receives endogenous input and input from a sensory field. The sensory field, inspired by earlier sensory processing, receives exogenous input. Habituation of the sensory field is implemented by a reciprocal coupling with a third field, the habituation field. The model generates IOR because, due to the habituation of the sensory field, the decision field receives a reduced target-induced input in cue-target-compatible situations. The model is consistent with single-unit recordings of neurons of monkeys that perform IOR tasks. Such recordings have revealed that IOR phenomena parallel the activity of neurons in the intermediate layer of the superior colliculus and that neurons in this layer receive reduced input in cue-target-compatible situations. The model is also consistent with behavioral data concerning temporal expectancy effects. In a discussion, the multi-layer dynamic field account of IOR is used to illustrate the broader view that behavior consists of a tuning of the organism to the environment that continuously and concurrently takes place at different spatiotemporal scales

Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors

[EN] Affective Computing has emerged as an important field of study that aims to develop systems that can automatically recognize emotions. Up to the present, elicitation has been carried out with nonimmersive stimuli. This study, on the other hand, aims to develop an emotion recognition system for affective states evoked through Immersive Virtual Environments. Four alternative virtual rooms were designed to elicit four possible arousal-valence combinations, as described in each quadrant of the Circumplex Model of Affects. An experiment involving the recording of the electroencephalography (EEG) and electrocardiography (ECG) of sixty participants was carried out. A set of features was extracted from these signals using various state-of-the-art metrics that quantify brain and cardiovascular linear and nonlinear dynamics, which were input into a Support Vector Machine classifier to predict the subject's arousal and valence perception. The model's accuracy was 75.00% along the arousal dimension and 71.21% along the valence dimension. Our findings validate the use of Immersive Virtual Environments to elicit and automatically recognize different emotional states from neural and cardiac dynamics; this development could have novel applications in fields as diverse as Architecture, Health, Education and Videogames.This work was supported by the Ministerio de Economia y Competitividad. Spain (Project TIN2013-45736-R).Marín-Morales, J.; Higuera-Trujillo, JL.; Greco, A.; Guixeres Provinciale, J.; Llinares Millán, MDC.; Scilingo, EP.; Alcañiz Raya, ML.... (2018). Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific Reports. 8:1-15. https://doi.org/10.1038/s41598-018-32063-4S1158Picard, R. W. Affective computing. (MIT press, 1997).Picard, R. W. Affective Computing: Challenges. Int. J. Hum. Comput. 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Early maternal mirroring predicts infant motor system activation during facial expression observation.

Processing facial expressions is an essential component of social interaction, especially for preverbal infants. In human adults and monkeys, this process involves the motor system, with a neural matching mechanism believed to couple self- and other-generated facial gestures. Here, we used electroencephalography to demonstrate recruitment of the human motor system during observation and execution of facial expressions in nine-month-old infants, implicating this system in facial expression processing from a very young age. Notably, examination of early video-recorded mother-infant interactions supported the common, but as yet untested, hypothesis that maternal mirroring of infant facial gestures is central to the development of a neural matching mechanism for these gestures. Specifically, the extent to which mothers mirrored infant facial expressions at two months postpartum predicted infant motor system activity during observation of the same expressions at nine months. This suggests that maternal mirroring strengthens mappings between visual and motor representations of facial gestures, which increases infant neural sensitivity to particularly relevant cues in the early social environment

HDNetDB: A Molecular Interaction Database for Network-Oriented Investigations into Huntington’s Disease

Huntington's disease (HD) is a progressive and fatal neurodegenerative disorder caused by an expanded CAG repeat in the huntingtin gene. Although HD is monogenic, its molecular manifestation appears highly complex and involves multiple cellular processes. The recent application of high throughput platforms such as microarrays and mass-spectrometry has indicated multiple pathogenic routes. The massive data generated by these techniques together with the complexity of the pathogenesis, however, pose considerable challenges to researchers. Network-based methods can provide valuable tools to consolidate newly generated data with existing knowledge, and to decipher the interwoven molecular mechanisms underlying HD. To facilitate research on HD in a network-oriented manner, we have developed HDNetDB, a database that integrates molecular interactions with many HD-relevant datasets. It allows users to obtain, visualize and prioritize molecular interaction networks using HD-relevant gene expression, phenotypic and other types of data obtained from human samples or model organisms. We illustrated several HDNetDB functionalities through a case study and identified proteins that constitute potential cross-talk between HD and the unfolded protein response (UPR). HDNetDB is publicly accessible at http://hdnetdb.sysbiolab.eu.CHDI Foundation [A-2666]; Portuguese Fundacao para a Ciencia e a Tecnologia [SFRH/BPD/70718/2010, SFRH/BPD/96890/2013, IF/00881/2013, UID/BIM/04773/2013 - CBMR, UID/Multi/04326/2013 - CCMAR]info:eu-repo/semantics/publishedVersio

Defective Sphingosine-1-phosphate metabolism is a druggable target in Huntington's disease

Huntington's disease is characterized by a complex and heterogeneous pathogenic profile. Studies have shown that disturbance in lipid homeostasis may represent a critical determinant in the progression of several neurodegenerative disorders. The recognition of perturbed lipid metabolism is only recently becoming evident in HD. In order to provide more insight into the nature of such a perturbation and into the effect its modulation may have in HD pathology, we investigated the metabolism of Sphingosine-1-phosphate (S1P), one of the most important bioactive lipids, in both animal models and patient samples. Here, we demonstrated that S1P metabolism is significantly disrupted in HD even at early stage of the disease and importantly, we revealed that such a dysfunction represents a common denominator among multiple disease models ranging from cells to humans through mouse models. Interestingly, the in vitro anti-apoptotic and the pro-survival actions seen after modulation of S1P-metabolizing enzymes allows this axis to emerge as a new druggable target and unfolds its promising therapeutic potential for the development of more effective and targeted interventions against this incurable condition