Ingurune aberastuaren eragina eskizofrenian

Eskizofrenia gure giza11eari eragiten dioten patologia psikiatriko ohikoen eta minusbaliagarrienetakoa da. Besteak beste, ikasteko zailtasunak eta oroimenaren hutsegiteak dira zailtasun garrantzitsuak gizarte-integrazioari begira. Eskizofrenia, degeneraziozko gaixotasun kronikoa da eta denboran zehar lanitzen doa, baina gaur egungo ikerketa desberdinek erakusten dute terapia-estrategia egoki batek posible egin dezakeela gaixotasuna pairatzen duten kideen integrazioa hobetzea egungo gizartean. Nahiz eta eragin handiko patologia izan , bere jatorri etiopatogenikoa ez da guztiz ezaguna, bere sorreran parte bar baitezakete hainbat eta hainbat faktore desberdinek, hala nola intlamazioak, substantzia psikoaktiboak eta batik bat neurona-zirkuitu kitzikatzaileen eta inhibitzaileen arteko desorekak garapenean zehar. Nerbio Sistema Zentralaren jaio ondorengo garapenean oso funtsezkoa da neurona kitzikatzaileek sortzen dituzten se inale aferenteen eta in terneurona GABAergikoek bideratutako zirkuitu inhibitzaileen arteko oreka. Bizimoduaren eta eskizofreniaren larritasun mailen arteko korrelazio zuzena dago, eta ikusi da sedentarismoak sintomatologia areagotzen duela. Patologia neurologikoak eta neuropsikiatrikoak aztertzeko erabiltzen diren animali modeloetan, ikusi izan da ingurune aberastua dela efektuak murri zteko ahalmena duen tresnetako bat. Ingurune aberastuak, zentzumenen erabi lera, ariketa fisikoa eta gizarte-elkarrekintza areagotzen ditu. lngurunea aberasteak ikas ahalmenen eta oroimena areagotzea eragiten ditu, bai baldintza patologikoetan, baita baldintza arruntetan ere. Halaber, aberastutako ingurunean hazitako animaliek neurogenesia, gliogenesia eta dendriten adarkatze handiago bat erakutsi dute eta horrek lagun dezake hainbat neurodegenerazio gaixotasunen efektuei aurka egiten.Eskizofreniaren ezaugarri etiopatogenikoen alderdian, garrantzi handia hartzen dute sistema sentsorialen garapenean inhibizio-zirkuituei gertatzen zaizkien eraldaketek. Hori dela eta, ingurune aberastuak eskizofreniako animali modeloetan duen eragin positiboa aztertuko dugu

Effects of Visual Experience on Vascular Endothelial Growth Factor Expression during the Postnatal Development of the Rat Visual Cortex

The development of the cortical vascular network depends on functional maturation. External inputs are an essential requirement in the modeling of the visual cortex, mainly during the critical period, when the functional and structural properties of visual cortical neurons are particularly susceptible to alterations. Vascular endothelial growth factor (VEGF) is the major angiogenic factor, a key signal in the induction of vessel growth. Our study focused on the role of visual stimuli on the development of the vascular pattern correlated with VEGF levels. Vascular density and the expression of VEGF were examined in the primary visual cortex of rats reared under different visual environments (dark rearing, dark-rearing in conditions of enriched environment, enriched environment, and laboratory standard conditions) during postnatal development (before, during, and after the critical period). Our results show a restricted VEGF cellular expression to astroglial cells. Quantitative differences appeared during the critical period: higher vascular density and VEGF protein levels were found in the enriched environment group; both dark-reared groups showed lower vascular density and VEGF levels, which means that enriched environment without the physical exercise component does not exert effects in dark-reared rats

Pure phase-locking of beta/gamma oscillation contributes to the N30 frontal component of somatosensory evoked potentials

BACKGROUND: Evoked potentials have been proposed to result from phase-locking of electroencephalographic (EEG) activities within specific frequency bands. However, the respective contribution of phasic activity and phase resetting of ongoing EEG oscillation remains largely debated. We here applied the EEGlab procedure in order to quantify the contribution of electroencephalographic oscillation in the generation of the frontal N30 component of the somatosensory evoked potentials (SEP) triggered by median nerve electrical stimulation at the wrist. Power spectrum and intertrial coherence analysis were performed on EEG recordings in relation to median nerve stimulation. RESULTS: The frontal N30 component was accompanied by a significant phase-locking of beta/gamma oscillation (25-35 Hz) and to a lesser extent of 80 Hz oscillation. After the selection in each subject of the trials for which the power spectrum amplitude remained unchanged, we found pure phase-locking of beta/gamma oscillation (25-35 Hz) peaking about 30 ms after the stimulation. Transition across trials from uniform to normal phase distribution revealed temporal phase reorganization of ongoing 30 Hz EEG oscillations in relation to stimulation. In a proportion of trials, this phase-locking was accompanied by a spectral power increase peaking in the 30 Hz frequency band. This corresponds to the complex situation of 'phase-locking with enhancement' in which the distinction between the contribution of phasic neural event versus EEG phase resetting is hazardous. CONCLUSION: The identification of a pure phase-locking in a large proportion of the SEP trials reinforces the contribution of the oscillatory model for the physiological correlates of the frontal N30. This may imply that ongoing EEG rhythms, such as beta/gamma oscillation, are involved in somatosensory information processing.Comparative StudyJournal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe

A review of estimation of distribution algorithms in bioinformatics

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain

A review on probabilistic graphical models in evolutionary computation

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms

Evolutionary computation based on Bayesian classifiers

Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). This paper contributes to the further development of this discipline by introducing a new evolutionary computation method based on the learning and later simulation of a Bayesian classifier in every generation. In the method we propose, at each iteration the selected group of individuals of the population is divided into different classes depending on their respective fitness value. Afterwards, a Bayesian classifier---either naive Bayes, seminaive Bayes, tree augmented naive Bayes or a similar one---is learned to model the corresponding supervised classification problem. The simulation of the latter Bayesian classifier provides individuals that form the next generation. Experimental results are presented to compare the performance of this new method with different types of EDAs and GAs. The problems chosen for this purpose are combinatorial optimization problems which are commonly used in the literature