Special Issue: Interactive Educational Media for the Neural and Cognitive Sciences

Understanding the functioning of brains is an extremely challenging endeavour - both for researches as well as for students. Interactive media and tools, like simulations, databases and visualizations or virtual laboratories proved to be not only indispensable in research but also in education to help understanding brain function. Accordingly, a wide range of such media and tools are now available and it is getting increasingly difficult to see an overall picture. Written by researchers, tool developers and experienced academic teachers, this special issue of Brains, Minds & Media covers a broad range of interactive research media and tools with a strong emphasis on their use in neural and cognitive sciences education. The focus lies not only on the tools themselves, but also on the question of how research tools can significantly enhance learning and teaching and how a curricular integration can be achieved. This collection gives a comprehensive overview of existing tools and their usage as well as the underlying educational ideas and thus provides an orientation guide not only for teaching researchers but also for interested teachers and students.

Psychomotor symptoms of schizophrenia map on the cerebral motor circuit

Schizophrenia is a devastating disorder thought to result mainly from cerebral pathology. Neuroimaging studies have provided a wealth of findings of brain dysfunction in schizophrenia. However, we are still far from understanding how particular symptoms can result from aberrant brain function. In this context, the high prevalence of motor symptoms in schizophrenia such as catatonia, neurological soft signs, parkinsonism, and abnormal involuntary movements is of particular interest. Here, the neuroimaging correlates of these motor symptoms are reviewed. For all investigated motor symptoms, neural correlates were found within the cerebral motor system. However, only a limited set of results exists for hypokinesia and neurological soft signs, while catatonia, abnormal involuntary movements and parkinsonian signs still remain understudied with neuroimaging methods. Soft signs have been associated with altered brain structure and function in cortical premotor and motor areas as well as cerebellum and thalamus. Hypokinesia is suggested to result from insufficient interaction of thalamocortical loops within the motor system. Future studies are needed to address the neural correlates of motor abnormalities in prodromal states, changes during the course of the illness, and the specific pathophysiology of catatonia, dyskinesia and parkinsonism in schizophrenia.

Why babies do not feel pain, or: How structure-derived functional interpretations can go wrong

The response to pain involves a non-conscious, reflexive action and a conscious perception. According to Key (2016), consciousness — and thus pain perception — depends on a neuronal correlate that has a “unique neural architecture” as realized in the human cortex. On the basis of the “bioengineering principle that structure determines function,” Key (2016) concludes that animal species such as fish, which lack the requisite cortex-like neuroanatomical structure, are unable to feel pain. This commentary argues that the relationship between brain structure and brain function is less straightforward than suggested in Key’s target article.

Análisis de conceptos básicos de memoria y aprendizaje : ¿Hasta qué punto los "dogmas" cognitivos son realmente "dogmas"?

El aprendizaje como proceso se puede considerar un avance evolutivo importante para todos los sistemas vivientes que lo adquirieron en las tempranas épocas del desarrollo de la vida. La percepción de un entorno que tiene “pasado" y “presente" permitió a los animales primitivos adquirir una visión más completa del mundo que los rodeaba. El uso cognitivo de la información que está disponible en un sistema viviente se le reconoce ahora como “aprendizaje". A pesar que han pasado muchos años y muchos investigadores han estado muy comprometidos en el estudio de la memoria y el aprendizaje, su intrincada naturaleza todavía no se comprende bien. En este trabajo se amplían muchos términos comunes en la investigación de este tema, como memoria, aprendizaje y ensayo redefiniéndose en un contexto más acotado con la intención de uniformar su comprensión. Se vuelve a discutir el modelo de aprendizaje en términos de un “circuito de aprendizaje". También se amplía el concepto unificador de “engrama de la unidad vectorial de la memoria" para el proceso de aprendizaje y el almacenamiento de la información, descrito con anterioridad. Finalmente, las implicaciones del modelo propuesto se consideran en el contexto de patologías que producen déficit de memoria, evaluándose las predicciones del modelo con la evidencia comportamental de pacientes con lesiones localizadas en ciertas partes del cerebro.

Magnetoencephalography as a putative biomarker for Alzheimer's disease.

Alzheimer’s Disease (AD) is the most common dementia in the elderly and is estimated to affect tens of millions of people worldwide. AD is believed to have a prodromal stage lasting ten or more years. While amyloid deposits, tau filaments, and loss of brain cells are characteristics of the disease, the loss of dendritic spines and of synapses predate such changes. Popular preclinical detection strategies mainly involve cerebrospinal fluid biomarkers, magnetic resonance imaging, metabolic PET scans, and amyloid imaging. One strategy missing from this list involves neurophysiological measures, which might be more sensitive to detect alterations in brain function. The Magnetoencephalography International Consortium of Alzheimer’s Disease arose out of the need to advance the use of Magnetoencephalography (MEG), as a tool in AD and pre-AD research. This paper presents a framework for using MEG in dementia research, and for short-term research priorities

ESPINA: a tool for the automated segmentation and counting of synapses in large stacks of electron microscopy images

The synapses in the cerebral cortex can be classified into two main types, Gray’s type I and type II, which correspond to asymmetric (mostly glutamatergic excitatory) and symmetric (inhibitory GABAergic) synapses, respectively. Hence, the quantification and identification of their different types and the proportions in which they are found, is extraordinarily important in terms of brain function. The ideal approach to calculate the number of synapses per unit volume is to analyze 3D samples reconstructed from serial sections. However, obtaining serial sections by transmission electron microscopy is an extremely time consuming and technically demanding task. Using focused ion beam/scanning electron microscope microscopy, we recently showed that virtually all synapses can be accurately identified as asymmetric or symmetric synapses when they are visualized, reconstructed, and quantified from large 3D tissue samples obtained in an automated manner. Nevertheless, the analysis, segmentation, and quantification of synapses is still a labor intensive procedure. Thus, novel solutions are currently necessary to deal with the large volume of data that is being generated by automated 3D electron microscopy. Accordingly, we have developed ESPINA, a software tool that performs the automated segmentation and counting of synapses in a reconstructed 3D volume of the cerebral cortex, and that greatly facilitates and accelerates these processes.

Functional connectivity measured with magnetoencephalography identifies persons with HIV disease

There is need for a valid and reliable biomarker for HIV Associated Neurocognitive Disorder (HAND). The purpose of the present study was to provide preliminary evidence of the potential utility of neuronal functional connectivity measures obtained using magnetoencephalography (MEG) to identify HIV-associated changes in brain function. Resting state, eyes closed, MEG data from 10 HIV-infected individuals and 8 seronegative controls were analyzed using mutual information (MI) between all pairs of MEG sensors to ...

A guided heuristic approach to the MEG inverse problem.

There is general agreement within the scientific community in considering Biology as the science with more potential to develop in the XXI century. This is due to several reasons, but probably the most important one is the state of development of the rest of experimental and technological sciences. In this context, there are a very rich variety of mathematical tools, physical techniques and computer resources that permit to do biological experiments that were unbelievable only a few years ago. Biology is nowadays taking advantage of all these newly developed technologies, which are been applied to life sciences opening new research fields and helping to give new insights in many biological problems. Consequently, biologists have improved a lot their knowledge in many key areas as human function and human diseases. However there is one human organ that is still barely understood compared with the rest: The human brain. The understanding of the human brain is one of the main challenges of the XXI century. In this regard, it is considered a strategic research field for the European Union and the USA. Thus, there is a big interest in applying new experimental techniques for the study of brain function. Magnetoencephalography (MEG) is one of these novel techniques that are currently applied for mapping the brain activity1. This technique has important advantages compared to the metabolic-based brain imagining techniques like Functional Magneto Resonance Imaging2 (fMRI). The main advantage is that MEG has a higher time resolution than fMRI. Another benefit of MEG is that it is a patient friendly clinical technique. The measure is performed with a wireless set up and the patient is not exposed to any radiation. Although MEG is widely applied in clinical studies, there are still open issues regarding data analysis. The present work deals with the solution of the inverse problem in MEG, which is the most controversial and uncertain part of the analysis process3. This question is addressed using several variations of a new solving algorithm based in a heuristic method. The performance of those methods is analyzed by applying them to several test cases with known solutions and comparing those solutions with the ones provided by our methods.

A review of Tai Chi Chuan and parameters related to balance

Introduction: Previous systematic reviews of the literature on the effects of Tai Chi Chuan(TCC)on balance have focussed either on determining the quality of the research design or have provided just a general description of the studies.To the best of our knowledge none have approached this topic by conducting an analysis from the point of view of the factors which affect balance.It is important to present this perspective as it will help to guide future research in this field. Methodology: Seven electronic data bases were searched for publications dated between 1996 and 2012.The inclusion criteria were;randomized controlled trials(RCT)written in English. Results: From a total of 397 articles identified, 27 randomized controlled trials were eligible for the analysis. Conclusions: Studies reviewed appear to confirm that TCC improves static and dynamic balance and in the functional factors which affect balance in persons of over 55 years of age.Only one study was identified on people affected with problems with the vestibular system. No studies on the influence of TCC on improvement in balance in individuals suffering from deteriorated brain function were identified.

Propuesta de integración de agentes software inteligentes sobre JADE con entornos virtuales en Unity3D

La unión de distintos sistemas software constituye un elemento principal de las nuevas Tecnologías de la Información y la Comunicación. La integración de entornos virtuales tridimensionales con agentes software inteligentes es el objetivo que persigue este trabajo de investigación. Para llevar a cabo esta integración se parte de la creación de un agente virtual, un personaje que es controlado por una mente desarrollada siguiendo un enfoque basado en agentes software. Se busca así dotar al sistema de cierto nivel de inteligencia, tomando como referencia el funcionamiento del cerebro humano. Lo que se consigue es que el agente capte los estímulos del entorno, los procese y genere comportamientos, tanto reactivos como deliberativos, que son ejecutados por el personaje. Los resultados obtenidos resaltan el dinamismo del sistema, a la vez que animan a seguir investigando en este campo lleno de aplicaciones directas y reales sobre el mundo. En conclusión, esta investigación busca y consigue un nuevo paso en el progreso de las nuevas tecnologías mediante una integración real de distintos sistemas software. ---ABSTRACT---The union of different software systems is a major element of Information and Communications Technology. The aim of this research is the integration of 3D virtual environments and intelligent software agents. This integration is based on the development of a virtual agent, a character that is controlled by a mind developed following a software agent approach. It is sought to provide the system with some intelligence level, taking the human brain function as a reference point. The consequence is that the agent captures environmental stimuli, processes them and creates reactive and deliberative behaviours that can be executed by the avatar. The findings emphasize the dynamism of the system as well as they encourage to research more in this field that has a lot of direct and real-life applications on the world. In conclusion, this research seeks and takes a new step in the progress of new technologies through a real integration of different software systems.

Caracterización y simulación de arborizaciones dentríticas con redes bayesianas incluyendo variables angulares

El funcionamiento interno del cerebro es todavía hoy en día un misterio, siendo su comprensión uno de los principales desafíos a los que se enfrenta la ciencia moderna. El córtex cerebral es el área del cerebro donde tienen lugar los procesos cerebrales de más alto nivel, cómo la imaginación, el juicio o el pensamiento abstracto. Las neuronas piramidales, un tipo específico de neurona, suponen cerca del 80% de los cerca de los 10.000 millones de que componen el córtex cerebral, haciendo de ellas un objetivo principal en el estudio del funcionamiento del cerebro. La morfología neuronal, y más específicamente la morfología dendrítica, determina cómo estas procesan la información y los patrones de conexión entre neuronas, siendo los modelos computacionales herramientas imprescindibles para el estudio de su rol en el funcionamiento del cerebro. En este trabajo hemos creado un modelo computacional, con más de 50 variables relativas a la morfología dendrítica, capaz de simular el crecimiento de arborizaciones dendríticas basales completas a partir de reconstrucciones de neuronas piramidales reales, abarcando desde el número de dendritas hasta el crecimiento los los árboles dendríticos. A diferencia de los trabajos anteriores, nuestro modelo basado en redes Bayesianas contempla la arborización dendrítica en su conjunto, teniendo en cuenta las interacciones entre dendritas y detectando de forma automática las relaciones entre las variables morfológicas que caracterizan la arborización. Además, el análisis de las redes Bayesianas puede ayudar a identificar relaciones hasta ahora desconocidas entre variables morfológicas. Motivado por el estudio de la orientación de las dendritas basales, en este trabajo se introduce una regularización L1 generalizada, aplicada al aprendizaje de la distribución von Mises multivariante, una de las principales distribuciones de probabilidad direccional multivariante. También se propone una distancia circular multivariante que puede utilizarse para estimar la divergencia de Kullback-Leibler entre dos muestras de datos circulares. Comparamos los modelos con y sin regularizaci ón en el estudio de la orientación de la dendritas basales en neuronas humanas, comprobando que, en general, el modelo regularizado obtiene mejores resultados. El muestreo, ajuste y representación de la distribución von Mises multivariante se implementa en un nuevo paquete de R denominado mvCircular.---ABSTRACT---The inner workings of the brain are, as of today, a mystery. To understand the brain is one of the main challenges faced by current science. The cerebral cortex is the region of the brain where all superior brain processes, like imagination, judge and abstract reasoning take place. Pyramidal neurons, a specific type of neurons, constitute approximately the 80% of the more than 10.000 million neurons that compound the cerebral cortex. It makes the study of the pyramidal neurons crucial in order to understand how the brain works. Neuron morphology, and specifically the dendritic morphology, determines how the information is processed in the neurons, as well as the connection patterns among neurons. Computational models are one of the main tools for studying dendritic morphology and its role in the brain function. We have built a computational model that contains more than 50 morphological variables of the dendritic arborizations. This model is able to simulate the growth of complete dendritic arborizations from real neuron reconstructions, starting with the number of basal dendrites, and ending modeling the growth of dendritic trees. One of the main diferences between our approach, mainly based on the use of Bayesian networks, and other models in the state of the art is that we model the whole dendritic arborization instead of focusing on individual trees, which makes us able to take into account the interactions between dendrites and to automatically detect relationships between the morphologic variables that characterize the arborization. Moreover, the posterior analysis of the relationships in the model can help to identify new relations between morphological variables. Motivated by the study of the basal dendrites orientation, a generalized L1 regularization applied to the multivariate von Mises distribution, one of the most used distributions in multivariate directional statistics, is also introduced in this work. We also propose a circular multivariate distance that can be used to estimate the Kullback-Leibler divergence between two circular data samples. We compare the regularized and unregularized models on basal dendrites orientation of human neurons and prove that regularized model achieves better results than non regularized von Mises model. Sampling, fitting and plotting functions for the multivariate von Mises are implemented in a new R packaged called mvCircular.

Deficits in memory and motor performance in synaptotagmin IV mutant mice

Synaptotagmin (Syt) IV is a synaptic vesicle protein. Syt IV expression is induced in the rat hippocampus after systemic kainic acid treatment. To examine the functional role of this protein in vivo, we derived Syt IV null [Syt IV(−/−)] mutant mice. Studies with the rotorod revealed that the Syt IV mutants have impaired motor coordination, a result consistent with constitutive Syt IV expression in the cerebellum. Because Syt IV is thought to modulate synaptic function, we also have examined Syt IV mutant mice in learning and memory tests. Our studies show that the Syt IV mutation disrupts contextual fear conditioning, a learning task sensitive to hippocampal and amygdala lesions. In contrast, cued fear conditioning is normal in the Syt IV mutants, suggesting that this mutation did not disrupt amygdala function. Conditioned taste aversion, which also depends on the amygdala, is normal in the Syt IV mutants. Consistent with the idea that the Syt IV mutation preferentially affects hippocampal function, Syt IV mutant mice also display impaired social transmission of food preference. These studies demonstrate that Syt IV is critical for brain function and suggest that the Syt IV mutation affects hippocampal-dependent learning and memory, as well as motor coordination.

Event-related functional MRI: Past, present, and future

The past two decades have seen an enormous growth in the field of human brain mapping. Investigators have extensively exploited techniques such as positron emission tomography and MRI to map patterns of brain activity based on changes in cerebral hemodynamics. However, until recently, most studies have investigated equilibrium changes in blood flow measured over time periods upward of 1 min. The advent of high-speed MRI methods, capable of imaging the entire brain with a temporal resolution of a few seconds, allows for brain mapping based on more transient aspects of the hemodynamic response. Today it is now possible to map changes in cerebrovascular parameters essentially in real time, conferring the ability to observe changes in brain state that occur over time periods of seconds. Furthermore, because robust hemodynamic alterations are detectable after neuronal stimuli lasting only a few tens of milliseconds, a new class of task paradigms designed to measure regional responses to single sensory or cognitive events can now be studied. Such “event related” functional MRI should provide for fundamentally new ways to interrogate brain function, and allow for the direct comparison and ultimately integration of data acquired by using more traditional behavioral and electrophysiological methods.

Functional neuroimaging studies of encoding, priming, and explicit memory retrieval

Human functional neuroimaging techniques provide a powerful means of linking neural level descriptions of brain function and cognition. The exploration of the functional anatomy underlying human memory comprises a prime example. Three highly reliable findings linking memory-related cognitive processes to brain activity are discussed. First, priming is accompanied by reductions in the amount of neural activation relative to naive or unprimed task performance. These reductions can be shown to be both anatomically and functionally specific and are found for both perceptual and conceptual task components. Second, verbal encoding, allowing subsequent conscious retrieval, is associated with activation of higher order brain regions including areas within the left inferior and dorsal prefrontal cortex. These areas also are activated by working memory and effortful word generation tasks, suggesting that these tasks, often discussed as separable, might rely on interdependent processes. Finally, explicit (intentional) retrieval shares much of the same functional anatomy as the encoding and word generation tasks but is associated with the recruitment of additional brain areas, including the anterior prefrontal cortex (right > left). These findings illustrate how neuroimaging techniques can be used to study memory processes and can both complement and extend data derived through other means. More recently developed methods, such as event-related functional MRI, will continue this progress and may provide additional new directions for research.

Neural codes: Firing rates and beyond

Computational neuroscience has contributed significantly to our understanding of higher brain function by combining experimental neurobiology, psychophysics, modeling, and mathematical analysis. This article reviews recent advances in a key area: neural coding and information processing. It is shown that synapses are capable of supporting computations based on highly structured temporal codes. Such codes could provide a substrate for unambiguous representations of complex stimuli and be used to solve difficult cognitive tasks, such as the binding problem. Unsupervised learning rules could generate the circuitry required for precise temporal codes. Together, these results indicate that neural systems perform a rich repertoire of computations based on action potential timing.