Imaging emotional brain functions: conceptual and methodological issues

This article reviews the psychophysiological and brain imaging literature on emotional brain function from a methodological point of view. The difficulties in defining, operationalising and measuring emotional activation and, in particular, aversive learning will be considered. Emotion is a response of the organism during an episode of major significance and involves physiological activation, motivational, perceptual, evaluative and learning processes, motor expression, action tendencies and monitoring/subjective feelings. Despite the advances in assessing the physiological correlates of emotional perception and learning processes, a critical appraisal shows that functional neuroimaging approaches encounter methodological difficulties regarding measurement precision (e.g., response scaling and reproducibility) and validity (e.g., response specificity, generalisation to other paradigms, subjects or settings). Since emotional processes are not only the result of localised but also of widely distributed activation, a more representative model of assessment is needed that systematically relates the hierarchy of high- and low-level emotion constructs with the corresponding patterns of activity and functional connectivity of the brain.

Native EEG and treatment effects in neuroleptic-naïve schizophrenic patients: time and frequency domain approaches

Time domain analysis of electroencephalography (EEG) can identify subsecond periods of quasi-stable brain states. These so-called microstates assumingly correspond to basic units of cognition and emotion. On the other hand, Global Field Synchronization (GFS) is a frequency domain measure to estimate functional synchronization of brain processes on a global level for each EEG frequency band [Koenig, T., Lehmann, D., Saito, N., Kuginuki, T., Kinoshita, T., Koukkou, M., 2001. Decreased functional connectivity of EEG theta-frequency activity in first-episode, neuroleptic-naive patients with schizophrenia: preliminary results. Schizophr Res. 50, 55-60.]. Using these time and frequency domain analyzes, several previous studies reported shortened microstate duration in specific microstate classes and decreased GFS in theta band in drug naïve schizophrenia compared to controls. The purpose of this study was to investigate changes of these EEG parameters after drug treatment in drug naïve schizophrenia. EEG analysis was performed in 21 drug-naive patients and 21 healthy controls. 14 patients were reevaluated 2-8 weeks (mean 4.3) after the initiation of drug administration. The results extended findings of treatment effect on brain functions in schizophrenia, and imply that shortened duration of specific microstate classes seems a state marker especially in patients with later neuroleptic responsive, while lower theta GFS seems a state-related phenomenon and that higher gamma GFS is a trait like phenomenon.

Fixed block configuration GDDs with block size 6 and (3, r)-regular graphs

Chapter 1 is used to introduce the basic tools and mechanics used within this thesis. Most of the definitions used in the thesis will be defined, and we provide a basic survey of topics in graph theory and design theory pertinent to the topics studied in this thesis. In Chapter 2, we are concerned with the study of fixed block configuration group divisible designs, GDD(n; m; k; λ1; λ2). We study those GDDs in which each block has configuration (s; t), that is, GDDs in which each block has exactly s points from one of the two groups and t points from the other. Chapter 2 begins with an overview of previous results and constructions for small group size and block sizes 3, 4 and 5. Chapter 2 is largely devoted to presenting constructions and results about GDDs with two groups and block size 6. We show the necessary conditions are sufficient for the existence of GDD(n, 2, 6; λ1, λ2) with fixed block configuration (3; 3). For configuration (1; 5), we give minimal or nearminimal index constructions for all group sizes n ≥ 5 except n = 10, 15, 160, or 190. For configuration (2, 4), we provide constructions for several families ofGDD(n, 2, 6; λ1, λ2)s. Chapter 3 addresses characterizing (3, r)-regular graphs. We begin with providing previous results on the well studied class of (2, r)-regular graphs and some results on the structure of large (t; r)-regular graphs. In Chapter 3, we completely characterize all (3, 1)-regular and (3, 2)-regular graphs, as well has sharpen existing bounds on the order of large (3, r)- regular graphs of a certain form for r ≥ 3. Finally, the appendix gives computational data resulting from Sage and C programs used to generate (3, 3)-regular graphs on less than 10 vertices.

EEG microstates associated with salience and frontoparietal networks in frontotemporal dementia, schizophrenia and Alzheimer's disease

OBJECTIVE: There are relevant links between resting-state fMRI networks, EEG microstate classes and psychopathological alterations in mental disorders associated with frontal lobe dysfunction. We hypothesized that a certain microstate class, labeled C and correlated with the salience network, was impaired early in frontotemporal dementia (FTD), and that microstate class D, correlated with the frontoparietal network, was impaired in schizophrenia. METHODS: We measured resting EEG microstate parameters in patients with mild FTD (n = 18), schizophrenia (n = 20), mild Alzheimer's disease (AD; n = 19) and age-matched controls (old n = 19, young n = 18) to investigate neuronal dynamics at the whole-brain level. RESULTS: The duration of class C was significantly shorter in FTD than in controls and AD, and the duration of class D was significantly shorter in schizophrenia than in controls, FTD and AD. Transition analysis showed a reversed sequence of activation of classes C and D in FTD and schizophrenia patients compared with that in controls, with controls preferring transitions from C to D, and patients preferring D to C. CONCLUSION: The duration and sequence of EEG microstates reflect specific aberrations of frontal lobe functions in FTD and schizophrenia. SIGNIFICANCE: This study highlights the importance of subsecond brain dynamics for understanding of psychiatric disorders.

Structural plasticity can produce metaplasticity.

BACKGROUND: Synaptic plasticity underlies many aspect of learning memory and development. The properties of synaptic plasticity can change as a function of previous plasticity and previous activation of synapses, a phenomenon called metaplasticity. Synaptic plasticity not only changes the functional connectivity between neurons but in some cases produces a structural change in synaptic spines; a change thought to form a basis for this observed plasticity. Here we examine to what extent structural plasticity of spines can be a cause for metaplasticity. This study is motivated by the observation that structural changes in spines are likely to affect the calcium dynamics in spines. Since calcium dynamics determine the sign and magnitude of synaptic plasticity, it is likely that structural plasticity will alter the properties of synaptic plasticity. METHODOLOGY/PRINCIPAL FINDINGS: In this study we address the question how spine geometry and alterations of N-methyl-D-aspartic acid (NMDA) receptors conductance may affect plasticity. Based on a simplified model of the spine in combination with a calcium-dependent plasticity rule, we demonstrated that after the induction phase of plasticity a shift of the long term potentiation (LTP) or long term depression (LTD) threshold takes place. This induces a refractory period for further LTP induction and promotes depotentiation as observed experimentally. That resembles the BCM metaplasticity rule but specific for the individual synapse. In the second phase, alteration of the NMDA response may bring the synapse to a state such that further synaptic weight alterations are feasible. We show that if the enhancement of the NMDA response is proportional to the area of the post synaptic density (PSD) the plasticity curves most likely return to the initial state. CONCLUSIONS/SIGNIFICANCE: Using simulations of calcium dynamics in synaptic spines, coupled with a biophysically motivated calcium-dependent plasticity rule, we find under what conditions structural plasticity can form the basis of synapse specific metaplasticity.

White matter matters: a multimodal approach to investigate white matter microstructure and metabolism

White matter connects different brain areas and applies electrical insulation to the neuron’s axons with myelin sheaths in order to enable quick signal transmission. Due to its modulatory properties in signal conduction, white matter plays an essential role in learning, cognition and psychiatric disorders (Fields, 2008a). In respect thereof, the non-invasive investigation of white matter anatomy and function in vivo provides the unique opportunity to explore the most complex organ of our body. Thus, the present thesis aimed to apply a multimodal neuroimaging approach to investigate different white matter properties in psychiatric and healthy populations. On the one hand, white matter microstructural properties were investigated in a psychiatric population; on the other hand, white matter metabolic properties were assessed in healthy adults providing basic information about the brain’s wiring entity. As a result, three research papers are presented here. The first paper assessed the microstructural properties of white matter in relation to a frequent epidemiologic finding in schizophrenia. As a result, reduced white matter integrity was observed in patients born in summer and autumn compared to patients born in winter and spring. Despite the large genetic basis of schizophrenia, accumulating evidence indicates that environmental exposures may be implicated in the development of schizophrenia (A. S. Brown, 2011). Notably, epidemiologic studies have shown a 5–8% excess of births during winter and spring for patients with schizophrenia on the Northern Hemisphere at higher latitudes (Torrey, Miller, Rawlings, & Yolken, 1997). Although the underlying mechanisms are unclear, the seasonal birth effect may indicate fluctuating environmental risk factors for schizophrenia. Thus, exposure to harmful factors during foetal development may result in the activation of pathologic neural circuits during adolescence or young adulthood, increasing the risk of schizophrenia (Fatemi & Folsom, 2009). While white matter development starts during the foetal period and continues until adulthood, its major development is accomplished by the age of two years (Brody, Kinney, Kloman, & Gilles, 1987; Huang et al., 2009). This indicates a vulnerability period of white matter that may coincide with the fluctuating environmental risk factors for schizophrenia. Since microstructural alterations of white matter in schizophrenia are frequently observed, the current study provided evidence for the neurodevelopmental hypothesis of schizophrenia. In the second research paper, the perfusion of white matter showed a positive correlation between white matter microstructure and its perfusion with blood across healthy adults. This finding was in line with clinical studies indicating a tight coupling between cerebral perfusion and WM health across subjects (Amann et al., 2012; Chen, Rosas, & Salat, 2013; Kitagawa et al., 2009). Although relatively little is known about the metabolic properties of white matter, different microstructural properties, such as axon diameter and myelination, might be coupled with the metabolic demand of white matter. Furthermore, the ability to detect perfusion signal in white matter was in accordance with a recent study showing that technical improvements, such as pseudo-continuous arterial spin labeling, enabled the reliable detection of white matter perfusion signal (van Osch et al., 2009). The third paper involved a collaboration within the same department to assess the interrelation between functional connectivity networks and their underlying structural connectivity.

Suppressing versus releasing a habit: frequency-dependent effects of prefrontal transcranial magnetic stimulation.

When subjects are required to generate a random sequence of numbers they typically produce too many forward and backward 'counts' (e.g. 5-6, 4-3). This counting bias is interpreted as the consequence of an interference by overlearned tendencies to arrange numbers according to their natural order. Inhibition of such well-learned routines is known to rely on frontal lobe functioning. We examined differential effects of slow (1 Hz) and fast (10 Hz) repetitive transcranial magnetic stimulation (rTMS) over the left or right dorsolateral prefrontal cortex (DLPFC) on random number generation (RNG) performance. Eighteen healthy men performed an RNG task. Those subjects stimulated over the left DLPFC showed a frequency-dependent rTMS effect: counting bias was significantly reduced after the 1 Hz stimulation compared with baseline, but significantly exaggerated after the 10 Hz stimulation compared with 1 Hz stimulation. In contrast, the sequences of the subjects stimulated over the right DLPFC showed the well-known excess of counting in all conditions (i.e. baseline, 1 Hz and 10 Hz). These findings confirm the functional importance of specifically the left DLPFC in sequential response production and show, for the first time, that rTMS affects cognitive processing in a frequency-dependent manner.

Interactions between dorsolateral and ventromedial prefrontal cortex underlie context-dependent stimulus valuation in goal-directed choice.

External circumstances and internal bodily states often change and require organisms to flexibly adapt valuation processes to select the optimal action in a given context. Here, we investigate the neurobiology of context-dependent valuation in 22 human subjects using functional magnetic resonance imaging. Subjects made binary choices between visual stimuli with three attributes (shape, color, and pattern) that were associated with monetary values. Context changes required subjects to deviate from the default shape valuation and to integrate a second attribute in order to comply with the goal to maximize rewards. Critically, this binary choice task did not involve any conflict between opposing monetary, temporal, or social preferences. We tested the hypothesis that interactions between regions of dorsolateral and ventromedial prefrontal cortex (dlPFC; vmPFC) implicated in self-control choices would also underlie the more general function of context-dependent valuation. Consistent with this idea, we found that the degree to which stimulus attributes were reflected in vmPFC activity varied as a function of context. In addition, activity in dlPFC increased when context changes required a reweighting of stimulus attribute values. Moreover, the strength of the functional connectivity between dlPFC and vmPFC was associated with the degree of context-specific attribute valuation in vmPFC at the time of choice. Our findings suggest that functional interactions between dlPFC and vmPFC are a key aspect of context-dependent valuation and that the role of this network during choices that require self-control to adjudicate between competing outcome preferences is a specific application of this more general neural mechanism.

The contributions of the amygdala, orbital frontal cortex and hippocampal formation to primate social cognition

Deficits in social cognition are prominent symptoms of many human psychiatric disorders, but the origin of such deficits remains largely unknown. To further current knowledge regarding the neural network mediating social cognition, the present research program investigated the individual contributions of two temporal lobe structures, the amygdala and hippocampal formation, and one frontal lobe region, the orbital frontal cortex (Areas 11 and 13), to primate social cognition. Based on previous research, we hypothesized that the amygdala, hippocampal formation and orbital frontal cortex contribute significantly to the formation of new social relationships, but less to the maintenance of familiar ones. ^ Thirty-six male rhesus macaques (Macaca mulatta) served as subjects, and were divided into four experimental groups: Neurotoxic amygdala lesion (A-ibo, n = 9), neurotoxic or aspiration orbital frontal cortex lesion (O, n = 9), neurotoxic hippocampal formation lesion (H-ibo, n = 9) or sham-operated control (C, n = 9). Six social groups (tetrads) were created, each containing one member from each experimental group. The effect of lesion on established social relationships was assessed during pre- and post-surgical unrestrained social interactions, whereas the effect of lesion on the formation of new relationships was assessed during an additional phase of post-surgical testing with shuffled tetrad membership. Results indicated that these three neural structures each contribute significantly to both the formation and maintenance of social relationships. Furthermore, the amygdala appears to primarily mediate normal responses to threatening social signals, whereas the orbital frontal cortex plays a more global role in social cognition by mediating responses to both threatening and affiliative social signals. By contrast, the hippocampal formation seems to contribute to social cognition indirectly by providing access to previous experience during social judgments. ^ These conclusions were further investigated with three experiments that measured behavioral and physiological (stress hormone) reactivity to threatening stimuli, and three additional experiments that measured subjects' ability to flexibly alter behavioral responses depending on the incentive value of a food reinforcer. Data from these six experiments further confirmed and strengthened the three conclusions originating from the social behavior experiments and, when combined with the current literature, helped to formulate a simple, but testable, theoretical model of primate social cognition. ^

Dynamic population coding in primary visual cortex

More than a century ago Ramon y Cajal pioneered the description of neural circuits. Currently, new techniques are being developed to streamline the characterization of entire neural circuits. Even if this 'connectome' approach is successful, it will represent only a static description of neural circuits. Thus, a fundamental question in neuroscience is to understand how information is dynamically represented by neural populations. In this thesis, I studied two main aspects of dynamical population codes. ^ First, I studied how the exposure or adaptation, for a fraction of a second to oriented gratings dynamically changes the population response of primary visual cortex neurons. The effects of adaptation to oriented gratings have been extensively explored in psychophysical and electrophysiological experiments. However, whether rapid adaptation might induce a change in the primary visual cortex's functional connectivity to dynamically impact the population coding accuracy is currently unknown. To address this issue, we performed multi-electrode recordings in primary visual cortex, where adaptation has been previously shown to induce changes in the selectivity and response amplitude of individual neurons. We found that adaptation improves the population coding accuracy. The improvement was more prominent for iso- and orthogonal orientation adaptation, consistent with previously reported psychophysical experiments. We propose that selective decorrelation is a metabolically inexpensive mechanism that the visual system employs to dynamically adapt the neural responses to the statistics of the input stimuli to improve coding efficiency. ^ Second, I investigated how ongoing activity modulates orientation coding in single neurons, neural populations and behavior. Cortical networks are never silent even in the absence of external stimulation. The ongoing activity can account for up to 80% of the metabolic energy consumed by the brain. Thus, a fundamental question is to understand the functional role of ongoing activity and its impact on neural computations. I studied how the orientation coding by individual neurons and cell populations in primary visual cortex depend on the spontaneous activity before stimulus presentation. We hypothesized that since the ongoing activity of nearby neurons is strongly correlated, it would influence the ability of the entire population of orientation-selective cells to process orientation depending on the prestimulus spontaneous state. Our findings demonstrate that ongoing activity dynamically filters incoming stimuli to shape the accuracy of orientation coding by individual neurons and cell populations and this interaction affects behavioral performance. In summary, this thesis is a contribution to the study of how dynamic internal states such as rapid adaptation and ongoing activity modulate the population code accuracy. ^

Detecting periodicity with horizontal visibility graphs

The horizontal visibility algorithm was recently introduced as a mapping between time series and networks. The challenge lies in characterizing the structure of time series (and the processes that generated those series) using the powerful tools of graph theory. Recent works have shown that the visibility graphs inherit several degrees of correlations from their associated series, and therefore such graph theoretical characterization is in principle possible. However, both the mathematical grounding of this promising theory and its applications are in its infancy. Following this line, here we address the question of detecting hidden periodicity in series polluted with a certain amount of noise. We first put forward some generic properties of horizontal visibility graphs which allow us to define a (graph theoretical) noise reduction filter. Accordingly, we evaluate its performance for the task of calculating the period of noisy periodic signals, and compare our results with standard time domain (autocorrelation) methods. Finally, potentials, limitations and applications are discussed.

Optimisation of algorithms to compute information theoretic indexes

Analysis of big amount of data is a field with many years of research. It is centred in getting significant values, to make it easier to understand and interpret data. Being the analysis of interdependence between time series an important field of research, mainly as a result of advances in the characterization of dynamical systems from the signals they produce. In the medicine sphere, it is easy to find many researches that try to understand the brain behaviour, its operation mode and its internal connections. The human brain comprises approximately 1011 neurons, each of which makes about 103 synaptic connections. This huge number of connections between individual processing elements provides the fundamental substrate for neuronal ensembles to become transiently synchronized or functionally connected. A similar complex network configuration and dynamics can also be found at the macroscopic scales of systems neuroscience and brain imaging. The emergence of dynamically coupled cell assemblies represents the neurophysiological substrate for cognitive function such as perception, learning, thinking. Understanding the complex network organization of the brain on the basis of neuroimaging data represents one of the most impervious challenges for systems neuroscience. Brain connectivity is an elusive concept that refers to diferent interrelated aspects of brain organization: structural, functional connectivity (FC) and efective connectivity (EC). Structural connectivity refers to a network of physical connections linking sets of neurons, it is the anatomical structur of brain networks. However, FC refers to the statistical dependence between the signals stemming from two distinct units within a nervous system, while EC refers to the causal interactions between them. This research opens the door to try to resolve diseases related with the brain, like Parkinson’s disease, senile dementia, mild cognitive impairment, etc. One of the most important project associated with Alzheimer’s research and other diseases are enclosed in the European project called Blue Brain. The center for Biomedical Technology (CTB) of Universidad Politecnica de Madrid (UPM) forms part of the project. The CTB researches have developed a magnetoencephalography (MEG) data processing tool that allow to visualise and analyse data in an intuitive way. This tool receives the name of HERMES, and it is presented in this document. Analysis of big amount of data is a field with many years of research. It is centred in getting significant values, to make it easier to understand and interpret data. Being the analysis of interdependence between time series an important field of research, mainly as a result of advances in the characterization of dynamical systems from the signals they produce. In the medicine sphere, it is easy to find many researches that try to understand the brain behaviour, its operation mode and its internal connections. The human brain comprises approximately 1011 neurons, each of which makes about 103 synaptic connections. This huge number of connections between individual processing elements provides the fundamental substrate for neuronal ensembles to become transiently synchronized or functionally connected. A similar complex network configuration and dynamics can also be found at the macroscopic scales of systems neuroscience and brain imaging. The emergence of dynamically coupled cell assemblies represents the neurophysiological substrate for cognitive function such as perception, learning, thinking. Understanding the complex network organization of the brain on the basis of neuroimaging data represents one of the most impervious challenges for systems neuroscience. Brain connectivity is an elusive concept that refers to diferent interrelated aspects of brain organization: structural, functional connectivity (FC) and efective connectivity (EC). Structural connectivity refers to a network of physical connections linking sets of neurons, it is the anatomical structur of brain networks. However, FC refers to the statistical dependence between the signals stemming from two distinct units within a nervous system, while EC refers to the causal interactions between them. This research opens the door to try to resolve diseases related with the brain, like Parkinson’s disease, senile dementia, mild cognitive impairment, etc. One of the most important project associated with Alzheimer’s research and other diseases are enclosed in the European project called Blue Brain. The center for Biomedical Technology (CTB) of Universidad Politecnica de Madrid (UPM) forms part of the project. The CTB researches have developed a magnetoencephalography (MEG) data processing tool that allow to visualise and analyse data in an intuitive way. This tool receives the name of HERMES, and it is presented in this document.

Sensitization profiles and cross-reactivity among plant allergens. Molecular mechanisms of food allergy development and the role of enhancers

La prevalencia de las alergias está aumentando desde mediados del siglo XX, y se estima que actualmente afectan a alrededor del 2-8 % de la población, pero las causas de este aumento aún no están claras. Encontrar el origen del mecanismo por el cual una proteína inofensiva se convierte en capaz de inducir una respuesta alérgica es de vital importancia para prevenir y tratar estas enfermedades. Aunque la caracterización de alérgenos relevantes ha ayudado a mejorar el manejo clínico y a aclarar los mecanismos básicos de las reacciones alérgicas, todavía queda un largo camino para establecer el origen de la alergenicidad y reactividad cruzada. El objetivo de esta tesis ha sido caracterizar las bases moleculares de la alergenicidad tomando como modelo dos familias de panalergenos (proteínas de transferencia de lípidos –LTPs- y taumatinas –TLPs-) y estudiando los mecanismos que median la sensibilización y la reactividad cruzada para mejorar tanto el diagnóstico como el tratamiento de la alergia. Para ello, se llevaron a cabo dos estrategias: estudiar la reactividad cruzada de miembros de familias de panalérgenos; y estudiar moléculas-co-adyuvantes que pudieran favorecer la capacidad alergénica de dichas proteínas. Para estudiar la reactividad cruzada entre miembros de la misma familia de proteínas, se seleccionaron LTPs y TLPs, descritas como alergenos, tomando como modelo la alergia a frutas. Por otra parte, se estudiaron los perfiles de sensibilización a alérgenos de trigo relacionados con el asma del panadero, la enfermedad ocupacional más relevante de origen alérgico. Estos estudios se llevaron a cabo estandarizando ensayos tipo microarrays con alérgenos y analizando los resultados por la teoría de grafos. En relación al estudiar moléculas-co-adyuvantes que pudieran favorecer la capacidad alergénica de dichas proteínas, se llevaron a cabo estudios sobre la interacción de los alérgenos alimentarios con células del sistema inmune humano y murino y el epitelio de las mucosas, analizando la importancia de moléculas co-transportadas con los alérgenos en el desarrollo de una respuesta Th2. Para ello, Pru p 3(LTP y alérgeno principal del melocotón) se selección como modelo para llevarlo a cabo. Por otra parte, se analizó el papel de moléculas activadoras del sistema inmune producidas por patógenos en la inducción de alergias alimentarias seleccionando el modelo kiwi-alternaria, y el papel de Alt a 1, alérgeno mayor de dicho hongo, en la sensibilización a Act d 2, alérgeno mayor de kiwi. En resumen, el presente trabajo presenta una investigación innovadora aportando resultados de gran utilidad tanto para la mejora del diagnóstico como para nuevas investigaciones sobre la alergia y el esclarecimiento final de los mecanismos que caracterizan esta enfermedad. ABSTRACT Allergies are increasing their prevalence from mid twentieth century, and they are currently estimated to affect around 2-8% of the population but the underlying causes of this increase remain still elusive. The understanding of the mechanism by which a harmless protein becomes capable of inducing an allergic response provides us the basis to prevent and treat these diseases. Although the characterization of relevant allergens has led to improved clinical management and has helped to clarify the basic mechanisms of allergic reactions, it seems justified in aspiring to molecularly dissecting these allergens to establish the structural basis of their allergenicity and cross-reactivity. The aim of this thesis was to characterize the molecular basis of the allergenicity of model proteins belonging to different families (Lipid Transfer Proteins –LTPs-, and Thaumatin-like Proteins –TLPs-) in order to identify mechanisms that mediate sensitization and cross reactivity for developing new strategies in the management of allergy, both diagnosis and treatment, in the near future. With this purpose, two strategies have been conducted: studies of cross-reactivity among panallergen families and molecular studies of the contribution of cofactors in the induction of the allergic response by these panallergens. Following the first strategy, we studied the cross-reactivity among members of two plant panallergens (LTPs , Lipid Transfer Proteins , and TLPs , Thaumatin-like Proteins) using the peach allergy as a model. Similarly, we characterized the sensitization profiles to wheat allergens in baker's asthma development, the most relevant occupational disease. These studies were performed using allergen microarrays and the graph theory for analyzing the results. Regarding the second approach, we analyzed the interaction of plant allergens with immune and epithelial cells. To perform these studies , we examined the importance of ligands and co-transported molecules of plant allergens in the development of Th2 responses. To this end, Pru p 3, nsLTP (non-specific Lipid Transfer Protein) and peach major allergen, was selected as a model to investigate its interaction with cells of the human and murine immune systems as well as with the intestinal epithelium and the contribution of its ligand in inducing an allergic response was studied. Moreover, we analyzed the role of pathogen associated molecules in the induction of food allergy. For that, we selected the kiwi- alternaria system as a model and the role of Alt a 1 , major allergen of the fungus, in the development of Act d 2-sensitization was studied. In summary, this work presents an innovative research providing useful results for improving diagnosis and leading to further research on allergy and the final clarification of the mechanisms that characterize this disease.

Knowledge representation tool for cognitiveprocesses modeling

In the last decades, neuropsychological theories tend to consider cognitive functions as a result of the whole brainwork and not as individual local areas of its cortex. Studies based on neuroimaging techniques have increased in the last years, promoting an exponential growth of the body of knowledge about relations between cognitive functions and brain structures [1]. However, so fast evolution make complicated to integrate them in verifiable theories and, even more, translated in to cognitive rehabilitation. The aim of this research work is to develop a cognitive process-modeling tool. The purpose of this system is, in the first term, to represent multidimensional data, from structural and functional connectivity, neuroimaging, data from lesion studies and derived data from clinical intervention [2][3]. This will allow to identify consolidated knowledge, hypothesis, experimental designs, new data from ongoing studies and emerging results from clinical interventions. In the second term, we pursuit to use Artificial Intelligence to assist in decision making allowing to advance towards evidence based and personalized treatments in cognitive rehabilitation. This work presents the knowledge base design of the knowledge representation tool. It is compound of two different taxonomies (structure and function) and a set of tags linking both taxonomies at different levels of structural and functional organization. The remainder of the abstract is organized as follows: Section 2 presents the web application used for gathering necessary information for generating the knowledge base, Section 3 describes knowledge base structure and finally Section 4 expounds reached conclusions.

Dynamics of brain networks in aesthetic appreciation

Neuroimage experiments have been essential for identifying active brain networks. During cognitive tasks as in, e.g., aesthetic appreciation, such networks include regions that belong to the default mode network (DMN). Theoretically, DMN activity should be interrupted during cognitive tasks demanding attention, as is the case for aesthetic appreciation. Analyzing the functional connectivity dynamics along three temporal windows and two conditions, beautiful and not beautiful stimuli, here we report experimental support for the hypothesis that aesthetic appreciation relies on the activation of two different networks, an initial aesthetic network and a delayed aesthetic network, engaged within distinct time frames. Activation of the DMN might correspond mainly to the delayed aesthetic network. We discuss adaptive and evolutionary explanations for the relationships existing between the DMN and aesthetic networks and offer unique inputs to debates on the mind/brain interaction.