Brain dynamics of mild cognitive impairment during face encoding

Brain oscillations are closely correlated with human information processing and fundamental aspects of cognition. Previous literature shows that due to the relation between brain oscillations and memory processes, spectral dynamics during such tasks are good candidates to study and characterize memory related pathologies. Mild cognitive impairment (MCI), defined as a clinical condition characterized by memory impairment and/ or deterioration of additional cognitive domains, is considered a preliminary stage in the dementia process. In consequence, the study of its brain patterns could help to achieve an early diagnosis of Alzheimer Disease.

Video-based tasks for emotional processing rehabilitation in schizophrenia

Schizophrenia is a mental disorder characterized by a breakdown of cognitive processes and by a deficit of typi-cal emotional responses. Effectiveness of computerized task has been demonstrated in the field of cognitive rehabilitation. However, current rehabilitation programs based on virtual environments normally focus on higher cognitive functions, not covering social cognition training. This paper presents a set of video-based tasks specifically designed for the rehabilita-tion of emotional processing deficits in patients in early stages of schizophrenia or schizoaffective disorders. These tasks are part of the Mental Health program of Guttmann NeuroPer-sonalTrainer® cognitive tele-rehabilitation platform, and entail innovation both from a clinical and technological per-spective in relation with former traditional therapeutic con-tents.

Three-dimensional mapping of real environments for cognitive robots navigation

Abstract The development of cognitive robots needs a strong “sensorial” support which should allow it to perceive the real world for interacting with it properly. Therefore the development of efficient visual-processing software to be equipped in effective artificial agents is a must. In this project we study and develop a visual-processing software that will work as the “eyes” of a cognitive robot. This software performs a three-dimensional mapping of the robot’s environment, providing it with the essential information required to make proper decisions during its navigation. Due to the complexity of this objective we have adopted the Scrum methodology in order to achieve an agile development process, which has allowed us to correct and improve in a fast way the successive versions of the product. The present project is structured in Sprints, which cover the different stages of the software development based on the requirements imposed by the robot and its real necessities. We have initially explored different commercial devices oriented to the acquisition of the required visual information, adopting the Kinect Sensor camera (Microsoft) as the most suitable option. Later on, we have studied the available software to manage the obtained visual information as well as its integration with the robot’s software, choosing the high-level platform Matlab as the common nexus to join the management of the camera, the management of the robot and the implementation of the behavioral algorithms. During the last stages the software has been developed to include the fundamental functionalities required to process the real environment, such as depth representation, segmentation, and clustering. Finally the software has been optimized to exhibit real-time processing and a suitable performance to fulfill the robot’s requirements during its operation in real situations.

Multimodal integration of functional and structural brain connectivity. An application to the characterization of Mild Cognitive Impairment

La investigación para el conocimiento del cerebro es una ciencia joven, su inicio se remonta a Santiago Ramón y Cajal en 1888. Desde esta fecha a nuestro tiempo la neurociencia ha avanzado mucho en el desarrollo de técnicas que permiten su estudio. Desde la neurociencia cognitiva hoy se explican muchos modelos que nos permiten acercar a nuestro entendimiento a capacidades cognitivas complejas. Aun así hablamos de una ciencia casi en pañales que tiene un lago recorrido por delante. Una de las claves del éxito en los estudios de la función cerebral ha sido convertirse en una disciplina que combina conocimientos de diversas áreas: de la física, de las matemáticas, de la estadística y de la psicología. Esta es la razón por la que a lo largo de este trabajo se entremezclan conceptos de diferentes campos con el objetivo de avanzar en el conocimiento de un tema tan complejo como el que nos ocupa: el entendimiento de la mente humana. Concretamente, esta tesis ha estado dirigida a la integración multimodal de la magnetoencefalografía (MEG) y la resonancia magnética ponderada en difusión (dMRI). Estas técnicas son sensibles, respectivamente, a los campos magnéticos emitidos por las corrientes neuronales, y a la microestructura de la materia blanca cerebral. A lo largo de este trabajo hemos visto que la combinación de estas técnicas permiten descubrir sinergias estructurofuncionales en el procesamiento de la información en el cerebro sano y en el curso de patologías neurológicas. Más específicamente en este trabajo se ha estudiado la relación entre la conectividad funcional y estructural y en cómo fusionarlas. Para ello, se ha cuantificado la conectividad funcional mediante el estudio de la sincronización de fase o la correlación de amplitudes entre series temporales, de esta forma se ha conseguido un índice que mide la similitud entre grupos neuronales o regiones cerebrales. Adicionalmente, la cuantificación de la conectividad estructural a partir de imágenes de resonancia magnética ponderadas en difusión, ha permitido hallar índices de la integridad de materia blanca o de la fuerza de las conexiones estructurales entre regiones. Estas medidas fueron combinadas en los capítulos 3, 4 y 5 de este trabajo siguiendo tres aproximaciones que iban desde el nivel más bajo al más alto de integración. Finalmente se utilizó la información fusionada de MEG y dMRI para la caracterización de grupos de sujetos con deterioro cognitivo leve, la detección de esta patología resulta relevante en la identificación precoz de la enfermedad de Alzheimer. Esta tesis está dividida en seis capítulos. En el capítulos 1 se establece un contexto para la introducción de la connectómica dentro de los campos de la neuroimagen y la neurociencia. Posteriormente en este capítulo se describen los objetivos de la tesis, y los objetivos específicos de cada una de las publicaciones científicas que resultaron de este trabajo. En el capítulo 2 se describen los métodos para cada técnica que fue empleada: conectividad estructural, conectividad funcional en resting state, redes cerebrales complejas y teoría de grafos y finalmente se describe la condición de deterioro cognitivo leve y el estado actual en la búsqueda de nuevos biomarcadores diagnósticos. En los capítulos 3, 4 y 5 se han incluido los artículos científicos que fueron producidos a lo largo de esta tesis. Estos han sido incluidos en el formato de la revista en que fueron publicados, estando divididos en introducción, materiales y métodos, resultados y discusión. Todos los métodos que fueron empleados en los artículos están descritos en el capítulo 2 de la tesis. Finalmente, en el capítulo 6 se concluyen los resultados generales de la tesis y se discuten de forma específica los resultados de cada artículo. ABSTRACT In this thesis I apply concepts from mathematics, physics and statistics to the neurosciences. This field benefits from the collaborative work of multidisciplinary teams where physicians, psychologists, engineers and other specialists fight for a common well: the understanding of the brain. Research on this field is still in its early years, being its birth attributed to the neuronal theory of Santiago Ramo´n y Cajal in 1888. In more than one hundred years only a very little percentage of the brain functioning has been discovered, and still much more needs to be explored. Isolated techniques aim at unraveling the system that supports our cognition, nevertheless in order to provide solid evidence in such a field multimodal techniques have arisen, with them we will be able to improve current knowledge about human cognition. Here we focus on the multimodal integration of magnetoencephalography (MEG) and diffusion weighted magnetic resonance imaging. These techniques are sensitive to the magnetic fields emitted by the neuronal currents and to the white matter microstructure, respectively. The combination of such techniques could bring up evidences about structural-functional synergies in the brain information processing and which part of this synergy fails in specific neurological pathologies. In particular, we are interested in the relationship between functional and structural connectivity, and how two integrate this information. We quantify the functional connectivity by studying the phase synchronization or the amplitude correlation between time series obtained by MEG, and so we get an index indicating similarity between neuronal entities, i.e. brain regions. In addition we quantify structural connectivity by performing diffusion tensor estimation from the diffusion weighted images, thus obtaining an indicator of the integrity of the white matter or, if preferred, the strength of the structural connections between regions. These quantifications are then combined following three different approaches, from the lowest to the highest level of integration, in chapters 3, 4 and 5. We finally apply the fused information to the characterization or prediction of mild cognitive impairment, a clinical entity which is considered as an early step in the continuum pathological process of dementia. The dissertation is divided in six chapters. In chapter 1 I introduce connectomics within the fields of neuroimaging and neuroscience. Later in this chapter we describe the objectives of this thesis, and the specific objectives of each of the scientific publications that were produced as result of this work. In chapter 2 I describe the methods for each of the techniques that were employed, namely structural connectivity, resting state functional connectivity, complex brain networks and graph theory, and finally, I describe the clinical condition of mild cognitive impairment and the current state of the art in the search for early biomarkers. In chapters 3, 4 and 5 I have included the scientific publications that were generated along this work. They have been included in in their original format and they contain introduction, materials and methods, results and discussion. All methods that were employed in these papers have been described in chapter 2. Finally, in chapter 6 I summarize all the results from this thesis, both locally for each of the scientific publications and globally for the whole work.

Cognitive risk perception system for obstacle avoidance in outdoor mUAV missions

La robótica ha evolucionado exponencialmente en las últimas décadas, permitiendo a los sistemas actuales realizar tareas sumamente complejas con gran precisión, fiabilidad y velocidad. Sin embargo, este desarrollo ha estado asociado a un mayor grado de especialización y particularización de las tecnologías implicadas, siendo estas muy eficientes en situaciones concretas y controladas, pero incapaces en entornos cambiantes, dinámicos y desestructurados. Por eso, el desarrollo de la robótica debe pasar por dotar a los sistemas de capacidad de adaptación a las circunstancias, de entendedimiento sobre los cambios observados y de flexibilidad a la hora de interactuar con el entorno. Estas son las caracteristicas propias de la interacción del ser humano con su entorno, las que le permiten sobrevivir y las que pueden proporcionar a un sistema inteligencia y capacidad suficientes para desenvolverse en un entorno real de forma autónoma e independiente. Esta adaptabilidad es especialmente importante en el manejo de riesgos e incetidumbres, puesto que es el mecanismo que permite contextualizar y evaluar las amenazas para proporcionar una respuesta adecuada. Así, por ejemplo, cuando una persona se mueve e interactua con su entorno, no evalúa los obstáculos en función de su posición, velocidad o dinámica (como hacen los sistemas robóticos tradicionales), sino mediante la estimación del riesgo potencial que estos elementos suponen para la persona. Esta evaluación se consigue combinando dos procesos psicofísicos del ser humano: por un lado, la percepción humana analiza los elementos relevantes del entorno, tratando de entender su naturaleza a partir de patrones de comportamiento, propiedades asociadas u otros rasgos distintivos. Por otro lado, como segundo nivel de evaluación, el entendimiento de esta naturaleza permite al ser humano conocer/estimar la relación de los elementos con él mismo, así como sus implicaciones en cuanto a nivel de riesgo se refiere. El establecimiento de estas relaciones semánticas -llamado cognición- es la única forma de definir el nivel de riesgo de manera absoluta y de generar una respuesta adecuada al mismo. No necesariamente proporcional, sino coherente con el riesgo al que se enfrenta. La investigación que presenta esta tesis describe el trabajo realizado para trasladar esta metodología de análisis y funcionamiento a la robótica. Este se ha centrado especialmente en la nevegación de los robots aéreos, diseñando e implementado procedimientos de inspiración humana para garantizar la seguridad de la misma. Para ello se han estudiado y evaluado los mecanismos de percepción, cognición y reacción humanas en relación al manejo de riesgos. También se ha analizado como los estímulos son capturados, procesados y transformados por condicionantes psicológicos, sociológicos y antropológicos de los seres humanos. Finalmente, también se ha analizado como estos factores motivan y descandenan las reacciones humanas frente a los peligros. Como resultado de este estudio, todos estos procesos, comportamientos y condicionantes de la conducta humana se han reproducido en un framework que se ha estructurado basadandose en factores análogos. Este emplea el conocimiento obtenido experimentalmente en forma de algoritmos, técnicas y estrategias, emulando el comportamiento humano en las mismas circunstancias. Diseñado, implementeado y validado tanto en simulación como con datos reales, este framework propone una manera innovadora -tanto en metodología como en procedimiento- de entender y reaccionar frente a las amenazas potenciales de una misión robótica. ABSTRACT Robotics has undergone a great revolution in the last decades. Nowadays this technology is able to perform really complex tasks with a high degree of accuracy and speed, however this is only true in precisely defined situations with fully controlled variables. Since the real world is dynamic, changing and unstructured, flexible and non context-dependent systems are required. The ability to understand situations, acknowledge changes and balance reactions is required by robots to successfully interact with their surroundings in a fully autonomous fashion. In fact, it is those very processes that define human interactions with the environment. Social relationships, driving or risk/incertitude management... in all these activities and systems, context understanding and adaptability are what allow human beings to survive: contrarily to the traditional robotics, people do not evaluate obstacles according to their position but according to the potential risk their presence imply. In this sense, human perception looks for information which goes beyond location, speed and dynamics (the usual data used in traditional obstacle avoidance systems). Specific features in the behaviour of a particular element allows the understanding of that element’s nature and therefore the comprehension of the risk posed by it. This process defines the second main difference between traditional obstacle avoidance systems and human behaviour: the ability to understand a situation/scenario allows to get to know the implications of the elements and their relationship with the observer. Establishing these semantic relationships -named cognition- is the only way to estimate the actual danger level of an element. Furthermore, only the application of this knowledge allows the generation of coherent, suitable and adjusted responses to deal with any risk faced. The research presented in this thesis summarizes the work done towards translating these human cognitive/reasoning procedures to the field of robotics. More specifically, the work done has been focused on employing human-based methodologies to enable aerial robots to navigate safely. To this effect, human perception, cognition and reaction processes concerning risk management have been experimentally studied; as well as the acquisition and processing of stimuli. How psychological, sociological and anthropological factors modify, balance and give shape to those stimuli has been researched. And finally, the way in which these factors motivate the human behaviour according to different mindsets and priorities has been established. This associative workflow has been reproduced by establishing an equivalent structure and defining similar factors and sources. Besides, all the knowledge obtained experimentally has been applied in the form of algorithms, techniques and strategies which emulate the analogous human behaviours. As a result, a framework capable of understanding and reacting in response to stimuli has been implemented and validated.

Distributed cognitive radio systems with temperature-interference constraints and overlay scheme

Cognitive radio represents a promising paradigm to further increase transmission rates in wireless networks, as well as to facilitate the deployment of self-organized networks such as femtocells. Within this framework, secondary users (SU) may exploit the channel under the premise to maintain the quality of service (QoS) on primary users (PU) above a certain level. To achieve this goal, we present a noncooperative game where SU maximize their transmission rates, and may act as well as relays of the PU in order to hold their perceived QoS above the given threshold. In the paper, we analyze the properties of the game within the theory of variational inequalities, and provide an algorithm that converges to one Nash Equilibrium of the game. Finally, we present some simulations and compare the algorithm with another method that does not consider SU acting as relays.

Cognitive Surveillance Architecture for Scenario Understanding

Uno de los mayores retos para la comunidad científica es conseguir que las máquinas posean en un futuro la capacidad del sistema visual y cognitivo humanos, de forma que, por ejemplo, en entornos de video vigilancia, puedan llegar a proporcionar de manera automática una descripción fiable de lo que está ocurriendo en la escena. En la presente tesis, mediante la propuesta de un marco de trabajo de referencia, se discuten y plantean los pasos necesarios para el desarrollo de sistemas más inteligentes capaces de extraer y analizar, a diferentes niveles de abstracción y mediante distintos módulos de procesamiento independientes, la información necesaria para comprender qué está sucediendo en un conjunto amplio de escenarios de distinta naturaleza. Se parte de un análisis de requisitos y se identifican los retos para este tipo de sistemas en la actualidad, lo que constituye en sí mismo los objetivos de esta tesis, contribuyendo así a un modelo de datos basado en el conocimiento que permitirá analizar distintas situaciones en las que personas y vehículos son los actores principales, dejando no obstante la puerta abierta a la adaptación a otros dominios. Así mismo, se estudian los distintos procesos que se pueden lanzar a nivel interno así como la necesidad de integrar mecanismos de realimentación a distintos niveles que permitan al sistema adaptarse mejor a cambios en el entorno. Como resultado, se propone un marco de referencia jerárquico que integra las capacidades de percepción, interpretación y aprendizaje para superar los retos identificados en este ámbito; y así poder desarrollar sistemas de vigilancia más robustos, flexibles e inteligentes, capaces de operar en una variedad de entornos. Resultados experimentales ejecutados sobre distintas muestras de datos (secuencias de vídeo principalmente) demuestran la efectividad del marco de trabajo propuesto respecto a otros propuestos en el pasado. Un primer caso de estudio, permite demostrar la creación de un sistema de monitorización de entornos de parking en exteriores para la detección de vehículos y el análisis de plazas libres de aparcamiento. Un segundo caso de estudio, permite demostrar la flexibilidad del marco de referencia propuesto para adaptarse a los requisitos de un entorno de vigilancia completamente distinto, como es un hogar inteligente donde el análisis automático de actividades de la vida cotidiana centra la atención del estudio. ABSTRACT One of the most ambitious objectives for the Computer Vision and Pattern Recognition research community is that machines can achieve similar capacities to the human's visual and cognitive system, and thus provide a trustworthy description of what is happening in the scene under surveillance. Thus, a number of well-established scenario understanding architectural frameworks to develop applications working on a variety of environments can be found in the literature. In this Thesis, a highly descriptive methodology for the development of scene understanding applications is presented. It consists of a set of formal guidelines to let machines extract and analyse, at different levels of abstraction and by means of independent processing modules that interact with each other, the necessary information to understand a broad set of different real World surveillance scenarios. Taking into account the challenges that working at both low and high levels offer, we contribute with a highly descriptive knowledge-based data model for the analysis of different situations in which people and vehicles are the main actors, leaving the door open for the development of interesting applications in diverse smart domains. Recommendations to let systems achieve high-level behaviour understanding will be also provided. Furthermore, feedback mechanisms are proposed to be integrated in order to let any system to understand better the environment and the logical context around, reducing thus the uncertainty and noise, and increasing its robustness and precision in front of low-level or high-level errors. As a result, a hierarchical cognitive architecture of reference which integrates the necessary perception, interpretation, attention and learning capabilities to overcome main challenges identified in this area of research is proposed; thus allowing to develop more robust, flexible and smart surveillance systems to cope with the different requirements of a variety of environments. Once crucial issues that should be treated explicitly in the design of this kind of systems have been formulated and discussed, experimental results shows the effectiveness of the proposed framework compared with other proposed in the past. Two case studies were implemented to test the capabilities of the framework. The first case study presents how the proposed framework can be used to create intelligent parking monitoring systems. The second case study demonstrates the flexibility of the system to cope with the requirements of a completely different environment, a smart home where activities of daily living are performed. Finally, general conclusions and future work lines to further enhancing the capabilities of the proposed framework are presented.

Granular computing as a basis of human–data interaction: a cognitive cities use case

The article proposes granular computing as a theoretical, formal and methodological basis for the newly emerging research field of human–data interaction (HDI). We argue that the ability to represent and reason with information granules is a prerequisite for data legibility. As such, it allows for extending the research agenda of HDI to encompass the topic of collective intelligence amplification, which is seen as an opportunity of today’s increasingly pervasive computing environments. As an example of collective intelligence amplification in HDI, we introduce a collaborative urban planning use case in a cognitive city environment and show how an iterative process of user input and human-oriented automated data processing can support collective decision making. As a basis for automated human-oriented data processing, we use the spatial granular calculus of granular geometry.

Majority versus minority influence: When, not whether, source status instigates heuristic or systematic processing

Two experiments investigated the extent of message processing of a persuasive communication proposed by either a numerical majority or minority. Both experiments crossed source status (majority versus minority) with message quality (strong versus weak arguments) to determine which source condition is associated with systematic processing. The first experiment showed a reliable difference between strong and weak messages, indicating systematic processing had occurred, for a minority irrespective of message direction (pro- versus counter-attitudinal), but not for a majority. The second experiment showed that message outcome moderates when a majority or a minority leads to systematic processing. When the message argued for a negative personal outcome, there was systematic processing only for the majority source; but when the message did not argue for a negative personal outcome, there was systematic processing only for the minority source. Thus one key moderator of whether a majority or minority source leads to message processing is whether the topic induces defensive processing motivated by self-interest. Copyright (C) 2002 John Wiley Sons, Ltd.

Genetic influences on cognitive processes associated with distraction: An event-related potential study of the slow wave

The efficiency of inhibitory control processes has been proposed as a mechanism constraining working-memory capacity. In order to investigate genetic influences on processes that may reflect interference control, event-related potential (ER-P) activity recorded at frontal sites, during distracting and nondistracting conditions of a working-memory task, in a sample of 509 twin pairs was examined. The ERP component of interest was the slow wave (SW). Considerable overlap in source of genetic influence was found, with a common genetic factor accounting for 37 - 45% of SW variance irrespective of condition. However, 3 - 8 % of SW variance in the distracting condition was influenced by an independent genetic source. These results suggest that neural responses to irrelevant and distracting information, that may disrupt working-memory performance, differ in a fundamental way from perceptual and memory-based processing in a working-memory task. Furthermore, the results are consistent with the view that cognition is a complex genetic trait influenced by numerous genes of small influence.

Multivariate genetic analysis of cognitive abilities in an adolescent twin sample

The sources of covariation among cognitive measures of Inspection Time, Choice Reaction Time, Delayed Response Speed and Accuracy, and IQ were examined in a classical twin design that included 245 monozygotic (MZ) and 298 dizygotic (DZ) twin pairs. Results indicated that a factor model comprising additive genetic and unique environmental effects was the most parsimonious. In this model, a general genetic cognitive factor emerged with factor loadings ranging from 0.28 to 0.64. Three other genetic factors explained the remaining genetic covariation between various speed and Delayed Response measures with IQ. However, a large proportion of the genetic variation in verbal (54%) and performance (25%) IQ was unrelated to these lower order cognitive measures. The independent genetic IQ variation may reflect information processes not captured by the elementary cognitive tasks, Inspection Time and Choice Reaction Time, nor our working memory task, Delayed Response. Unique environmental effects were mostly nonoverlapping, and partly represented test measurement error.

Experimental hand pain delays recognition of the contralateral hand: Evidence that acute and chronic pain have opposite effects on information processing?

Recognising the laterality of a pictured hand involves making an initial decision and confirming that choice by mentally moving one's own hand to match the picture. This depends on an intact body schema. Because patients with complex regional pain syndrome type 1 (CRPS1) take longer to recognise a hand's laterality when it corresponds to their affected hand, it has been proposed that nociceptive input disrupts the body schema. However, chronic pain is associated with physiological and psychosocial complexities that may also explain the results. In three studies, we investigated whether the effect is simply due to nociceptive input. Study one evaluated the temporal and perceptual characteristics of acute hand pain elicited by intramuscular injection of hypertonic saline into the thenar eminence. In studies two and three, subjects performed a hand laterality recognition task before, during, and after acute experimental hand pain, and experimental elbow pain, respectively. During hand pain and during elbow pain, when the laterality of the pictured hand corresponded to the painful side, there was no effect on response time (RT). That suggests that nociceptive input alone is not sufficient to disrupt the working body schema. Conversely to patients with CRPS1, when the laterality of the pictured hand corresponded to the non-painful hand, RT increased similar to 380 ms (95% confidence interval 190 ms-590 ms). The results highlight the differences between acute and chronic pain and may reflect a bias in information processing in acute pain toward the affected part.

Caffeine, cognition, and persuasion: Evidence for caffeine increasing the systematic processing of persuasive messages

Caffeine is known to increase arousal, attention, and information processing-all factors implicated in facilitating persuasion. In a standard attitude-change paradigm, participants consumed an orange-juice drink that either contained caffeine (3.5 mg/kg body weight) or did not (placebo) prior to reading a counterattitudinal communication (anti-voluntary euthanasia). Participants then completed a thought-listing task and a number of attitude scales. The first experiment showed that those who consumed caffeine showed greater agreement with the communication (direct attitude: voluntary euthanasia) and on an issue related to, but not contained in, the communication (indirect attitude: abortion). The order in which direct and indirect attitudes were measured did not affect the results. A second experiment manipulated the quality of the arguments in the message (strong vs. weak) to determine whether systematic processing had occurred. There was evidence that systematic processing occurred in both drink conditions, but was greater for those who had consumed caffeine. In both experiments, the amount of message-congruent thinking mediated persuasion. These results show that caffeine can increase the extent to which people systematically process and are influenced by a persuasive communication.

Turning configural processing upside down: Part and whole body postures

Like faces, body postures are susceptible to an inversion effect in untrained viewers. The inversion effect may be indicative of configural processing, but what kind of configural processing is used for the recognition of body postures must be specified. The information available in the body stimulus was manipulated. The presence and magnitude of inversion effects were compared for body parts, scrambled bodies, and body halves relative to whole bodies and to corresponding conditions for faces and houses. Results suggest that configural body posture recognition relies on the structural hierarchy of body parts, not the parts themselves or a complete template match. Configural recognition of body postures based on information about the structural hierarchy of parts defines an important point on the configural processing continuum, between recognition based on first-order spatial relations and recognition based on holistic undifferentiated template matching.