Intravenous mesenchymal stem cell therapy for traumatic brain injury.

OBJECT: Cell therapy has shown preclinical promise in the treatment of many diseases, and its application is being translated to the clinical arena. Intravenous mesenchymal stem cell (MSC) therapy has been shown to improve functional recovery after traumatic brain injury (TBI). Herein, the authors report on their attempts to reproduce such observations, including detailed characterizations of the MSC population, non-bromodeoxyuridine-based cell labeling, macroscopic and microscopic cell tracking, quantification of cells traversing the pulmonary microvasculature, and well-validated measurement of motor and cognitive function recovery. METHODS: Rat MSCs were isolated, expanded in vitro, immunophenotyped, and labeled. Four million MSCs were intravenously infused into Sprague-Dawley rats 24 hours after receiving a moderate, unilateral controlled cortical impact TBI. Infrared macroscopic cell tracking was used to identify cell distribution. Immunohistochemical analysis of brain and lung tissues 48 hours and 2 weeks postinfusion revealed transplanted cells in these locations, and these cells were quantified. Intraarterial blood sampling and flow cytometry were used to quantify the number of transplanted cells reaching the arterial circulation. Motor and cognitive behavioral testing was performed to evaluate functional recovery. RESULTS: At 48 hours post-MSC infusion, the majority of cells were localized to the lungs. Between 1.5 and 3.7% of the infused cells were estimated to traverse the lungs and reach the arterial circulation, 0.295% reached the carotid artery, and a very small percentage reached the cerebral parenchyma (0.0005%) and remained there. Almost no cells were identified in the brain tissue at 2 weeks postinfusion. No motor or cognitive functional improvements in recovery were identified. CONCLUSIONS: The intravenous infusion of MSCs appeared neither to result in significant acute or prolonged cerebral engraftment of cells nor to modify the recovery of motor or cognitive function. Less than 4% of the infused cells were likely to traverse the pulmonary microvasculature and reach the arterial circulation, a phenomenon termed the "pulmonary first-pass effect," which may limit the efficacy of this therapeutic approach. The data in this study contradict the findings of previous reports and highlight the potential shortcomings of acute, single-dose, intravenous MSC therapy for TBI.

Subacute neural stem cell therapy for traumatic brain injury.

INTRODUCTION: Traumatic brain injury (TBI) frequently results in devastating and prolonged morbidity. Cellular therapy is a burgeoning field of experimental treatment that has shown promise in the management of many diseases, including TBI. Previous work suggests that certain stem and progenitor cell populations migrate to sites of inflammation and improve functional outcome in rodents after neural injury. Unfortunately, recent study has revealed potential limitations of acute and intravenous stem cell therapy. We studied subacute, direct intracerebral neural stem and progenitor cell (NSC) therapy for TBI. MATERIALS AND METHODS: The NSCs were characterized by flow cytometry and placed (400,000 cells in 50 muL 1x phosphate-buffered saline) into and around the direct injury area, using stereotactic guidance, of female Sprague Dawley rats 1 wk after undergoing a controlled cortical impact injury. Immunohistochemistry was used to identify cells located in the brain at 48 h and 2 wk after administration. Motor function was assessed using the neurological severity score, foot fault, rotarod, and beam balance. Cognitive function was assessed using the Morris water maze learning paradigm. Repeated measures analysis of variance with post-hoc analysis were used to determine significance at P < 0.05. RESULTS: Immunohistochemistry analysis revealed that 1.4-1.9% of infused cells remained in the neural tissue at 48 h and 2 wk post placement. Nearly all cells were located along injection tracks at 48 h. At 2 wk some cell dispersion was apparent. Rotarod motor testing revealed significant increases in maximal speed among NSC-treated rats compared with saline controls at d 4 (36.4 versus 27.1 rpm, P < 0.05) and 5 (35.8 versus 28.9 rpm, P < 0.05). All other motor and cognitive evaluations were not significantly different compared to controls. CONCLUSIONS: Placement of NSCs led to the cells incorporating and remaining in the tissues 2 wk after placement. Motor function tests revealed improvements in the ability to run on a rotating rod; however, other motor and cognitive functions were not significantly improved by NSC therapy. Further examination of a dose response and optimization of placement strategy may improve long-term cell survival and maximize functional recovery.

BMZI-HEA. Adaption des Berner Motiv- und Zielinventars im Freizeit- und Gesundheitssport für Menschen im höheren Erwachsenenalter

Die Förderung regelmäßiger Bewegungs- und Sportaktivitäten bei älteren Menschen gewinnt zunehmend an Bedeutung. Für eine effiziente Bewegungs- und Sportförderung werden zielgruppenspezifische Maßnahmen gefordert. Sportbezogene Motive und Ziele von Älteren werden aktuell selten systematisch in die Konzeption von Interventionen miteinbezogen, wenngleich sie für das Wohlbefinden und die Aufrechterhaltung des Sportverhaltens eine zentrale Rolle einnehmen. Das bereits bestehende BMZI ermöglicht die Individualdiagnose von Motiven und Zielen im Freizeit- und Gesundheitssport bei Personen im mittleren Erwachsenenalter. Der vorliegende Beitrag zielt auf eine Adaption des Fragebogens für Menschen im höheren Erwachsenenalter. Das BMZI-HEA deckt mit insgesamt 27 Items folgende Motive und Ziele ab: Figur/Aussehen, Kontakt, Wettkampf/Leistung, Alltagskompetenz/Gesundheit, Positive Bewegungserfahrungen, Kognitive Funktionsfähigkeit, Stimmungsregulation. Der Fragebogen wurde an drei Stichproben explorativ und konfirmatorisch überprüft. Der globale Modell-Fit des BMZI-HEA ist als zufriedenstellend zu beurteilen. Die erwartungskonformen Zusammenhänge mit der Selbstkonkordanz verweisen auf eine gute Konstruktvalidität des Instruments. Das BMZI-HEA empfiehlt sich als ökonomisches Instrument für die Individualdiagnose der psychischen Handlungsvoraussetzungen für das Sporttreiben von Menschen im höheren Erwachsenenalter.

Exploring the Impact of Decreased AMPK Pathway Activity on Brain Injury Outcome

Each year, 150 million people sustain a Traumatic Brain Injury (TBI). TBI results in life-long cognitive impairments for many survivors. One observed pathological alteration following TBI are changes in glucose metabolism. Altered glucose uptake occurs in the periphery as well as in the nervous system, with an acute increase in glucose uptake, followed by a prolonged metabolic suppression. Chronic, persistent suppression of brain glucose uptake occurs in TBI patients experiencing memory loss. Abberant post-injury activation of energy-sensing signaling cascades could result in perturbed cellular metabolism. AMP-activated kinase (AMPK) is a kinase that senses low ATP levels, and promotes efficient cell energy usage. AMPK promotes energy production through increasing glucose uptake via glucose transporter 4 (GLUT4). When AMPK is activated, it phosphorylates Akt Substrate of 160 kDa (AS160), a Rab GTPase activating protein that controls Glut4 translocation. Additionally, AMPK negatively regulates energy-consumption by inhibiting protein synthesis via the mechanistic Target of Rapamycin (mTOR) pathway. Given that metabolic suppression has been observed post-injury, we hypothesized that activity of the AMPK pathway is transiently decreased. As AMPK activation increases energy efficiency of the cell, we proposed that increasing AMPK activity to combat the post-injury energy crisis would improve cognitive outcome. Additionally, we expected that inhibiting AMPK targets would be detrimental. We first investigated the role of an existing state of hyperglycemia on TBI outcome, as hyperglycemia correlates with increased mortality and decreased cognitive outcome in clinical studies. Inducing hyperglycemia had no effect on outcome; however, we discovered that AMPK and AS160 phosphorylation were altered post-injury. We conducted vii work to characterize this period of AMPK suppression and found that AMPK phosphorylation was significantly decreased in the hippocampus and cortex between 24 hours and 3 days post-injury, and phosphorylation of its downstream targets was consistently altered. Based on this period of observed decreased AMPK activity, we administered an AMPK activator post-injury, and this improved cognitive outcome. Finally, to examine whether AMPK-regulated target Glut4 is involved in post-injury glucose metabolism, we applied an inhibitor and found this treatment impaired post-injury cognitive function. This work is significant, as AMPK activation may represent a new TBI therapeutic target.

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.

Análisis de la actividad neuronal y cerebral mediante técnicas de redes complejas

El cerebro humano es probablemente uno de los sistemas más complejos a los que nos enfrentamos en la actualidad, si bien es también uno de los más fascinantes. Sin embargo, la compresión de cómo el cerebro organiza su actividad para llevar a cabo tareas complejas es un problema plagado de restos y obstáculos. En sus inicios la neuroimagen y la electrofisiología tenían como objetivo la identificación de regiones asociadas a activaciones relacionadas con tareas especificas, o con patrones locales que variaban en el tiempo dada cierta actividad. Sin embargo, actualmente existe un consenso acerca de que la actividad cerebral tiene un carácter temporal multiescala y espacialmente extendido, lo que lleva a considerar el cerebro como una gran red de áreas cerebrales coordinadas, cuyas conexiones funcionales son continuamente creadas y destruidas. Hasta hace poco, el énfasis de los estudios de la actividad cerebral funcional se han centrado en la identidad de los nodos particulares que forman estas redes, y en la caracterización de métricas de conectividad entre ellos: la hipótesis subyacente es que cada nodo, que es una representación mas bien aproximada de una región cerebral dada, ofrece a una única contribución al total de la red. Por tanto, la neuroimagen funcional integra los dos ingredientes básicos de la neuropsicología: la localización de la función cognitiva en módulos cerebrales especializados y el rol de las fibras de conexión en la integración de dichos módulos. Sin embargo, recientemente, la estructura y la función cerebral han empezado a ser investigadas mediante la Ciencia de la Redes, una interpretación mecánico-estadística de una antigua rama de las matemáticas: La teoría de grafos. La Ciencia de las Redes permite dotar a las redes funcionales de una gran cantidad de propiedades cuantitativas (robustez, centralidad, eficiencia, ...), y así enriquecer el conjunto de elementos que describen objetivamente la estructura y la función cerebral a disposición de los neurocientíficos. La conexión entre la Ciencia de las Redes y la Neurociencia ha aportado nuevos puntos de vista en la comprensión de la intrincada anatomía del cerebro, y de cómo las patrones de actividad cerebral se pueden sincronizar para generar las denominadas redes funcionales cerebrales, el principal objeto de estudio de esta Tesis Doctoral. Dentro de este contexto, la complejidad emerge como el puente entre las propiedades topológicas y dinámicas de los sistemas biológicos y, específicamente, en la relación entre la organización y la dinámica de las redes funcionales cerebrales. Esta Tesis Doctoral es, en términos generales, un estudio de cómo la actividad cerebral puede ser entendida como el resultado de una red de un sistema dinámico íntimamente relacionado con los procesos que ocurren en el cerebro. Con este fin, he realizado cinco estudios que tienen en cuenta ambos aspectos de dichas redes funcionales: el topológico y el dinámico. De esta manera, la Tesis está dividida en tres grandes partes: Introducción, Resultados y Discusión. En la primera parte, que comprende los Capítulos 1, 2 y 3, se hace un resumen de los conceptos más importantes de la Ciencia de las Redes relacionados al análisis de imágenes cerebrales. Concretamente, el Capitulo 1 está dedicado a introducir al lector en el mundo de la complejidad, en especial, a la complejidad topológica y dinámica de sistemas acoplados en red. El Capítulo 2 tiene como objetivo desarrollar los fundamentos biológicos, estructurales y funcionales del cerebro, cuando éste es interpretado como una red compleja. En el Capítulo 3, se resumen los objetivos esenciales y tareas que serán desarrolladas a lo largo de la segunda parte de la Tesis. La segunda parte es el núcleo de la Tesis, ya que contiene los resultados obtenidos a lo largo de los últimos cuatro años. Esta parte está dividida en cinco Capítulos, que contienen una versión detallada de las publicaciones llevadas a cabo durante esta Tesis. El Capítulo 4 está relacionado con la topología de las redes funcionales y, específicamente, con la detección y cuantificación de los nodos mas importantes: aquellos denominados “hubs” de la red. En el Capítulo 5 se muestra como las redes funcionales cerebrales pueden ser vistas no como una única red, sino más bien como una red-de-redes donde sus componentes tienen que coexistir en una situación de balance funcional. De esta forma, se investiga cómo los hemisferios cerebrales compiten para adquirir centralidad en la red-de-redes, y cómo esta interacción se mantiene (o no) cuando se introducen fallos deliberadamente en la red funcional. El Capítulo 6 va un paso mas allá al considerar las redes funcionales como sistemas vivos. En este Capítulo se muestra cómo al analizar la evolución de la topología de las redes, en vez de tratarlas como si estas fueran un sistema estático, podemos caracterizar mejor su estructura. Este hecho es especialmente relevante cuando se quiere tratar de encontrar diferencias entre grupos que desempeñan una tarea de memoria, en la que las redes funcionales tienen fuertes fluctuaciones. En el Capítulo 7 defino cómo crear redes parenclíticas a partir de bases de datos de actividad cerebral. Este nuevo tipo de redes, recientemente introducido para estudiar las anormalidades entre grupos de control y grupos anómalos, no ha sido implementado nunca en datos cerebrales y, en este Capítulo explico cómo hacerlo cuando se quiere evaluar la consistencia de la dinámica cerebral. Para concluir esta parte de la Tesis, el Capítulo 8 se centra en la relación entre las propiedades topológicas de los nodos dentro de una red y sus características dinámicas. Como mostraré más adelante, existe una relación entre ellas que revela que la posición de un nodo dentro una red está íntimamente correlacionada con sus propiedades dinámicas. Finalmente, la última parte de esta Tesis Doctoral está compuesta únicamente por el Capítulo 9, el cual contiene las conclusiones y perspectivas futuras que pueden surgir de los trabajos expuestos. En vista de todo lo anterior, espero que esta Tesis aporte una perspectiva complementaria sobre uno de los más extraordinarios sistemas complejos frente a los que nos encontramos: El cerebro humano. ABSTRACT The human brain is probably one of the most complex systems we are facing, thus being a timely and fascinating object of study. Characterizing how the brain organizes its activity to carry out complex tasks is highly non-trivial. While early neuroimaging and electrophysiological studies typically aimed at identifying patches of task-specific activations or local time-varying patterns of activity, there has now been consensus that task-related brain activity has a temporally multiscale, spatially extended character, as networks of coordinated brain areas are continuously formed and destroyed. Up until recently, though, the emphasis of functional brain activity studies has been on the identity of the particular nodes forming these networks, and on the characterization of connectivity metrics between them, the underlying covert hypothesis being that each node, constituting a coarse-grained representation of a given brain region, provides a unique contribution to the whole. Thus, functional neuroimaging initially integrated the two basic ingredients of early neuropsychology: localization of cognitive function into specialized brain modules and the role of connection fibres in the integration of various modules. Lately, brain structure and function have started being investigated using Network Science, a statistical mechanics understanding of an old branch of pure mathematics: graph theory. Network Science allows endowing networks with a great number of quantitative properties, thus vastly enriching the set of objective descriptors of brain structure and function at neuroscientists’ disposal. The link between Network Science and Neuroscience has shed light about how the entangled anatomy of the brain is, and how cortical activations may synchronize to generate the so-called functional brain networks, the principal object under study along this PhD Thesis. Within this context, complexity appears to be the bridge between the topological and dynamical properties of biological systems and, more specifically, the interplay between the organization and dynamics of functional brain networks. This PhD Thesis is, in general terms, a study of how cortical activations can be understood as the output of a network of dynamical systems that are intimately related with the processes occurring in the brain. In order to do that, I performed five studies that encompass both the topological and the dynamical aspects of such functional brain networks. In this way, the Thesis is divided into three major parts: Introduction, Results and Discussion. In the first part, comprising Chapters 1, 2 and 3, I make an overview of the main concepts of Network Science related to the analysis of brain imaging. More specifically, Chapter 1 is devoted to introducing the reader to the world of complexity, specially to the topological and dynamical complexity of networked systems. Chapter 2 aims to develop the biological, topological and functional fundamentals of the brain when it is seen as a complex network. Next, Chapter 3 summarizes the main objectives and tasks that will be developed along the forthcoming Chapters. The second part of the Thesis is, in turn, its core, since it contains the results obtained along these last four years. This part is divided into five Chapters, containing a detailed version of the publications carried out during the Thesis. Chapter 4 is related to the topology of functional networks and, more specifically, to the detection and quantification of the leading nodes of the network: the hubs. In Chapter 5 I will show that functional brain networks can be viewed not as a single network, but as a network-of-networks, where its components have to co-exist in a trade-off situation. In this way, I investigate how the brain hemispheres compete for acquiring the centrality of the network-of-networks and how this interplay is maintained (or not) when failures are introduced in the functional network. Chapter 6 goes one step beyond by considering functional networks as living systems. In this Chapter I show how analyzing the evolution of the network topology instead of treating it as a static system allows to better characterize functional networks. This fact is especially relevant when trying to find differences between groups performing certain memory tasks, where functional networks have strong fluctuations. In Chapter 7 I define how to create parenclitic networks from brain imaging datasets. This new kind of networks, recently introduced to study abnormalities between control and anomalous groups, have not been implemented with brain datasets and I explain in this Chapter how to do it when evaluating the consistency of brain dynamics. To conclude with this part of the Thesis, Chapter 8 is devoted to the interplay between the topological properties of the nodes within a network and their dynamical features. As I will show, there is an interplay between them which reveals that the position of a node in a network is intimately related with its dynamical properties. Finally, the last part of this PhD Thesis is composed only by Chapter 9, which contains the conclusions and future perspectives that may arise from the exposed results. In view of all, I hope that reading this Thesis will give a complementary perspective of one of the most extraordinary complex systems: The human brain.

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.

Nontropic actions of neurotrophins: Subcortical nerve growth factor gene delivery reverses age-related degeneration of primate cortical cholinergic innervation

Normal aging is associated with a significant reduction in cognitive function across primate species. However, the structural and molecular basis for this age-related decline in neural function has yet to be defined clearly. Extensive cell loss does not occur as a consequence of normal aging in human and nonhuman primate species. More recent studies have demonstrated significant reductions in functional neuronal markers in subcortical brain regions in primates as a consequence of aging, including dopaminergic and cholinergic systems, although corresponding losses in cortical innervation from these neurons have not been investigated. In the present study, we report that aging is associated with a significant 25% reduction in cortical innervation by cholinergic systems in rhesus monkeys (P < 0.001). Further, these age-related reductions are ameliorated by cellular delivery of human nerve growth factor to cholinergic somata in the basal forebrain, restoring levels of cholinergic innervation in the cortex to those of young monkeys (P = 0.89). Thus, (i) aging is associated with a significant reduction in cortical cholinergic innervation; (ii) this reduction is reversible by growth-factor delivery; and (iii) growth factors can remodel axonal terminal fields at a distance, representing a nontropic action of growth factors in modulating adult neuronal structure and function (i.e., administration of growth factors to cholinergic somata significantly increases axon density in terminal fields). These findings are relevant to potential clinical uses of growth factors to treat neurological disorders.

Mice transgenically overexpressing sulfonylurea receptor 1 in forebrain resist seizure induction and excitotoxic neuron death

The ability of the sulfonylurea receptor (SUR) 1 to suppress seizures and excitotoxic neuron damage was assessed in mice transgenically overexpressing this receptor. Fertilized eggs from FVB mice were injected with a construct containing SUR cDNA and a calcium-calmodulin kinase IIα promoter. The resulting mice showed normal gross anatomy, brain morphology and histology, and locomotor and cognitive behavior. However, they overexpressed the SUR1 transgene, yielding a 9- to 12-fold increase in the density of [3H]glibenclamide binding to the cortex, hippocampus, and striatum. These mice resisted kainic acid-induced seizures, showing a 36% decrease in average maximum seizure intensity and a 75% survival rate at a dose that killed 53% of the wild-type mice. Kainic acid-treated transgenic mice showed no significant loss of hippocampal pyramidal neurons or expression of heat shock protein 70, whereas wild-type mice lost 68–79% of pyramidal neurons in the CA1–3 subfields and expressed high levels of heat shock protein 70 after kainate administration. These results indicate that the transgenic overexpression of SUR1 alone in forebrain structures significantly protects mice from seizures and neuronal damage without interfering with locomotor or cognitive function.

Hormone replacement therapy and everyday memory in mid-aged New Zealand women

While empirical research to date has generally supported positive effects of estrogen on verbal memory performance in women, the literature examining specific effects of Hormone Replacement Therapy (HRT) on cognitive functioning in mid-life women is more equivocal. The Rivermead Behavioural Memory Test-Extended Version (RBMT-E), a measure of everyday memory functioning in adults within an average range of cognitive functioning, was administered to a sample of 104 New Zealand women aged 40 to 60 years who had self-selected to either use or not use HRT (53 HRT users and 51 non-users). Self-report. measures of mood, stress, general health and menopausal symptoms were also administered. These variables, along with age and education level, were used in analyses of group differences on the everyday memory measures. Results showed significant differences between the groups for three sub-tests of the RBMT-E:'Story Immediate', 'Story Delayed', and 'Message Delayed'. Women who use HRT scored higher on these subtests than those who do not use HRT. After calculation of a total profile score (adjusting for age and IQ), HRT users score higher than HRT non-users on the RBMT-E overall measure of Everyday Memory. These pilot results suggest that HRT use in this sample-is related to enhanced verbal memory in everyday memory tasks and that the RBMT-E may be a useful tool for further work in this area of research.

Exploring the Etiology of the Association Between Birthweight and IQ in an Adolescent Twin Sample

The negative effects of very low birthweight on intellectual development have been well documented, and more recently this effect has been shown to generalise to birthweights within the normal range. In this study we investigate the etiology of this relationship by using a classical twin design to disentangle the contributions of genes and environment. A previous Dutch study (Boomsma et al., 2001) examining these effects indicated that genes were important in mediating the association of birthweight to full IQ measured at ages 7 and 10, but not at ages 5 and 12. Here the association between birthweight and IQ at age 16 is considered (N = 523 twin pairs). Using variance components modeling we found that the genetic variance in birthweight (4%) completely overlapped with that in verbal IQ but not performance or full IQ. Results further showed the importance of shared environmental effects on birthweight (similar to 60%) but not on IQ (with genes explaining up to 72% of IQ variance). Models incorporating a direction of causation parameter between birthweight and IQ provided adequate fit to the data in either causal direction for performance and full IQ, but the model with verbal 10 causing birthweight was preferred to one in which birthweight influenced verbal IQ. As the measurement of birthweight precedes the measurement of twins' IQ at age 16, the influence of verbal IQ might be better considered as a proxy for parents' 10 or education, and it is possible that brighter mothers provide better prenatal environments for their children.

Quality of life compared during pharmacological treatments and clinical monitoring for non-localized prostate cancer: a randomized controlled trial

To investigate the effects of different management strategies for non-localized prostate cancer on men's quality of life and cognitive functioning. Men with prostate cancer were randomly assigned to one of four treatment arms: leuprorelin, goserelin, cyproterone acetate (CPA), or close clinical monitoring. In a repeated-measures design, men were assessed before treatment (baseline) and after 6 and 12 months of treatment. A community comparison group of men of the same age with no prostate cancer participated for the same length of time. The men were recruited from public and private urology departments from university teaching hospitals. All those with prostate cancer who were eligible for hormonal therapy had no symptoms requiring immediate therapy. In all, 82 patients were randomized and 62 completed the 1-year study, and of the 20 community participants, 15 completed the study. The main outcome measures were obtained from questionnaires on emotional distress, existential satisfaction, physical function and symptoms, social and role function, subjective cognitive function, and sexual function, combined with standard neuropsychological tests of memory, attention, and executive functions. Sexual dysfunction increased for patients on androgen-suppressing therapies, and emotional distress increased in those assigned to CPA or close clinical monitoring. Compared with before treatment there was evidence of an adverse effect of leuprorelin, goserelin, and CPA on cognitive function. In deciding the timing of androgen suppression therapy for prostate cancer, consideration should be given to potential adverse effects on quality of life and cognitive function.

Caregiver burden, time spent caring and health status in the first 12 months following stroke

Objective: To quantify time caring, burden and health status in carers of stroke patients after discharge from rehabilitation; to identify the potentially modifiable sociodemographic and clinical characteristics associated with these outcomes. Methods: Patients and carers prospectively interviewed 6 (n = 71) and 12 (n = 57) months after discharge. Relationships of carer and patient variables with burden, health status and time analysed by Gaussian and Poisson regression. Results: Carers showed considerable burden at 6 and 12 months. Carers spent 4.6 and 3.6 hours per day assisting patients with daily activities at 6 and 12 months, respectively. Improved patient motor and cognitive function were associated with reductions of up to 20 minutes per day in time spent in daily activities. Better patient mental health and cognitive function were associated with better carer mental health. Conclusions: Potentially modifiable factors such as these may be able to be targeted by caregiver training, support and education programmes and outpatient therapy for patients.

Season of birth is associated with anthropometric and neurocognitive outcomes during infancy and childhood in a general population birth cohort

The 'season of birth' effect is one of the most consistently replicated associations in schizophrenia epidemiology. In contrast, the association between season of birth and development in the general Population is relatively poorly understood. The aim of this study was to explore the impact of season of birth on various anthropometric and neurocognitive variables from birth to age seven in a large, community-based birth cohort. A sample of white singleton infants born after 37 weeks gestation (n =22,123) was drawn from the US Collaborative Perinatal Project. Anthropometric variables (weight, head circumference, length/height) and various measures of neurocognitive development, were assessed at birth, 8 months, 4 and 7 years of age. Compared to surnmer/autumn born infants, winter/spring born infants were significantly longer at birth, and at age seven were significantly heavier, taller and had larger head circumference. Winter/spring born infants were achieving significantly higher scores on the Bayley Motor Score at 8 months, the Graham-Ernhart Block Test at age 4, the Wechsler Intelligence Performance and Full Scale scores at age 7, but had significantly lower scores on the Bender-Gestalt Test at age 7 years. Winter/spring birth, while associated with an increased risk of schizophrenia, is generally associated with superior outcomes with respect to physical and cognitive development. (c) 2005 Elsevier B.V. All rights reserved.