Redox dysregulation in schizophrenia : implication for oligodendrocyte maturation and structural connectivity

Schizophrenia, which results from an interaction between gene and environmental factors, is a psychiatric disorder characterized by reality distortion. The clinical symptoms, which are generally diagnosed in late adolescence or early adulthood, partly derive from altered brain connectivity especially in prefrontal cortex. Disruption of neuronal networks implies oligodendrocyte and myelin abnormalities in schizophrenia pathophysiology. The mechanisms of these impairments are still unclear. Converging evidences indicate a role of redox dysregulation, generated by an imbalance between pro-oxidants and antioxidant defense mechanisms, in the development of schizophrenia pathophysiology. In particular, genetic and biochemical data indicate impaired synthesis of glutathione, the main cellular antioxidant and redox regulator. As oligodendrocyte maturation is dependent on redox state, we evaluated whether abnormal redox control could contribute to oligodendrocyte and myelin impairments in schizophrenia. We found that glutathione in prefrontal cortex of early psychosis patients and control subjects positively correlated with white matter integrity. We then further explored the interplay between glutathione and myelin using a translational approach. Our data showed that in mice with genetically impaired glutathione synthesis, oligodendrocyte late maturation as well as myelination was delayed in the anterior cingulate cortex. Specifically, oligodendrocyte number and myelin levels were lowered at peripubertal age, coincident in time with the peak of myelin- related gene expression during normal brain development. These data suggest that early adolescence is a vulnerable developmental period during which an adequate redox control is required for oligodendrocyte maturation and active myelination process. Consistently, oxidative stress mediated by psychosocial stress also delayed myelination in peripubertal mice. At cellular levels, impaired glutathione synthesis altered oligodendrocyte development at several levels. Using oligodendrocyte progenitor cells cultures, our data showed that glutathione deficiency was associated with (i) cell cycle arrest and a reduction in oligodendrocyte proliferation, and (ii) an impairment in oligodendrocyte maturation. Abnormal oligodendrocyte proliferation was mediated by upregulation of Fyn kinase activity. Consistently, under oxidative stress conditions, we observed abnormal regulation of Fyn kinase in fibroblasts of patients deficient in glutathione synthesis. Together, our data support that a redox dysregulation due to glutathione deficit could underlie myelination impairment in schizophrenia, possibly mediated by dysregulated Fyn pathway. Better characterization of Fyn mechanisms would pave the way towards new drug targets. -- La schizophrénie est une maladie psychiatrique qui se définit par une distorsion de la perception de la réalité. Les symptômes cliniques sont généralement diagnostiqués durant l'adolescence ou au début de l'âge adulte et proviennent de troubles de la connectivité, principalement au niveau du cortex préfrontal. Les dysfonctionnements des réseaux neuronaux impliquent des anomalies au niveau des oligodendrocytes et de la myéline dans la pathophysiologie de la schizophrénie. Les mécanismes responsables des ces altérations restent encore mal compris. Dans le développement de la schizophrénie, des évidences mettent en avant un rôle de la dérégulation rédox, traduit par un déséquilibre entre facteurs pro-oxydants et défenses antioxydantes. Des données génétiques et biochimiques indiquent notamment un défaut de la synthèse du glutathion, le principal antioxydant et rédox régulateur des cellules. Etant donné que la maturation des oligodendrocytes est dépendante de l'état rédox, nous avons regardé si une dérégulation rédox contribue aux anomalies de la myéline dans le cadre de la schizophrénie. Dans le cortex préfrontal des sujets contrôles et des patients en phase précoce de psychose, nous avons montré que le glutathion était positivement associé à l'intégrité de matière blanche. Afin d'explorer plus en détail la relation entre le glutathion et la myéline, nous avons mené une étude translationnelle. Nos résultats ont montré que des souris ayant un déficit de la synthèse du glutathion présentaient un retard dans les processus de maturation des oligodendrocytes et de la myélinisation dans le cortex cingulaire antérieure. Plus précisément, le nombre d'oligodendrocytes et le taux de myéline étaient uniquement diminués durant la période péripubertaire. Cette même période correspond au pic de l'expression des gènes en lien avec la myéline. Ces données soulignent le fait que l'adolescence est une période du développement particulièrement sensible durant laquelle un contrôle adéquat de l'état rédox est nécessaire aux processus de maturation des oligodendrocytes et de myélinisation. Ceci est en accord avec la diminution de myéline observée suite à un stress oxydatif généré par un stress psychosocial. Au niveau cellulaire, un déficit du glutathion affecte le développement des oligodendrocytes à différents stades. En effet, dans des cultures de progéniteurs d'oligodendrocytes, nos résultats montrent qu'une réduction du taux de glutathion était associée à (i) un arrêt du cycle cellulaire ainsi qu'une diminution de la prolifération des oligodendrocytes, et à (ii) des dysfonctionnements de la maturation des oligodendrocytes. Par ailleurs, au niveau moléculaire, les perturbations de la prolifération étaient générées par une augmentation de l'activité de la kinase Fyn. Ceci est en accord avec la dérégulation de Fyn observée dans les fibroblastes de patients ayant une déficience en synthèse du glutathion en condition de stress oxydatif. Les résultats de cette thèse soulignent qu'une dérégulation rédox induite par un déficit en glutathion peut contribuer aux anomalies des oligodendrocytes et de la myéline via le dysfonctionnement des voies de signalisation Fyn. Une recherche plus avancée de l'implication de Fyn dans la maladie pourrait ouvrir la voie à de nouvelles cibles thérapeutiques.

Extreme prematurity and intra uterine growth restriction effects in brain network topology at school age

Higher risk for long-term behavioral and emotional sequelae, with attentional problems (with or without hyperactivity) is now becoming one of the hallmarks of extreme premature (EP) birth and birth after pregancy conditions leading to poor intra uterine growth restriction (IUGR) [1,2]. However, little is know so far about the neurostructural basis of these complexe brain functional abnormalities that seem to have their origins in early critical periods of brain development. The development of cortical axonal pathways happens in a series of sequential events. The preterm phase (24-36 post conecptional weeks PCW) is known for being crucial for growth of the thalamocortical fiber bundles as well as for the development of long projectional, commisural and projectional fibers [3]. Is it logical to expect, thus, that being exposed to altered intrauterine environment (altered nutrition) or to extrauterine environment earlier that expected, lead to alterations in the structural organization and, consequently, alter the underlying white matter (WM) structure. Understanding rate and variability of normal brain development, and detect differences from typical development may offer insight into the neurodevelopmental anomalies that can be imaged at later stages. Due to its unique ability to non-invasively visualize and quantify in vivo white matter tracts in the brain, in this study we used diffusion MRI (dMRI) tractography to derive brain graphs [4,5,6]. This relatively simple way of modeling the brain enable us to use graph theory to study topological properties of brain graphs in order to study the effects of EP and IUGR on childrens brain connectivity at age 6 years old.

Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity.

Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc; our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (<0.11Hz) of region-averaged time series, and a pattern recognition technique was used to learn a discriminant function indicating which particular functional connections are most affected by disease. Classification performance using strict cross-validation yielded a sensitivity of 82% (above chance at p<0.005) and specificity of 86% (p<0.01) to distinguish between MS patients and controls. The most discriminative connectivity changes were found in subcortical and temporal regions, and contralateral connections were more discriminative than ipsilateral connections. The pattern of decreased discriminative connections can be summarized post hoc in an index that correlates positively (ρ=0.61) with white matter lesion load, possibly indicating functional reorganisation to cope with increasing lesion load. These results are consistent with a subtle but widespread impact of lesions in white matter and in gray matter structures serving as high-level integrative hubs. These findings suggest that predictive models of resting state fMRI can reveal specific anomalies due to MS with high sensitivity and specificity, potentially leading to new non-invasive markers.

Microtubule-associated protein 6 mediates neuronal connectivity through Semaphorin 3E-dependent signalling for axonal growth.

Structural microtubule associated proteins (MAPs) stabilize microtubules, a property that was thought to be essential for development, maintenance and function of neuronal circuits. However, deletion of the structural MAPs in mice does not lead to major neurodevelopment defects. Here we demonstrate a role for MAP6 in brain wiring that is independent of microtubule binding. We find that MAP6 deletion disrupts brain connectivity and is associated with a lack of post-commissural fornix fibres. MAP6 contributes to fornix development by regulating axonal elongation induced by Semaphorin 3E. We show that MAP6 acts downstream of receptor activation through a mechanism that requires a proline-rich domain distinct from its microtubule-stabilizing domains. We also show that MAP6 directly binds to SH3 domain proteins known to be involved in neurite extension and semaphorin function. We conclude that MAP6 is critical to interface guidance molecules with intracellular signalling effectors during the development of cerebral axon tracts.

Simulation-based evaluation of susceptibility distortion correction methods in diffusion MRI for connectivity analysis

Connectivity analysis on diffusion MRI data of the whole- brain suffers from distortions caused by the standard echo- planar imaging acquisition strategies. These images show characteristic geometrical deformations and signal destruction that are an important drawback limiting the success of tractography algorithms. Several retrospective correction techniques are readily available. In this work, we use a digital phantom designed for the evaluation of connectivity pipelines. We subject the phantom to a âeurooetheoretically correctâeuro and plausible deformation that resembles the artifact under investigation. We correct data back, with three standard methodologies (namely fieldmap-based, reversed encoding-based, and registration- based). Finally, we rank the methods based on their geometrical accuracy, the dropout compensation, and their impact on the resulting connectivity matrices.

Connectivity and tissue microstructural alterations in right and left temporal lobe epilepsy revealed by diffusion spectrum imaging.

Focal epilepsy is increasingly recognized as the result of an altered brain network, both on the structural and functional levels and the characterization of these widespread brain alterations is crucial for our understanding of the clinical manifestation of seizure and cognitive deficits as well as for the management of candidates to epilepsy surgery. Tractography based on Diffusion Tensor Imaging allows non-invasive mapping of white matter tracts in vivo. Recently, diffusion spectrum imaging (DSI), based on an increased number of diffusion directions and intensities, has improved the sensitivity of tractography, notably with respect to the problem of fiber crossing and recent developments allow acquisition times compatible with clinical application. We used DSI and parcellation of the gray matter in regions of interest to build whole-brain connectivity matrices describing the mutual connections between cortical and subcortical regions in patients with focal epilepsy and healthy controls. In addition, the high angular and radial resolution of DSI allowed us to evaluate also some of the biophysical compartment models, to better understand the cause of the changes in diffusion anisotropy. Global connectivity, hub architecture and regional connectivity patterns were altered in TLE patients and showed different characteristics in RTLE vs LTLE with stronger abnormalities in RTLE. The microstructural analysis suggested that disturbed axonal density contributed more than fiber orientation to the connectivity changes affecting the temporal lobes whereas fiber orientation changes were more involved in extratemporal lobe changes. Our study provides further structural evidence that RTLE and LTLE are not symmetrical entities and DSI-based imaging could help investigate the microstructural correlate of these imaging abnormalities.

Redox dysregulation in schizophrenia : effect on myelination of cortical structures and connectivity

Cette thèse traite du rôle qu'un facteur de risque génétique développé chez les patients souffrant de schizophrénie, à savoir un déficit de la synthèse du glutathion, peut jouer dans les anomalies de la connectivité cérébrale trouvées chez ces patients. L'essentiel du travail a été consacré à évaluer la structure de la substance blanche dans l'ensemble du cerveau chez un modèle animal par une méthode similaire à celle utilisée en recherche clinique avec l'imagerie par résonance magnétique (IRM). Cette approche de translation inverse chez la souris knock-out de glutamate-cystéine ligase modulateur sous-unité (Gclm KO), avait l'objectif d'étudier l'effet des défenses redox déficientes sur le développement des connexions cérébrales, tout en excluant celui des facteurs non liés au génotype. Après avoir établi le protocole de recherche, l'influence d'une manipulation environnementale a également été étudiée. Pour effectuer une analyse statistique fiable des données d'IRM obtenues, nous .avons d'abord créé un atlas du cerveau de la souris afin de l'utiliser comme modèle pour une segmentation précise des différentes régions du cerveau sur les images IRM obtenues in vivo. Les données provenant de chaque région d'intérêt ont ensuite été étudiées séparément. La qualité de cette méthode a été évaluée dans une expérience de simulation pour déduire la puissance statistique réalisable dans chaque région en fonction du nombre d'animaux utilisés. Ces outils d'analyse nous ont permis d'évaluer l'intégrité de la substance blanche dans le cerveau des souris durant le développement grâce à une expérience longitudinale, en utilisant l'imagerie du tenseur de diffusion (DTI). Nous avons ainsi observé des anomalies dans les paramètres dérivés du tenseur (diffusivité et anisotropie) dans la Commissure Antérieure et le Fimbria/Fornix des souris Gclm KO, par rapport aux animaux contrôles. Ces résultats suggèrent une substance blanche endommagée dans ces régions. Dans une expérience électrophysiologique, Pascal Steullet a montré que ces anomalies ont des conséquences fonctionnelles caractérisées par une réduction de la vitesse de conduction dans les fibres nerveuses. Ces données renforcent les conclusions des analyses d'imagerie. Le mécanisme par lequel une dérégulation redox affecte la structure de la substance blanche reste encore à définir, car une analyse immunohistochimique des protéines constituantes de la couche de myéline des fibres concernées n'a pas donné de résultats concluants. Nous avons également constaté un élargissement des ventricules dans les jeunes souris Gclm KO, mais pas chez les adultes et des anomalies neurochimiques déjà connues chez ces animaux (Duarte et al. 2011), à savoir une réduction du Glutathion et une augmentation de l'acide N-acétylaspartique, de l'Alanine et du ratio Glutamine/Glutamate. Nous avons ensuite testé l'effet d'un stress environnemental supplémentaire, l'élevage en isolement social, sur le phénotype. Ce stress n'a eu aucun effet sur la structure de la substance blanche évaluée par DTI, mais a réduit la concentration de myo-Inositol et augmenté le ratio de Glutamine/Glutamate dans le cortex frontal. Nous avons aussi reproduit dans ce groupe indépendant d'animaux les effets du génotype sur le profil neurochimique, sur la taille des ventricules et aussi sur les paramètres dérivés du tenseur de diffusion dans le Fimbria/Fornix, mais pas dans la Commissure Antérieure. Nos résultats montrent qu'une dérégulation redox d'origine génétique perturbe la structure et la fonction de la substance blanche dans des régions spécifiques, causant ainsi l'élargissement des ventricules. Ces phénotypes rassemblent certaines caractéristiques neuro-anatomiques de la schizophrénie, mais les mécanismes qui en sont responsables demeurent encore inconnus. L'isolement social n'a pas d'effet sur la structure de la substance blanche évaluée par DTI, alors qu'il est prouvé qu'il affecte la maturation des oligodendrocytes. La neurochimie corticale et en particulier le rapport Glutamine/Glutamate a été affecté par le dérèglement redox ainsi que par l'isolement social. En conséquence, ce ratio représente un indice prometteur dans la recherche sur l'interaction du stress environnemental avec le déséquilibre redox dans le domaine de la schizophrénie. -- The present doctoral thesis is concerned with the role that a genetic risk factor for the development of schizophrenia, namely a deficit in Glutathione synthesis, may play in the anomalies of brain connectivity found in patients. Most of the effort was devoted to perform a whole-brain assessment of white matter structure in the Glutamate-Cysteine ligase modulatory knockout mouse model (Gclm KO) using Magnetic Resonance Imaging (MRI) techniques similar to those used in state-of-the-art clinical research. Such reverse translational approach taking brain imaging from the bedside to the bench aimed to investigate the role that deficient redox defenses may play in the development of brain connections while excluding all influencing factors beside the genotype. After establishing the protocol, the influence of further environmental manipulations was also studied. Analysis of MRI images acquired in vivo was one of the main challenges of the project. Our strategy consisted in creating an atlas of the mouse brain to use as segmentation guide and then analyze the data from each region of interest separately. The quality of the method was assessed in a simulation experiment by calculating the statistical power achievable in each brain region at different sample sizes. This analysis tool enabled us to assess white matter integrity in the mouse brain along development in a longitudinal experiment using Diffusion Tensor Imaging (DTI). We discovered anomalies in diffusivity parameters derived from the tensor in the Anterior Commissure and Fimbria/Fornix of Gclm KO mice when compared to wild-type animals, which suggest that the structure of these tracts is compromised in the KO mice. In an elegant electrophysiological experiment, Pascal Steullet has provided evidence that these anomalies have functional consequences in form of reduced conduction velocity in the concerned tracts, thus supporting the DTI findings. The mechanism by which redox dysregulation affects WM structure remains unknown, for the immunohistochemical analysis of myelin constituent proteins in the concerned tracts produced inconclusive results. Our experiments also detected an enlargement of the lateral ventricles in young but not adult Gclm KO mice and confirmed neurochemical anomalies already known to affect this animals (Duarte et al. 2011), namely a reduction in Glutathione and an increase in Glutamine/Glutamate ratio, N-acetylaspartate and Alanine. Using the same methods, we tested the effect of an additional environmental stress on the observed phenotype: rearing in social isolation had no effect on white matter structure as assessed by DTI, but it reduced the concentration of myo-Inositol and increased the Glutamine/Glutamate ratio in the frontal cortex. We could also replicate in this separate group of animals the effects of genotype on the frontal neurochemical profile, ventricular size and diffusivity parameters in the Fimbria/Fornix but not in the Anterior Commissure. Our data show that a redox dysregulation of genetic origin may disrupt white matter structure and function in specific tracts and cause a ventricular enlargement, phenotypes that resemble some neuroanatomical features of schizophrenia. The mechanism responsible remains however unknown. We have also demonstrated that environmental stress in form of social isolation does not affect white matter structure as assessed by DTI even though it is known to affect oligodendrocyte maturation. Cortical neurochemistry, and specifically the Glutamine to Glutamate balance was affected both by redox dysregulation and social isolation, and is thus a good target for further research on the interaction of redox imbalance and environmental stress in schizophrenia.

Learning about brain physiology and complexity from the study of the epilepsies

The brain is a complex system, which produces emergent properties such as those associated with activity-dependent plasticity in processes of learning and memory. Therefore, understanding the integrated structures and functions of the brain is well beyond the scope of either superficial or extremely reductionistic approaches. Although a combination of zoom-in and zoom-out strategies is desirable when the brain is studied, constructing the appropriate interfaces to connect all levels of analysis is one of the most difficult challenges of contemporary neuroscience. Is it possible to build appropriate models of brain function and dysfunctions with computational tools? Among the best-known brain dysfunctions, epilepsies are neurological syndromes that reach a variety of networks, from widespread anatomical brain circuits to local molecular environments. One logical question would be: are those complex brain networks always producing maladaptive emergent properties compatible with epileptogenic substrates? The present review will deal with this question and will try to answer it by illustrating several points from the literature and from our laboratory data, with examples at the behavioral, electrophysiological, cellular and molecular levels. We conclude that, because the brain is a complex system compatible with the production of emergent properties, including plasticity, its functions should be approached using an integrated view. Concepts such as brain networks, graphics theory, neuroinformatics, and e-neuroscience are discussed as new transdisciplinary approaches dealing with the continuous growth of information about brain physiology and its dysfunctions. The epilepsies are discussed as neurobiological models of complex systems displaying maladaptive plasticity.

Repeatability analysis of global and local metrics of brain structural networks

Computational network analysis provides new methods to analyze the human connectome. Brain structural networks can be characterized by global and local metrics that recently gave promising insights for diagnosis and further understanding of neurological, psychiatric and neurodegenerative disorders. In order to ensure the validity of results in clinical settings the precision and repeatability of the networks and the associated metrics must be evaluated. In the present study, nineteen healthy subjects underwent two consecutive measurements enabling us to test reproducibility of the brain network and its global and local metrics. As it is known that the network topology depends on the network density, the effects of setting a common density threshold for all networks were also assessed. Results showed good to excellent repeatability for global metrics, while for local metrics it was more variable and some metrics were found to have locally poor repeatability. Moreover, between subjects differences were slightly inflated when the density was not fixed. At the global level, these findings confirm previous results on the validity of global network metrics as clinical biomarkers. However, the new results in our work indicate that the remaining variability at the local level as well as the effect of methodological characteristics on the network topology should be considered in the analysis of brain structural networks and especially in networks comparisons.

Cognitive rehabilitation of working memory in juvenile multiple sclerosis-effects on cognitive functioning, functional MRI and network related connectivity.

PURPOSE To assess possible effects of working memory (WM) training on cognitive functionality, functional MRI and brain connectivity in patients with juvenile MS. METHODS Cognitive status, fMRI and inter-network connectivity were assessed in 5 cases with juvenile MS aged between 12 and 18 years. Afterwards they received a computerized WM training for four weeks. Primary cognitive outcome measures were WM (visual and verbal) and alertness. Activation patterns related to WM were assessed during fMRI using an N-Back task with increasing difficulty. Inter-network connectivity analyses were focused on fronto-parietal (left and right), default-mode (dorsal and ventral) and the anterior salience network. Cognitive functioning, fMRI and inter-network connectivity were reassessed directly after the training and again nine months following training. RESULTS Response to treatment was seen in two patients. These patients showed increased performance in WM and alertness after the training. These behavioural changes were accompanied by increased WM network activation and systematic changes in inter-network connectivity. The remaining participants were non-responders to treatment. Effects on cognitive performance were maintained up to nine months after training, whereas effects observed by fMRI disappeared. CONCLUSIONS Responders revealed training effects on all applied outcome measures. Disease activity and general intelligence may be factors associated with response to treatment.

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.

Functional and effective connectivity in MEG. Application to the study of epilepsy

Nuestro cerebro contiene cerca de 1014 sinapsis neuronales. Esta enorme cantidad de conexiones proporciona un entorno ideal donde distintos grupos de neuronas se sincronizan transitoriamente para provocar la aparición de funciones cognitivas, como la percepción, el aprendizaje o el pensamiento. Comprender la organización de esta compleja red cerebral en base a datos neurofisiológicos, representa uno de los desafíos más importantes y emocionantes en el campo de la neurociencia. Se han propuesto recientemente varias medidas para evaluar cómo se comunican las diferentes partes del cerebro a diversas escalas (células individuales, columnas corticales, o áreas cerebrales). Podemos clasificarlos, según su simetría, en dos grupos: por una parte, la medidas simétricas, como la correlación, la coherencia o la sincronización de fase, que evalúan la conectividad funcional (FC); mientras que las medidas asimétricas, como la causalidad de Granger o transferencia de entropía, son capaces de detectar la dirección de la interacción, lo que denominamos conectividad efectiva (EC). En la neurociencia moderna ha aumentado el interés por el estudio de las redes funcionales cerebrales, en gran medida debido a la aparición de estos nuevos algoritmos que permiten analizar la interdependencia entre señales temporales, además de la emergente teoría de redes complejas y la introducción de técnicas novedosas, como la magnetoencefalografía (MEG), para registrar datos neurofisiológicos con gran resolución. Sin embargo, nos hallamos ante un campo novedoso que presenta aun varias cuestiones metodológicas sin resolver, algunas de las cuales trataran de abordarse en esta tesis. En primer lugar, el creciente número de aproximaciones para determinar la existencia de FC/EC entre dos o más señales temporales, junto con la complejidad matemática de las herramientas de análisis, hacen deseable organizarlas todas en un paquete software intuitivo y fácil de usar. Aquí presento HERMES (http://hermes.ctb.upm.es), una toolbox en MatlabR, diseñada precisamente con este fin. Creo que esta herramienta será de gran ayuda para todos aquellos investigadores que trabajen en el campo emergente del análisis de conectividad cerebral y supondrá un gran valor para la comunidad científica. La segunda cuestión practica que se aborda es el estudio de la sensibilidad a las fuentes cerebrales profundas a través de dos tipos de sensores MEG: gradiómetros planares y magnetómetros, esta aproximación además se combina con un enfoque metodológico, utilizando dos índices de sincronización de fase: phase locking value (PLV) y phase lag index (PLI), este ultimo menos sensible a efecto la conducción volumen. Por lo tanto, se compara su comportamiento al estudiar las redes cerebrales, obteniendo que magnetómetros y PLV presentan, respectivamente, redes más densamente conectadas que gradiómetros planares y PLI, por los valores artificiales que crea el problema de la conducción de volumen. Sin embargo, cuando se trata de caracterizar redes epilépticas, el PLV ofrece mejores resultados, debido a la gran dispersión de las redes obtenidas con PLI. El análisis de redes complejas ha proporcionado nuevos conceptos que mejoran caracterización de la interacción de sistemas dinámicos. Se considera que una red está compuesta por nodos, que simbolizan sistemas, cuyas interacciones se representan por enlaces, y su comportamiento y topología puede caracterizarse por un elevado número de medidas. Existe evidencia teórica y empírica de que muchas de ellas están fuertemente correlacionadas entre sí. Por lo tanto, se ha conseguido seleccionar un pequeño grupo que caracteriza eficazmente estas redes, y condensa la información redundante. Para el análisis de redes funcionales, la selección de un umbral adecuado para decidir si un determinado valor de conectividad de la matriz de FC es significativo y debe ser incluido para un análisis posterior, se convierte en un paso crucial. En esta tesis, se han obtenido resultados más precisos al utilizar un test de subrogadas, basado en los datos, para evaluar individualmente cada uno de los enlaces, que al establecer a priori un umbral fijo para la densidad de conexiones. Finalmente, todas estas cuestiones se han aplicado al estudio de la epilepsia, caso práctico en el que se analizan las redes funcionales MEG, en estado de reposo, de dos grupos de pacientes epilépticos (generalizada idiopática y focal frontal) en comparación con sujetos control sanos. La epilepsia es uno de los trastornos neurológicos más comunes, con más de 55 millones de afectados en el mundo. Esta enfermedad se caracteriza por la predisposición a generar ataques epilépticos de actividad neuronal anormal y excesiva o bien síncrona, y por tanto, es el escenario perfecto para este tipo de análisis al tiempo que presenta un gran interés tanto desde el punto de vista clínico como de investigación. Los resultados manifiestan alteraciones especificas en la conectividad y un cambio en la topología de las redes en cerebros epilépticos, desplazando la importancia del ‘foco’ a la ‘red’, enfoque que va adquiriendo relevancia en las investigaciones recientes sobre epilepsia. ABSTRACT There are about 1014 neuronal synapses in the human brain. This huge number of connections provides the substrate for neuronal ensembles to become transiently synchronized, producing the emergence of cognitive functions such as perception, learning or thinking. Understanding the complex brain network organization on the basis of neuroimaging data represents one of the most important and exciting challenges for systems neuroscience. Several measures have been recently proposed to evaluate at various scales (single cells, cortical columns, or brain areas) how the different parts of the brain communicate. We can classify them, according to their symmetry, into two groups: symmetric measures, such as correlation, coherence or phase synchronization indexes, evaluate functional connectivity (FC); and on the other hand, the asymmetric ones, such as Granger causality or transfer entropy, are able to detect effective connectivity (EC) revealing the direction of the interaction. In modern neurosciences, the interest in functional brain networks has increased strongly with the onset of new algorithms to study interdependence between time series, the advent of modern complex network theory and the introduction of powerful techniques to record neurophysiological data, such as magnetoencephalography (MEG). However, when analyzing neurophysiological data with this approach several questions arise. In this thesis, I intend to tackle some of the practical open problems in the field. First of all, the increase in the number of time series analysis algorithms to study brain FC/EC, along with their mathematical complexity, creates the necessity of arranging them into a single, unified toolbox that allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of them. I developed such a toolbox for this aim, it is named HERMES (http://hermes.ctb.upm.es), and encompasses several of the most common indexes for the assessment of FC and EC running for MatlabR environment. I believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis and will entail a great value for the scientific community. The second important practical issue tackled in this thesis is the evaluation of the sensitivity to deep brain sources of two different MEG sensors: planar gradiometers and magnetometers, in combination with the related methodological approach, using two phase synchronization indexes: phase locking value (PLV) y phase lag index (PLI), the latter one being less sensitive to volume conduction effect. Thus, I compared their performance when studying brain networks, obtaining that magnetometer sensors and PLV presented higher artificial values as compared with planar gradiometers and PLI respectively. However, when it came to characterize epileptic networks it was the PLV which gives better results, as PLI FC networks where very sparse. Complex network analysis has provided new concepts which improved characterization of interacting dynamical systems. With this background, networks could be considered composed of nodes, symbolizing systems, whose interactions with each other are represented by edges. A growing number of network measures is been applied in network analysis. However, there is theoretical and empirical evidence that many of these indexes are strongly correlated with each other. Therefore, in this thesis I reduced them to a small set, which could more efficiently characterize networks. Within this framework, selecting an appropriate threshold to decide whether a certain connectivity value of the FC matrix is significant and should be included in the network analysis becomes a crucial step, in this thesis, I used the surrogate data tests to make an individual data-driven evaluation of each of the edges significance and confirmed more accurate results than when just setting to a fixed value the density of connections. All these methodologies were applied to the study of epilepsy, analysing resting state MEG functional networks, in two groups of epileptic patients (generalized and focal epilepsy) that were compared to matching control subjects. Epilepsy is one of the most common neurological disorders, with more than 55 million people affected worldwide, characterized by its predisposition to generate epileptic seizures of abnormal excessive or synchronous neuronal activity, and thus, this scenario and analysis, present a great interest from both the clinical and the research perspective. Results revealed specific disruptions in connectivity and network topology and evidenced that networks’ topology is changed in epileptic brains, supporting the shift from ‘focus’ to ‘networks’ which is gaining importance in modern epilepsy research.

Neuroanatomic-based detection algorithm for automatic labeling of brain structures in brain injury

The number and grade of injured neuroanatomic structures and the type of injury determine the degree of impairment after a brain injury event and the recovery options of the patient. However, the body of knowledge and clinical intervention guides are basically focused on functional disorder and they still do not take into account the location of injuries. The prognostic value of location information is not known in detail either. This paper proposes a feature-based detection algorithm, named Neuroanatomic-Based Detection Algorithm (NBDA), based on SURF (Speeded Up Robust Feature) to label anatomical brain structures on cortical and sub-cortical areas. Themain goal is to register injured neuroanatomic structures to generate a database containing patient?s structural impairment profile. This kind of information permits to establish a relation with functional disorders and the prognostic evolution during neurorehabilitation procedures.

Cognitive reserve is associated with the functional organization of brain networks in healthy aging: a MEG study

The proportion of elderly people in the population has increased rapidly in the last century and consequently "healthy aging" is expected to become a critical area of research in neuroscience. Evidence reveals how healthy aging depends on three main behavioral factors: social lifestyle, cognitive activity and physical activity. In this study, we focused on the role of cognitive activity, concentrating specifically on educational and occupational attainment factors, which were considered two of the main pillars of cognitive reserve. 21 subjects with similar rates of social lifestyle, physical and cognitive activity were selected from a sample of 55 healthy adults. These subjects were divided into two groups according to their level of cognitive reserve; one group comprised subjects with high cognitive reserve (9 members) and the other contained those with low cognitive reserve (12 members). To evaluate the cortical brain connectivity network, all participants were recorded by Magnetoencephalography (MEG) while they performed a memory task (modified version of the Sternberg¿s Task). We then applied two algorithms (Phase Locking Value & Phase-Lag Index) to study the dynamics of functional connectivity. In response to the same task, the subjects with lower cognitive reserve presented higher functional connectivity than those with higher cognitive reserve. These results may indicate that participants with low cognitive reserve needed a greater 'effort' than those with high cognitive reserve to achieve the same level of cognitive performance. Therefore, we conclude that cognitive reserve contributes to the modulation of the functional connectivity patterns of the aging brain.

Post-mortem CT and MR brain imaging of putrefied corpses

INTRODUCTION Putrefaction of the brain is a challenge to a forensic pathologist because it may lead to considerable organ alterations and restrict documenting reliable autopsy findings. OBJECTIVES This study aims to present a new and systematic evaluation of possible benefits of post-mortem MR Neuroimaging (1.5 Tesla, sequences: T1w, T2w) in putrefied corpses in comparison to PMCT and autopsy. METHODS A post-mortem MRI brain examination was conducted on 35 adult, putrefied corpses after performing a whole body CT scan prior to a forensic autopsy. Imaging data and autopsy findings were compared with regard to brain symmetry, gray and white matter junction, ventricular system, basal ganglia, cerebellum, brain stem, and possible pathological findings. RESULTS At autopsy, a reliable assessment of the anatomical brain structures was often restricted. MR imaging offered an assessment of the anatomical brain structures, even at advanced stages of putrefaction. In two cases, MR imaging revealed pathological findings that were detectable neither by CT scans nor at autopsy. CONCLUSIONS Post-mortem MR imaging of putrefied brains offers the possibility to assess brain morphology, even if the brain is liquefied. Post-mortem MR imaging of the brain should be considered if the assessment of a putrefied brain is crucial to the evaluation of a forensic autopsy case.