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.

The default mode network is functionally and structurally disrupted in amnestic mild cognitive impairment — a bimodal MEG–DTI study

Over the past years, several studies on Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) have reported Default Mode Network (DMN) deficits. This network is attracting increasing interest in the AD community, as it seems to play an important role in cognitive functioning and in beta amyloid deposition. Attention has been particularly drawn to how different DMN regions are connected using functional or structural connectivity. To this end, most studies have used functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET) or Diffusion Tensor Imaging (DTI). In this study we evaluated (1) functional connectivity from resting state magnetoencephalography (MEG) and (2) structural connectivity from DTI in 26 MCI patients and 31 age-matched controls. Compared to controls, the DMN in the MCI group was functionally disrupted in the alpha band, while no differences were found for delta, theta, beta and gamma frequency bands. In addition, structural disconnection could be assessed through a decreased fractional anisotropy along tracts connecting different DMN regions. This suggests that the DMN functional and anatomical disconnection could represent a core feature of MCI.

Tractographie de la matière blanche orientée par a priori anatomiques et microstructurels = White matter tractography guided by anatomical and microstructural priors

Résumé : L'imagerie par résonance magnétique pondérée en diffusion est une modalité unique sensible aux mouvements microscopiques des molécules d'eau dans les tissus biologiques. Il est possible d'utiliser les caractéristiques de ce mouvement pour inférer la structure macroscopique des faisceaux de la matière blanche du cerveau. La technique, appelée tractographie, est devenue l'outil de choix pour étudier cette structure de façon non invasive. Par exemple, la tractographie est utilisée en planification neurochirurgicale et pour le suivi du développement de maladies neurodégénératives. Dans cette thèse, nous exposons certains des biais introduits lors de reconstructions par tractographie, et des méthodes sont proposées pour les réduire. D'abord, nous utilisons des connaissances anatomiques a priori pour orienter la reconstruction. Ainsi, nous montrons que l'information anatomique sur la nature des tissus permet d'estimer des faisceaux anatomiquement plausibles et de réduire les biais dans l'estimation de structures complexes de la matière blanche. Ensuite, nous utilisons des connaissances microstructurelles a priori dans la reconstruction, afin de permettre à la tractographie de suivre le mouvement des molécules d'eau non seulement le long des faisceaux, mais aussi dans des milieux microstructurels spécifiques. La tractographie peut ainsi distinguer différents faisceaux, réduire les erreurs de reconstruction et permettre l'étude de la microstructure le long de la matière blanche. Somme toute, nous montrons que l'utilisation de connaissances anatomiques et microstructurelles a priori, en tractographie, augmente l'exactitude des reconstructions de la matière blanche du cerveau.

Análise das mudanças espaciais e temporais nos padrões de paisagem na região do oeste da Bahia sobre a formação do grupo urucuia (1988-2011) e suas implicações para a conservação do Cerrado

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós Graduação em Geografia, 2015.

Structural Integration of Tellurium Oxide into Mixed-Network-Former Glasses: Connectivity Distribution in the System NaPO3–TeO2

FSodium phosphate tellurite glasses in the system (NaPO3)(x)(TeO2)(1-x) were prepared and structurally characterized by thermal analysis, vibrational spectroscopy, X-ray photoelectron spectroscopy (XPS) and a variety of complementary solid-state nuclear magnetic resonance (NMR) techniques. Unlike the situation in other mixed-network-former glasses, the interaction between the two network formers tellurium oxide and phosphorus oxide produces no new structural units, and no sharing of the network modifier Na2O takes place. The glass structure can be regarded as a network of interlinked metaphosphate-type P(2) tetrahedral and TeO4/2 antiprismotic units. The combined interpretation of the O 1s XPS data and the P-31 solid-state NMR spectra presents clear quantitative evidence for a nonstatistical connectivity distribution. Rather the formation of homootomic P-O-P and Te-O-Te linkages is favored over mixed P-O-Te connectivities. As a consequence of this chemical segregation effect, the spatial sodium distribution is not random, as also indicated by a detailed analysis of P-31/No-23 rotational echo double-resonance (REDOR) experiments. ACHTUNGTRENUNG(TeO2)1 x were prepared and structurally characterized by thermal analysis,vibrat ional spectroscopy,X-ray photoelectron spectroscopy (XPS) and a variety of complementary solid-state nuclear magnetic resonance (NMR) techniques. Unlike the situation in other mixed-network-former glasses,the interaction between the two network formers tellurium oxide and phosphorus oxide produces no new structural units,and no sharing of the network modifier Na2O takes place. The glass structure can be regarded as a network of interlinked metaphosphate-type P(2) tetrahedral and TeO4/2 antiprismatic units. The combined interpretation of the O 1s XPS data and the 31P solid-state NMR spectra presents clear quantitative evidence for a nonstatistical connectivity distribution. Rather,the formation of homoatomic P O P and Te O Te linkages is favored over mixed P O Te connectivities. As a consequence of this chemical segregation effect,the spatial sodium distribution is not random,as also indicated by a detailed analysis of 31P/23Na rotational echo double-resonance (REDOR) experiments.

Cross-hemispheric collaboration and segregation associated with task difficulty as revealed by structural and functional connectivity.

Although it is known that brain regions in one hemisphere may interact very closely with their corresponding contralateral regions (collaboration) or operate relatively independent of them (segregation), the specific brain regions (where) and conditions (how) associated with collaboration or segregation are largely unknown. We investigated these issues using a split field-matching task in which participants matched the meaning of words or the visual features of faces presented to the same (unilateral) or to different (bilateral) visual fields. Matching difficulty was manipulated by varying the semantic similarity of words or the visual similarity of faces. We assessed the white matter using the fractional anisotropy (FA) measure provided by diffusion tensor imaging (DTI) and cross-hemispheric communication in terms of fMRI-based connectivity between homotopic pairs of cortical regions. For both perceptual and semantic matching, bilateral trials became faster than unilateral trials as difficulty increased (bilateral processing advantage, BPA). The study yielded three novel findings. First, whereas FA in anterior corpus callosum (genu) correlated with word-matching BPA, FA in posterior corpus callosum (splenium-occipital) correlated with face-matching BPA. Second, as matching difficulty intensified, cross-hemispheric functional connectivity (CFC) increased in domain-general frontopolar cortex (for both word and face matching) but decreased in domain-specific ventral temporal lobe regions (temporal pole for word matching and fusiform gyrus for face matching). Last, a mediation analysis linking DTI and fMRI data showed that CFC mediated the effect of callosal FA on BPA. These findings clarify the mechanisms by which the hemispheres interact to perform complex cognitive tasks.

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge.

Disparate connectivity for structural and functional networks is revealed when physical location of the connected nodes is considered

Macroscopic brain networks have been widely described with the manifold of metrics available using graph theory. However, most analyses do not incorporate information about the physical position of network nodes. Here, we provide a multimodal macroscopic network characterization while considering the physical positions of nodes. To do so, we examined anatomical and functional macroscopic brain networks in a sample of twenty healthy subjects. Anatomical networks are obtained with a graph based tractography algorithm from diffusion-weighted magnetic resonance images (DW-MRI). Anatomical con- nections identified via DW-MRI provided probabilistic constraints for determining the connectedness of 90 dif- ferent brain areas. Functional networks are derived from temporal linear correlations between blood-oxygenation level-dependent signals derived from the same brain areas. Rentian Scaling analysis, a technique adapted from very- large-scale integration circuits analyses, shows that func- tional networks are more random and less optimized than the anatomical networks. We also provide a new metric that allows quantifying the global connectivity arrange- ments for both structural and functional networks. While the functional networks show a higher contribution of inter-hemispheric connections, the anatomical networks highest connections are identified in a dorsal?ventral arrangement. These results indicate that anatomical and functional networks present different connectivity organi- zations that can only be identified when the physical locations of the nodes are included in the analysis.

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

Connectivity of workflow nets : the foundations of stepwise verification

Behavioral models capture operational principles of real-world or designed systems. Formally, each behavioral model defines the state space of a system, i.e., its states and the principles of state transitions. Such a model is the basis for analysis of the system’s properties. In practice, state spaces of systems are immense, which results in huge computational complexity for their analysis. Behavioral models are typically described as executable graphs, whose execution semantics encodes a state space. The structure theory of behavioral models studies the relations between the structure of a model and the properties of its state space. In this article, we use the connectivity property of graphs to achieve an efficient and extensive discovery of the compositional structure of behavioral models; behavioral models get stepwise decomposed into components with clear structural characteristics and inter-component relations. At each decomposition step, the discovered compositional structure of a model is used for reasoning on properties of the whole state space of the system. The approach is exemplified by means of a concrete behavioral model and verification criterion. That is, we analyze workflow nets, a well-established tool for modeling behavior of distributed systems, with respect to the soundness property, a basic correctness property of workflow nets. Stepwise verification allows the detection of violations of the soundness property by inspecting small portions of a model, thereby considerably reducing the amount of work to be done to perform soundness checks. Besides formal results, we also report on findings from applying our approach to an industry model collection.

The structure and connectivity of semantic memory in the healthy older adult brain

The anterior temporal lobes (ATLs) have been proposed to serve as a "hub" linking amodal or domain general information about the meaning of words, objects, facts and people distributed throughout the brain in semantic memory. The two primary sources of evidence supporting this proposal, viz. structural imaging studies in semantic dementia (SD) patients and functional imaging investigations, are not without problems. Similarly, knowledge about the anatomo-functional connectivity of semantic memory is limited to a handful of intra-operative electrocortical stimulation (IES) investigations in patients. Here, using principal components analyses (PCA) of a battery of conceptual and non-conceptual tests coupled with voxel based morphometry (VBM) and diffusion tensor imaging (DTI) in a sample of healthy older adults aged 55-85. years, we show that amodal semantic memory relies on a predominantly left lateralised network of grey matter regions involving the ATL, posterior temporal and posterior inferior parietal lobes, with prominent involvement of the left inferior fronto-occipital fasciculus (IFOF) and uncinate fasciculus fibre pathways. These results demonstrate relationships between semantic memory, brain structure and connectivity essential for human communication and cognition.

Scalar connectivity measures from fast-marching tractography reveal heritability of white matter architecture

Recent advances in diffusion-weighted MRI (DWI) have enabled studies of complex white matter tissue architecture in vivo. To date, the underlying influence of genetic and environmental factors in determining central nervous system connectivity has not been widely studied. In this work, we introduce new scalar connectivity measures based on a computationally-efficient fast-marching algorithm for quantitative tractography. We then calculate connectivity maps for a DTI dataset from 92 healthy adult twins and decompose the genetic and environmental contributions to the variance in these metrics using structural equation models. By combining these techniques, we generate the first maps to directly examine genetic and environmental contributions to brain connectivity in humans. Our approach is capable of extracting statistically significant measures of genetic and environmental contributions to neural connectivity.

Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.

Structural and functional characterization of dendritic arbors and GABAergic synaptic inputs on interneurons and principal cells in the rat basolateral amygdala

The basolateral amygdala (BLA) is a complex brain region associated with processing emotional states, such as fear, anxiety, and stress. Some aspects of these emotional states are driven by the network activity of synaptic connections, derived from both local circuitry and projections to the BLA from other regions. Although the synaptic physiology and general morphological characteristics are known for many individual cell types within the BLA, the combination of morphological, electrophysiological, and distribution of neurochemical GABAergic synapses in a three-dimensional neuronal arbor has not been reported for single neurons from this region. The aim of this study was to assess differences in morphological characteristics of BLA principal cells and interneurons, quantify the distribution of GABAergic neurochemical synapses within the entire neuronal arbor of each cell type, and determine whether GABAergic synaptic density correlates with electrophysiological recordings of inhibitory postsynaptic currents. We show that BLA principal neurons form complex dendritic arborizations, with proximal dendrites having fewer spines but higher densities of neurochemical GABAergic synapses compared with distal dendrites. Furthermore, we found that BLA interneurons exhibited reduced dendritic arbor lengths and spine densities but had significantly higher densities of putative GABAergic synapses compared with principal cells, which was correlated with an increased frequency of spontaneous inhibitory postsynaptic currents. The quantification of GABAergic connectivity, in combination with morphological and electrophysiological measurements of the BLA cell types, is the first step toward a greater understanding of how fear and stress lead to changes in morphology, local connectivity, and/or synaptic reorganization of the BLA.

Anisochrony of O-methylene protons of ethyl ester functions and structural factors in the connected chiral entities

(i) Incistrans pairs of cyclic 1,3-dicarboxylic acid ethyl esters thecis-foms exhibit higher O-methylene proton (HA, HB) anisochrony than thetrans-forms; (ii) anisochrony, easily observed in certain decalin-10-carboxylic ethyl esters, ‘disappears’ on one of the rings attaining the possibility of transforming into a ‘twist’ form; (iii) in certain pairs of chiralsecethyl esters and theirtert-methylated analogues anisochrony is higher in the latter, contrary to expectation, while, in certain others, the reverse is observed. Attempted explanations are based on assessments whether H A and H B are or are not in highly different magnetic environments in confomers regarded as preferred. This subsumes the possibility thatXYZC-CO2H A H B Me chiral ethyl acetates differ fromXYZC-CH A H B Me ethanes because intervention by the carboxyl group insulates the prochiral centre and allows anisotropic effects to gain somewhat in importance among mechanisms that discriminate between H A and H B so long as rotamerpopulation inequalities persist. Background information on why rotamer-population inequalities will always persist and on a heuristic that attempts to generalize the effects ofXYZ inXYZC - CU AUB V is provided. Possible effects when connectivity exists between a pair amongX, Y, Z or when specific interactions occur betweenV andX, Y orZ are considered. An interpretation in terms of ‘increasing conformational mobility’ has been suggested for the observed increase in the rate of temperature-dependence of O-methylene anisochrony down a series of chiral ethyl esters.