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.

Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations.

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.

ConnectomeViewer - Multi-Modal Multi-Level Network Visualization and Analysis.

Introduction: The field of Connectomic research is growing rapidly, resulting from methodological advances in structural neuroimaging on many spatial scales. Especially progress in Diffusion MRI data acquisition and processing made available macroscopic structural connectivity maps in vivo through Connectome Mapping Pipelines (Hagmann et al, 2008) into so-called Connectomes (Hagmann 2005, Sporns et al, 2005). They exhibit both spatial and topological information that constrain functional imaging studies and are relevant in their interpretation. The need for a special-purpose software tool for both clinical researchers and neuroscientists to support investigations of such connectome data has grown. Methods: We developed the ConnectomeViewer, a powerful, extensible software tool for visualization and analysis in connectomic research. It uses the novel defined container-like Connectome File Format, specifying networks (GraphML), surfaces (Gifti), volumes (Nifti), track data (TrackVis) and metadata. Usage of Python as programming language allows it to by cross-platform and have access to a multitude of scientific libraries. Results: Using a flexible plugin architecture, it is possible to enhance functionality for specific purposes easily. Following features are already implemented: * Ready usage of libraries, e.g. for complex network analysis (NetworkX) and data plotting (Matplotlib). More brain connectivity measures will be implemented in a future release (Rubinov et al, 2009). * 3D View of networks with node positioning based on corresponding ROI surface patch. Other layouts possible. * Picking functionality to select nodes, select edges, get more node information (ConnectomeWiki), toggle surface representations * Interactive thresholding and modality selection of edge properties using filters * Arbitrary metadata can be stored for networks, thereby allowing e.g. group-based analysis or meta-analysis. * Python Shell for scripting. Application data is exposed and can be modified or used for further post-processing. * Visualization pipelines using filters and modules can be composed with Mayavi (Ramachandran et al, 2008). * Interface to TrackVis to visualize track data. Selected nodes are converted to ROIs for fiber filtering The Connectome Mapping Pipeline (Hagmann et al, 2008) processed 20 healthy subjects into an average Connectome dataset. The Figures show the ConnectomeViewer user interface using this dataset. Connections are shown that occur in all 20 subjects. The dataset is freely available from the homepage (connectomeviewer.org). Conclusions: The ConnectomeViewer is a cross-platform, open-source software tool that provides extensive visualization and analysis capabilities for connectomic research. It has a modular architecture, integrates relevant datatypes and is completely scriptable. Visit www.connectomics.org to get involved as user or developer.

Functional and topological characterization of transcriptional cooperativity in yeast

This work was supported by grants from Spanish Ministry of Science andInnovation (MICINN) BIO2011-22568 & BIO2008-205.

Investigation of high-resolution functional magnetic resonance imaging by means of surface and array radiofrequency coils at 7 T.

In this investigation, high-resolution, 1x1x1-mm(3) functional magnetic resonance imaging (fMRI) at 7 T is performed using a multichannel array head coil and a surface coil approach. Scan geometry was optimized for each coil separately to exploit the strengths of both coils. Acquisitions with the surface coil focused on partial brain coverage, while whole-brain coverage fMRI experiments were performed with the array head coil. BOLD sensitivity in the occipital lobe was found to be higher with the surface coil than with the head array, suggesting that restriction of signal detection to the area of interest may be beneficial for localized activation studies. Performing independent component analysis (ICA) decomposition of the fMRI data, we consistently detected BOLD signal changes and resting state networks. In the surface coil data, a small negative BOLD response could be detected in these resting state network areas. Also in the data acquired with the surface coil, two distinct components of the positive BOLD signal were consistently observed. These two components were tentatively assigned to tissue and venous signal changes.

Gene Promoter Evolution Targets the Center of the Human Protein Interaction Network

Assessing the contribution of promoters and coding sequences to gene evolution is an important step toward discovering the major genetic determinants of human evolution. Many specific examples have revealed the evolutionary importance of cis-regulatory regions. However, the relative contribution of regulatory and coding regions to the evolutionary process and whether systemic factors differentially influence their evolution remains unclear. To address these questions, we carried out an analysis at the genome scale to identify signatures of positive selection in human proximal promoters. Next, we examined whether genes with positively selected promoters (Prom+ genes) show systemic differences with respect to a set of genes with positively selected protein-coding regions (Cod+ genes). We found that the number of genes in each set was not significantly different (8.1% and 8.5%, respectively). Furthermore, a functional analysis showed that, in both cases, positive selection affects almost all biological processes and only a few genes of each group are located in enriched categories, indicating that promoters and coding regions are not evolutionarily specialized with respect to gene function. On the other hand, we show that the topology of the human protein network has a different influence on the molecular evolution of proximal promoters and coding regions. Notably, Prom+ genes have an unexpectedly high centrality when compared with a reference distribution (P = 0.008, for Eigenvalue centrality). Moreover, the frequency of Prom+ genes increases from the periphery to the center of the protein network (P = 0.02, for the logistic regression coefficient). This means that gene centrality does not constrain the evolution of proximal promoters, unlike the case with coding regions, and further indicates that the evolution of proximal promoters is more efficient in the center of the protein network than in the periphery. These results show that proximal promoters have had a systemic contribution to human evolution by increasing the participation of central genes in the evolutionary process.

EEG-based functional networks in schizophrenia.

Schizophrenia is often considered as a dysconnection syndrome in which, abnormal interactions between large-scale functional brain networks result in cognitive and perceptual deficits. In this article we apply the graph theoretic measures to brain functional networks based on the resting EEGs of fourteen schizophrenic patients in comparison with those of fourteen matched control subjects. The networks were extracted from common-average-referenced EEG time-series through partial and unpartial cross-correlation methods. Unpartial correlation detects functional connectivity based on direct and/or indirect links, while partial correlation allows one to ignore indirect links. We quantified the network properties with the graph metrics, including mall-worldness, vulnerability, modularity, assortativity, and synchronizability. The schizophrenic patients showed method-specific and frequency-specific changes especially pronounced for modularity, assortativity, and synchronizability measures. However, the differences between schizophrenia patients and normal controls in terms of graph theory metrics were stronger for the unpartial correlation method.

Subjective mental time: the functional architecture of projecting the self to past and future.

Abstract Human experience takes place in the line of mental time (MT) created through 'self-projection' of oneself to different time-points in the past or future. Here we manipulated self-projection in MT not only with respect to one's life events but also with respect to one's faces from different past and future time-points. Behavioural and event-related functional magnetic resonance imaging activity showed three independent effects characterized by (i) similarity between past recollection and future imagination, (ii) facilitation of judgements related to the future as compared with the past, and (iii) facilitation of judgements related to time-points distant from the present. These effects were found with respect to faces and events, and also suggest that brain mechanisms of MT are independent of whether actual life episodes have to be re-experienced or pre-experienced, recruiting a common cerebral network including the anteromedial temporal, posterior parietal, inferior frontal, temporo-parietal and insular cortices. These behavioural and neural data suggest that self-projection in time is a fundamental aspect of MT, relying on neural structures encoding memory, mental imagery and self.

Regulation of the AKAP-LBC signaling complex : molecular characterization and functional implications

RÉSUMÉ Les protéines d'ancrage de la protéine kinase A (AKAPs) constituent une grande famille de protéines qui ciblent la protéine kinase A (PKA) à proximité de ses substrats physiologiques pour assurer leur régulation. Une nouvelle protéine de cette famille, appelée AKAP-Lbc, a été récemment caractérisée et fonctionne comme un facteur d'échange de nucléotides guanine (GEF) pour la petite GTPase Rho. AKAP-Lbc est régulée par différents signaux qui activent et désactivent son activité Rho-GEF. Son activation est assurée par la sous-unité alpha de la protéine G hétérotrimérique G12, tandis que son inhibition dépend de son interaction avec la PKA et 14-3-3. AKAP-Lbc est principalement exprimée dans le coeur et pourrait réguler des processus importants tels que l'hypertrophie et la différenciation des cardiomyocytes. Ainsi, il est crucial d'élucider les mécanismes moléculaires impliqués dans la régulation de son activité Rho-GEF. Le but général de ce travail de thèse est la caractérisation de deux nouveaux mécanismes impliqués dans la régulation de l'activité de AKAP-Lbc. Le premier mécanisme consiste en la régulation de l'activité de AKAP-Lbc par son homo-oligomérisation. Mes travaux montrent que l'homo-oligomérisation maintient AKAP-Lbc inactive, dans une conformation permettant à la PKA ancrée et à 14-3-3 d'exercer leur effet inhibiteur sur l'activité de AKAP-Lbc. Le second mécanisme concerne la régulation de l'activité de AKAP-Lbc via une nouvelle interaction entre AKAP-Lbc et la protéine LC3. LC3 joue un rôle crucial dans l'autophagie, un processus cellulaire qui adresse les protéines cytoplasmiques au lysosome pour leur dégradation. Ce mécanisme est particulièrement important pour le survie des cardiomyocytes durant les périodes d'absence de nutriments. Mes travaux mettent en évidence que LC3 inhibe l'activité Rho-GEF de AKAP-Lbc, ce qui suggère que, au-delà son rôle bien établi dans l'autophagie, LC3 participerait à la régulation de la signalisation de Rho. Prises ensembles, ces études contribuent à comprendre comment le complexe de signalisation formé par AKAP-Lbc régule la signalisation de Rho dans les cellules. Au-delà de leur intérêt au niveau biochimique, ces travaux pourraient aussi contribuer à élucider les réseaux de signalisation qui régulent des phénomènes physiologiques dans le coeur. ABSTRACT A-kinase anchoring proteins (AKAPs) are a group of functionally related proteins, which target the cAMP dependent protein kinase A (PKA) in close proximity to its physiological substrates for ensuring their regulation. A novel PKA anchoring protein, termed AKAP-Lbc, has been recently characterized, which also functions as a guanine nucleotide exchange factor (GEF) for the small GTPase Rho. AKAP-Lbc is regulated in a bi-directional manner by signals which activate or deactivate its Rho-GEF activity. Activation is mediated by the alpha subunit of the heterotrimeric G protein G12, whereas inhibition occurs following its interaction with PKA and 14-3-3. AKAP-Lbc is predominantly expressed in the heart and might regulate important processes such as hypertrophy and differentiation of cardiomyocytes. Therefore ít is crucial to elucidate the molecular mechanisms involved in the regulation of the Rho-GEF activity of AKAP-Lbc. The general aim of the present thesis work is the characterization of two novel molecular mechanisms involved in the regulation of the Rho-GEF activity of AKAP-Lbc. The first mechanism consists of the. regulation of AKAP-Lbc activity through its homooligomerization. I report here that homo-oligomerization maintains AKAP-Lbc inactive, under a conformation suitable for ensuring the inhibitory effect of anchored PKA and 14-33 on AKAP-Lbc activity. The second mechanism concerns the regulation of AKAP-Lbc activity through a novel interaction between AKAP-Lbc and ubiquitin-like protein LC3. LC3 is a key mediator of autophagy, which is a cellular process that targets cytosolic proteins to the lysosome for degradation. This process is particularly important for cardiomyocyte survival during conditions of nutrient starvation. Here, I show that LC3 is a negative regulator of the Rho-GEF activity of AKAP-Lbc, which suggests that, beyond its well established role in autophagy, LC3 can participate in the regulation of Rho signaling in cells. Overall, these findings contribute to understand how the AKAP-Lbc signaling complex can regulate the Rho signaling in cells. Beyond its interest at the biochemical level, this work might also contribute to elucidate the signaling network that regulate physiological events in the heart.

Revisiting sodium and water reabsorption with functional genomics tools.

PURPOSE OF REVIEW: The kidney plays an essential role in maintaining sodium and water balance, thereby controlling the volume and osmolarity of the extracellular body fluids, the blood volume and the blood pressure. The final adjustment of sodium and water reabsorption in the kidney takes place in cells of the distal part of the nephron in which a set of apical and basolateral transporters participate in vectorial sodium and water transport from the tubular lumen to the interstitium and, finally, to the general circulation. According to a current model, the activity and/or cell-surface expression of these transporters is/are under the control of a gene network composed of the hormonally regulated, as well as constitutively expressed, genes. It is proposed that this gene network may include new candidate genes for salt- and water-losing syndromes and for salt-sensitive hypertension. A new generation of functional genomics techniques have recently been applied to the characterization of this gene network. The purpose of this review is to summarize these studies and to discuss the potential of the different techniques for characterization of the renal transcriptome. RECENT FINDINGS: Recently, DNA microarrays and serial analysis of gene expression have been applied to characterize the kidney transcriptome in different in-vivo and in-vitro models. In these studies, a set of new interesting genes potentially involved in the regulation of sodium and water reabsorption by the kidney have been identified and are currently under detailed investigation. SUMMARY: Characterization of the kidney transcriptome is greatly expanding our knowledge of the gene networks involved in multiple kidney functions, including the maintenance of sodium and water homeostasis.

Brain functional abnormality in schizo-affective disorder: an fMRI study.

Background.Schizo-affective disorder has not been studied to any significant extent using functional imaging. The aim of this study was to examine patterns of brain activation and deactivation in patients meeting strict diagnostic criteria for the disorder. METHOD: Thirty-two patients meeting research diagnostic criteria (RDC) for schizo-affective disorder (16 schizomanic and 16 schizodepressive) and 32 matched healthy controls underwent functional magnetic resonance imaging (fMRI) during performance of the n-back task. Linear models were used to obtain maps of activations and deactivations in the groups. RESULTS: Controls showed activation in a network of frontal and other areas and also deactivation in the medial frontal cortex, the precuneus and the parietal cortex. Schizo-affective patients activated significantly less in prefrontal, parietal and temporal regions than the controls, and also showed failure of deactivation in the medial frontal cortex. When task performance was controlled for, the reduced activation in the dorsolateral prefrontal cortex (DLPFC) and the failure of deactivation of the medial frontal cortex remained significant. CONCLUSIONS: Schizo-affective disorder shows a similar pattern of reduced frontal activation to schizophrenia. The disorder is also characterized by failure of deactivation suggestive of default mode network dysfunction.

Brain functional abnormality in schizo-affective disorder: an fMRI study.

Background.Schizo-affective disorder has not been studied to any significant extent using functional imaging. The aim of this study was to examine patterns of brain activation and deactivation in patients meeting strict diagnostic criteria for the disorder. METHOD: Thirty-two patients meeting research diagnostic criteria (RDC) for schizo-affective disorder (16 schizomanic and 16 schizodepressive) and 32 matched healthy controls underwent functional magnetic resonance imaging (fMRI) during performance of the n-back task. Linear models were used to obtain maps of activations and deactivations in the groups. RESULTS: Controls showed activation in a network of frontal and other areas and also deactivation in the medial frontal cortex, the precuneus and the parietal cortex. Schizo-affective patients activated significantly less in prefrontal, parietal and temporal regions than the controls, and also showed failure of deactivation in the medial frontal cortex. When task performance was controlled for, the reduced activation in the dorsolateral prefrontal cortex (DLPFC) and the failure of deactivation of the medial frontal cortex remained significant. CONCLUSIONS: Schizo-affective disorder shows a similar pattern of reduced frontal activation to schizophrenia. The disorder is also characterized by failure of deactivation suggestive of default mode network dysfunction.

Brain functional abnormality in schizo-affective disorder: an fMRI study.

Background.Schizo-affective disorder has not been studied to any significant extent using functional imaging. The aim of this study was to examine patterns of brain activation and deactivation in patients meeting strict diagnostic criteria for the disorder. METHOD: Thirty-two patients meeting research diagnostic criteria (RDC) for schizo-affective disorder (16 schizomanic and 16 schizodepressive) and 32 matched healthy controls underwent functional magnetic resonance imaging (fMRI) during performance of the n-back task. Linear models were used to obtain maps of activations and deactivations in the groups. RESULTS: Controls showed activation in a network of frontal and other areas and also deactivation in the medial frontal cortex, the precuneus and the parietal cortex. Schizo-affective patients activated significantly less in prefrontal, parietal and temporal regions than the controls, and also showed failure of deactivation in the medial frontal cortex. When task performance was controlled for, the reduced activation in the dorsolateral prefrontal cortex (DLPFC) and the failure of deactivation of the medial frontal cortex remained significant. CONCLUSIONS: Schizo-affective disorder shows a similar pattern of reduced frontal activation to schizophrenia. The disorder is also characterized by failure of deactivation suggestive of default mode network dysfunction.

Functional connectivity of reward processing in the brain

Controversial results have been reported concerning the neural mechanisms involved in the processing of rewards and punishments. On the one hand, there is evidence suggesting that monetary gains and losses activate a similar fronto-subcortical network. On the other hand, results of recent studies imply that reward and punishment may engage distinct neural mechanisms. Using functional magnetic resonance imaging (fMRI) we investigated both regional and interregional functional connectivity patterns while participants performed a gambling task featuring unexpectedly high monetary gains and losses. Classical univariate statistical analysis showed that monetary gains and losses activated a similar fronto-striatallimbic network, in which main activation peaks were observed bilaterally in the ventral striatum. Functional connectivity analysis showed similar responses for gain and loss conditions in the insular cortex, the amygdala, and the hippocampus that correlated with the activity observed in the seed region ventral striatum, with the connectivity to the amygdala appearing more pronounced after losses. Larger functional connectivity was found to the medial orbitofrontal cortex for negative outcomes. The fact that different functional patterns were obtained with both analyses suggests that the brain activations observed in the classical univariate approach identifi es the involvement of different functional networks in the current task. These results stress the importance of studying functional connectivity in addition to standard fMRI analysis in reward-related studies.

Predicting human resting-state functional connectivity from structural connectivity

In the cerebral cortex, the activity levels of neuronal populations are continuously fluctuating. When neuronal activity, as measured using functional MRI (fMRI), is temporally coherent across 2 populations, those populations are said to be functionally connected. Functional connectivity has previously been shown to correlate with structural (anatomical) connectivity patterns at an aggregate level. In the present study we investigate, with the aid of computational modeling, whether systems-level properties of functional networks-including their spatial statistics and their persistence across time-can be accounted for by properties of the underlying anatomical network. We measured resting state functional connectivity (using fMRI) and structural connectivity (using diffusion spectrum imaging tractography) in the same individuals at high resolution. Structural connectivity then provided the couplings for a model of macroscopic cortical dynamics. In both model and data, we observed (i) that strong functional connections commonly exist between regions with no direct structural connection, rendering the inference of structural connectivity from functional connectivity impractical; (ii) that indirect connections and interregional distance accounted for some of the variance in functional connectivity that was unexplained by direct structural connectivity; and (iii) that resting-state functional connectivity exhibits variability within and across both scanning sessions and model runs. These empirical and modeling results demonstrate that although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex.