Where there's smoke, there's fire : the brain reactivity of chronic smokers to anti-smoking stimuli

Contexte: Plusieurs études ont démontré que les indices environnementaux associés à la cigarette peuvent provoquer des envies de consommer (« cravings ») chez les fumeurs, ce qui nuit aux efforts d’abandon de la substance et favorise le maintien du tabagisme. Un bon nombre d’études en imagerie cérébrale ont examiné les bases neurophysiologiques de cette caractéristique clinique. Le tabagisme se caractérise aussi par l’incapacité des représentations négatives de la consommation (méfaits médicaux et sociaux) d’influencer la consommation des fumeurs. Étonnamment toutefois, très peu de travaux de recherche se sont intéressés à examiner les bases neurophysiologiques de cette insouciance envers les méfaits de la cigarette chez les fumeurs. En utilisant l'imagerie cérébrale fonctionnelle, l'objectif de cette étude était: d’examiner la réponse neurophysiologique des fumeurs chroniques à des images qui illustrent les effets négatifs de la cigarette (campagne anti-tabac); d’examiner le caractère affectif de cette réactivité utilisant des conditions contrôles (c.-à-d., images aversives non-liées au tabac et appétitives liées au tabac); d'examiner la connectivité fonctionnelle durant cette tâche entre les systèmes affectifs et exécutifs (une interaction qui peut favoriser ou entraver l'impact des évènements aversifs). Méthodes: 30 fumeurs chroniques ont passé une session de neuroimagerie durant laquelle ils devaient regarder des images appétitives et aversives de cigarettes, des images aversives non-reliées au tabac et des images neutres. Résultats: Les images aversives liés au tabagisme suscitent une plus grande activation dans le cortex médial préfrontal, l'amygdale, le gyrus frontal inférieur et le cortex orbitofrontal latéral en comparaison avec les images neutres, mais une moins grande activation dans des structures médiaux / sous-corticales comparé aux images aversives non-reliés et images appétitives reliées aux tabac. L’activité du système exécutif présente une connectivité fonctionnelle négative avec le système affectif lorsque les images aversives sont liées au tabac, mais pas quand elles ne le sont pas. Conclusions: Le modèle d'activation du cerveau observé suggère qu’il y a un biais dans la réactivité des fumeurs chroniques lorsqu’ils observent des représentations négatives de la consommation du tabac. L’activité du système exécutif cérébral semble promouvoir chez les fumeurs une baisse d’activité dans des régions impliquées dans la genèse d’une réponse physiologique affective; il s’agit d’un mécanisme qui permettrait de réduire l’impact persuasif de ces représentations des méfaits de la cigarette sur la consommation des fumeurs.

Mécanismes cellulaires et moléculaires impliqués dans le développement des synapses GABAergiques périsomatiques et dans la plasticité corticale : rôle de l’activité neuronale et de proBDNF/p75NTR

Dans le cortex visuel des mammifères, une cellule à panier (BC) qui représente un sous-type majoritaire d’interneurones GABAergiques, innerve une centaine de neurones par une multitude de synapses localisées sur le soma et sur les dendrites proximales de chacune de ses cibles. De plus, ces cellules sont importantes pour la génération des rythmes gammas, qui régulent de nombreuses fonctions cognitives, et pour la régulation de la plasticité corticale. Bien que la fonction des BC au sein des réseaux corticaux est à l'étude, les mécanismes qui contrôlent le développement de leur arborisation complexe ainsi que de leurs nombreux contacts synaptiques n’ont pas été entièrement déterminés. En utilisant les récepteurs allatostatines couplés aux protéines G de la drosophile (AlstR), nous démontrons in vitro que la réduction de l'excitation ainsi que la réduction de la libération des neurotransmetteurs par les BCs corticales individuelles des souris, diminuent le nombre de cellules innervées sans modifier le patron d'innervation périsomatique, durant et après la phase de prolifération des synapses périsomatiques. Inversement, lors de la suppression complète de la libération des neurotransmetteurs par les BCs individuelles avec l’utilisation de la chaîne légère de la toxine tétanus, nous observons des effets contraires selon le stade de développement. Les BCs exprimant TeNT-Lc pendant la phase de prolifération sont caractérisées par des arborisations axonales plus denses et un nombre accru de petits boutons homogènes autour des somas innervés. Toutefois, les cellules transfectées avec TeNT-Lc après la phase de la prolifération forment une innervation périsomatique avec moins de branchements terminaux d’axones et un nombre réduit de boutons avec une taille irrégulière autour des somas innervés. Nos résultats révèlent le rôle spécifique des niveaux de l’activité neuronale et de la neurotransmission dans l'établissement du territoire synaptique des cellules GABAergiques corticaux. Le facteur neurotrophique dérivé du cerveau (BDNF) est un modulateur puissant de la maturation activité-dépendante des synapses GABAergiques. Grâce à l'activation et à la signalisation de son récepteur tyrosine kinase B (TrkB), la liaison de mBDNF module fortement la prolifération des synapses périsomatiques GABAergiques formés par les BCs. Par contre, le rôle du récepteur neurotrophique de faible affinité, p75NTR, dans le développement du territoire synaptique des cellules reste encore inconnu. Dans ce projet, nous démontrons que la suppression de p75NTR au niveau des BCs individuelles in vitro provenant de souris p75NTRlox induit la formation d'une innervation périsomatique exubérante. BDNF est synthétisé sous une forme précurseur, proBDNF, qui est par la suite clivée par des enzymes, y compris la plasmine activée par tPA, pour produire une forme mature de BDNF (m)BDNF. mBDNF et proBDNF se lient avec une forte affinité à TrkB et p75NTR, respectivement. Nos résultats démontrent qu’un traitement des cultures organotypiques avec la forme résistante au clivage de proBDNF (mut-proBDNF) réduit fortement le territoire synaptique des BCs. Les cultures traitées avec le peptide PPACK, qui inactive tPA, ou avec tPA altèrent et favorisent respectivement la maturation de l’innervation synaptique des BCs. Nous démontrons aussi que l’innervation exubérante formée par les BCs p75NTR-/- n’est pas affectée par un traitement avec mut-proBDNF. L’ensemble de ces résultats suggère que l'activation de p75NTR via proBDNF régule négativement le territoire synaptique des BCs corticaux. Nous avons ensuite examiné si mut-proBDNF affecte l’innervation périsomatique formée par les BCs in vivo, chez la souris adulte. Nous avons constaté que les boutons GABAergiques périsomatiques sont significativement diminués dans le cortex infusé avec mut-proBDNF par rapport à l’hémisphère non-infusé ou traité avec de la saline. En outre, la plasticité de la dominance oculaire (OD) est rétablie par ce traitement chez la souris adulte. Enfin, en utilisant des souris qui ne possèdent pas le récepteur p75NTR dans leurs BCs spécifiquement, nous avons démontré que l'activation de p75NTR via proBDNF est nécessaire pour induire la plasticité de la OD chez les souris adultes. L’ensemble de ces résultats démontre un rôle critique de l'activation de p75NTR dans la régulation et le maintien de la connectivité des circuits GABAergiques, qui commencent lors du développement postnatal précoce jusqu’à l'âge adulte. De plus, nous suggérons que l'activation contrôlée de p75NTR pourrait être un outil utile pour restaurer la plasticité dans le cortex adulte.

Further characterization of muscarinic agonist-induced epileptiform bursting activity in immature rat piriform cortex, in vitro

The characteristics of muscarinic acetylcholine receptor agonist-induced epileptiform bursting seen in immature rat piriform cortex slices in vitro were further investigated using intracellular recording, with particular focus on its postnatal age-dependence (P+14-P+30), pharmacology, site(s) of origin and the likely contribution of the muscarinic acetylcholine receptor agonist-induced post-stimulus slow afterdepolarization and gap junction functionality toward its generation. The muscarinic agonist, oxotremorine-M (10 microM), induced rhythmic bursting only in immature piriform cortex slices; however, paroxysmal depolarizing shift amplitude, burst duration and burst incidence were inversely related to postnatal age. No significant age-dependent changes in neuronal membrane properties or postsynaptic muscarinic responsiveness accounted for this decline. Burst incidence was higher when recorded in anterior and posterior regions of the immature piriform cortex. In adult and immature neurones, oxotremorine-M effects were abolished by M1-, but not M2-muscarinic acetylcholine receptor-selective antagonists. Rostrocaudal lesions, between piriform cortex layers I and II, or layer III and endopiriform nucleus in adult or immature slices did not influence oxotremorine-M effects; however, the slow afterdepolarization in adult (but not immature) lesioned slices was abolished. Gap junction blockers (carbenoxolone or octanol) disrupted muscarinic bursting and diminished the slow afterdepolarization in immature slices, suggesting that gap junction connectivity was important for bursting. Our data show that neural networks within layers II-III function as primary oscillatory circuits for burst initiation in immature rat piriform cortex during persistent muscarinic receptor activation. Furthermore, we propose that muscarinic slow afterdepolarization induction and gap junction communication could contribute towards the increased epileptiform susceptibility of this brain area.

A management architecture for active networks

In this paper we present an architecture for network and applications management, which is based on the Active Networks paradigm and shows the advantages of network programmability. The stimulus to develop this architecture arises from an actual need to manage a cluster of active nodes, where it is often required to redeploy network assets and modify nodes connectivity. In our architecture, a remote front-end of the managing entity allows the operator to design new network topologies, to check the status of the nodes and to configure them. Moreover, the proposed framework allows to explore an active network, to monitor the active applications, to query each node and to install programmable traps. In order to take advantage of the Active Networks technology, we introduce active SNMP-like MIBs and agents, which are dynamic and programmable. The programmable management agents make tracing distributed applications a feasible task. We propose a general framework that can inter-operate with any active execution environment. In this framework, both the manager and the monitor front-ends communicate with an active node (the Active Network Access Point) through the XML language. A gateway service performs the translation of the queries from XML to an active packet language and injects the code in the network. We demonstrate the implementation of an active network gateway for PLAN (Packet Language for Active Networks) in a forty active nodes testbed. Finally, we discuss an application of the active management architecture to detect the causes of network failures by tracing network events in time.

Effectiveness of landmark analysis for establishing locality in p2p networks

Locality to other nodes on a peer-to-peer overlay network can be established by means of a set of landmarks shared among the participating nodes. Each node independently collects a set of latency measures to landmark nodes, which are used as a multi-dimensional feature vector. Each peer node uses the feature vector to generate a unique scalar index which is correlated to its topological locality. A popular dimensionality reduction technique is the space filling Hilbert’s curve, as it possesses good locality preserving properties. However, there exists little comparison between Hilbert’s curve and other techniques for dimensionality reduction. This work carries out a quantitative analysis of their properties. Linear and non-linear techniques for scaling the landmark vectors to a single dimension are investigated. Hilbert’s curve, Sammon’s mapping and Principal Component Analysis have been used to generate a 1d space with locality preserving properties. This work provides empirical evidence to support the use of Hilbert’s curve in the context of locality preservation when generating peer identifiers by means of landmark vector analysis. A comparative analysis is carried out with an artificial 2d network model and with a realistic network topology model with a typical power-law distribution of node connectivity in the Internet. Nearest neighbour analysis confirms Hilbert’s curve to be very effective in both artificial and realistic network topologies. Nevertheless, the results in the realistic network model show that there is scope for improvements and better techniques to preserve locality information are required.

Reduced capacity to sustain positive emotion in major depression reflects diminished maintenance of fronto-striatal brain activation

Anhedonia, the loss of pleasure or interest in previously rewarding stimuli, is a core feature of major depression. While theorists have argued that anhedonia reflects a reduced capacity to experience pleasure, evidence is mixed as to whether anhedonia is caused by a reduction in hedonic capacity. An alternative explanation is that anhedonia is due to the inability to sustain positive affect across time. Using positive images, we used an emotion regulation task to test whether individuals with depression are unable to sustain activation in neural circuits underlying positive affect and reward. While up-regulating positive affect, depressed individuals failed to sustain nucleus accumbens activity over time compared with controls. This decreased capacity was related to individual differences in self-reported positive affect. Connectivity analyses further implicated the fronto-striatal network in anhedonia. These findings support the hypothesis that anhedonia in depressed patients reflects the inability to sustain engagement of structures involved in positive affect and reward.

Controlling a mobile robot with a biological brain

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Some recent research is ongoing in which biological neurons are being cultured and trained to act as the brain of an interactive real world robot�thereby either completely replacing, or operating in a cooperative fashion with, a computer system. Studying such hybrid systems can provide distinct insights into the operation of biological neural structures, and therefore, such research has immediate medical implications as well as enormous potential in robotics. The main aim of the research is to assess the computational and learning capacity of dissociated cultured neuronal networks. A hybrid system incorporating closed-loop control of a mobile robot by a dissociated culture of neurons has been created. The system is flexible and allows for closed-loop operation, either with hardware robot or its software simulation. The paper provides an overview of the problem area, gives an idea of the breadth of present ongoing research, establises a new system architecture and, as an example, reports on the results of conducted experiments with real-life robots.

Assembly rules for protein networks derived from phylogenetic-statistical analysis of whole genomes

Background: We report an analysis of a protein network of functionally linked proteins, identified from a phylogenetic statistical analysis of complete eukaryotic genomes. Phylogenetic methods identify pairs of proteins that co-evolve on a phylogenetic tree, and have been shown to have a high probability of correctly identifying known functional links. Results: The eukaryotic correlated evolution network we derive displays the familiar power law scaling of connectivity. We introduce the use of explicit phylogenetic methods to reconstruct the ancestral presence or absence of proteins at the interior nodes of a phylogeny of eukaryote species. We find that the connectivity distribution of proteins at the point they arise on the tree and join the network follows a power law, as does the connectivity distribution of proteins at the time they are lost from the network. Proteins resident in the network acquire connections over time, but we find no evidence that 'preferential attachment' - the phenomenon of newly acquired connections in the network being more likely to be made to proteins with large numbers of connections - influences the network structure. We derive a 'variable rate of attachment' model in which proteins vary in their propensity to form network interactions independently of how many connections they have or of the total number of connections in the network, and show how this model can produce apparent power-law scaling without preferential attachment. Conclusion: A few simple rules can explain the topological structure and evolutionary changes to protein-interaction networks: most change is concentrated in satellite proteins of low connectivity and small phenotypic effect, and proteins differ in their propensity to form attachments. Given these rules of assembly, power law scaled networks naturally emerge from simple principles of selection, yielding protein interaction networks that retain a high-degree of robustness on short time scales and evolvability on longer evolutionary time scales.

Simulating global properties of electroencephalograms with minimal random neural networks

The human electroencephalogram (EEG) is globally characterized by a 1/f power spectrum superimposed with certain peaks, whereby the "alpha peak" in a frequency range of 8-14 Hz is the most prominent one for relaxed states of wakefulness. We present simulations of a minimal dynamical network model of leaky integrator neurons attached to the nodes of an evolving directed and weighted random graph (an Erdos-Renyi graph). We derive a model of the dendritic field potential (DFP) for the neurons leading to a simulated EEG that describes the global activity of the network. Depending on the network size, we find an oscillatory transition of the simulated EEG when the network reaches a critical connectivity. This transition, indicated by a suitably defined order parameter, is reflected by a sudden change of the network's topology when super-cycles are formed from merging isolated loops. After the oscillatory transition, the power spectra of simulated EEG time series exhibit a 1/f continuum superimposed with certain peaks. (c) 2007 Elsevier B.V. All rights reserved.

Prediction of Parkinson's disease tremor onset using artificial neural networks

In this paper we present the initial results using an artificial neural network to predict the onset of Parkinson's Disease tremors in a human subject. Data for the network was obtained from implanted deep brain electrodes. A tuned artificial neural network was shown to be able to identify the pattern of the onset tremor from these real time recordings.

Identification of Parkinson’s disease tremor onset using artificial neural networks

In this paper we consider the possibility of using an artificial neural network to accurately identify the onset of Parkinson’s Disease tremors in human subjects. Data for the network is obtained by means of deep brain implantation in the human brain. Results presented have been obtained from a practical study (i.e. real not simulated data) but should be regarded as initial trials to be discussed further. It can be seen that a tuned artificial neural network can act as an extremely effective predictor in these circumstances.

Models for evolving networks: with applications in telecommunication and online activities

Driven by a range of modern applications that includes telecommunications, e-business and on-line social interaction, recent ideas in complex networks can be extended to the case of time-varying connectivity. Here we propose a general frame- work for modelling and simulating such dynamic networks, and we explain how the long time behaviour may reveal important information about the mechanisms underlying the evolution.

Maturation of limbic corticostriatal activation and connectivity associated with developmental changes in temporal discounting

Temporal discounting (TD) matures with age, alongside other markers of increased impulse control, and coherent, self-regulated behaviour. Discounting paradigms quantify the ability to refrain from preference of immediate rewards, in favour of delayed, larger rewards. As such, they measure temporal foresight and the ability to delay gratification, functions that develop slowly into adulthood. We investigated the neural maturation that accompanies the previously observed age-related behavioural changes in discounting, from early adolescence into mid-adulthood. We used functional magnetic resonance imaging of a hypothetical discounting task with monetary rewards delayed in the week to year range. We show that age-related reductions in choice impulsivity were associated with changes in activation in ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), ventral striatum (VS), insula, inferior temporal gyrus, and posterior parietal cortex. Limbic frontostriatal activation changes were specifically associated with age-dependent reductions in impulsive choice, as part of a more extensive network of brain areas showing age-related changes in activation, including dorsolateral PFC, inferior parietal cortex, and subcortical areas. The maturational pattern of functional connectivity included strengthening in activation coupling between ventromedial and dorsolateral PFC, parietal and insular cortices during selection of delayed alternatives, and between vmPFC and VS during selection of immediate alternatives. We conclude that maturational mechanisms within limbic frontostriatal circuitry underlie the observed post-pubertal reductions in impulsive choice with increasing age, and that this effect is dependent on increased activation coherence within a network of areas associated with discounting behaviour and inter-temporal decision-making.

Prediction of Parkinson’s disease tremor onset using radial basis function neural networks

The possibility of using a radial basis function neural network (RBFNN) to accurately recognise and predict the onset of Parkinson’s disease tremors in human subjects is discussed in this paper. The data for training the RBFNN are obtained by means of deep brain electrodes implanted in a Parkinson disease patient’s brain. The effectiveness of a RBFNN is initially demonstrated by a real case study.