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.

HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ‘traditional’ set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ?traditional? set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified, easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

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 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.

Phase synchronization in brain networks derived from correlation between probabilities of recurrences in functional MRI data

It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric ``correlation between probabilities of recurrence'' (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.

Connectivity and Function of the Primate Insula

The insula is a mammalian cortical structure that has been implicated in a wide range of low- and high-level functions governing one’s sensory, emotional, and cognitive experiences. One particular role of this region is considered to be processing of olfactory stimuli. The ability to detect and evaluate odors has significant effects on an organism’s eating behavior and survival and, in case of humans, on complex decision making. Despite such importance of this function, the mechanism in which olfactory information is processed in the insula has not been thoroughly studied. Moreover, due to the structure’s close spatial relationship with the neighboring claustrum, it is not entirely clear whether the connectivity and olfactory functions attributed to the insula are truly those of the insula, rather than of the claustrum. My graduate work, consisting of two studies, seeks to help fill these gaps. In the first, the structural connectivity patterns of the insula and the claustrum in a non-human primate brain is assayed using an ultra-high-quality diffusion magnetic resonance image, and the results suggest dissociation of connectivity — and hence function — between the two structures. In the second study, a functional neuroimaging experiment investigates the insular activity during odor evaluation tasks in humans, and uncovers a potential spatial organization within the anterior portion of the insula for processing different aspects of odor characteristics.

A Dynamic Directional Model for Effective Brain Connectivity using Electrocorticographic (ECoG) Time Series.

We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on intracranial electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components (state equations), and observation equations linking the underlying neuronal states to observed data. When applied to functional MRI or EEG data, DDMs usually have complex formulations and thus can accommodate only a few regions, due to limitations in spatial resolution and/or temporal resolution of these imaging modalities. In contrast, we formulate our model in the context of ECoG data. The combined high temporal and spatial resolution of ECoG data result in a much simpler DDM, allowing investigation of complex connections between many regions. To identify functionally segregated sub-networks, a form of biologically economical brain networks, we propose the Potts model for the DDM parameters. The neuronal states of brain components are represented by cubic spline bases and the parameters are estimated by minimizing a log-likelihood criterion that combines the state and observation equations. The Potts model is converted to the Potts penalty in the penalized regression approach to achieve sparsity in parameter estimation, for which a fast iterative algorithm is developed. The methods are applied to an auditory ECoG dataset.

Network Connectivity Proxy: Architecture, Implementation, and Performance Analysis

Several studies in the last decade have pointed out that many devices, such as computers, are often left powered on even when idle, just to make them available and reachable on the network, leading to large energy waste. The concept of network connectivity proxy (NCP) has been proposed as an effective means to improve energy efficiency. It impersonates the presence of networked devices that are temporally unavailable, by carrying out background networking routines on their behalf. Hence, idle devices could be put into low-power states and save energy. Several architectural alternatives and the applicability of this concept to different protocols and applications have been investigated. However, there is no clear understanding of the limitations and issues of this approach in current networking scenarios. This paper extends the knowledge about the NCP by defining an extended set of tasks that the NCP can carry out, by introducing a suitable communication interface to control NCP operation, and by designing, implementing, and evaluating a functional prototype.

Properties of functional brain networks correlate with frequency of psychogenic non-epileptic seizures.

Abnormalities in the topology of brain networks may be an important feature and etiological factor for psychogenic non-epileptic seizures (PNES). To explore this possibility, we applied a graph theoretical approach to functional networks based on resting state EEGs from 13 PNES patients and 13 age- and gender-matched controls. The networks were extracted from Laplacian-transformed time-series by a cross-correlation method. PNES patients showed close to normal local and global connectivity and small-world structure, estimated with clustering coefficient, modularity, global efficiency, and small-worldness (SW) metrics, respectively. Yet the number of PNES attacks per month correlated with a weakness of local connectedness and a skewed balance between local and global connectedness quantified with SW, all in EEG alpha band. In beta band, patients demonstrated above-normal resiliency, measured with assortativity coefficient, which also correlated with the frequency of PNES attacks. This interictal EEG phenotype may help improve differentiation between PNES and epilepsy. The results also suggest that local connectivity could be a target for therapeutic interventions in PNES. Selective modulation (strengthening) of local connectivity might improve the skewed balance between local and global connectivity and so prevent PNES events.

Pre-existing astrocytes form functional perisynaptic processes on neurons generated in the adult hippocampus.

The adult dentate gyrus produces new neurons that morphologically and functionally integrate into the hippocampal network. In the adult brain, most excitatory synapses are ensheathed by astrocytic perisynaptic processes that regulate synaptic structure and function. However, these processes are formed during embryonic or early postnatal development and it is unknown whether astrocytes can also ensheathe synapses of neurons born during adulthood and, if so, whether they play a role in their synaptic transmission. Here, we used a combination of serial-section immuno-electron microscopy, confocal microscopy, and electrophysiology to examine the formation of perisynaptic processes on adult-born neurons. We found that the afferent and efferent synapses of newborn neurons are ensheathed by astrocytic processes, irrespective of the age of the neurons or the size of their synapses. The quantification of gliogenesis and the distribution of astrocytic processes on synapses formed by adult-born neurons suggest that the majority of these processes are recruited from pre-existing astrocytes. Furthermore, the inhibition of astrocytic glutamate re-uptake significantly reduced postsynaptic currents and increased paired-pulse facilitation in adult-born neurons, suggesting that perisynaptic processes modulate synaptic transmission on these cells. Finally, some processes were found intercalated between newly formed dendritic spines and potential presynaptic partners, suggesting that they may also play a structural role in the connectivity of new spines. Together, these results indicate that pre-existing astrocytes remodel their processes to ensheathe synapses of adult-born neurons and participate to the functional and structural integration of these cells into the hippocampal network.

Proteornics analysis unravels the functional repertoire of coronavirus nonstructural protein 3

Severe acute respiratory syndrome (SARS) coronavirus infection and growth are dependent on initiating signaling and enzyme actions upon viral entry into the host cell. Proteins packaged during virus assembly may subsequently form the first line of attack and host manipulation upon infection. A complete characterization of virion components is therefore important to understanding the dynamics of early stages of infection. Mass spectrometry and kinase profiling techniques identified nearly 200 incorporated host and viral proteins. We used published interaction data to identify hubs of connectivity with potential significance for virion formation. Surprisingly, the hub with the most potential connections was not the viral M protein but the nonstructurall protein 3 (nsp3), which is one of the novel virion components identified by mass spectrometry. Based on new experimental data and a bioinformatics analysis across the Coronaviridae, we propose a higher-resolution functional domain architecture for nsp3 that determines the interaction capacity of this protein. Using recombinant protein domains expressed in Escherichia coli, we identified two additional RNA-binding domains of nsp3. One of these domains is located within the previously described SARS-unique domain, and there is a nucleic acid chaperone-like domain located immediately downstream of the papain-like proteinase domain. We also identified a novel cysteine-coordinated metal ion-binding domain. Analyses of interdomain interactions and provisional functional annotation of the remaining, so-far-uncharacterized domains are presented. Overall, the ensemble of data surveyed here paint a more complete picture of nsp3 as a conserved component of the viral protein processing machinery, which is intimately associated with viral RNA in its role as a virion component.

The functional neuroanatomy of the evolving parent–infant relationship

Infant survival and the development of secure and cooperative relationships are central to the future of the species. In humans, this relies heavily on the evolving early parent–infant social and affective relationship. While much is known about the behavioural and psychological components of this relationship, relatively little is known about the underlying functional neuroanatomy. Affective and social neuroscience has helped to describe the main adult brain networks involved, but has so far engaged very little with developmental findings. In this review, we seek to highlight future avenues for research by providing a coherent framework for describing the parent–infant relationship over the first 18 months. We provide an outline of the evolving nature of the relationship, starting with basic orienting and recognition processes, and culminating in the infant's attainment of higher socio-emotional and cognitive capacities. Key social and affective interactions, such as communication, cooperative play and the establishment of specific attachments propel the development of the parent–infant relationship. We summarise our current knowledge of the developing infant brain in terms of structure and function, and how these relate to the emergent abilities necessary for the formation of a secure and cooperative relationship with parents or other caregivers. Important roles have been found for brain regions including the orbitofrontal, cingulate, and insular cortices in parent–infant interactions, but it has become clear that much more information is needed about the developmental time course and connectivity of these regions.