Computational Flux Balance Analysis Predicts that Stimulation of Energy Metabolism in Astrocytes and their Metabolic Interactions with Neurons Depend on Uptake of K(+) Rather than Glutamate

Brain activity involves essential functional and metabolic interactions between neurons and astrocytes. The importance of astrocytic functions to neuronal signaling is supported by many experiments reporting high rates of energy consumption and oxidative metabolism in these glial cells. In the brain, almost all energy is consumed by the Na(+)/K(+) ATPase, which hydrolyzes 1 ATP to move 3 Na(+) outside and 2 K(+) inside the cells. Astrocytes are commonly thought to be primarily involved in transmitter glutamate cycling, a mechanism that however only accounts for few % of brain energy utilization. In order to examine the participation of astrocytic energy metabolism in brain ion homeostasis, here we attempted to devise a simple stoichiometric relation linking glutamatergic neurotransmission to Na(+) and K(+) ionic currents. To this end, we took into account ion pumps and voltage/ligand-gated channels using the stoichiometry derived from available energy budget for neocortical signaling and incorporated this stoichiometric relation into a computational metabolic model of neuron-astrocyte interactions. We aimed at reproducing the experimental observations about rates of metabolic pathways obtained by (13)C-NMR spectroscopy in rodent brain. When simulated data matched experiments as well as biophysical calculations, the stoichiometry for voltage/ligand-gated Na(+) and K(+) fluxes generated by neuronal activity was close to a 1:1 relationship, and specifically 63/58 Na(+)/K(+) ions per glutamate released. We found that astrocytes are stimulated by the extracellular K(+) exiting neurons in excess of the 3/2 Na(+)/K(+) ratio underlying Na(+)/K(+) ATPase-catalyzed reaction. Analysis of correlations between neuronal and astrocytic processes indicated that astrocytic K(+) uptake, but not astrocytic Na(+)-coupled glutamate uptake, is instrumental for the establishment of neuron-astrocytic metabolic partnership. Our results emphasize the importance of K(+) in stimulating the activation of astrocytes, which is relevant to the understanding of brain activity and energy metabolism at the cellular level. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11064-016-2048-0) contains supplementary material, which is available to authorized users

Task-Related modulations of BOLD low-frequency fluctuations within the default mode Network

Spontaneous low-frequency Blood-Oxygenation Level-Dependent (BOLD) signals acquired during resting state are characterized by spatial patterns of synchronous fluctuations, ultimately leading to the identification of robust brain networks. The resting-state brain networks, including the Default Mode Network (DMN), are demonstrated to persist during sustained task execution, but the exact features of task-related changes of network properties are still not well characterized. In this work we sought to examine in a group of 20 healthy volunteers (age 33 ± 6 years, 8 F/12 M) the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the continuous execution of a working memory n-back task. We found that task execution impacted on both functional connectivity and amplitude of BOLD fluctuations within large parts of the DMN, but these changes correlated between each other only in a small area of the posterior cingulate. We conclude that combined analysis of multiple parameters related to connectivity, and their changes during the transition from resting state to continuous task execution, can contribute to a better understanding of how brain networks rearrange themselves in response to a task

CENTRE OF MASS TRAJECTORY IN SNOWBOARD GIANT SLALOM USING INERTIAL SENSORS: LABORATORY AND IN-FIELD PRELIMINARY EVALUATION

The purpose of the present study was to evaluate the reconstruction accuracy of the centre of mass during snowboard giant slalom using inertial sensors (Opal, APDM, 128 Hz). Two approaches were implemented and tested: i) a multi-segment model using 7 inertial sensors on the trunk, the pelvis, the thighs, the shanks, and the board; and ii) a double integration of the acceleration at L5 level measured with one inertial sensor. The accuracy of the algorithms was verified in two laboratory conditions: a) the multi-segment model approach was tested indoor during controlled movements using stereo-photogrammetry as gold standard, and b) the double integration of acceleration approach was tested outdoor in simulated movements on a longboard using GPS as gold standard. Successively, to verify the application in real conditions, an in-field acquisition of a forerunner athlete during a snowboard world cup competition was performed. The position of the centre of mass estimated indoor with multi-segmental model approach reported in the local reference frame of the board showed high correlation with respect to stereo-photogrammetry (r=0.87) and a RMS error of 3.8 [%] expressed as percentage of the range of motion during the trial (1.32m). For the simulated movements test in outdoor conditions on the longboard applying the double integration approach, high correlation was found with respect to the GPS data (r=0.95) on the trajectory but , for the 4 turns trial, a RMS difference on the distance equal to 15.3 [%] expressed as percentage of the whole distance covered (46m). Finally, the in-field acquisition showed how using inertial sensors is a viable option for collecting centre of mass data during training session useful for coaches and athletes. The approach using one sensors at L5 level showed low level of accuracy with respect to the one using a multi-segment model. Further developments should be performed in the direction of a better estimation of the orientation of the inertial sensors and of the boundary conditions for the integration algorithm

Scale-invariant rearrangement of resting state networks in the human brain under sustained stimulation

Brain activity at rest is characterized by widely distributed and spatially specific patterns of synchronized low-frequency blood-oxygenation level-dependent (BOLD) fluctuations, which correspond to physiologically relevant brain networks. This network behaviour is known to persist also during task execution, yet the details underlying task-associated modulations of within- and between-network connectivity are largely unknown. In this study we exploited a multi-parametric and multi-scale approach to investigate how low-frequency fluctuations adapt to a sustained n-back working memory task. We found that the transition from the resting state to the task state involves a behaviourally relevant and scale-invariant modulation of synchronization patterns within both task-positive and default mode networks. Specifically, decreases of connectivity within networks are accompanied by increases of connectivity between networks. In spite of large and widespread changes of connectivity strength, the overall topology of brain networks is remarkably preserved. We show that these findings are strongly influenced by connectivity at rest, suggesting that the absolute change of connectivity (i.e., disregarding the baseline) may not be the most suitable metric to study dynamic modulations of functional connectivity. Our results indicate that a task can evoke scale-invariant, distributed changes of BOLD fluctuations, further confirming that low frequency BOLD oscillations show a specialized response and are tightly bound to task-evoked activation

Evaluation of denoising strategies for task-based functional connectivity: Equalizing residual motion artifacts between rest and cognitively demanding tasks

In-scanner head motion represents a major confounding factor in functional connectivity studies and it raises particular concerns when motion correlates with the effect of interest. One such instance regards research focused on functional connectivity modulations induced by sustained cognitively demanding tasks. Indeed, cognitive engagement is generally associated with substantially lower in-scanner movement compared with unconstrained, or minimally constrained, conditions. Consequently, the reliability of condition-dependent changes in functional connectivity relies on effective denoising strategies. In this study, we evaluated the ability of common denoising pipelines to minimize and balance residual motion-related artifacts between resting-state and task conditions. Denoising pipelines—including realignment/tissue-based regression, PCA/ICA-based methods (aCompCor and ICA-AROMA, respectively), global signal regression, and censoring of motion-contaminated volumes—were evaluated according to a set of benchmarks designed to assess either residual artifacts or network identifiability. We found a marked heterogeneity in pipeline performance, with many approaches showing a differential efficacy between rest and task conditions. The most effective approaches included aCompCor, optimized to increase the noise prediction power of the extracted confounding signals, and global signal regression, although both strategies performed poorly in mitigating the spurious distance-dependent association between motion and connectivity. Censoring was the only approach that substantially reduced distance-dependent artifacts, yet this came at the great cost of reduced network identifiability. The implications of these findings for best practice in denoising task-based functional connectivity data, and more generally for resting-state data, are discussed

An anti-CD45RO/RB monoclonal antibody modulates T cell responses via induction of apoptosis and generation of regulatory T cells

The effects of a chimeric monoclonal antibody (chA6 mAb) that recognizes both the RO and RB isoforms of the transmembrane protein tyrosine phosphatase CD45 on human T cells were investigated. Chimeric A6 (chA6) mAb potently inhibited antigen-specific and polyclonal T cell responses. ChA6 mAb induced activation-independent apoptosis in CD4+CD45RO/RBhigh T cells but not in CD8+ T cells. In addition, CD4+ T cell lines specific for tetanus toxoid (TT) generated in the presence of chA6 mAb were anergic and suppressed the proliferation and interferon (IFN)-γ production by TT-specific effector T cells by an interleukin-10–dependent mechanism, indicating that these cells were equivalent to type 1 regulatory T cells. Similarly, CD8+ T cell lines specific for the influenza A matrix protein-derived peptide (MP.58-66) generated in the presence of chA6 mAb were anergic and suppressed IFN-γ production by MP.58-66–specific effector CD8+ T cells. Furthermore, chA6 mAb significantly prolonged human pancreatic islet allograft survival in nonobese diabetic/severe combined immunodeficiency mice injected with human peripheral blood lymphocytes (hu-PBL-NOD/SCID). Together, these results demonstrate that the chA6 mAb is a new immunomodulatory agent with multiple modes of action, including deletion of preexisting memory and recently activated T cells and induction of anergic CD4+ and CD8+ regulatory T cells