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Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary, but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations reflect the transient formation and dissolution of multiple communities of synchronized brain regions. Synchronized communities reoccurred intermittently in time and across scanning sessions. We found that the synchronization communities relate to previously defined functional networks known to be engaged in sensory-motor or cognitive function, called resting-state networks (RSNs), including the default mode network, the somato-motor network, the visual network, the auditory network, the cognitive control networks, the self-referential network, and combinations of these and other RSNs. We studied the mechanism originating the observed spatiotemporal synchronization dynamics by using a network model of phase oscillators connected through the brain's anatomical connectivity estimated using diffusion imaging human data. The model consistently approximates the temporal and spatial synchronization patterns of the empirical data, and reveals that multiple clusters that transiently synchronize and desynchronize emerge from the complex topology of anatomical connections, provided that oscillators are heterogeneous.

Significance The brain is never at rest, and patterns of ongoing correlated activity have been found to resemble patterns during active behavior. A fundamental problem in neuroscience concerns the relationship between spontaneous and task-driven activity. During a demanding task that requires selective attention to sensory stimuli, correlated patterns of spontaneous (rest) activity are generally preserved. However, specific changes in synchronization occur within and between networks that correlate with behavioral performance. These results indicate that attention modifies spontaneous activity patterns in support of task performance. Fundamental problems in neuroscience today are understanding how patterns of ongoing spontaneous activity are modified by task performance and whether/how these intrinsic patterns influence task-evoked activation and behavior. We examined these questions by comparing instantaneous functional connectivity (IFC) and directed functional connectivity (DFC) changes in two networks that are strongly correlated and segregated at rest: the visual (VIS) network and the dorsal attention network (DAN). We measured how IFC and DFC during a visuospatial attention task, which requires dynamic selective rerouting of visual information across hemispheres, changed with respect to rest. During the attention task, the two networks remained relatively segregated, and their general pattern of within-network correlation was maintained. However, attention induced a decrease of correlation in the VIS network and an increase of the DAN→VIS IFC and DFC, especially in a top-down direction. In contrast, within the DAN, IFC was not modified by attention, whereas DFC was enhanced. Importantly, IFC modulations were behaviorally relevant. We conclude that a stable backbone of within-network functional connectivity topography remains in place when transitioning between resting wakefulness and attention selection. However, relative decrease of correlation of ongoing “idling” activity in visual cortex and synchronization between frontoparietal and visual cortex were behaviorally relevant, indicating that modulations of resting activity patterns are important for task performance. Higher order resting connectivity in the DAN was relatively unaffected during attention, potentially indicating a role for simultaneous ongoing activity as a “prior” for attention selection.

Functional MRI (fMRI) studies have shown that low-frequency (<0.1 Hz) spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal during restful wakefulness are coherent within distributed large-scale cortical and subcortical networks (resting state networks, RSNs). The neuronal mechanisms underlying RSNs remain poorly understood. Here, we describe magnetoencephalographic correspondents of two well-characterized RSNs: the dorsal attention and the default mode networks. Seed-based correlation mapping was performed using time-dependent MEG power reconstructed at each voxel within the brain. The topography of RSNs computed on the basis of extended (5 min) epochs was similar to that observed with fMRI but confined to the same hemisphere as the seed region. Analyses taking into account the nonstationarity of MEG activity showed transient formation of more complete RSNs, including nodes in the contralateral hemisphere. Spectral analysis indicated that RSNs manifest in MEG as synchronous modulation of band-limited power primarily within the theta, alpha, and beta bands--that is, in frequencies slower than those associated with the local electrophysiological correlates of event-related BOLD responses.

The brain is not a passive sensory-motor analyzer driven by environmental stimuli, but actively maintains ongoing representations that may be involved in the coding of expected sensory stimuli, prospective motor responses, and prior experience. Spontaneous cortical activity has been proposed to play an important part in maintaining these ongoing, internal representations, although its functional role is not well understood. One spontaneous signal being intensely investigated in the human brain is the interregional temporal correlation of the blood-oxygen level-dependent (BOLD) signal recorded at rest by functional MRI (functional connectivity-by-MRI, fcMRI, or BOLD connectivity). This signal is intrinsic and coherent within a number of distributed networks whose topography closely resembles that of functional networks recruited during tasks. While it is apparent that fcMRI networks reflect anatomical connectivity, it is less clear whether they have any dynamic functional importance. Here, we demonstrate that visual perceptual learning, an example of adult neural plasticity, modifies the resting covariance structure of spontaneous activity between networks engaged by the task. Specifically, after intense training on a shape-identification task constrained to one visual quadrant, resting BOLD functional connectivity and directed mutual interaction between trained visual cortex and frontal-parietal areas involved in the control of spatial attention were significantly modified. Critically, these changes correlated with the degree of perceptual learning. We conclude that functional connectivity serves a dynamic role in brain function, supporting the consolidation of previous experience.

The research on two-stroke engines has been focused lately on the development of direct injection systems for reducing the emissions of hydrocarbons by minimizing the fuel short-circuiting. Low temperature combustion (LTC) may be the next step to further improve emissions and fuel consumption; however, LTC requires unconventional ignition systems. Jet ignition, i.e., the use of prechambers to accelerate the combustion process, turned out to be an effective way to perform LTC. The present work aims at proving the feasibility of adopting passive prechambers in a high-pressure, direct injection, two-stroke engine through non-reactive computational fluid dynamics analyses. The goal of the analysis is the evaluation of the prechamber performance in terms of both scavenging efficiency of burnt gases and fuel/air mixture formation inside the prechamber volume itself, in order to guarantee the mixture ignitability. Two prechamber geometries, featuring different aspect ratios and orifice numbers, were investigated. The analyses were replicated for two different locations of the injection and for three operating conditions of the engine in terms of revolution speed and load. Upon examination of the results, the effectiveness of both prechambers was found to be strongly dependent on the injection setup.

People differ in their ability to perform novel perceptual tasks, both during initial exposure and in the rate of improvement with practice. It is also known that regions of the brain recruited by particular tasks change their activity during learning. Here we investigate neural signals predictive of individual variability in performance. We used resting-state functional MRI to assess functional connectivity before training on a novel visual discrimination task. Subsequent task performance was related to functional connectivity measures within portions of visual cortex and between visual cortex and prefrontal association areas. Our results indicate that individual differences in performing novel perceptual tasks can be related to individual differences in spontaneous cortical activity.

Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. However, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain.

It has been proposed that neurons in the intraparietal cortex gradually accumulate evidence supporting different response options. Here the authors show that this model generalizes to arbitrary stimulus-response associations in humans. The neural mechanism underlying simple perceptual decision-making in monkeys has been recently conceptualized as an integrative process in which sensory evidence supporting different response options accumulates gradually over time. For example, intraparietal neurons accumulate motion information in favor of a specific oculomotor choice over time. It is unclear, however, whether this mechanism generalizes to more complex decisions that are based on arbitrary stimulus-response associations. In a task requiring arbitrary association of visual stimuli (faces or places) with different actions (eye or hand-pointing movements), we found that activity of effector-specific regions in human posterior parietal cortex reflected the 'strength' of the sensory evidence in favor of the preferred response. These regions did not respond to sensory stimuli per se but integrated sensory evidence toward the decision outcome. We conclude that even arbitrary decisions can be mediated by sensory-motor mechanisms that are completely triggered by contextual stimulus-response associations.

Recent innovations in engine control and diagnostics are providing room for development of innovative combustion approaches (e.g., low-temperature combustion) able to minimize the creation of pollutants. To ensure the constant fulfillment of the prescribed thermodynamic conditions, however, a fast real-time monitoring of the in-cylinder pressure is needed. To this end, dynamic pressure sensors, flush-mounted on the cylinder head, are commonly used. With this approach, the measurement accuracy is high, but the durability is limited by the harsh working conditions. The installation on the cylinder head is also complex. The development of robust and effective indirect measurement systems could then represent the enabler of a further development of this technology. In the present study, an innovative methodology to measure the in-cylinder pressure has been conceived and extensively tested on a four-stroke single-cylinder engine. The proposed approach is based on the analysis of the mechanical stress on the engine studs by means of a piezoelectric strain washer. This solution allows the user for a rapid and cost-effective sensor installation, described in the paper along with the signal post-processing techniques. Results showed good accuracy and robustness of the methodology, making the results of practical use for engine control.

Inhibiting inappropriate behavior and thoughts is an essential ability for humans, but the regions responsible for inhibitory processing are a matter of continuous debate. This is the first study of somatosensory go/nogo tasks using event-related functional magnetic resonance imaging (fMRI). Fifteen subjects preformed two different types of go/nogo task, i.e. (1) Movement and (2) Count, to compare with previous studies using visual go/nogo tasks, and confirm whether the inhibitory processing is dependent on sensory modalities. Go and nogo stimuli were presented with an even probability. Our data indicated that the response inhibition network involved the dorsolateral (DLPFC) and ventrolateral (VLPFC) prefrontal cortices, pre-supplementary motor area (pre-SMA), anterior cingulate cortex (ACC), inferior parietal lobule (IPL), insula, and temporoparietal junction (TPJ), which were consistent with previous results obtained using visual go/nogo tasks. These activities existed in both Movement and Count Nogo trials. Therefore, our results suggest that the network for inhibitory processing is not dependent on sensory modalities but reflects common neural activities. In addition, there were differences of activation intensity between Movement and Count Nogo trials in the prefrontal cortex, temporal lobe, and ACC. Thus, inhibitory processing would involve two neural networks, common and uncommon regions, depending on the required response mode.