Reorganizations of latency structure within white matter from wakefulness to sleep

Previous studies based on resting-state fMRI (rsfMRI) data have revealed the existence of highly reproducible latency structure, reflecting the propagation of BOLD fMRI signals, in white matter (WM). Here, based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data collected from 35 healthy subjects who were instructed to sleep, we explored the alterations of propagations in WM across wakefulness and nonrapid eye movement (NREM) sleep stages. Lagged cross-covariance was computed among voxel-wise time series, followed by parabolic interpolation to determine the actual latency value in-between. In WM, regions including cerebellar peduncle, internal capsule, posterior thalamic radiation, genu of corpus callosum, and corona radiata, were found to change their temporal roles drastically, as revealed by applying linear mixed-effect model on voxel-wise latency projections across wakefulness and NREM sleep stages. Using these regions as seeds, further seed-based latency analysis revealed that variations of latency projections across different stages were underlain by inconsistent temporal shifts between each seed and the remaining part of WM. Finally, latency analysis on resting-state networks (RSNs), obtained by applying k-means clustering technique on group-level functional connectivity matrix, identified a path of signal propagations similar to previous findings in EEG during wakefulness, which propagated mainly from the brainstem upward to internal capsule and further to corona radiata. This path showed inter-RSN temporal reorganizations depending on the paired stages between which the brain transitioned, e.g., it changed, between internal capsule and corona radiata, from mainly unidirectional to clearly reciprocal when the brain transitioned from wakefulness to N3 stage. These findings suggested the functional role of BOLD signals in white matter as a slow process, dynamically modulated across wakefulness and NREM sleep stages, and involving in maintaining different levels of consciousness and cognitive processes.

An experimental study of fog and cloud computing in CEP-based Real-Time IoT applications

Internet of Things (IoT) has posed new requirements to the underlying processing architecture, specially for real-time applications, such as event-detection services. Complex Event Processing (CEP) engines provide a powerful tool to implement these services. Fog computing has raised as a solution to support IoT real-time applications, in contrast to the Cloud-based approach. This work is aimed at analysing a CEP-based Fog architecture for real-time IoT applications that uses a publish-subscribe protocol. A testbed has been developed with low-cost and local resources to verify the suitability of CEP-engines to low-cost computing resources. To assess performance we have analysed the effectiveness and cost of the proposal in terms of latency and resource usage, respectively. Results show that the fog computing architecture reduces event-detection latencies up to 35%, while the available computing resources are being used more efficiently, when compared to a Cloud deployment. Performance evaluation also identifies the communication between the CEP-engine and the final users as the most time consuming component of latency. Moreover, the latency analysis concludes that the time required by CEP-engine is related to the compute resources, but is nonlinear dependent of the number of things connected.