Recent resting-state functional connectivity fMRI (RS-fcMRI) research has demonstrated that head motion during fMRI acquisition systematically influences connectivity estimates despite bandpass ...filtering and nuisance regression, which are intended to reduce such nuisance variability. We provide evidence that the effects of head motion and other nuisance signals are poorly controlled when the fMRI time series are bandpass-filtered but the regressors are unfiltered, resulting in the inadvertent reintroduction of nuisance-related variation into frequencies previously suppressed by the bandpass filter, as well as suboptimal correction for noise signals in the frequencies of interest. This is important because many RS-fcMRI studies, including some focusing on motion-related artifacts, have applied this approach. In two cohorts of individuals (n=117 and 22) who completed resting-state fMRI scans, we found that the bandpass–regress approach consistently overestimated functional connectivity across the brain, typically on the order of r=.10–.35, relative to a simultaneous bandpass filtering and nuisance regression approach. Inflated correlations under the bandpass–regress approach were associated with head motion and cardiac artifacts. Furthermore, distance-related differences in the association of head motion and connectivity estimates were much weaker for the simultaneous filtering approach. We recommend that future RS-fcMRI studies ensure that the frequencies of nuisance regressors and fMRI data match prior to nuisance regression, and we advocate a simultaneous bandpass filtering and nuisance regression strategy that better controls nuisance-related variability.
•Bandpass filtering and nuisance regression are intended to reduce noise in RS-fMRI.•When RS-fMRI data are filtered, but regressors are not, noise is poorly controlled.•In addition, this approach reintroduces synchronous noise into RS-fMRI data.•Such noise leads to systematically inflated estimates of functional connectivity.•Simultaneous bandpass filtering and regression eliminates this source of bias.
The thalamus is globally connected with distributed cortical regions, yet the functional significance of this extensive thalamocortical connectivity remains largely unknown. By performing ...graph-theoretic analyses on thalamocortical functional connectivity data collected from human participants, we found that most thalamic subdivisions display network properties that are capable of integrating multimodal information across diverse cortical functional networks. From a meta-analysis of a large dataset of functional brain-imaging experiments, we further found that the thalamus is involved in multiple cognitive functions. Finally, we found that focal thalamic lesions in humans have widespread distal effects, disrupting the modular organization of cortical functional networks. This converging evidence suggests that the human thalamus is a critical hub region that could integrate diverse information being processed throughout the cerebral cortex as well as maintain the modular structure of cortical functional networks.
The thalamus is traditionally viewed as a passive relay station of information from sensory organs or subcortical structures to the cortex. However, the thalamus has extensive connections with the entire cerebral cortex, which can also serve to integrate information processing between cortical regions. In this study, we demonstrate that multiple thalamic subdivisions display network properties that are capable of integrating information across multiple functional brain networks. Moreover, the thalamus is engaged by tasks requiring multiple cognitive functions. These findings support the idea that the thalamus is involved in integrating information across cortical networks.
In this paper, we review the background and state-of-the-art of the narrow-band Internet of Things (NB-IoT). We first introduce NB-IoT general background, development history, and standardization. ...Then, we present NB-IoT features through the review of current national and international studies on NB-IoT technology, where we focus on basic theories and key technologies, i.e., connection count analysis theory, delay analysis theory, coverage enhancement mechanism, ultra-low power consumption technology, and coupling relationship between signaling and data. Subsequently, we compare several performances of NB-IoT and other wireless and mobile communication technologies in aspects of latency, security, availability, data transmission rate, energy consumption, spectral efficiency, and coverage area. Moreover, we analyze five intelligent applications of NB-IoT, including smart cities, smart buildings, intelligent environment monitoring, intelligent user services, and smart metering. Finally, we summarize security requirements of NB-IoT, which need to be solved urgently. These discussions aim to provide a comprehensive overview of NB-IoT, which can help readers to understand clearly the scientific problems and future research directions of NB-IoT.
Trust and security have prevented businesses from fully accepting cloud platforms. To protect clouds, providers must first secure virtualized data center resources, uphold user privacy, and preserve ...data integrity. The authors suggest using a trust-overlay network over multiple data centers to implement a reputation system for establishing trust between service providers and data owners. Data coloring and software watermarking techniques protect shared data objects and massively distributed software modules. These techniques safeguard multi-way authentications, enable single sign-on in the cloud, and tighten access control for sensitive data in both public and private clouds.
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•Canonical EEG analyses conflate neural oscillations with the aperiodic signal.•From early childhood to adulthood, the aperiodic signal flattens in slope.•Dominant posterior ...oscillations shift from theta to alpha around 7–8 years old.•Theta oscillations transition from posterior dominant to anterior dominant with age.
Intrinsic, unconstrained neural activity exhibits rich spatial, temporal, and spectral organization that undergoes continuous refinement from childhood through adolescence. The goal of this study was to investigate the development of theta (4−8 Hertz) and alpha (8−12 Hertz) oscillations from early childhood to adulthood (years 3–24), as these oscillations play a fundamental role in cognitive function. We analyzed eyes-open, resting-state EEG data from 96 participants to estimate genuine oscillations separately from the aperiodic (1/f) signal. We examined age-related differences in the aperiodic signal (slope and offset), as well as the peak frequency and power of the dominant posterior oscillation. For the aperiodic signal, we found that both the aperiodic slope and offset decreased with age. For the dominant oscillation, we found that peak frequency, but not power, increased with age. Critically, early childhood (ages 3–7) was characterized by a dominance of theta oscillations in posterior electrodes, whereas peak frequency of the dominant oscillation in the alpha range increased between ages 7 and 24. Furthermore, theta oscillations displayed a topographical transition from dominance in posterior electrodes in early childhood to anterior electrodes in adulthood. Our results provide a quantitative description of the development of theta and alpha oscillations.
Functional hubs are brain regions that play a crucial role in facilitating communication among parallel, distributed brain networks. The developmental emergence and stability of hubs, however, is not ...well understood. The current study used measures of network topology drawn from graph theory to investigate the development of functional hubs in 99 participants, 10-20 years of age. We found that hub architecture was evident in late childhood and was stable from adolescence to early adulthood. Connectivity between hub and non-hub ("spoke") regions, however, changed with development. From childhood to adolescence, the strength of connections between frontal hubs and cortical and subcortical spoke regions increased. From adolescence to adulthood, hub-spoke connections with frontal hubs were stable, whereas connectivity between cerebellar hubs and cortical spoke regions increased. Our findings suggest that a developmentally stable functional hub architecture provides the foundation of information flow in the brain, whereas connections between hubs and spokes continue to develop, possibly supporting mature cognitive function.
The ability to voluntarily inhibit responses to task-irrelevant stimuli, which is a fundamental component of cognitive control, has a protracted development through adolescence. Previous human ...developmental imaging studies have found immaturities in localized brain activity in children and adolescents. However, little is known about how these regions integrate with age to form the distributed networks known to support cognitive control. In the present study, we used Granger causality analysis to characterize developmental changes in effective connectivity underlying inhibitory control (antisaccade task) compared with reflexive responses (prosaccade task) in human participants. By childhood, few top-down connectivities were evident with increased parietal interconnectivity. By adolescence, connections from prefrontal cortex increased and parietal interconnectivity decreased. From adolescence to adulthood, there was evidence of increased number and strength of frontal connections to cortical regions as well as subcortical regions. Together, results suggest that developmental improvements in inhibitory control may be supported by age-related enhancements in top-down effective connectivity between frontal, oculomotor, and subcortical regions.
With the development of network-enabled sensors and artificial intelligence algorithms, various human-centered smart systems are proposed to provide services with higher quality, such as smart ...healthcare, affective interaction, and autonomous driving. Considering cognitive computing is an indispensable technology to develop these smart systems, this paper proposes human-centered computing assisted by cognitive computing and cloud computing. First, we provide a comprehensive investigation of cognitive computing, including its evolution from knowledge discovery, cognitive science, and big data. Then, the system architecture of cognitive computing is proposed, which consists of three critical technologies, i.e., networking (e.g., Internet of Things), analytics (e.g., reinforcement learning and deep learning), and cloud computing. Finally, it describes the representative applications of human-centered cognitive computing, including robot technology, emotional communication system, and medical cognitive system.
Future industrial cyber-physical system (CPS) devices are expected to request a large amount of delay-sensitive services that need to be processed at the edge of a network. Due to limited resources, ...service placement at the edge of the cloud has attracted significant attention. Although there are many methods of design schemes, the service placement problem in industrial CPS has not been well studied. Furthermore, none of existing schemes can optimize service placement, workload scheduling, and resource allocation under uncertain service demands. To address these issues, we first formulate a joint optimization problem of service placement, workload scheduling, and resource allocation in order to minimize service response delay. We then propose an improved deep Q-network (DQN)-based service placement algorithm. The proposed algorithm can achieve an optimal resource allocation by means of convex optimization where the service placement and workload scheduling decisions are assisted by means of DQN technology. The experimental results verify that the proposed algorithm, compared with existing algorithms, can reduce the average service response time by 8-10%.