Hierarchical organization of brain functional networks during visual tasks.

PubMed

Zhuo, Zhao; Cai, Shi-Min; Fu, Zhong-Qian; Zhang, Jie

2011-09-01

The functional network of the brain is known to demonstrate modular structure over different hierarchical scales. In this paper, we systematically investigated the hierarchical modular organizations of the brain functional networks that are derived from the extent of phase synchronization among high-resolution EEG time series during a visual task. In particular, we compare the modular structure of the functional network from EEG channels with that of the anatomical parcellation of the brain cortex. Our results show that the modular architectures of brain functional networks correspond well to those from the anatomical structures over different levels of hierarchy. Most importantly, we find that the consistency between the modular structures of the functional network and the anatomical network becomes more pronounced in terms of vision, sensory, vision-temporal, motor cortices during the visual task, which implies that the strong modularity in these areas forms the functional basis for the visual task. The structure-function relationship further reveals that the phase synchronization of EEG time series in the same anatomical group is much stronger than that of EEG time series from different anatomical groups during the task and that the hierarchical organization of functional brain network may be a consequence of functional segmentation of the brain cortex.

Decreased Efficiency of Task-Positive and Task-Negative Networks During Working Memory in Schizophrenia

PubMed Central

Metzak, Paul D.; Riley, Jennifer D.; Wang, Liang; Whitman, Jennifer C.; Ngan, Elton T. C.; Woodward, Todd S.

2012-01-01

Working memory (WM) is one of the most impaired cognitive processes in schizophrenia. Functional magnetic resonance imaging (fMRI) studies in this area have typically found a reduction in information processing efficiency but have focused on the dorsolateral prefrontal cortex. In the current study using the Sternberg Item Recognition Test, we consider networks of regions supporting WM and measure the activation of functionally connected neural networks over different WM load conditions. We used constrained principal component analysis with a finite impulse response basis set to compare the estimated hemodynamic response associated with different WM load condition for 15 healthy control subjects and 15 schizophrenia patients. Three components emerged, reflecting activated (task-positive) and deactivated (task-negative or default-mode) neural networks. Two of the components (with both task-positive and task-negative aspects) were load dependent, were involved in encoding and delay phases (one exclusively encoding and the other both encoding and delay), and both showed evidence for decreased efficiency in patients. The results suggest that WM capacity is reached sooner for schizophrenia patients as the overt levels of WM load increase, to the point that further increases in overt memory load do not increase fMRI activation, and lead to performance impairments. These results are consistent with an account holding that patients show reduced efficiency in task-positive and task-negative networks during WM and also partially support the shifted inverted-U-shaped curve theory of the relationship between WM load and fMRI activation in schizophrenia. PMID:21224491

ICA model order selection of task co-activation networks.

PubMed

Ray, Kimberly L; McKay, D Reese; Fox, Peter M; Riedel, Michael C; Uecker, Angela M; Beckmann, Christian F; Smith, Stephen M; Fox, Peter T; Laird, Angela R

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.

ICA model order selection of task co-activation networks

PubMed Central

Ray, Kimberly L.; McKay, D. Reese; Fox, Peter M.; Riedel, Michael C.; Uecker, Angela M.; Beckmann, Christian F.; Smith, Stephen M.; Fox, Peter T.; Laird, Angela R.

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders. PMID:24339802

Improving the Quality of Service and Security of Military Networks with a Network Tasking Order Process

DTIC Science & Technology

2010-09-01

IMPROVING THE QUALITY OF SERVICE AND SECURITY OF MILITARY NETWORKS WITH A NETWORK TASKING ORDER...United States. AFIT/DCS/ENG/10-09 IMPROVING THE QUALITY OF SERVICE AND SECURITY OF MILITARY NETWORKS WITH A NETWORK TASKING ORDER PROCESS...USAF September 2010 APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED AFIT/DCS/ENG/10-09 IMPROVING THE QUALITY OF SERVICE AND

The prestimulus default mode network state predicts cognitive task performance levels on a mental rotation task.

PubMed

Kamp, Tabea; Sorger, Bettina; Benjamins, Caroline; Hausfeld, Lars; Goebel, Rainer

2018-06-22

Linking individual task performance to preceding, regional brain activation is an ongoing goal of neuroscientific research. Recently, it could be shown that the activation and connectivity within large-scale brain networks prior to task onset influence performance levels. More specifically, prestimulus default mode network (DMN) effects have been linked to performance levels in sensory near-threshold tasks, as well as cognitive tasks. However, it still remains uncertain how the DMN state preceding cognitive tasks affects performance levels when the period between task trials is long and flexible, allowing participants to engage in different cognitive states. We here investigated whether the prestimulus activation and within-network connectivity of the DMN are predictive of the correctness and speed of task performance levels on a cognitive (match-to-sample) mental rotation task, employing a sparse event-related functional magnetic resonance imaging (fMRI) design. We found that prestimulus activation in the DMN predicted the speed of correct trials, with a higher amplitude preceding correct fast response trials compared to correct slow response trials. Moreover, we found higher connectivity within the DMN before incorrect trials compared to correct trials. These results indicate that pre-existing activation and connectivity states within the DMN influence task performance on cognitive tasks, both effecting the correctness and speed of task execution. The findings support existing theories and empirical work on relating mind-wandering and cognitive task performance to the DMN and expand these by establishing a relationship between the prestimulus DMN state and the speed of cognitive task performance. © 2018 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.

Cerebellum and Integration of Neural Networks in Dual-Task Processing

PubMed Central

Wu, Tao; Liu, Jun; Hallett, Mark; Zheng, Zheng; Chan, Piu

2014-01-01

Performing two tasks simultaneously (dual-task) is common in human daily life. The neural correlates of dual-task processing remain unclear. In the current study, we used a dual motor and counting task with functional MRI (fMRI) to determine whether there are any areas additionally activated for dual-task performance. Moreover, we investigated the functional connectivity of these added activated areas, as well as the training effect on brain activity and connectivity. We found that the right cerebellar vermis, left lobule V of the cerebellar anterior lobe and precuneus are additionally activated for this type of dual-tasking. These cerebellar regions had functional connectivity with extensive motor- and cognitive-related regions. Dual-task training induced less activation in several areas, but increased the functional connectivity between these cerebellar regions and numbers of motor- and cognitive-related areas. Our findings demonstrate that some regions within the cerebellum can be additionally activated with dual-task performance. Their role in dual motor and cognitive task processes is likely to integrate motor and cognitive networks, and may be involved in adjusting these networks to be more efficient in order to perform dual-tasking properly. The connectivity of the precuneus differs from the cerebellar regions. A possible role of the precuneus in dual-task may be monitoring the operation of active brain networks. PMID:23063842

Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.

PubMed

Stephen, Emily P; Lepage, Kyle Q; Eden, Uri T; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S; Guenther, Frank H; Kramer, Mark A

2014-01-01

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty-both in the functional network edges and the corresponding aggregate measures of network topology-are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here-appropriate for static and dynamic network inference and different statistical measures of coupling-permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.

Distinct brain networks for adaptive and stable task control in humans

PubMed Central

Dosenbach, Nico U. F.; Fair, Damien A.; Miezin, Francis M.; Cohen, Alexander L.; Wenger, Kristin K.; Dosenbach, Ronny A. T.; Fox, Michael D.; Snyder, Abraham Z.; Vincent, Justin L.; Raichle, Marcus E.; Schlaggar, Bradley L.; Petersen, Steven E.

2007-01-01

Control regions in the brain are thought to provide signals that configure the brain's moment-to-moment information processing. Previously, we identified regions that carried signals related to task-control initiation, maintenance, and adjustment. Here we characterize the interactions of these regions by applying graph theory to resting state functional connectivity MRI data. In contrast to previous, more unitary models of control, this approach suggests the presence of two distinct task-control networks. A frontoparietal network included the dorsolateral prefrontal cortex and intraparietal sulcus. This network emphasized start-cue and error-related activity and may initiate and adapt control on a trial-by-trial basis. The second network included dorsal anterior cingulate/medial superior frontal cortex, anterior insula/frontal operculum, and anterior prefrontal cortex. Among other signals, these regions showed activity sustained across the entire task epoch, suggesting that this network may control goal-directed behavior through the stable maintenance of task sets. These two independent networks appear to operate on different time scales and affect downstream processing via dissociable mechanisms. PMID:17576922

Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses

PubMed Central

Stephen, Emily P.; Lepage, Kyle Q.; Eden, Uri T.; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S.; Guenther, Frank H.; Kramer, Mark A.

2014-01-01

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience. PMID:24678295

Development of task network models of human performance in microgravity

NASA Technical Reports Server (NTRS)

Diaz, Manuel F.; Adam, Susan

1992-01-01

This paper discusses the utility of task-network modeling for quantifying human performance variability in microgravity. The data are gathered for: (1) improving current methodologies for assessing human performance and workload in the operational space environment; (2) developing tools for assessing alternative system designs; and (3) developing an integrated set of methodologies for the evaluation of performance degradation during extended duration spaceflight. The evaluation entailed an analysis of the Remote Manipulator System payload-grapple task performed on many shuttle missions. Task-network modeling can be used as a tool for assessing and enhancing human performance in man-machine systems, particularly for modeling long-duration manned spaceflight. Task-network modeling can be directed toward improving system efficiency by increasing the understanding of basic capabilities of the human component in the system and the factors that influence these capabilities.

Regression to the Mean in SYMPLICITY HTN-3: Implications for Design and Reporting of Future Trials.

PubMed

Pocock, Stuart J; Bakris, George; Bhatt, Deepak L; Brar, Sandeep; Fahy, Martin; Gersh, Bernard J

2016-11-01

Regression to the mean (RTM) describes the tendency for an extreme measurement on 1 occasion to become less extreme when measured again. RTM may affect clinical trial data interpretation when the outcome measure has high variability. We investigated RTM in the SYMPLICITY HTN-3 (Renal Denervation in Patients With Uncontrolled Hypertension) trial of renal denervation versus a sham procedure. Analysis of covariance was performed on the 6-month change in systolic blood pressure, estimating a mean treatment difference of -4.11 mm Hg (95% confidence interval: -8.44 to 0.22 mm Hg; p = 0.064), which was similar to the unadjusted difference but with a smaller confidence interval. RTM occurred in both arms, but it had a negligible effect on the observed treatment difference. A second example concerns changes in hemoglobin A1c in a nonrandomized study. These findings emphasize the importance of incorporating RTM and analysis of covariance into the design and reporting of clinical studies of how treatments affect time changes in quantitative outcomes. (Renal Denervation in Patients With Uncontrolled Hypertension [SYMPLICITY HTN-3]; NCT01418261). Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Integration and segregation of large-scale brain networks during short-term task automatization

PubMed Central

Mohr, Holger; Wolfensteller, Uta; Betzel, Richard F.; Mišić, Bratislav; Sporns, Olaf; Richiardi, Jonas; Ruge, Hannes

2016-01-01

The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes. PMID:27808095

Resting-State Network Topology Differentiates Task Signals across the Adult Life Span.

PubMed

Chan, Micaela Y; Alhazmi, Fahd H; Park, Denise C; Savalia, Neil K; Wig, Gagan S

2017-03-08

Brain network connectivity differs across individuals. For example, older adults exhibit less segregated resting-state subnetworks relative to younger adults (Chan et al., 2014). It has been hypothesized that individual differences in network connectivity impact the recruitment of brain areas during task execution. While recent studies have described the spatial overlap between resting-state functional correlation (RSFC) subnetworks and task-evoked activity, it is unclear whether individual variations in the connectivity pattern of a brain area (topology) relates to its activity during task execution. We report data from 238 cognitively normal participants (humans), sampled across the adult life span (20-89 years), to reveal that RSFC-based network organization systematically relates to the recruitment of brain areas across two functionally distinct tasks (visual and semantic). The functional activity of brain areas (network nodes) were characterized according to their patterns of RSFC: nodes with relatively greater connections to nodes in their own functional system ("non-connector" nodes) exhibited greater activity than nodes with relatively greater connections to nodes in other systems ("connector" nodes). This "activation selectivity" was specific to those brain systems that were central to each of the tasks. Increasing age was accompanied by less differentiated network topology and a corresponding reduction in activation selectivity (or differentiation) across relevant network nodes. The results provide evidence that connectional topology of brain areas quantified at rest relates to the functional activity of those areas during task. Based on these findings, we propose a novel network-based theory for previous reports of the "dedifferentiation" in brain activity observed in aging. SIGNIFICANCE STATEMENT Similar to other real-world networks, the organization of brain networks impacts their function. As brain network connectivity patterns differ across

Task-Related Modulations of BOLD Low-Frequency Fluctuations within the Default Mode Network

PubMed Central

Tommasin, Silvia; Mascali, Daniele; Gili, Tommaso; Assan, Ibrahim Eid; Moraschi, Marta; Fratini, Michela; Wise, Richard G.; Macaluso, Emiliano; Mangia, Silvia; Giove, Federico

2017-01-01

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. PMID:28845420

Community-aware task allocation for social networked multiagent systems.

PubMed

Wang, Wanyuan; Jiang, Yichuan

2014-09-01

In this paper, we propose a novel community-aware task allocation model for social networked multiagent systems (SN-MASs), where the agent' cooperation domain is constrained in community and each agent can negotiate only with its intracommunity member agents. Under such community-aware scenarios, we prove that it remains NP-hard to maximize system overall profit. To solve this problem effectively, we present a heuristic algorithm that is composed of three phases: 1) task selection: select the desirable task to be allocated preferentially; 2) allocation to community: allocate the selected task to communities based on a significant task-first heuristics; and 3) allocation to agent: negotiate resources for the selected task based on a nonoverlap agent-first and breadth-first resource negotiation mechanism. Through the theoretical analyses and experiments, the advantages of our presented heuristic algorithm and community-aware task allocation model are validated. 1) Our presented heuristic algorithm performs very closely to the benchmark exponential brute-force optimal algorithm and the network flow-based greedy algorithm in terms of system overall profit in small-scale applications. Moreover, in the large-scale applications, the presented heuristic algorithm achieves approximately the same overall system profit, but significantly reduces the computational load compared with the greedy algorithm. 2) Our presented community-aware task allocation model reduces the system communication cost compared with the previous global-aware task allocation model and improves the system overall profit greatly compared with the previous local neighbor-aware task allocation model.

Cooperative network clustering and task allocation for heterogeneous small satellite network

NASA Astrophysics Data System (ADS)

Qin, Jing

The research of small satellite has emerged as a hot topic in recent years because of its economical prospects and convenience in launching and design. Due to the size and energy constraints of small satellites, forming a small satellite network(SSN) in which all the satellites cooperate with each other to finish tasks is an efficient and effective way to utilize them. In this dissertation, I designed and evaluated a weight based dominating set clustering algorithm, which efficiently organizes the satellites into stable clusters. The traditional clustering algorithms of large monolithic satellite networks, such as formation flying and satellite swarm, are often limited on automatic formation of clusters. Therefore, a novel Distributed Weight based Dominating Set(DWDS) clustering algorithm is designed to address the clustering problems in the stochastically deployed SSNs. Considering the unique features of small satellites, this algorithm is able to form the clusters efficiently and stably. In this algorithm, satellites are separated into different groups according to their spatial characteristics. A minimum dominating set is chosen as the candidate cluster head set based on their weights, which is a weighted combination of residual energy and connection degree. Then the cluster heads admit new neighbors that accept their invitations into the cluster, until the maximum cluster size is reached. Evaluated by the simulation results, in a SSN with 200 to 800 nodes, the algorithm is able to efficiently cluster more than 90% of nodes in 3 seconds. The Deadline Based Resource Balancing (DBRB) task allocation algorithm is designed for efficient task allocations in heterogeneous LEO small satellite networks. In the task allocation process, the dispatcher needs to consider the deadlines of the tasks as well as the residue energy of different resources for best energy utilization. We assume the tasks adopt a Map-Reduce framework, in which a task can consist of multiple

Convolutional neural networks and face recognition task

NASA Astrophysics Data System (ADS)

Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.

2017-09-01

Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

Task-related modulations of BOLD low-frequency fluctuations within the default mode network

NASA Astrophysics Data System (ADS)

Tommasin, Silvia; Mascali, Daniele; Gili, Tommaso; Eid Assan, Ibrahim; Moraschi, Marta; Fratini, Michela; Wise, Richard G.; Macaluso, Emiliano; Mangia, Silvia; Giove, Federico

2017-07-01

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, 8F/12M) the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the steady-state execution of a sustained working memory n-back task. We found that the steady state execution of such a task 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 steady-state task execution, can contribute to a better understanding of how brain networks rearrange themselves in response of a task.

Task modulates functional connectivity networks in free viewing behavior.

PubMed

Seidkhani, Hossein; Nikolaev, Andrey R; Meghanathan, Radha Nila; Pezeshk, Hamid; Masoudi-Nejad, Ali; van Leeuwen, Cees

2017-10-01

In free visual exploration, eye-movement is immediately followed by dynamic reconfiguration of brain functional connectivity. We studied the task-dependency of this process in a combined visual search-change detection experiment. Participants viewed two (nearly) same displays in succession. First time they had to find and remember multiple targets among distractors, so the ongoing task involved memory encoding. Second time they had to determine if a target had changed in orientation, so the ongoing task involved memory retrieval. From multichannel EEG recorded during 200 ms intervals time-locked to fixation onsets, we estimated the functional connectivity using a weighted phase lag index at the frequencies of theta, alpha, and beta bands, and derived global and local measures of the functional connectivity graphs. We found differences between both memory task conditions for several network measures, such as mean path length, radius, diameter, closeness and eccentricity, mainly in the alpha band. Both the local and the global measures indicated that encoding involved a more segregated mode of operation than retrieval. These differences arose immediately after fixation onset and persisted for the entire duration of the lambda complex, an evoked potential commonly associated with early visual perception. We concluded that encoding and retrieval differentially shape network configurations involved in early visual perception, affecting the way the visual input is processed at each fixation. These findings demonstrate that task requirements dynamically control the functional connectivity networks involved in early visual perception. Copyright © 2017 Elsevier Inc. All rights reserved.

Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks

PubMed Central

Miconi, Thomas

2017-01-01

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior. DOI: http://dx.doi.org/10.7554/eLife.20899.001 PMID:28230528

Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

PubMed

Miconi, Thomas

2017-02-23

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

Dynamic Tasking of Networked Sensors Using Covariance Information

DTIC Science & Technology

2010-09-01

has been created under an effort called TASMAN (Tasking Autonomous Sensors in a Multiple Application Network). One of the first studies utilizing this...environment was focused on a novel resource management approach, namely covariance-based tasking. Under this scheme, the state error covariance of...resident space objects (RSO), sensor characteristics, and sensor- target geometry were used to determine the effectiveness of future observations in

Distributed task coding throughout the multiple demand network of the human frontal-insular cortex.

PubMed

Stiers, Peter; Mennes, Maarten; Sunaert, Stefan

2010-08-01

The large variety of tasks that humans can perform is governed by a small number of key frontal-insular regions that are commonly active during task performance. Little is known about how this network distinguishes different tasks. We report on fMRI data in twelve participants while they performed four cognitive tasks. Of 20 commonly active frontal-insular regions in each hemisphere, five showed a BOLD response increase with increased task demands, regardless of the task. Although active in all tasks, each task invoked a unique response pattern across the voxels in each area that proved reliable in split-half multi-voxel correlation analysis. Consequently, voxels differed in their preference for one or more of the tasks. Voxel-based functional connectivity analyses revealed that same preference voxels distributed across all areas of the network constituted functional sub-networks that characterized the task being executed. Copyright 2010 Elsevier Inc. All rights reserved.

12-month blood pressure results of catheter-based renal artery denervation for resistant hypertension: the SYMPLICITY HTN-3 trial.

PubMed

Bakris, George L; Townsend, Raymond R; Flack, John M; Brar, Sandeep; Cohen, Sidney A; D'Agostino, Ralph; Kandzari, David E; Katzen, Barry T; Leon, Martin B; Mauri, Laura; Negoita, Manuela; O'Neill, William W; Oparil, Suzanne; Rocha-Singh, Krishna; Bhatt, Deepak L

2015-04-07

Results of the SYMPLICITY HTN-3 (Renal Denervation in Patients With Uncontrolled Hypertension) trial confirmed the safety but not the efficacy of renal denervation for treatment-resistant hypertension at 6 months post procedure. This study sought to analyze the 12-month SYMPLICITY HTN-3 results for the original denervation group, the sham subjects who underwent denervation after the 6-month endpoint (crossover group), and the sham subjects who did not undergo denervation after 6 months (non-crossover group). Eligible subjects were randomized 2:1 to denervation or sham procedure. Subjects were unblinded to their treatment group after the 6-month primary endpoint was ascertained; subjects in the sham group meeting eligibility requirements could undergo denervation. Change in blood pressure (BP) at 12 months post randomization (6 months for crossover subjects) was analyzed. The 12-month follow-up was available for 319 of 361 denervation subjects and 48 of 101 non-crossover subjects; 6-month denervation follow-up was available for 93 of 101 crossover subjects. In denervation subjects, the 12-month office systolic BP (SBP) change was greater than that observed at 6 months (-15.5 ± 24.1 mm Hg vs. -18.9 ± 25.4 mm Hg, respectively; p = 0.025), but the 24-h SBP change was not significantly different at 12 months (p = 0.229). The non-crossover group office SBP decreased by -32.9 ± 28.1 mm Hg at 6 months, but this response regressed to -21.4 ± 19.9 mm Hg (p = 0.01) at 12 months, increasing to 11.5 ± 29.8 mm Hg. These data support no further reduction in office or ambulatory BP after 1-year follow-up. Loss of BP reduction in the non-crossover group may reflect decreased medication adherence or other related factors. (Renal Denervation in Patients With Uncontrolled Hypertension [SYMPLICITY HTN-3]; NCT01418261). Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

The default mode network and the working memory network are not anti-correlated during all phases of a working memory task.

PubMed

Piccoli, Tommaso; Valente, Giancarlo; Linden, David E J; Re, Marta; Esposito, Fabrizio; Sack, Alexander T; Di Salle, Francesco

2015-01-01

The default mode network and the working memory network are known to be anti-correlated during sustained cognitive processing, in a load-dependent manner. We hypothesized that functional connectivity among nodes of the two networks could be dynamically modulated by task phases across time. To address the dynamic links between default mode network and the working memory network, we used a delayed visuo-spatial working memory paradigm, which allowed us to separate three different phases of working memory (encoding, maintenance, and retrieval), and analyzed the functional connectivity during each phase within and between the default mode network and the working memory network networks. We found that the two networks are anti-correlated only during the maintenance phase of working memory, i.e. when attention is focused on a memorized stimulus in the absence of external input. Conversely, during the encoding and retrieval phases, when the external stimulation is present, the default mode network is positively coupled with the working memory network, suggesting the existence of a dynamically switching of functional connectivity between "task-positive" and "task-negative" brain networks. Our results demonstrate that the well-established dichotomy of the human brain (anti-correlated networks during rest and balanced activation-deactivation during cognition) has a more nuanced organization than previously thought and engages in different patterns of correlation and anti-correlation during specific sub-phases of a cognitive task. This nuanced organization reinforces the hypothesis of a direct involvement of the default mode network in cognitive functions, as represented by a dynamic rather than static interaction with specific task-positive networks, such as the working memory network.

The Default Mode Network and the Working Memory Network Are Not Anti-Correlated during All Phases of a Working Memory Task

PubMed Central

Piccoli, Tommaso; Valente, Giancarlo; Linden, David E. J.; Re, Marta; Esposito, Fabrizio; Sack, Alexander T.; Salle, Francesco Di

2015-01-01

Introduction The default mode network and the working memory network are known to be anti-correlated during sustained cognitive processing, in a load-dependent manner. We hypothesized that functional connectivity among nodes of the two networks could be dynamically modulated by task phases across time. Methods To address the dynamic links between default mode network and the working memory network, we used a delayed visuo-spatial working memory paradigm, which allowed us to separate three different phases of working memory (encoding, maintenance, and retrieval), and analyzed the functional connectivity during each phase within and between the default mode network and the working memory network networks. Results We found that the two networks are anti-correlated only during the maintenance phase of working memory, i.e. when attention is focused on a memorized stimulus in the absence of external input. Conversely, during the encoding and retrieval phases, when the external stimulation is present, the default mode network is positively coupled with the working memory network, suggesting the existence of a dynamically switching of functional connectivity between “task-positive” and “task-negative” brain networks. Conclusions Our results demonstrate that the well-established dichotomy of the human brain (anti-correlated networks during rest and balanced activation-deactivation during cognition) has a more nuanced organization than previously thought and engages in different patterns of correlation and anti-correlation during specific sub-phases of a cognitive task. This nuanced organization reinforces the hypothesis of a direct involvement of the default mode network in cognitive functions, as represented by a dynamic rather than static interaction with specific task-positive networks, such as the working memory network. PMID:25848951

Is a Responsive Default Mode Network Required for Successful Working Memory Task Performance?

PubMed

Čeko, Marta; Gracely, John L; Fitzcharles, Mary-Ann; Seminowicz, David A; Schweinhardt, Petra; Bushnell, M Catherine

2015-08-19

In studies of cognitive processing using tasks with externally directed attention, regions showing increased (external-task-positive) and decreased or "negative" [default-mode network (DMN)] fMRI responses during task performance are dynamically responsive to increasing task difficulty. Responsiveness (modulation of fMRI signal by increasing load) has been linked directly to successful cognitive task performance in external-task-positive regions but not in DMN regions. To investigate whether a responsive DMN is required for successful cognitive performance, we compared healthy human subjects (n = 23) with individuals shown to have decreased DMN engagement (chronic pain patients, n = 28). Subjects performed a multilevel working-memory task (N-back) during fMRI. If a responsive DMN is required for successful performance, patients having reduced DMN responsiveness should show worsened performance; if performance is not reduced, their brains should show compensatory activation in external-task-positive regions or elsewhere. All subjects showed decreased accuracy and increased reaction times with increasing task level, with no significant group differences on either measure at any level. Patients had significantly reduced negative fMRI response (deactivation) of DMN regions (posterior cingulate/precuneus, medial prefrontal cortex). Controls showed expected modulation of DMN deactivation with increasing task difficulty. Patients showed significantly reduced modulation of DMN deactivation by task difficulty, despite their successful task performance. We found no evidence of compensatory neural recruitment in external-task-positive regions or elsewhere. Individual responsiveness of the external-task-positive ventrolateral prefrontal cortex, but not of DMN regions, correlated with task accuracy. These findings suggest that a responsive DMN may not be required for successful cognitive performance; a responsive external-task-positive network may be sufficient. We studied the

Combining Partial Directed Coherence and Graph Theory to Analyse Effective Brain Networks of Different Mental Tasks.

PubMed

Huang, Dengfeng; Ren, Aifeng; Shang, Jing; Lei, Qiao; Zhang, Yun; Yin, Zhongliang; Li, Jun; von Deneen, Karen M; Huang, Liyu

2016-01-01

The aim of this study is to qualify the network properties of the brain networks between two different mental tasks (play task or rest task) in a healthy population. EEG signals were recorded from 19 healthy subjects when performing different mental tasks. Partial directed coherence (PDC) analysis, based on Granger causality (GC), was used to assess the effective brain networks during the different mental tasks. Moreover, the network measures, including degree, degree distribution, local and global efficiency in delta, theta, alpha, and beta rhythms were calculated and analyzed. The local efficiency is higher in the beta frequency and lower in the theta frequency during play task whereas the global efficiency is higher in the theta frequency and lower in the beta frequency in the rest task. This study reveals the network measures during different mental states and efficiency measures may be used as characteristic quantities for improvement in attentional performance.

Meditation leads to reduced default mode network activity beyond an active task.

PubMed

Garrison, Kathleen A; Zeffiro, Thomas A; Scheinost, Dustin; Constable, R Todd; Brewer, Judson A

2015-09-01

Meditation has been associated with relatively reduced activity in the default mode network, a brain network implicated in self-related thinking and mind wandering. However, previous imaging studies have typically compared meditation to rest, despite other studies having reported differences in brain activation patterns between meditators and controls at rest. Moreover, rest is associated with a range of brain activation patterns across individuals that has only recently begun to be better characterized. Therefore, in this study we compared meditation to another active cognitive task, both to replicate the findings that meditation is associated with relatively reduced default mode network activity and to extend these findings by testing whether default mode activity was reduced during meditation, beyond the typical reductions observed during effortful tasks. In addition, prior studies had used small groups, whereas in the present study we tested these hypotheses in a larger group. The results indicated that meditation is associated with reduced activations in the default mode network, relative to an active task, for meditators as compared to controls. Regions of the default mode network showing a Group × Task interaction included the posterior cingulate/precuneus and anterior cingulate cortex. These findings replicate and extend prior work indicating that the suppression of default mode processing may represent a central neural process in long-term meditation, and they suggest that meditation leads to relatively reduced default mode processing beyond that observed during another active cognitive task.

Markov Task Network: A Framework for Service Composition under Uncertainty in Cyber-Physical Systems.

PubMed

Mohammed, Abdul-Wahid; Xu, Yang; Hu, Haixiao; Agyemang, Brighter

2016-09-21

In novel collaborative systems, cooperative entities collaborate services to achieve local and global objectives. With the growing pervasiveness of cyber-physical systems, however, such collaboration is hampered by differences in the operations of the cyber and physical objects, and the need for the dynamic formation of collaborative functionality given high-level system goals has become practical. In this paper, we propose a cross-layer automation and management model for cyber-physical systems. This models the dynamic formation of collaborative services pursuing laid-down system goals as an ontology-oriented hierarchical task network. Ontological intelligence provides the semantic technology of this model, and through semantic reasoning, primitive tasks can be dynamically composed from high-level system goals. In dealing with uncertainty, we further propose a novel bridge between hierarchical task networks and Markov logic networks, called the Markov task network. This leverages the efficient inference algorithms of Markov logic networks to reduce both computational and inferential loads in task decomposition. From the results of our experiments, high-precision service composition under uncertainty can be achieved using this approach.

Visual pathways from the perspective of cost functions and multi-task deep neural networks.

PubMed

Scholte, H Steven; Losch, Max M; Ramakrishnan, Kandan; de Haan, Edward H F; Bohte, Sander M

2018-01-01

Vision research has been shaped by the seminal insight that we can understand the higher-tier visual cortex from the perspective of multiple functional pathways with different goals. In this paper, we try to give a computational account of the functional organization of this system by reasoning from the perspective of multi-task deep neural networks. Machine learning has shown that tasks become easier to solve when they are decomposed into subtasks with their own cost function. We hypothesize that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of a ventral pathway dedicated to vision for perception, and a dorsal pathway dedicated to vision for action. To evaluate the functional organization in multi-task deep neural networks, we propose a method that measures the contribution of a unit towards each task, applying it to two networks that have been trained on either two related or two unrelated tasks, using an identical stimulus set. Results show that the network trained on the unrelated tasks shows a decreasing degree of feature representation sharing towards higher-tier layers while the network trained on related tasks uniformly shows high degree of sharing. We conjecture that the method we propose can be used to analyze the anatomical and functional organization of the visual system and beyond. We predict that the degree to which tasks are related is a good descriptor of the degree to which they share downstream cortical-units. Copyright © 2017 Elsevier Ltd. All rights reserved.

Differential recruitment of theory of mind brain network across three tasks: An independent component analysis.

PubMed

Thye, Melissa D; Ammons, Carla J; Murdaugh, Donna L; Kana, Rajesh K

2018-07-16

Social neuroscience research has focused on an identified network of brain regions primarily associated with processing Theory of Mind (ToM). However, ToM is a broad cognitive process, which encompasses several sub-processes, such as mental state detection and intentional attribution, and the connectivity of brain regions underlying the broader ToM network in response to paradigms assessing these sub-processes requires further characterization. Standard fMRI analyses which focus only on brain activity cannot capture information about ToM processing at a network level. An alternative method, independent component analysis (ICA), is a data-driven technique used to isolate intrinsic connectivity networks, and this approach provides insight into network-level regional recruitment. In this fMRI study, three complementary, but distinct ToM tasks assessing mental state detection (e.g. RMIE: Reading the Mind in the Eyes; RMIV: Reading the Mind in the Voice) and intentional attribution (Causality task) were each analyzed using ICA in order to separately characterize the recruitment and functional connectivity of core nodes in the ToM network in response to the sub-processes of ToM. Based on visual comparison of the derived networks for each task, the spatiotemporal network patterns were similar between the RMIE and RMIV tasks, which elicited mentalizing about the mental states of others, and these networks differed from the network derived for the Causality task, which elicited mentalizing about goal-directed actions. The medial prefrontal cortex, precuneus, and right inferior frontal gyrus were seen in the components with the highest correlation with the task condition for each of the tasks highlighting the role of these regions in general ToM processing. Using a data-driven approach, the current study captured the differences in task-related brain response to ToM in three distinct ToM paradigms. The findings of this study further elucidate the neural mechanisms associated

Is a Responsive Default Mode Network Required for Successful Working Memory Task Performance?

PubMed Central

Čeko, Marta; Gracely, John L.; Fitzcharles, Mary-Ann; Seminowicz, David A.; Schweinhardt, Petra

2015-01-01

In studies of cognitive processing using tasks with externally directed attention, regions showing increased (external-task-positive) and decreased or “negative” [default-mode network (DMN)] fMRI responses during task performance are dynamically responsive to increasing task difficulty. Responsiveness (modulation of fMRI signal by increasing load) has been linked directly to successful cognitive task performance in external-task-positive regions but not in DMN regions. To investigate whether a responsive DMN is required for successful cognitive performance, we compared healthy human subjects (n = 23) with individuals shown to have decreased DMN engagement (chronic pain patients, n = 28). Subjects performed a multilevel working-memory task (N-back) during fMRI. If a responsive DMN is required for successful performance, patients having reduced DMN responsiveness should show worsened performance; if performance is not reduced, their brains should show compensatory activation in external-task-positive regions or elsewhere. All subjects showed decreased accuracy and increased reaction times with increasing task level, with no significant group differences on either measure at any level. Patients had significantly reduced negative fMRI response (deactivation) of DMN regions (posterior cingulate/precuneus, medial prefrontal cortex). Controls showed expected modulation of DMN deactivation with increasing task difficulty. Patients showed significantly reduced modulation of DMN deactivation by task difficulty, despite their successful task performance. We found no evidence of compensatory neural recruitment in external-task-positive regions or elsewhere. Individual responsiveness of the external-task-positive ventrolateral prefrontal cortex, but not of DMN regions, correlated with task accuracy. These findings suggest that a responsive DMN may not be required for successful cognitive performance; a responsive external-task-positive network may be sufficient

Analysis of Time-Dependent Brain Network on Active and MI Tasks for Chronic Stroke Patients

PubMed Central

Chang, Won Hyuk; Kim, Yun-Hee; Lee, Seong-Whan; Kwon, Gyu Hyun

2015-01-01

Several researchers have analyzed brain activities by investigating brain networks. However, there is a lack of the research on the temporal characteristics of the brain network during a stroke by EEG and the comparative studies between motor execution and imagery, which became known to have similar motor functions and pathways. In this study, we proposed the possibility of temporal characteristics on the brain networks of a stroke. We analyzed the temporal properties of the brain networks for nine chronic stroke patients by the active and motor imagery tasks by EEG. High beta band has a specific role in the brain network during motor tasks. In the high beta band, for the active task, there were significant characteristics of centrality and small-worldness on bilateral primary motor cortices at the initial motor execution. The degree centrality significantly increased on the contralateral primary motor cortex, and local efficiency increased on the ipsilateral primary motor cortex. These results indicate that the ipsilateral primary motor cortex constructed a powerful subnetwork by influencing the linked channels as compensatory effect, although the contralateral primary motor cortex organized an inefficient network by using the connected channels due to lesions. For the MI task, degree centrality and local efficiency significantly decreased on the somatosensory area at the initial motor imagery. Then, there were significant correlations between the properties of brain networks and motor function on the contralateral primary motor cortex and somatosensory area for each motor execution/imagery task. Our results represented that the active and MI tasks have different mechanisms of motor acts. Based on these results, we indicated the possibility of customized rehabilitation according to different motor tasks. We expect these results to help in the construction of the customized rehabilitation system depending on motor tasks by understanding temporal functional

Meditation leads to reduced default mode network activity beyond an active task

PubMed Central

Garrison, Kathleen A.; Zeffiro, Thomas A.; Scheinost, Dustin; Constable, R. Todd; Brewer, Judson A.

2015-01-01

Meditation has been associated with relatively reduced activity in the default mode network, a brain network implicated in self-related thinking and mind wandering. However, previous imaging studies have typically compared meditation to rest despite other studies reporting differences in brain activation patterns between meditators and controls at rest. Moreover, rest is associated with a range of brain activation patterns across individuals that has only recently begun to be better characterized. Therefore, this study compared meditation to another active cognitive task, both to replicate findings that meditation is associated with relatively reduced default mode network activity, and to extend these findings by testing whether default mode activity was reduced during meditation beyond the typical reductions observed during effortful tasks. In addition, prior studies have used small groups, whereas the current study tested these hypotheses in a larger group. Results indicate that meditation is associated with reduced activations in the default mode network relative to an active task in meditators compared to controls. Regions of the default mode showing a group by task interaction include the posterior cingulate/precuneus and anterior cingulate cortex. These findings replicate and extend prior work indicating that suppression of default mode processing may represent a central neural process in long-term meditation, and suggest that meditation leads to relatively reduced default mode processing beyond that observed during another active cognitive task. PMID:25904238

Persistency and flexibility of complex brain networks underlie dual-task interference.

PubMed

Alavash, Mohsen; Hilgetag, Claus C; Thiel, Christiane M; Gießing, Carsten

2015-09-01

Previous studies on multitasking suggest that performance decline during concurrent task processing arises from interfering brain modules. Here, we used graph-theoretical network analysis to define functional brain modules and relate the modular organization of complex brain networks to behavioral dual-task costs. Based on resting-state and task fMRI we explored two organizational aspects potentially associated with behavioral interference when human subjects performed a visuospatial and speech task simultaneously: the topological overlap between persistent single-task modules, and the flexibility of single-task modules in adaptation to the dual-task condition. Participants showed a significant decline in visuospatial accuracy in the dual-task compared with single visuospatial task. Global analysis of topological similarity between modules revealed that the overlap between single-task modules significantly correlated with the decline in visuospatial accuracy. Subjects with larger overlap between single-task modules showed higher behavioral interference. Furthermore, lower flexible reconfiguration of single-task modules in adaptation to the dual-task condition significantly correlated with larger decline in visuospatial accuracy. Subjects with lower modular flexibility showed higher behavioral interference. At the regional level, higher overlap between single-task modules and less modular flexibility in the somatomotor cortex positively correlated with the decline in visuospatial accuracy. Additionally, higher modular flexibility in cingulate and frontal control areas and lower flexibility in right-lateralized nodes comprising the middle occipital and superior temporal gyri supported dual-tasking. Our results suggest that persistency and flexibility of brain modules are important determinants of dual-task costs. We conclude that efficient dual-tasking benefits from a specific balance between flexibility and rigidity of functional brain modules. © 2015 Wiley

A Novel Connectionist Network for Solving Long Time-Lag Prediction Tasks

NASA Astrophysics Data System (ADS)

Johnson, Keith; MacNish, Cara

Traditional Recurrent Neural Networks (RNNs) perform poorly on learning tasks involving long time-lag dependencies. More recent approaches such as LSTM and its variants significantly improve on RNNs ability to learn this type of problem. We present an alternative approach to encoding temporal dependencies that associates temporal features with nodes rather than state values, where the nodes explicitly encode dependencies over variable time delays. We show promising results comparing the network's performance to LSTM variants on an extended Reber grammar task.

Dynamic Task Allocation in Multi-Hop Multimedia Wireless Sensor Networks with Low Mobility

PubMed Central

Jin, Yichao; Vural, Serdar; Gluhak, Alexander; Moessner, Klaus

2013-01-01

This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines. PMID:24135992

Genetic network inference as a series of discrimination tasks.

PubMed

Kimura, Shuhei; Nakayama, Satoshi; Hatakeyama, Mariko

2009-04-01

Genetic network inference methods based on sets of differential equations generally require a great deal of time, as the equations must be solved many times. To reduce the computational cost, researchers have proposed other methods for inferring genetic networks by solving sets of differential equations only a few times, or even without solving them at all. When we try to obtain reasonable network models using these methods, however, we must estimate the time derivatives of the gene expression levels with great precision. In this study, we propose a new method to overcome the drawbacks of inference methods based on sets of differential equations. Our method infers genetic networks by obtaining classifiers capable of predicting the signs of the derivatives of the gene expression levels. For this purpose, we defined a genetic network inference problem as a series of discrimination tasks, then solved the defined series of discrimination tasks with a linear programming machine. Our experimental results demonstrated that the proposed method is capable of correctly inferring genetic networks, and doing so more than 500 times faster than the other inference methods based on sets of differential equations. Next, we applied our method to actual expression data of the bacterial SOS DNA repair system. And finally, we demonstrated that our approach relates to the inference method based on the S-system model. Though our method provides no estimation of the kinetic parameters, it should be useful for researchers interested only in the network structure of a target system. Supplementary data are available at Bioinformatics online.

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

PubMed Central

Jang, Hojin; Plis, Sergey M.; Calhoun, Vince D.; Lee, Jong-Hwan

2016-01-01

Feedforward deep neural networks (DNN), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean ± standard deviation; %) of 6.9 (± 3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4 ± 4.6) and the two-layer network (7.4 ± 4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the

The role of effective connectivity between the task-positive and task-negative network for evidence gathering [Evidence gathering and connectivity].

PubMed

Andreou, Christina; Steinmann, Saskia; Kolbeck, Katharina; Rauh, Jonas; Leicht, Gregor; Moritz, Steffen; Mulert, Christoph

2018-06-01

Reports linking a 'jumping-to-conclusions' bias to delusions have led to growing interest in the neurobiological correlates of probabilistic reasoning. Several brain areas have been implicated in probabilistic reasoning; however, findings are difficult to integrate into a coherent account. The present study aimed to provide additional evidence by investigating, for the first time, effective connectivity among brain areas involved in different stages of evidence gathering. We investigated evidence gathering in 25 healthy individuals using fMRI and a new paradigm (Box Task) designed such as to minimize the effects of cognitive effort and reward processing. Decisions to collect more evidence ('draws') were contrasted to decisions to reach a final choice ('conclusions') with respect to BOLD activity. Psychophysiological interaction analysis was used to investigate effective connectivity. Conclusion events were associated with extensive brain activations in widely distributed brain areas associated with the task-positive network. In contrast, draw events were characterized by higher activation in areas assumed to be part of the task-negative network. Effective connectivity between the two networks decreased during draws and increased during conclusion events. Our findings indicate that probabilistic reasoning may depend on the balance between the task-positive and task-negative network, and that shifts in connectivity between the two may be crucial for evidence gathering. Thus, abnormal connectivity between the two systems may significantly contribute to the jumping-to-conclusions bias. Copyright © 2018 Elsevier Inc. All rights reserved.

Schizophrenic patients and their unaffected siblings share increased resting-state connectivity in the task-negative network but not its anticorrelated task-positive network.

PubMed

Liu, Haihong; Kaneko, Yoshio; Ouyang, Xuan; Li, Li; Hao, Yihui; Chen, Eric Y H; Jiang, Tianzi; Zhou, Yuan; Liu, Zhening

2012-03-01

Abnormal connectivity of the anticorrelated intrinsic networks, the task-negative network (TNN), and the task-positive network (TPN) is implicated in schizophrenia. Comparisons between schizophrenic patients and their unaffected siblings enable further understanding of illness susceptibility and pathophysiology. We examined the resting-state connectivity differences in the intrinsic networks between schizophrenic patients, their unaffected siblings, and healthy controls. Resting-state functional magnetic resonance images were obtained from 25 individuals in each subject group. The posterior cingulate cortex/precuneus and right dorsolateral prefrontal cortex were used as seed regions to identify the TNN and TPN through functional connectivity analysis. Interregional connectivity strengths were analyzed using overlapped intrinsic networks composed of regions common to all subject groups. Schizophrenic patients and their unaffected siblings showed increased connectivity in the TNN between the bilateral inferior temporal gyri. By contrast, schizophrenic patients alone demonstrated increased connectivity between the posterior cingulate cortex/precuneus and left inferior temporal gyrus and between the ventral medial prefrontal cortex and right lateral parietal cortex in the TNN. Schizophrenic patients exhibited increased connectivity between the left dorsolateral prefrontal cortex and right inferior frontal gyrus in the TPN relative to their unaffected siblings, though this trend only approached statistical significance in comparison to healthy controls. Resting-state hyperconnectivity of the intrinsic networks may disrupt network coordination and thereby contribute to the pathophysiology of schizophrenia. Similar, though milder, hyperconnectivity of the TNN in unaffected siblings of schizophrenic patients may contribute to the identification of schizophrenia endophenotypes and ultimately to the determination of schizophrenia risk genes.

Attenuated anticorrelation between the default and dorsal attention networks with aging: evidence from task and rest.

PubMed

Spreng, R Nathan; Stevens, W Dale; Viviano, Joseph D; Schacter, Daniel L

2016-09-01

Anticorrelation between the default and dorsal attention networks is a central feature of human functional brain organization. Hallmarks of aging include impaired default network modulation and declining medial temporal lobe (MTL) function. However, it remains unclear if this anticorrelation is preserved into older adulthood during task performance, or how this is related to the intrinsic architecture of the brain. We hypothesized that older adults would show reduced within- and increased between-network functional connectivity (FC) across the default and dorsal attention networks. To test this hypothesis, we examined the effects of aging on task-related and intrinsic FC using functional magnetic resonance imaging during an autobiographical planning task known to engage the default network and during rest, respectively, with young (n = 72) and older (n = 79) participants. The task-related FC analysis revealed reduced anticorrelation with aging. At rest, there was a robust double dissociation, with older adults showing a pattern of reduced within-network FC, but increased between-network FC, across both networks, relative to young adults. Moreover, older adults showed reduced intrinsic resting-state FC of the MTL with both networks suggesting a fractionation of the MTL memory system in healthy aging. These findings demonstrate age-related dedifferentiation among these competitive large-scale networks during both task and rest, consistent with the idea that age-related changes are associated with a breakdown in the intrinsic functional architecture within and among large-scale brain networks. Copyright © 2016 Elsevier Inc. All rights reserved.

A computer program for the generation of logic networks from task chart data

NASA Technical Reports Server (NTRS)

Herbert, H. E.

1980-01-01

The Network Generation Program (NETGEN), which creates logic networks from task chart data is presented. NETGEN is written in CDC FORTRAN IV (Extended) and runs in a batch mode on the CDC 6000 and CYBER 170 series computers. Data is input via a two-card format and contains information regarding the specific tasks in a project. From this data, NETGEN constructs a logic network of related activities with each activity having unique predecessor and successor nodes, activity duration, descriptions, etc. NETGEN then prepares this data on two files that can be used in the Project Planning Analysis and Reporting System Batch Network Scheduling program and the EZPERT graphics program.

Bayesian network analysis revealed the connectivity difference of the default mode network from the resting-state to task-state

PubMed Central

Wu, Xia; Yu, Xinyu; Yao, Li; Li, Rui

2014-01-01

Functional magnetic resonance imaging (fMRI) studies have converged to reveal the default mode network (DMN), a constellation of regions that display co-activation during resting-state but co-deactivation during attention-demanding tasks in the brain. Here, we employed a Bayesian network (BN) analysis method to construct a directed effective connectivity model of the DMN and compared the organizational architecture and interregional directed connections under both resting-state and task-state. The analysis results indicated that the DMN was consistently organized into two closely interacting subsystems in both resting-state and task-state. The directed connections between DMN regions, however, changed significantly from the resting-state to task-state condition. The results suggest that the DMN intrinsically maintains a relatively stable structure whether at rest or performing tasks but has different information processing mechanisms under varied states. PMID:25309414

Proposal of Constraints Analysis Method Based on Network Model for Task Planning

NASA Astrophysics Data System (ADS)

Tomiyama, Tomoe; Sato, Tatsuhiro; Morita, Toyohisa; Sasaki, Toshiro

Deregulation has been accelerating several activities toward reengineering business processes, such as railway through service and modal shift in logistics. Making those activities successful, business entities have to regulate new business rules or know-how (we call them ‘constraints’). According to the new constraints, they need to manage business resources such as instruments, materials, workers and so on. In this paper, we propose a constraint analysis method to define constraints for task planning of the new business processes. To visualize each constraint's influence on planning, we propose a network model which represents allocation relations between tasks and resources. The network can also represent task ordering relations and resource grouping relations. The proposed method formalizes the way of defining constraints manually as repeatedly checking the network structure and finding conflicts between constraints. Being applied to crew scheduling problems shows that the method can adequately represent and define constraints of some task planning problems with the following fundamental features, (1) specifying work pattern to some resources, (2) restricting the number of resources for some works, (3) requiring multiple resources for some works, (4) prior allocation of some resources to some works and (5) considering the workload balance between resources.

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

PubMed

Jang, Hojin; Plis, Sergey M; Calhoun, Vince D; Lee, Jong-Hwan

2017-01-15

Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the

Dual Arm Work Package performance estimates and telerobot task network simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Draper, J.V.; Blair, L.M.

1997-02-01

This paper describes the methodology and results of a network simulation study of the Dual Arm Work Package (DAWP), to be employed for dismantling the Argonne National Laboratory CP-5 reactor. The development of the simulation model was based upon the results of a task analysis for the same system. This study was performed by the Oak Ridge National Laboratory (ORNL), in the Robotics and Process Systems Division. Funding was provided the US Department of Energy`s Office of Technology Development, Robotics Technology Development Program (RTDP). The RTDP is developing methods of computer simulation to estimate telerobotic system performance. Data were collectedmore » to provide point estimates to be used in a task network simulation model. Three skilled operators performed six repetitions of a pipe cutting task representative of typical teleoperation cutting operations.« less

Catheter-based renal denervation for resistant hypertension: 12-month results of the EnligHTN I first-in-human study using a multielectrode ablation system.

PubMed

Papademetriou, Vasilios; Tsioufis, Costas P; Sinhal, Ajay; Chew, Derek P; Meredith, Ian T; Malaiapan, Yuvi; Worthley, Matthew I; Worthley, Stephen G

2014-09-01

Renal denervation has emerged as a novel approach for the treatment of patients with drug-resistant hypertension. To date, only limited data have been published using multielectrode radiofrequency ablation systems. In this article, we present the 12-month data of EnligHTN I, a first-in-human study using a multielectrode ablation catheter. EnligHTN I enrolled 46 patients (average age, 60±10 years; on average 4.7±1.0 medications) with drug-resistant hypertension. Eligible patients were on ≥3 antihypertensive medications and had a systolic blood pressure (BP) ≥160 mm Hg (≥150 mm Hg for diabetics). Bilateral renal artery ablation was performed using a percutaneous femoral approach and standardized techniques. The average baseline office BP was 176/96 mm Hg, average 24-hour ambulatory BP was 150/83 mm Hg, and average home BP was 158/90 mm Hg. The average reductions (mm Hg) at 1, 3, 6, and 12 months were as follows: office: -28/-10, -27/-10, -26/-10, and -27/-11 mm Hg (P<0.001 for all); 24-hour ambulatory: -10/-5, -10/-5, -10/-6 (P<0.001 for all), and -7/-4 for 12 months (P<0.0094). Reductions in home measurements (based on 2-week average) were -9/-4, -8/-5,-10/-7, and -11/-6 mm Hg (P<0.001 at 12 months). At 12 months, there were no signals of worsening renal function and no new serious or life-threatening adverse events. One patient with baseline nonocclusive renal artery stenosis progressed to 75% diameter stenosis, requiring renal artery stenting. The 12-month data continue to demonstrate safety and efficacy of the EnligHTN ablation system in patients with drug-resistant hypertension. Home BP measurements parallel measurements obtained with 24-hour ambulatory monitoring. © 2014 American Heart Association, Inc.

At-sea demonstration of RF sensor tasking using XML over a worldwide network

NASA Astrophysics Data System (ADS)

Kellogg, Robert L.; Lee, Tom; Dumas, Diane; Raggo, Barbara

2003-07-01

As part of an At-Sea Demonstration for Space and Naval Warfare Command (SPAWAR, PMW-189), a prototype RF sensor for signal acquisition and direction finding queried and received tasking via a secure worldwide Automated Data Network System (ADNS). Using extended mark-up language (XML) constructs, both mission and signal tasking were available for push and pull Battlespace management. XML tasking was received by the USS Cape St George (CG-71) during an exercise along the Gulf Coast of the US from a test facility at SPAWAR, San Diego, CA. Although only one ship was used in the demonstration, the intent of the software initiative was to show that a network of different RF sensors on different platforms with different capabilitis could be tasked by a common web agent. A sensor software agent interpreted the XML task to match the sensor's capability. Future improvements will focus on enlarging the domain of mission tasking and incorporate report management.

The Richness of Task-Evoked Hemodynamic Responses Defines a Pseudohierarchy of Functionally Meaningful Brain Networks

PubMed Central

Orban, Pierre; Doyon, Julien; Petrides, Michael; Mennes, Maarten; Hoge, Richard; Bellec, Pierre

2015-01-01

Functional magnetic resonance imaging can measure distributed and subtle variations in brain responses associated with task performance. However, it is unclear whether the rich variety of responses observed across the brain is functionally meaningful and consistent across individuals. Here, we used a multivariate clustering approach that grouped brain regions into clusters based on the similarity of their task-evoked temporal responses at the individual level, and then established the spatial consistency of these individual clusters at the group level. We observed a stable pseudohierarchy of task-evoked networks in the context of a delayed sequential motor task, where the fractionation of networks was driven by a gradient of involvement in motor sequence preparation versus execution. In line with theories about higher-level cognitive functioning, this gradient evolved in a rostro-caudal manner in the frontal lobe. In addition, parcellations in the cerebellum and basal ganglia matched with known anatomical territories and fiber pathways with the cerebral cortex. These findings demonstrate that subtle variations in brain responses associated with task performance are systematic enough across subjects to define a pseudohierarchy of task-evoked networks. Such networks capture meaningful functional features of brain organization as shaped by a given cognitive context. PMID:24729172

Functional Connectivity of Cognitive Brain Networks in Schizophrenia during a Working Memory Task

PubMed Central

Godwin, Douglass; Ji, Andrew; Kandala, Sridhar; Mamah, Daniel

2017-01-01

Task-based connectivity studies facilitate the understanding of how the brain functions during cognition, which is commonly impaired in schizophrenia (SZ). Our aim was to investigate functional connectivity during a working memory task in SZ. We hypothesized that the task-negative (default mode) network and the cognitive control (frontoparietal) network would show dysconnectivity. Twenty-five SZ patient and 31 healthy control scans were collected using the customized 3T Siemens Skyra MRI scanner, previously used to collect data for the Human Connectome Project. Blood oxygen level dependent signal during the 0-back and 2-back conditions were extracted within a network-based parcelation scheme. Average functional connectivity was assessed within five brain networks: frontoparietal (FPN), default mode (DMN), cingulo-opercular (CON), dorsal attention (DAN), and ventral attention network; as well as between the DMN or FPN and other networks. For within-FPN connectivity, there was a significant interaction between n-back condition and group (p = 0.015), with decreased connectivity at 0-back in SZ subjects compared to controls. FPN-to-DMN connectivity also showed a significant condition × group effect (p = 0.003), with decreased connectivity at 0-back in SZ. Across groups, connectivity within the CON and DAN were increased during the 2-back condition, while DMN connectivity with either CON or DAN were decreased during the 2-back condition. Our findings support the role of the FPN, CON, and DAN in working memory and indicate that the pattern of FPN functional connectivity differs between SZ patients and control subjects during the course of a working memory task. PMID:29312020

Functional Connectivity of Cognitive Brain Networks in Schizophrenia during a Working Memory Task.

PubMed

Godwin, Douglass; Ji, Andrew; Kandala, Sridhar; Mamah, Daniel

2017-01-01

Task-based connectivity studies facilitate the understanding of how the brain functions during cognition, which is commonly impaired in schizophrenia (SZ). Our aim was to investigate functional connectivity during a working memory task in SZ. We hypothesized that the task-negative (default mode) network and the cognitive control (frontoparietal) network would show dysconnectivity. Twenty-five SZ patient and 31 healthy control scans were collected using the customized 3T Siemens Skyra MRI scanner, previously used to collect data for the Human Connectome Project. Blood oxygen level dependent signal during the 0-back and 2-back conditions were extracted within a network-based parcelation scheme. Average functional connectivity was assessed within five brain networks: frontoparietal (FPN), default mode (DMN), cingulo-opercular (CON), dorsal attention (DAN), and ventral attention network; as well as between the DMN or FPN and other networks. For within-FPN connectivity, there was a significant interaction between n -back condition and group ( p  = 0.015), with decreased connectivity at 0-back in SZ subjects compared to controls. FPN-to-DMN connectivity also showed a significant condition × group effect ( p  = 0.003), with decreased connectivity at 0-back in SZ. Across groups, connectivity within the CON and DAN were increased during the 2-back condition, while DMN connectivity with either CON or DAN were decreased during the 2-back condition. Our findings support the role of the FPN, CON, and DAN in working memory and indicate that the pattern of FPN functional connectivity differs between SZ patients and control subjects during the course of a working memory task.

Renal denervation after Symplicity HTN-3: an update.

PubMed

Persu, Alexandre; Jin, Yu; Fadl Elmula, Fadl Elmula Mohamed; Jacobs, Lotte; Renkin, Jean; Kjeldsen, Sverre

2014-08-01

After three years of excessive confidence, overoptimistic expectations and performance of 15 to 20,000 renal denervation procedures in Europe, the failure of a single well-designed US trial--Symplicity HTN-3--to meet its primary efficacy endpoint has cast doubt on renal denervation as a whole. The use of a sound methodology, including randomisation and blinded endpoint assessment was enough to see the typical 25-30 mmHg systolic blood pressure decrease observed after renal denervation melt down to less than 3 mmHg, the rest being likely explained by Hawthorne and placebo effects, attenuation of white coat effect, regression to the mean and other physician and patient-related biases. The modest blood pressure benefit directly assignable to renal denervation should be balanced with unresolved safety issues, such as potentially increased risk of renal artery stenosis after the procedure (more than ten cases reported up to now, most of them in 2014), unclear long-term impact on renal function and lack of morbidity-mortality data. Accordingly, there is no doubt that renal denervation is not ready for clinical use. Still, renal denervation is supported by a strong rationale and is occasionally followed by major blood pressure responses in at-risk patients who may otherwise have remained uncontrolled. Upcoming research programmes should focus on identification of those few patients with truly resistant hypertension who may derive a substantial benefit from the technique, within the context of well-designed randomised trials and independent registries. While electrical stimulation of baroreceptors and other interventional treatments of hypertension are already "knocking at the door", the premature and uncontrolled dissemination of renal denervation should remain an example of what should not be done, and trigger radical changes in evaluation processes of new devices by national and European health authorities.

Low-Resolution Screening of Early Stage Acquisition Simulation Scenario Development Decisions

DTIC Science & Technology

2012-12-01

6 seconds) incorporating reload times and assumptions. Phit for min range is assumed to be 100% (excepting FGM- 148, which was estimated for a...User Interface HTN Hierarchical Task Network MCCDC Marine Corps Combat Development Command Phit Probability to hit the intended target Pkill...well beyond the scope of this study. 5. Weapon Capabilities Translation COMBATXXI develops situation probabilities to hit ( Phit ) and probabilities to

Leveraging Large-Scale Semantic Networks for Adaptive Robot Task Learning and Execution.

PubMed

Boteanu, Adrian; St Clair, Aaron; Mohseni-Kabir, Anahita; Saldanha, Carl; Chernova, Sonia

2016-12-01

This work seeks to leverage semantic networks containing millions of entries encoding assertions of commonsense knowledge to enable improvements in robot task execution and learning. The specific application we explore in this project is object substitution in the context of task adaptation. Humans easily adapt their plans to compensate for missing items in day-to-day tasks, substituting a wrap for bread when making a sandwich, or stirring pasta with a fork when out of spoons. Robot plan execution, however, is far less robust, with missing objects typically leading to failure if the robot is not aware of alternatives. In this article, we contribute a context-aware algorithm that leverages the linguistic information embedded in the task description to identify candidate substitution objects without reliance on explicit object affordance information. Specifically, we show that the task context provided by the task labels within the action structure of a task plan can be leveraged to disambiguate information within a noisy large-scale semantic network containing hundreds of potential object candidates to identify successful object substitutions with high accuracy. We present two extensive evaluations of our work on both abstract and real-world robot tasks, showing that the substitutions made by our system are valid, accepted by users, and lead to a statistically significant reduction in robot learning time. In addition, we report the outcomes of testing our approach with a large number of crowd workers interacting with a robot in real time.

Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state.

PubMed

Yang, Yan-Li; Deng, Hong-Xia; Xing, Gui-Yang; Xia, Xiao-Luan; Li, Hai-Fang

2015-02-01

It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we investigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state. Z-values in the vision-related brain regions were calculated, confirming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental findings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.

A Hardware-Supported Algorithm for Self-Managed and Choreographed Task Execution in Sensor Networks.

PubMed

Bordel, Borja; Miguel, Carlos; Alcarria, Ramón; Robles, Tomás

2018-03-07

Nowadays, sensor networks are composed of a great number of tiny resource-constraint nodes, whose management is increasingly more complex. In fact, although collaborative or choreographic task execution schemes are which fit in the most perfect way with the nature of sensor networks, they are rarely implemented because of the high resource consumption of these algorithms (especially if networks include many resource-constrained devices). On the contrary, hierarchical networks are usually designed, in whose cusp it is included a heavy orchestrator with a remarkable processing power, being able to implement any necessary management solution. However, although this orchestration approach solves most practical management problems of sensor networks, a great amount of the operation time is wasted while nodes request the orchestrator to address a conflict and they obtain the required instructions to operate. Therefore, in this paper it is proposed a new mechanism for self-managed and choreographed task execution in sensor networks. The proposed solution considers only a lightweight gateway instead of traditional heavy orchestrators and a hardware-supported algorithm, which consume a negligible amount of resources in sensor nodes. The gateway avoids the congestion of the entire sensor network and the hardware-supported algorithm enables a choreographed task execution scheme, so no particular node is overloaded. The performance of the proposed solution is evaluated through numerical and electronic ModelSim-based simulations.

A Hardware-Supported Algorithm for Self-Managed and Choreographed Task Execution in Sensor Networks

PubMed Central

2018-01-01

Nowadays, sensor networks are composed of a great number of tiny resource-constraint nodes, whose management is increasingly more complex. In fact, although collaborative or choreographic task execution schemes are which fit in the most perfect way with the nature of sensor networks, they are rarely implemented because of the high resource consumption of these algorithms (especially if networks include many resource-constrained devices). On the contrary, hierarchical networks are usually designed, in whose cusp it is included a heavy orchestrator with a remarkable processing power, being able to implement any necessary management solution. However, although this orchestration approach solves most practical management problems of sensor networks, a great amount of the operation time is wasted while nodes request the orchestrator to address a conflict and they obtain the required instructions to operate. Therefore, in this paper it is proposed a new mechanism for self-managed and choreographed task execution in sensor networks. The proposed solution considers only a lightweight gateway instead of traditional heavy orchestrators and a hardware-supported algorithm, which consume a negligible amount of resources in sensor nodes. The gateway avoids the congestion of the entire sensor network and the hardware-supported algorithm enables a choreographed task execution scheme, so no particular node is overloaded. The performance of the proposed solution is evaluated through numerical and electronic ModelSim-based simulations. PMID:29518986

The Reference Ability Neural Network Study: Life-time stability of reference-ability neural networks derived from task maps of young adults.

PubMed

Habeck, C; Gazes, Y; Razlighi, Q; Steffener, J; Brickman, A; Barulli, D; Salthouse, T; Stern, Y

2016-01-15

Analyses of large test batteries administered to individuals ranging from young to old have consistently yielded a set of latent variables representing reference abilities (RAs) that capture the majority of the variance in age-related cognitive change: Episodic Memory, Fluid Reasoning, Perceptual Processing Speed, and Vocabulary. In a previous paper (Stern et al., 2014), we introduced the Reference Ability Neural Network Study, which administers 12 cognitive neuroimaging tasks (3 for each RA) to healthy adults age 20-80 in order to derive unique neural networks underlying these 4 RAs and investigate how these networks may be affected by aging. We used a multivariate approach, linear indicator regression, to derive a unique covariance pattern or Reference Ability Neural Network (RANN) for each of the 4 RAs. The RANNs were derived from the neural task data of 64 younger adults of age 30 and below. We then prospectively applied the RANNs to fMRI data from the remaining sample of 227 adults of age 31 and above in order to classify each subject-task map into one of the 4 possible reference domains. Overall classification accuracy across subjects in the sample age 31 and above was 0.80±0.18. Classification accuracy by RA domain was also good, but variable; memory: 0.72±0.32; reasoning: 0.75±0.35; speed: 0.79±0.31; vocabulary: 0.94±0.16. Classification accuracy was not associated with cross-sectional age, suggesting that these networks, and their specificity to the respective reference domain, might remain intact throughout the age range. Higher mean brain volume was correlated with increased overall classification accuracy; better overall performance on the tasks in the scanner was also associated with classification accuracy. For the RANN network scores, we observed for each RANN that a higher score was associated with a higher corresponding classification accuracy for that reference ability. Despite the absence of behavioral performance information in the

The Global Invasive Species Information Network: contributing to GEO Task BI-07-01b

NASA Astrophysics Data System (ADS)

Graham, J.; Morisette, J. T.; Simpson, A.

2009-12-01

Invasive alien species (IAS) threaten biodiversity and exert a tremendous cost on society for IAS prevention and eradication. They endanger natural ecosystem functioning and seriously impact biodiversity and agricultural production. The task definition for the GEO task BI-07-01b: Invasive Species Monitoring System is to characterize, monitor, and predict changes in the distribution of invasive species. This includes characterizing the current requirements and capacity for invasive species monitoring and developing strategies for implementing cross-search functionality among existing online invasive species information systems from around the globe. The Task is being coordinated by members of the Global Invasive Species Information Network (GISIN) and their partners. Information on GISIN and a prototype of the network is available at www.gisin.org. This talk will report on the current status of GISIN and review how researchers can either contribute to or utilize data from this network.

Impact of renal denervation on 24-hour ambulatory blood pressure: results from SYMPLICITY HTN-3.

PubMed

Bakris, George L; Townsend, Raymond R; Liu, Minglei; Cohen, Sidney A; D'Agostino, Ralph; Flack, John M; Kandzari, David E; Katzen, Barry T; Leon, Martin B; Mauri, Laura; Negoita, Manuela; O'Neill, William W; Oparil, Suzanne; Rocha-Singh, Krishna; Bhatt, Deepak L

2014-09-16

Prior studies of catheter-based renal artery denervation have not systematically performed ambulatory blood pressure monitoring (ABPM) to assess the efficacy of the procedure. SYMPLICITY HTN-3 (Renal Denervation in Patients With Uncontrolled Hypertension) was a prospective, blinded, randomized, sham-controlled trial. The current analysis details the effect of renal denervation or a sham procedure on ABPM measurements 6 months post-randomization. Patients with resistant hypertension were randomized 2:1 to renal denervation or sham control. Patients were on a stable antihypertensive regimen including maximally tolerated doses of at least 3 drugs including a diuretic before randomization. The powered secondary efficacy endpoint was a change in mean 24-h ambulatory systolic blood pressure (SBP). Nondipper to dipper (nighttime blood pressure [BP] 10% to 20% lower than daytime BP) conversion was calculated at 6 months. The 24-h ambulatory SBP changed -6.8 ± 15.1 mm Hg in the denervation group and -4.8 ± 17.3 mm Hg in the sham group: difference of -2.0 mm Hg (95% confidence interval [CI]: -5.0 to 1.1; p = 0.98 with a 2 mm Hg superiority margin). The daytime ambulatory SBP change difference between groups was -1.1 (95% CI: -4.3 to 2.2; p = 0.52). The nocturnal ambulatory SBP change difference between groups was -3.3 (95 CI: -6.7 to 0.1; p = 0.06). The percent of nondippers converted to dippers was 21.2% in the denervation group and 15.0% in the sham group (95% CI: -3.8% to 16.2%; p = 0.30). Change in 24-h heart rate was -1.4 ± 7.4 in the denervation group and -1.3 ± 7.3 in the sham group; (95% CI: -1.5 to 1.4; p = 0.94). This trial did not demonstrate a benefit of renal artery denervation on reduction in ambulatory BP in either the 24-h or day and night periods compared with sham (Renal Denervation in Patients With Uncontrolled Hypertension [SYMPLICITY HTN-3]; NCT01418261). Copyright © 2014 American College of Cardiology Foundation. Published by

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task

PubMed Central

2017-01-01

Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making. PMID:28961245

Cognitive task analysis of network analysts and managers for network situational awareness

NASA Astrophysics Data System (ADS)

Erbacher, Robert F.; Frincke, Deborah A.; Wong, Pak Chung; Moody, Sarah; Fink, Glenn

2010-01-01

The goal of our project is to create a set of next-generation cyber situational-awareness capabilities with applications to other domains in the long term. The situational-awareness capabilities being developed focus on novel visualization techniques as well as data analysis techniques designed to improve the comprehensibility of the visualizations. The objective is to improve the decision-making process to enable decision makers to choose better actions. To this end, we put extensive effort into ensuring we had feedback from network analysts and managers and understanding what their needs truly are. This paper discusses the cognitive task analysis methodology we followed to acquire feedback from the analysts. This paper also provides the details we acquired from the analysts on their processes, goals, concerns, etc. A final result we describe is the generation of a task-flow diagram.

Cognitive Task Analysis of Network Analysts and Managers for Network Situational Awareness

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erbacher, Robert; Frincke, Deborah A.; Wong, Pak C.

The goal of the project was to create a set of next generation cyber situational awareness capabilities with applications to other domains in the long term. The goal is to improve the decision making process such that decision makers can choose better actions. To this end, we put extensive effort into ensuring we had feedback from network analysts and managers and understood what their needs truly were. Consequently, this is the focus of this portion of the research. This paper discusses the methodology we followed to acquire this feedback from the analysts, namely a cognitive task analysis. Additionally, this papermore » provides the details we acquired from the analysts. This essentially provides details on their processes, goals, concerns, the data and meta-data they analyze, etc. A final result we describe is the generation of a task-flow diagram.« less

A Bayesian Network Approach to Modeling Learning Progressions and Task Performance. CRESST Report 776

ERIC Educational Resources Information Center

West, Patti; Rutstein, Daisy Wise; Mislevy, Robert J.; Liu, Junhui; Choi, Younyoung; Levy, Roy; Crawford, Aaron; DiCerbo, Kristen E.; Chappel, Kristina; Behrens, John T.

2010-01-01

A major issue in the study of learning progressions (LPs) is linking student performance on assessment tasks to the progressions. This report describes the challenges faced in making this linkage using Bayesian networks to model LPs in the field of computer networking. The ideas are illustrated with exemplar Bayesian networks built on Cisco…

Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition

NASA Astrophysics Data System (ADS)

Yin, Xi; Liu, Xiaoming

2018-02-01

This paper explores multi-task learning (MTL) for face recognition. We answer the questions of how and why MTL can improve the face recognition performance. First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weight to each side task, which is a crucial problem in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the variations from the learnt identity features. Extensive experiments on the entire Multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in Multi-PIE for face recognition. Our approach is also applicable to in-the-wild datasets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.

The Time Course of Task-Specific Memory Consolidation Effects in Resting State Networks

PubMed Central

Sami, Saber; Robertson, Edwin M.

2014-01-01

Previous studies have reported functionally localized changes in resting-state brain activity following a short period of motor learning, but their relationship with memory consolidation and their dependence on the form of learning is unclear. We investigate these questions with implicit or explicit variants of the serial reaction time task (SRTT). fMRI resting-state functional connectivity was measured in human subjects before the tasks, and 0.1, 0.5, and 6 h after learning. There was significant improvement in procedural skill in both groups, with the group learning under explicit conditions showing stronger initial acquisition, and greater improvement at the 6 h retest. Immediately following acquisition, this group showed enhanced functional connectivity in networks including frontal and cerebellar areas and in the visual cortex. Thirty minutes later, enhanced connectivity was observed between cerebellar nuclei, thalamus, and basal ganglia, whereas at 6 h there was enhanced connectivity in a sensory-motor cortical network. In contrast, immediately after acquisition under implicit conditions, there was increased connectivity in a network including precentral and sensory-motor areas, whereas after 30 min a similar cerebello-thalamo-basal ganglionic network was seen as in explicit learning. Finally, 6 h after implicit learning, we found increased connectivity in medial temporal cortex, but reduction in precentral and sensory-motor areas. Our findings are consistent with predictions that two variants of the SRTT task engage dissociable functional networks, although there are also networks in common. We also show a converging and diverging pattern of flux between prefrontal, sensory-motor, and parietal areas, and subcortical circuits across a 6 h consolidation period. PMID:24623776

Socio-contextual Network Mining for User Assistance in Web-based Knowledge Gathering Tasks

NASA Astrophysics Data System (ADS)

Rajendran, Balaji; Kombiah, Iyakutti

Web-based Knowledge Gathering (WKG) is a specialized and complex information seeking task carried out by many users on the web, for their various learning, and decision-making requirements. We construct a contextual semantic structure by observing the actions of the users involved in WKG task, in order to gain an understanding of their task and requirement. We also build a knowledge warehouse in the form of a master Semantic Link Network (SLX) that accommodates and assimilates all the contextual semantic structures. This master SLX, which is a socio-contextual network, is then mined to provide contextual inputs to the current users through their agents. We validated our approach through experiments and analyzed the benefits to the users in terms of resource explorations and the time saved. The results are positive enough to motivate us to implement in a larger scale.

Network analysis of exploratory behaviors of mice in a spatial learning and memory task

PubMed Central

Suzuki, Yusuke

2017-01-01

The Barnes maze is one of the main behavioral tasks used to study spatial learning and memory. The Barnes maze is a task conducted on “dry land” in which animals try to escape from a brightly lit exposed circular open arena to a small dark escape box located under one of several holes at the periphery of the arena. In comparison with another classical spatial learning and memory task, the Morris water maze, the negative reinforcements that motivate animals in the Barnes maze are less severe and less stressful. Furthermore, the Barnes maze is more compatible with recently developed cutting-edge techniques in neural circuit research, such as the miniature brain endoscope or optogenetics. For this study, we developed a lift-type task start system and equipped the Barnes maze with it. The subject mouse is raised up by the lift and released into the maze automatically so that it can start navigating the maze smoothly from exactly the same start position across repeated trials. We believe that a Barnes maze test with a lift-type task start system may be useful for behavioral experiments when combined with head-mounted or wire-connected devices for online imaging and intervention in neural circuits. Furthermore, we introduced a network analysis method for the analysis of the Barnes maze data. Each animal’s exploratory behavior in the maze was visualized as a network of nodes and their links, and spatial learning in the maze is described by systematic changes in network structures of search behavior. Network analysis was capable of visualizing and quantitatively analyzing subtle but significant differences in an animal’s exploratory behavior in the maze. PMID:28700627

Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks.

PubMed

Calhoun, Vince D; Kiehl, Kent A; Pearlson, Godfrey D

2008-07-01

Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called "resting state networks"); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer's disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail. (c) 2008 Wiley-Liss, Inc.

Differences between child and adult large-scale functional brain networks for reading tasks.

PubMed

Liu, Xin; Gao, Yue; Di, Qiqi; Hu, Jiali; Lu, Chunming; Nan, Yun; Booth, James R; Liu, Li

2018-02-01

Reading is an important high-level cognitive function of the human brain, requiring interaction among multiple brain regions. Revealing differences between children's large-scale functional brain networks for reading tasks and those of adults helps us to understand how the functional network changes over reading development. Here we used functional magnetic resonance imaging data of 17 adults (19-28 years old) and 16 children (11-13 years old), and graph theoretical analyses to investigate age-related changes in large-scale functional networks during rhyming and meaning judgment tasks on pairs of visually presented Chinese characters. We found that: (1) adults had stronger inter-regional connectivity and nodal degree in occipital regions, while children had stronger inter-regional connectivity in temporal regions, suggesting that adults rely more on visual orthographic processing whereas children rely more on auditory phonological processing during reading. (2) Only adults showed between-task differences in inter-regional connectivity and nodal degree, whereas children showed no task differences, suggesting the topological organization of adults' reading network is more specialized. (3) Children showed greater inter-regional connectivity and nodal degree than adults in multiple subcortical regions; the hubs in children were more distributed in subcortical regions while the hubs in adults were more distributed in cortical regions. These findings suggest that reading development is manifested by a shift from reliance on subcortical to cortical regions. Taken together, our study suggests that Chinese reading development is supported by developmental changes in brain connectivity properties, and some of these changes may be domain-general while others may be specific to the reading domain. © 2017 Wiley Periodicals, Inc.

Temporal motifs reveal collaboration patterns in online task-oriented networks

NASA Astrophysics Data System (ADS)

Xuan, Qi; Fang, Huiting; Fu, Chenbo; Filkov, Vladimir

2015-05-01

Real networks feature layers of interactions and complexity. In them, different types of nodes can interact with each other via a variety of events. Examples of this complexity are task-oriented social networks (TOSNs), where teams of people share tasks towards creating a quality artifact, such as academic research papers or software development in commercial or open source environments. Accomplishing those tasks involves both work, e.g., writing the papers or code, and communication, to discuss and coordinate. Taking into account the different types of activities and how they alternate over time can result in much more precise understanding of the TOSNs behaviors and outcomes. That calls for modeling techniques that can accommodate both node and link heterogeneity as well as temporal change. In this paper, we report on methodology for finding temporal motifs in TOSNs, limited to a system of two people and an artifact. We apply the methods to publicly available data of TOSNs from 31 Open Source Software projects. We find that these temporal motifs are enriched in the observed data. When applied to software development outcome, temporal motifs reveal a distinct dependency between collaboration and communication in the code writing process. Moreover, we show that models based on temporal motifs can be used to more precisely relate both individual developer centrality and team cohesion to programmer productivity than models based on aggregated TOSNs.

Temporal motifs reveal collaboration patterns in online task-oriented networks.

PubMed

Xuan, Qi; Fang, Huiting; Fu, Chenbo; Filkov, Vladimir

2015-05-01

Real networks feature layers of interactions and complexity. In them, different types of nodes can interact with each other via a variety of events. Examples of this complexity are task-oriented social networks (TOSNs), where teams of people share tasks towards creating a quality artifact, such as academic research papers or software development in commercial or open source environments. Accomplishing those tasks involves both work, e.g., writing the papers or code, and communication, to discuss and coordinate. Taking into account the different types of activities and how they alternate over time can result in much more precise understanding of the TOSNs behaviors and outcomes. That calls for modeling techniques that can accommodate both node and link heterogeneity as well as temporal change. In this paper, we report on methodology for finding temporal motifs in TOSNs, limited to a system of two people and an artifact. We apply the methods to publicly available data of TOSNs from 31 Open Source Software projects. We find that these temporal motifs are enriched in the observed data. When applied to software development outcome, temporal motifs reveal a distinct dependency between collaboration and communication in the code writing process. Moreover, we show that models based on temporal motifs can be used to more precisely relate both individual developer centrality and team cohesion to programmer productivity than models based on aggregated TOSNs.

Practice Level Costs of Office-Based Hypertension Performance Improvement: The Heart Healthy Lenoir Study.

PubMed

Primary care practice leaders who consider engaging in quality improvement (QI) need to understand the practice level costs incurred when asking staff to take on new tasks. The Heart Healthy Lenoir study is a prospective cohort trial in which QI methods were used to enhance hypertension (HTN) care and reduce racial disparities in blood pressure control in small rural primary care practices in North Carolina. As part of this effort, we performed an activity-based costing analysis to describe the costs incurred to develop, implement, and maintain key tasks.We interviewed 20 practice stakeholders and phone-based health coaches during 2012-2014. We calculated the time invested by individuals to perform each task within each study phase and applied national hourly wages to generate cost estimates. Our descriptive analyses focus on four of the most widely used practices. Activities included time to abstract HTN control data, participate in project meetings, identify patients with uncontrolled HTN, create standardized work, and provide additional health coaching for patients with uncontrolled HTN. Despite practice and staffing differences, the developmental phase costs were similar, ranging from $879 to $1,417. Implementation costs varied more widely as practices took different approaches to identifying patients with uncontrolled HTN. Practice-specific phone health coaching costs ranged from $19,508 to more than $38,000. This study adds to the growing literature regarding practice level costs of engaging in systems change. Understanding these costs and balancing them against practice incentives may be helpful as stakeholders make decisions regarding HTN QI.

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy.

PubMed

Kell, Alexander J E; Yamins, Daniel L K; Shook, Erica N; Norman-Haignere, Sam V; McDermott, Josh H

2018-05-02

A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy-primary and non-primary responses were best predicted by intermediate and late network layers, respectively. The results suggest that task optimization provides a powerful set of tools for modeling sensory systems. Copyright © 2018 Elsevier Inc. All rights reserved.

Immune networks: multi-tasking capabilities at medium load

NASA Astrophysics Data System (ADS)

Agliari, E.; Annibale, A.; Barra, A.; Coolen, A. C. C.; Tantari, D.

2013-08-01

Associative network models featuring multi-tasking properties have been introduced recently and studied in the low-load regime, where the number P of simultaneously retrievable patterns scales with the number N of nodes as P ˜ log N. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium-load regime, P ˜ Nδ with δ ∈ (0, 1]. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for δ < 1 the existence of large regions in the phase diagram where the network can retrieve all stored patterns simultaneously. Finally, in the high-load regime δ = 1 we find that the system behaves as a spin-glass, suggesting that finite-connectivity frameworks are required to achieve effective retrieval.

A multi-phase network situational awareness cognitive task analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erbacher, Robert; Frincke, Deborah A.; Wong, Pak C.

Abstract The goal of our project is to create a set of next-generation cyber situational-awareness capabilities with applications to other domains in the long term. The objective is to improve the decision-making process to enable decision makers to choose better actions. To this end, we put extensive effort into making certain that we had feedback from network analysts and managers and understand what their genuine needs are. This article discusses the cognitive task-analysis methodology that we followed to acquire feedback from the analysts. This article also provides the details we acquired from the analysts on their processes, goals, concerns, themore » data and metadata that they analyze. Finally, we describe the generation of a novel task-flow diagram representing the activities of the target user base.« less

Modulation of Temporally Coherent Brain Networks Estimated Using ICA at Rest and During Cognitive Tasks

PubMed Central

Calhoun, Vince D.; Kiehl, Kent A.; Pearlson, Godfrey D.

2009-01-01

Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called “resting state networks”); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer’s disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail. PMID:18438867

Design and Analysis of Self-Adapted Task Scheduling Strategies in Wireless Sensor Networks

PubMed Central

Guo, Wenzhong; Xiong, Naixue; Chao, Han-Chieh; Hussain, Sajid; Chen, Guolong

2011-01-01

In a wireless sensor network (WSN), the usage of resources is usually highly related to the execution of tasks which consume a certain amount of computing and communication bandwidth. Parallel processing among sensors is a promising solution to provide the demanded computation capacity in WSNs. Task allocation and scheduling is a typical problem in the area of high performance computing. Although task allocation and scheduling in wired processor networks has been well studied in the past, their counterparts for WSNs remain largely unexplored. Existing traditional high performance computing solutions cannot be directly implemented in WSNs due to the limitations of WSNs such as limited resource availability and the shared communication medium. In this paper, a self-adapted task scheduling strategy for WSNs is presented. First, a multi-agent-based architecture for WSNs is proposed and a mathematical model of dynamic alliance is constructed for the task allocation problem. Then an effective discrete particle swarm optimization (PSO) algorithm for the dynamic alliance (DPSO-DA) with a well-designed particle position code and fitness function is proposed. A mutation operator which can effectively improve the algorithm’s ability of global search and population diversity is also introduced in this algorithm. Finally, the simulation results show that the proposed solution can achieve significant better performance than other algorithms. PMID:22163971

Working memory activation of neural networks in the elderly as a function of information processing phase and task complexity.

PubMed

Charroud, Céline; Steffener, Jason; Le Bars, Emmanuelle; Deverdun, Jérémy; Bonafe, Alain; Abdennour, Meriem; Portet, Florence; Molino, François; Stern, Yaakov; Ritchie, Karen; Menjot de Champfleur, Nicolas; Akbaraly, Tasnime N

2015-11-01

Changes in working memory are sensitive indicators of both normal and pathological brain aging and associated disability. The present study aims to further understanding of working memory in normal aging using a large cohort of healthy elderly in order to examine three separate phases of information processing in relation to changes in task load activation. Using covariance analysis, increasing and decreasing neural activation was observed on fMRI in response to a delayed item recognition task in 337 cognitively healthy elderly persons as part of the CRESCENDO (Cognitive REServe and Clinical ENDOphenotypes) study. During three phases of the task (stimulation, retention, probe), increased activation was observed with increasing task load in bilateral regions of the prefrontal cortex, parietal lobule, cingulate gyrus, insula and in deep gray matter nuclei, suggesting an involvement of central executive and salience networks. Decreased activation associated with increasing task load was observed during the stimulation phase, in bilateral temporal cortex, parietal lobule, cingulate gyrus and prefrontal cortex. This spatial distribution of decreased activation is suggestive of the default mode network. These findings support the hypothesis of an increased activation in salience and central executive networks and a decreased activation in default mode network concomitant to increasing task load. Copyright © 2015 Elsevier Inc. All rights reserved.

Same task, different strategies: How brain networks can be influenced by memory strategy

PubMed Central

Sanfratello, Lori; Caprihan, Arvind; Stephen, Julia M.; Knoefel, Janice E.; Adair, John C.; Qualls, Clifford; Lundy, S. Laura; Aine, Cheryl J.

2015-01-01

Previous functional neuroimaging studies demonstrated that different neural networks underlie different types of cognitive processing by engaging participants in particular tasks, such as verbal or spatial working memory (WM) tasks. However, we report here that even when a working memory task is defined as verbal or spatial, different types of memory strategies may be employed to complete it, with concomitant variations in brain activity. We developed a questionnaire to characterize the type of strategy used by individual members in a group of 28 young healthy participants (18–25 years) during a spatial WM task. A cluster analysis was performed to differentiate groups. We acquired functional magnetoencephalography (MEG) and structural diffusion tensor imaging (DTI) measures to characterize the brain networks associated with the use of different strategies. We found two types of strategies were utilized during the spatial WM task, a visuospatial and a verbal strategy, and brain regions and timecourses of activation differed between participants who used each. Task performance also varied by type of strategy used, with verbal strategies showing an advantage. In addition, performance on neuropsychological tests (indices from WAIS-IV, REY-D Complex Figure) correlated significantly with fractional anisotropy (FA) measures for the visuospatial strategy group in white matter tracts implicated in other WM/attention studies. We conclude that differences in memory strategy can have a pronounced effect on the locations and timing of brain activation, and that these differences need further investigation as a possible confounding factor for studies using group averaging as a means for summarizing results. PMID:24931401

Same task, different strategies: how brain networks can be influenced by memory strategy.

PubMed

Sanfratello, Lori; Caprihan, Arvind; Stephen, Julia M; Knoefel, Janice E; Adair, John C; Qualls, Clifford; Lundy, S Laura; Aine, Cheryl J

2014-10-01

Previous functional neuroimaging studies demonstrated that different neural networks underlie different types of cognitive processing by engaging participants in particular tasks, such as verbal or spatial working memory (WM) tasks. However, we report here that even when a WM task is defined as verbal or spatial, different types of memory strategies may be used to complete it, with concomitant variations in brain activity. We developed a questionnaire to characterize the type of strategy used by individual members in a group of 28 young healthy participants (18-25 years) during a spatial WM task. A cluster analysis was performed to differentiate groups. We acquired functional magnetoencephalography and structural diffusion tensor imaging measures to characterize the brain networks associated with the use of different strategies. We found two types of strategies were used during the spatial WM task, a visuospatial and a verbal strategy, and brain regions and time courses of activation differed between participants who used each. Task performance also varied by type of strategy used with verbal strategies showing an advantage. In addition, performance on neuropsychological tests (indices from Wechsler Adult Intelligence Scale-IV, Rey Complex Figure Test) correlated significantly with fractional anisotropy measures for the visuospatial strategy group in white matter tracts implicated in other WM and attention studies. We conclude that differences in memory strategy can have a pronounced effect on the locations and timing of brain activation and that these differences need further investigation as a possible confounding factor for studies using group averaging as a means for summarizing results. Copyright © 2014 Wiley Periodicals, Inc.

Self-powered information measuring wireless networks using the distribution of tasks within multicore processors

NASA Astrophysics Data System (ADS)

Zhuravska, Iryna M.; Koretska, Oleksandra O.; Musiyenko, Maksym P.; Surtel, Wojciech; Assembay, Azat; Kovalev, Vladimir; Tleshova, Akmaral

2017-08-01

The article contains basic approaches to develop the self-powered information measuring wireless networks (SPIM-WN) using the distribution of tasks within multicore processors critical applying based on the interaction of movable components - as in the direction of data transmission as wireless transfer of energy coming from polymetric sensors. Base mathematic model of scheduling tasks within multiprocessor systems was modernized to schedule and allocate tasks between cores of one-crystal computer (SoC) to increase energy efficiency SPIM-WN objects.

A new intrusion prevention model using planning knowledge graph

NASA Astrophysics Data System (ADS)

Cai, Zengyu; Feng, Yuan; Liu, Shuru; Gan, Yong

2013-03-01

Intelligent plan is a very important research in artificial intelligence, which has applied in network security. This paper proposes a new intrusion prevention model base on planning knowledge graph and discuses the system architecture and characteristics of this model. The Intrusion Prevention based on plan knowledge graph is completed by plan recognition based on planning knowledge graph, and the Intrusion response strategies and actions are completed by the hierarchical task network (HTN) planner in this paper. Intrusion prevention system has the advantages of intelligent planning, which has the advantage of the knowledge-sharing, the response focused, learning autonomy and protective ability.

Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks

PubMed Central

Shi, Heyuan; Song, Xiaoyu; Gu, Ming; Sun, Jiaguang

2016-01-01

The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN. PMID:27916807

Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks.

PubMed

Shi, Heyuan; Song, Xiaoyu; Gu, Ming; Sun, Jiaguang

2016-11-28

The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN.

Psychopathic traits associated with abnormal hemodynamic activity in salience and default mode networks during auditory oddball task.

PubMed

Anderson, Nathaniel E; Maurer, J Michael; Steele, Vaughn R; Kiehl, Kent A

2018-06-01

Psychopathy is a personality disorder accompanied by abnormalities in emotional processing and attention. Recent theoretical applications of network-based models of cognition have been used to explain the diverse range of abnormalities apparent in psychopathy. Still, the physiological basis for these abnormalities is not well understood. A significant body of work has examined psychopathy-related abnormalities in simple attention-based tasks, but these studies have largely been performed using electrocortical measures, such as event-related potentials (ERPs), and they often have been carried out among individuals with low levels of psychopathic traits. In this study, we examined neural activity during an auditory oddball task using functional magnetic resonance imaging (fMRI) during a simple auditory target detection (oddball) task among 168 incarcerated adult males, with psychopathic traits assessed via the Hare Psychopathy Checklist-Revised (PCL-R). Event-related contrasts demonstrated that the largest psychopathy-related effects were apparent between the frequent standard stimulus condition and a task-off, implicit baseline. Negative correlations with interpersonal-affective dimensions (Factor 1) of the PCL-R were apparent in regions comprising default mode and salience networks. These findings support models of psychopathy describing impaired integration across functional networks. They additionally corroborate reports which have implicated failures of efficient transition between default mode and task-positive networks. Finally, they demonstrate a neurophysiological basis for abnormal mobilization of attention and reduced engagement with stimuli that have little motivational significance among those with high psychopathic traits.

From an Executive Network to Executive Control: A Computational Model of the "n"-Back Task

ERIC Educational Resources Information Center

Chatham, Christopher H.; Herd, Seth A.; Brant, Angela M.; Hazy, Thomas E.; Miyake, Akira; O'Reilly, Randy; Friedman, Naomi P.

2011-01-01

A paradigmatic test of executive control, the n-back task, is known to recruit a widely distributed parietal, frontal, and striatal "executive network," and is thought to require an equally wide array of executive functions. The mapping of functions onto substrates in such a complex task presents a significant challenge to any theoretical…

Florida Model Task Force on Diabetic Retinopathy: Development of an Interagency Network.

ERIC Educational Resources Information Center

Groff, G.; And Others

1990-01-01

This article describes the development of a mechanism to organize a network in Florida for individuals who are at risk for diabetic retinopathy. The task force comprised representatives from governmental, academic, professional, and voluntary organizations. It worked to educate professionals, patients, and the public through brochures, resource…

Cyber-physical approach to the network-centric robotics control task

NASA Astrophysics Data System (ADS)

Muliukha, Vladimir; Ilyashenko, Alexander; Zaborovsky, Vladimir; Lukashin, Alexey

2016-10-01

Complex engineering tasks concerning control for groups of mobile robots are developed poorly. In our work for their formalization we use cyber-physical approach, which extends the range of engineering and physical methods for a design of complex technical objects by researching the informational aspects of communication and interaction between objects and with an external environment [1]. The paper analyzes network-centric methods for control of cyber-physical objects. Robots or cyber-physical objects interact with each other by transmitting information via computer networks using preemptive queueing system and randomized push-out mechanism [2],[3]. The main field of application for the results of our work is space robotics. The selection of cyber-physical systems as a special class of designed objects is due to the necessity of integrating various components responsible for computing, communications and control processes. Network-centric solutions allow using universal means for the organization of information exchange to integrate different technologies for the control system.

Networking observers and observatories with remote telescope markup language

NASA Astrophysics Data System (ADS)

Hessman, Frederic V.; Tuparev, Georg; Allan, Alasdair

2006-06-01

Remote Telescope Markup Language (RTML) is an XML-based protocol for the transport of the high-level description of a set of observations to be carried out on a remote, robotic or service telescope. We describe how RTML is being used in a wide variety of contexts: the transport of service and robotic observing requests in the Hands-On Universe TM, ACP, eSTAR, and MONET networks; how RTML is easily combined with other XML protocols for more localized control of telescopes; RTML as a secondary observation report format for the IVOA's VOEvent protocol; the input format for a general-purpose observation simulator; and the observatory-independent means for carrying out request transactions for the international Heterogeneous Telescope Network (HTN).

Large-Scale Cooperative Task Distribution on Peer-to-Peer Networks

DTIC Science & Technology

2012-01-01

SUBTITLE Large-scale cooperative task distribution on peer-to-peer networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6...of agents, and each agent attempts to form a coalition with its most profitable partner. The second algorithm builds upon the Shapley for- mula [37...ters at the second layer. These Category Layer clusters each represent a single resource, and agents join one or more clusters based on their

Altered characteristic of brain networks in mild cognitive impairment during a selective attention task: An EEG study.

PubMed

Wei, Ling; Li, Yingjie; Yang, Xiaoli; Xue, Qing; Wang, Yuping

2015-10-01

The present study evaluated the topological properties of whole brain networks using graph theoretical concepts and investigated the time-evolution characteristic of brain network in mild cognitive impairment patients during a selective attention task. Electroencephalography (EEG) activities were recorded in 10 MCI patients and 17 healthy subjects when they performed a color match task. We calculated the phase synchrony index between each possible pairs of EEG channels in alpha and beta frequency bands and analyzed the local interconnectedness, overall connectedness and small-world characteristic of brain network in different degree for two groups. Relative to healthy normal controls, the properties of cortical networks in MCI patients tend to be a shift of randomization. Lower σ of MCI had suggested that patients had a further loss of small-world attribute both during active and resting states. Our results provide evidence for the functional disconnection of brain regions in MCI. Furthermore, we found the properties of cortical networks could reflect the processing of conflict information in the selective attention task. The human brain tends to be a more regular and efficient neural architecture in the late stage of information processing. In addition, the processing of conflict information needs stronger information integration and transfer between cortical areas. Copyright © 2015 Elsevier B.V. All rights reserved.

Cerebrocerebellar networks during articulatory rehearsal and verbal working memory tasks.

PubMed

Chen, S H Annabel; Desmond, John E

2005-01-15

Converging evidence has implicated the cerebellum in verbal working memory. The current fMRI study sought to further characterize cerebrocerebellar participation in this cognitive process by revealing regions of activation common to a verbal working task and an articulatory control task, as well as regions that are uniquely activated by working memory. Consistent with our model's predictions, load-dependent activations were observed in Broca's area (BA 44/6) and the superior cerebellar hemisphere (VI/CrusI) for both working memory and motoric rehearsal. In contrast, activations unique to verbal working memory were found in the inferior parietal lobule (BA 40) and the right inferior cerebellum hemisphere (VIIB). These findings provide evidence for two cerebrocerebellar networks for verbal working memory: a frontal/superior cerebellar articulatory control system and a parietal/inferior cerebellar phonological storage system.

Predicting human protein function with multi-task deep neural networks.

PubMed

Fa, Rui; Cozzetto, Domenico; Wan, Cen; Jones, David T

2018-01-01

Machine learning methods for protein function prediction are urgently needed, especially now that a substantial fraction of known sequences remains unannotated despite the extensive use of functional assignments based on sequence similarity. One major bottleneck supervised learning faces in protein function prediction is the structured, multi-label nature of the problem, because biological roles are represented by lists of terms from hierarchically organised controlled vocabularies such as the Gene Ontology. In this work, we build on recent developments in the area of deep learning and investigate the usefulness of multi-task deep neural networks (MTDNN), which consist of upstream shared layers upon which are stacked in parallel as many independent modules (additional hidden layers with their own output units) as the number of output GO terms (the tasks). MTDNN learns individual tasks partially using shared representations and partially from task-specific characteristics. When no close homologues with experimentally validated functions can be identified, MTDNN gives more accurate predictions than baseline methods based on annotation frequencies in public databases or homology transfers. More importantly, the results show that MTDNN binary classification accuracy is higher than alternative machine learning-based methods that do not exploit commonalities and differences among prediction tasks. Interestingly, compared with a single-task predictor, the performance improvement is not linearly correlated with the number of tasks in MTDNN, but medium size models provide more improvement in our case. One of advantages of MTDNN is that given a set of features, there is no requirement for MTDNN to have a bootstrap feature selection procedure as what traditional machine learning algorithms do. Overall, the results indicate that the proposed MTDNN algorithm improves the performance of protein function prediction. On the other hand, there is still large room for deep learning

Artificial Epigenetic Networks: Automatic Decomposition of Dynamical Control Tasks Using Topological Self-Modification.

PubMed

Turner, Alexander P; Caves, Leo S D; Stepney, Susan; Tyrrell, Andy M; Lones, Michael A

2017-01-01

This paper describes the artificial epigenetic network, a recurrent connectionist architecture that is able to dynamically modify its topology in order to automatically decompose and solve dynamical problems. The approach is motivated by the behavior of gene regulatory networks, particularly the epigenetic process of chromatin remodeling that leads to topological change and which underlies the differentiation of cells within complex biological organisms. We expected this approach to be useful in situations where there is a need to switch between different dynamical behaviors, and do so in a sensitive and robust manner in the absence of a priori information about problem structure. This hypothesis was tested using a series of dynamical control tasks, each requiring solutions that could express different dynamical behaviors at different stages within the task. In each case, the addition of topological self-modification was shown to improve the performance and robustness of controllers. We believe this is due to the ability of topological changes to stabilize attractors, promoting stability within a dynamical regime while allowing rapid switching between different regimes. Post hoc analysis of the controllers also demonstrated how the partitioning of the networks could provide new insights into problem structure.

Reward-based training of recurrent neural networks for cognitive and value-based tasks

PubMed Central

Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

2017-01-01

Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal’s internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task. DOI: http://dx.doi.org/10.7554/eLife.21492.001 PMID:28084991

Amygdala task-evoked activity and task-free connectivity independently contribute to feelings of arousal.

PubMed

Touroutoglou, Alexandra; Bickart, Kevin C; Barrett, Lisa Feldman; Dickerson, Bradford C

2014-10-01

Individual differences in the intensity of feelings of arousal while viewing emotional pictures have been associated with the magnitude of task-evoked blood-oxygen dependent (BOLD) response in the amygdala. Recently, we reported that individual differences in feelings of arousal are associated with task-free (resting state) connectivity within the salience network. There has not yet been an investigation of whether these two types of functional magnetic resonance imaging (MRI) measures are redundant or independent in their relationships to behavior. Here we tested the hypothesis that a combination of task-evoked amygdala activation and task-free amygdala connectivity within the salience network relate to individual differences in feelings of arousal while viewing of negatively potent images. In 25 young adults, results revealed that greater task-evoked amygdala activation and stronger task-free amygdala connectivity within the salience network each contributed independently to feelings of arousal, predicting a total of 45% of its variance. Individuals who had both increased task-evoked amygdala activation and stronger task-free amygdala connectivity within the salience network had the most heightened levels of arousal. Task-evoked amygdala activation and task-free amygdala connectivity within the salience network were not related to each other, suggesting that resting-state and task-evoked dynamic brain imaging measures may provide independent and complementary information about affective experience, and likely other kinds of behaviors as well. Copyright © 2014 Wiley Periodicals, Inc.

Functional connectivity in task-negative network of the Deaf: effects of sign language experience

PubMed Central

Talavage, Thomas M.; Wilbur, Ronnie B.

2014-01-01

Prior studies investigating cortical processing in Deaf signers suggest that life-long experience with sign language and/or auditory deprivation may alter the brain’s anatomical structure and the function of brain regions typically recruited for auditory processing (Emmorey et al., 2010; Pénicaud et al., 2013 inter alia). We report the first investigation of the task-negative network in Deaf signers and its functional connectivity—the temporal correlations among spatially remote neurophysiological events. We show that Deaf signers manifest increased functional connectivity between posterior cingulate/precuneus and left medial temporal gyrus (MTG), but also inferior parietal lobe and medial temporal gyrus in the right hemisphere- areas that have been found to show functional recruitment specifically during sign language processing. These findings suggest that the organization of the brain at the level of inter-network connectivity is likely affected by experience with processing visual language, although sensory deprivation could be another source of the difference. We hypothesize that connectivity alterations in the task negative network reflect predictive/automatized processing of the visual signal. PMID:25024915

Effects of Stress and Task Difficulty on Working Memory and Cortical Networking.

PubMed

Kim, Yujin; Woo, Jihwan; Woo, Minjung

2017-12-01

This study investigated interactive effects of stress and task difficulty on working memory and cortico-cortical communication during memory encoding. Thirty-eight adolescent participants (mean age of 15.7 ± 1.5 years) completed easy and hard working memory tasks under low- and high-stress conditions. We analyzed the accuracy and reaction time (RT) of working memory performance and inter- and intrahemispheric electroencephalogram coherences during memory encoding. Working memory accuracy was higher, and RT shorter, in the easy versus the hard task. RT was shorter under the high-stress (TENS) versus low-stress (no-TENS) condition, while there was no difference in memory accuracy between the two stress conditions. For electroencephalogram coherence, we found higher interhemispheric coherence in all bands but only at frontal electrode sites in the easy versus the hard task. On the other hand, intrahemispheric coherence was higher in the left hemisphere in the easy (versus hard task) and higher in the right hemisphere (with one exception) in the hard (versus easy task). Inter- and intracoherences were higher in the low- versus high-stress condition. Significant interactions between task difficulty and stress condition were observed in coherences of the beta frequency band. The difference in coherence between low- and high-stress conditions was greater in the hard compared with the easy task, with lower coherence under the high-stress condition relative to the low-stress condition. Stress seemed to cause a decrease in cortical network communications between memory-relevant cortical areas as task difficulty increased.

Resting State Default Mode Network Connectivity, Dual Task Performance, Gait Speed, and Postural Sway in Older Adults with Mild Cognitive Impairment

PubMed Central

Crockett, Rachel A.; Hsu, Chun Liang; Best, John R.; Liu-Ambrose, Teresa

2017-01-01

Aging is associated with an increased risk of falling. In particular, older adults with mild cognitive impairment (MCI) are more vulnerable to falling compared with their healthy counterparts. Major contributors to this increased falls risk include a decline in dual task performance, gait speed, and postural sway. Recent evidence highlights the potential influence of the default mode network (DMN), the frontoparietal network (FPN), and the supplementary motor area (SMA) on dual task performance, gait speed, and postural sway. The DMN is active during rest and deactivates during task-oriented processes, to maintain attention and stay on task. The FPN and SMA are involved in top-down attentional control, motor planning, and motor execution. The DMN shows less deactivation during task in older adults with MCI. This lack of deactivation is theorized to increase competition for resources between the DMN and task-related brain regions (e.g., the FPN and SMA), increasing distraction from the task and reducing task performance. However, no study has yet investigated the relationship between the between-network connectivity of the DMN with these regions and dual task walking, gait speed or postural sway. We hypothesized that greater functional connectivity both within the DMN and between DMN–FPN and DMN–SMA, will be associated with poorer performance during dual task walking, slower gait speed, and greater postural sway in older adults with MCI. Forty older adults with MCI were measured on a dual task-walking paradigm, gait speed over a 4-m walk, and postural sway using a sway-meter. Greater within-DMN connectivity was significantly correlated with poorer dual task performance. Furthermore, greater inter-network connectivity between the DMN and SMA was significantly correlated with slower gait speed and greater postural sway on the eyes open floor sway task. Thus, greater resting state DMN functional connectivity may be an underlying neural mechanism for reduced dual

Resting State Default Mode Network Connectivity, Dual Task Performance, Gait Speed, and Postural Sway in Older Adults with Mild Cognitive Impairment.

PubMed

Crockett, Rachel A; Hsu, Chun Liang; Best, John R; Liu-Ambrose, Teresa

2017-01-01

Aging is associated with an increased risk of falling. In particular, older adults with mild cognitive impairment (MCI) are more vulnerable to falling compared with their healthy counterparts. Major contributors to this increased falls risk include a decline in dual task performance, gait speed, and postural sway. Recent evidence highlights the potential influence of the default mode network (DMN), the frontoparietal network (FPN), and the supplementary motor area (SMA) on dual task performance, gait speed, and postural sway. The DMN is active during rest and deactivates during task-oriented processes, to maintain attention and stay on task. The FPN and SMA are involved in top-down attentional control, motor planning, and motor execution. The DMN shows less deactivation during task in older adults with MCI. This lack of deactivation is theorized to increase competition for resources between the DMN and task-related brain regions (e.g., the FPN and SMA), increasing distraction from the task and reducing task performance. However, no study has yet investigated the relationship between the between-network connectivity of the DMN with these regions and dual task walking, gait speed or postural sway. We hypothesized that greater functional connectivity both within the DMN and between DMN-FPN and DMN-SMA, will be associated with poorer performance during dual task walking, slower gait speed, and greater postural sway in older adults with MCI. Forty older adults with MCI were measured on a dual task-walking paradigm, gait speed over a 4-m walk, and postural sway using a sway-meter. Greater within-DMN connectivity was significantly correlated with poorer dual task performance. Furthermore, greater inter-network connectivity between the DMN and SMA was significantly correlated with slower gait speed and greater postural sway on the eyes open floor sway task. Thus, greater resting state DMN functional connectivity may be an underlying neural mechanism for reduced dual task

Modulating Intrinsic Connectivity: Adjacent Subregions within Supplementary Motor Cortex, Dorsolateral Prefrontal Cortex, and Parietal Cortex Connect to Separate Functional Networks during Task and Also Connect during Rest

PubMed Central

Roth, Jennifer K.; Johnson, Marcia K.; Tokoglu, Fuyuze; Murphy, Isabella; Constable, R. Todd

2014-01-01

Supplementary motor area (SMA), the inferior frontal junction (IFJ), superior frontal junction (SFJ) and parietal cortex are active in many cognitive tasks. In a previous study, we found that subregions of each of these major areas were differentially active in component processes of executive function during working memory tasks. In the present study, each of these subregions was used as a seed in a whole brain functional connectivity analysis of working memory and resting state data. These regions show functional connectivity to different networks, thus supporting the parcellation of these major regions into functional subregions. Many regions showing significant connectivity during the working memory residual data (with task events regressed from the data) were also significantly connected during rest suggesting that these network connections to subregions within major regions of cortex are intrinsic. For some of these connections, task demands modulate activity in these intrinsic networks. Approximately half of the connections significant during task were significant during rest, indicating that some of the connections are intrinsic while others are recruited only in the service of the task. Furthermore, the network connections to traditional ‘task positive’ and ‘task negative’ (a.k.a ‘default mode’) regions shift from positive connectivity to negative connectivity depending on task demands. These findings demonstrate that such task-identified subregions are part of distinct networks, and that these networks have different patterns of connectivity for task as they do during rest, engaging connections both to task positive and task negative regions. These results have implications for understanding the parcellation of commonly active regions into more specific functional networks. PMID:24637793

Altered segregation between task-positive and task-negative regions in mild traumatic brain injury.

PubMed

Sours, Chandler; Kinnison, Joshua; Padmala, Srikanth; Gullapalli, Rao P; Pessoa, Luiz

2018-06-01

Changes in large-scale brain networks that accompany mild traumatic brain injury (mTBI) were investigated using functional magnetic resonance imaging (fMRI) during the N-back working memory task at two cognitive loads (1-back and 2-back). Thirty mTBI patients were examined during the chronic stage of injury and compared to 28 control participants. Demographics and behavioral performance were matched across groups. Due to the diffuse nature of injury, we hypothesized that there would be an imbalance in the communication between task-positive and Default Mode Network (DMN) regions in the context of effortful task execution. Specifically, a graph-theoretic measure of modularity was used to quantify the extent to which groups of brain regions tended to segregate into task-positive and DMN sub-networks. Relative to controls, mTBI patients showed reduced segregation between the DMN and task-positive networks, but increased functional connectivity within the DMN regions during the more cognitively demanding 2-back task. Together, our findings reveal that patients exhibit alterations in the communication between and within neural networks during a cognitively demanding task. These findings reveal altered processes that persist through the chronic stage of injury, highlighting the need for longitudinal research to map the neural recovery of mTBI patients.

Complex network analysis of brain functional connectivity under a multi-step cognitive task

NASA Astrophysics Data System (ADS)

Cai, Shi-Min; Chen, Wei; Liu, Dong-Bai; Tang, Ming; Chen, Xun

2017-01-01

Functional brain network has been widely studied to understand the relationship between brain organization and behavior. In this paper, we aim to explore the functional connectivity of brain network under a multi-step cognitive task involving consecutive behaviors, and further understand the effect of behaviors on the brain organization. The functional brain networks are constructed based on a high spatial and temporal resolution fMRI dataset and analyzed via complex network based approach. We find that at voxel level the functional brain network shows robust small-worldness and scale-free characteristics, while its assortativity and rich-club organization are slightly restricted to the order of behaviors performed. More interestingly, the functional connectivity of brain network in activated ROIs strongly correlates with behaviors and is obviously restricted to the order of behaviors performed. These empirical results suggest that the brain organization has the generic properties of small-worldness and scale-free characteristics, and its diverse functional connectivity emerging from activated ROIs is strongly driven by these behavioral activities via the plasticity of brain.

Longitudinal relationship of parental hypertension with body mass index, blood pressure, and cardiovascular reactivity in children.

PubMed

Li, Rongling; Alpert, Bruce S; Walker, Sammie S; Somes, Grant W

2007-05-01

To investigate whether parental hypertension (HTN) affects children's body mass index (BMI) and cardiovascular reactivity (CVR) over time. A longitudinal study of 315 students (black: 23 females, 19 males; white: 142 females, 131 males) was conducted in the public schools of Obion County, Tennessee, between 1987 and 1992. BMI and BMI z scores were calculated. The CVR task was a series of video games (taking approximately 10 minutes to play) given to the same students in their third-, fourth-, fifth-, seventh-, and eighth-grade years. CVR was defined as the change in blood pressure (delta_BP) or heart rate (delta_HR) between before playing and while playing the video game. Positive parental history of HTN (27.6%) was defined as at least 1 parent with HTN. Multivariable regression analyses were performed to estimate the effects of parental HTN on children's BMI and CVR over time. Children with parental HTN had significant higher BMI, BMI z score, and R_BP than did children without parental HTN (BMI: 21.6 vs 19.9, P = .001; BMI z score: 1.6 vs 1.1, P = .003; R_SBP: 112.6 vs 110.4 mm Hg, P = .01; R_DBP 62.7 vs 60.6 mm Hg, P = .003) after adjustment for covariates. Increased CVR was observed in children with parental HTN compared with children without parental HTN but was statistically significant only for SBP (delta_SBP: 17.2 vs 14.9 mm Hg; P = .01) after adjustment for covariates. Parental HTN independently predicted children's BMI, BMI z score, resting BP, and BP reactivity.

Cognitive Task Analysis and Work-Centered Support System Recommendations for a Deployed Network Operations Support Center (NOSC-D)

DTIC Science & Technology

2001-08-01

This report presents the results of a preliminary Cognitive Task Analysis (CTA) of the deployed Network Operations Support Center (NOSC-D), and the...conducted Cognitive Task Analysis interviews with four (4) NOSC-D personnel. Because of the preliminary nature of the finding, the analysis is

Brain functional network changes following Prelimbic area inactivation in a spatial memory extinction task.

PubMed

Méndez-Couz, Marta; Conejo, Nélida M; Vallejo, Guillermo; Arias, Jorge L

2015-01-01

Several studies suggest a prefrontal cortex involvement during the acquisition and consolidation of spatial memory, suggesting an active modulating role at late stages of acquisition processes. Recently, we have reported that the prelimbic and infralimbic areas of the prefrontal cortex, among other structures, are also specifically involved in the late phases of spatial memory extinction. This study aimed to evaluate whether the inactivation of the prelimbic area of the prefrontal cortex impaired spatial memory extinction. For this purpose, male Wistar rats were implanted bilaterally with cannulae into the prelimbic region of the prefrontal cortex. Animals were trained during 5 consecutive days in a hidden platform task and tested for reference spatial memory immediately after the last training session. One day after completing the training task, bilateral infusion of the GABAA receptor agonist Muscimol was performed before the extinction protocol was carried out. Additionally, cytochrome c oxidase histochemistry was applied to map the metabolic brain activity related to the spatial memory extinction under prelimbic cortex inactivation. Results show that animals acquired the reference memory task in the water maze, and the extinction task was successfully completed without significant impairment. However, analysis of the functional brain networks involved by cytochrome oxidase activity interregional correlations showed changes in brain networks between the group treated with Muscimol as compared to the saline-treated group, supporting the involvement of the mammillary bodies at a the late stage in the memory extinction process. Copyright © 2015 Elsevier B.V. All rights reserved.

Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis

PubMed Central

2015-01-01

Background Modern methods for mining biomolecular interactions from literature typically make predictions based solely on the immediate textual context, in effect a single sentence. No prior work has been published on extending this context to the information automatically gathered from the whole biomedical literature. Thus, our motivation for this study is to explore whether mutually supporting evidence, aggregated across several documents can be utilized to improve the performance of the state-of-the-art event extraction systems. In this paper, we describe our participation in the latest BioNLP Shared Task using the large-scale text mining resource EVEX. We participated in the Genia Event Extraction (GE) and Gene Regulation Network (GRN) tasks with two separate systems. In the GE task, we implemented a re-ranking approach to improve the precision of an existing event extraction system, incorporating features from the EVEX resource. In the GRN task, our system relied solely on the EVEX resource and utilized a rule-based conversion algorithm between the EVEX and GRN formats. Results In the GE task, our re-ranking approach led to a modest performance increase and resulted in the first rank of the official Shared Task results with 50.97% F-score. Additionally, in this paper we explore and evaluate the usage of distributed vector representations for this challenge. In the GRN task, we ranked fifth in the official results with a strict/relaxed SER score of 0.92/0.81 respectively. To try and improve upon these results, we have implemented a novel machine learning based conversion system and benchmarked its performance against the original rule-based system. Conclusions For the GRN task, we were able to produce a gene regulatory network from the EVEX data, warranting the use of such generic large-scale text mining data in network biology settings. A detailed performance and error analysis provides more insight into the relatively low recall rates. In the GE task we

Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

PubMed

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

An Electroencephalography Network and Connectivity Analysis for Deception in Instructed Lying Tasks

PubMed Central

Wang, Yue; Ng, Wu Chun; Ng, Khoon Siong; Yu, Ke; Wu, Tiecheng; Li, Xiaoping

2015-01-01

Deception is an impactful social event that has been the focus of an abundance of researches over recent decades. In this paper, an electroencephalography (EEG) study is presented regarding the cognitive processes of an instructed liar/truth-teller during the time window of stimulus (question) delivery period (SDP) prior to their deceptive/truthful responses towards questions related to authentic (WE: with prior experience) and fictional experience (NE: no prior experience). To investigate deception in non-experienced events, the subjects were given stimuli in a mock interview scenario that induced them to fabricate lies. To analyze the data, frequency domain network and connectivity analysis was performed in the source space in order to provide a more systematic level understanding of deception during SDP. This study reveals several groups of neuronal generators underlying both the instructed lying (IL) and the instructed truth-telling (IT) conditions for both tasks during the SDP. Despite the similarities existed in these group components, significant differences were found in the intra- and inter-group connectivity between the IL and IT conditions in either task. Additionally, the response time was found to be positively correlated with the clustering coefficient of the inferior frontal gyrus (44R) in the WE-IL condition and positively correlated with the clustering coefficient of the precuneus (7L) and the angular gyrus (39R) in the WE-IT condition. However, the response time was found to be marginally negatively correlated with the clustering coefficient of the secondary auditory cortex (42L) in the NE-IL condition and negatively correlated with the clustering coefficient of the somatosensory association cortex (5L, R) in the NE-IT condition. Therefore, these results provide complementary and intuitive evidence for the differences between the IL and IT conditions in SDP for two types of deception tasks, thus elucidating the electrophysiological mechanisms

Hallucination- and speech-specific hypercoupling in frontotemporal auditory and language networks in schizophrenia using combined task-based fMRI data: An fBIRN study.

PubMed

Lavigne, Katie M; Woodward, Todd S

2018-04-01

Hypercoupling of activity in speech-perception-specific brain networks has been proposed to play a role in the generation of auditory-verbal hallucinations (AVHs) in schizophrenia; however, it is unclear whether this hypercoupling extends to nonverbal auditory perception. We investigated this by comparing schizophrenia patients with and without AVHs, and healthy controls, on task-based functional magnetic resonance imaging (fMRI) data combining verbal speech perception (SP), inner verbal thought generation (VTG), and nonverbal auditory oddball detection (AO). Data from two previously published fMRI studies were simultaneously analyzed using group constrained principal component analysis for fMRI (group fMRI-CPCA), which allowed for comparison of task-related functional brain networks across groups and tasks while holding the brain networks under study constant, leading to determination of the degree to which networks are common to verbal and nonverbal perception conditions, and which show coordinated hyperactivity in hallucinations. Three functional brain networks emerged: (a) auditory-motor, (b) language processing, and (c) default-mode (DMN) networks. Combining the AO and sentence tasks allowed the auditory-motor and language networks to separately emerge, whereas they were aggregated when individual tasks were analyzed. AVH patients showed greater coordinated activity (deactivity for DMN regions) than non-AVH patients during SP in all networks, but this did not extend to VTG or AO. This suggests that the hypercoupling in AVH patients in speech-perception-related brain networks is specific to perceived speech, and does not extend to perceived nonspeech or inner verbal thought generation. © 2017 Wiley Periodicals, Inc.

Lung nodule malignancy prediction using multi-task convolutional neural network

NASA Astrophysics Data System (ADS)

Li, Xiuli; Kao, Yueying; Shen, Wei; Li, Xiang; Xie, Guotong

2017-03-01

In this paper, we investigated the problem of diagnostic lung nodule malignancy prediction using thoracic Computed Tomography (CT) screening. Unlike most existing studies classify the nodules into two types benign and malignancy, we interpreted the nodule malignancy prediction as a regression problem to predict continuous malignancy level. We proposed a joint multi-task learning algorithm using Convolutional Neural Network (CNN) to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. We trained a CNN regression model to predict the nodule malignancy, and designed a multi-task learning mechanism to simultaneously share knowledge among 9 different nodule characteristics (Subtlety, Calcification, Sphericity, Margin, Lobulation, Spiculation, Texture, Diameter and Malignancy), and improved the final prediction result. Each CNN would generate characteristic-specific feature representations, and then we applied multi-task learning on the features to predict the corresponding likelihood for that characteristic. We evaluated the proposed method on 2620 nodules CT scans from LIDC-IDRI dataset with the 5-fold cross validation strategy. The multitask CNN regression result for regression RMSE and mapped classification ACC were 0.830 and 83.03%, while the results for single task regression RMSE 0.894 and mapped classification ACC 74.9%. Experiments show that the proposed method could predict the lung nodule malignancy likelihood effectively and outperforms the state-of-the-art methods. The learning framework could easily be applied in other anomaly likelihood prediction problem, such as skin cancer and breast cancer. It demonstrated the possibility of our method facilitating the radiologists for nodule staging assessment and individual therapeutic planning.

Time-Perception Network and Default Mode Network Are Associated with Temporal Prediction in a Periodic Motion Task

PubMed Central

Carvalho, Fabiana M.; Chaim, Khallil T.; Sanchez, Tiago A.; de Araujo, Draulio B.

2016-01-01

The updating of prospective internal models is necessary to accurately predict future observations. Uncertainty-driven internal model updating has been studied using a variety of perceptual paradigms, and have revealed engagement of frontal and parietal areas. In a distinct literature, studies on temporal expectations have also characterized a time-perception network, which relies on temporal orienting of attention. However, the updating of prospective internal models is highly dependent on temporal attention, since temporal attention must be reoriented according to the current environmental demands. In this study, we used functional magnetic resonance imaging (fMRI) to evaluate to what extend the continuous manipulation of temporal prediction would recruit update-related areas and the time-perception network areas. We developed an exogenous temporal task that combines rhythm cueing and time-to-contact principles to generate implicit temporal expectation. Two patterns of motion were created: periodic (simple harmonic oscillation) and non-periodic (harmonic oscillation with variable acceleration). We found that non-periodic motion engaged the exogenous temporal orienting network, which includes the ventral premotor and inferior parietal cortices, and the cerebellum, as well as the presupplementary motor area, which has previously been implicated in internal model updating, and the motion-sensitive area MT+. Interestingly, we found a right-hemisphere preponderance suggesting the engagement of explicit timing mechanisms. We also show that the periodic motion condition, when compared to the non-periodic motion, activated a particular subset of the default-mode network (DMN) midline areas, including the left dorsomedial prefrontal cortex (DMPFC), anterior cingulate cortex (ACC), and bilateral posterior cingulate cortex/precuneus (PCC/PC). It suggests that the DMN plays a role in processing contextually expected information and supports recent evidence that the DMN may

Impaired orienting in youth with Internet Addiction: Evidence from the Attention Network Task (ANT).

PubMed

Fu, Jia; Xu, Peng; Zhao, Lun; Yu, Guoming

2018-06-01

An important theory of attention suggests that there are three separate networks that execute discrete cognitive functions: alerting, orienting and conflict networks. Recent studies showed that there was a dysfunction of attention in Internet Addiction. In order to investigate the underlying mechanism of attention dysfunction in Internet Addiction, we recorded performance related to the Attentional Network Test (ANT) in youth. The ANT, a behavioral assay of the functional integrity of attention networks, was used to examine the performance in Internet Addiction and healthy controls. Performance on the ANT clearly differentiated the participants with and without Internet Addiction in terms of mean reaction times (RTs). Compared with control group, the Internet Addiction group detected targets more slowly and this effect was evident only for spatial cue condition. The Internet Addiction group demonstrated deficits in the orienting network in terms of slower RT. There was no demonstration of a deficit in both the alerting and conflict network in Internet Addiction on this task. The youth with Internet Addiction demonstrated deficits in the orienting network but normal functioning of the alerting and conflict attention networks. Copyright © 2017 Elsevier B.V. All rights reserved.

Abnormal Neural Network of Primary Insomnia: Evidence from Spatial Working Memory Task fMRI.

PubMed

Li, Yongli; Liu, Liya; Wang, Enfeng; Zhang, Hongju; Dou, Shewei; Tong, Li; Cheng, Jingliang; Chen, Chuanliang; Shi, Dapeng

2016-01-01

Contemporary functional MRI (fMRI) methods can provide a wealth of information about the neural mechanisms associated with primary insomnia (PI), which centrally involve neural network circuits related to spatial working memory. A total of 30 participants diagnosed with PI and without atypical brain anatomy were selected along with 30 age- and gender-matched healthy controls. Subjects were administered the Pittsburgh Sleep Quality Index (PSQI), Hamilton Rating Scale for Depression and clinical assessments of spatial working memory, followed by an MRI scan and fMRI in spatial memory task state. Statistically significant differences between PSQI and spatial working memory were observed between PI patients and controls (p < 0.01). Activation of neural networks related to spatial memory task state in the PI group was observed at the left temporal lobe, left occipital lobe and right frontal lobe. Lower levels of activation were observed in the left parahippocampal gyrus, right parahippocampal gyrus, bilateral temporal cortex, frontal cortex and superior parietal lobule. Participants with PI exhibited characteristic abnormalities in the neural network connectivity related to spatial working memory. These results may be indicative of an underlying pathological mechanism related to spatial working memory deterioration in PI, analogous to recently described mechanisms in other mental health disorders. © 2016 S. Karger AG, Basel.

Modulations of the executive control network by stimulus onset asynchrony in a Stroop task

PubMed Central

2013-01-01

Background Manipulating task difficulty is a useful way of elucidating the functional recruitment of the brain’s executive control network. In a Stroop task, pre-exposing the irrelevant word using varying stimulus onset asynchronies (‘negative’ SOAs) modulates the amount of behavioural interference and facilitation, suggesting disparate mechanisms of cognitive processing in each SOA. The current study employed a Stroop task with three SOAs (−400, -200, 0 ms), using functional magnetic resonance imaging to investigate for the first time the neural effects of SOA manipulation. Of specific interest were 1) how SOA affects the neural representation of interference and facilitation; 2) response priming effects in negative SOAs; and 3) attentional effects of blocked SOA presentation. Results The results revealed three regions of the executive control network that were sensitive to SOA during Stroop interference; the 0 ms SOA elicited the greatest activation of these areas but experienced relatively smaller behavioural interference, suggesting that the enhanced recruitment led to more efficient conflict processing. Response priming effects were localized to the right inferior frontal gyrus, which is consistent with the idea that this region performed response inhibition in incongruent conditions to overcome the incorrectly-primed response, as well as more general action updating and response preparation. Finally, the right superior parietal lobe was sensitive to blocked SOA presentation and was most active for the 0 ms SOA, suggesting that this region is involved in attentional control. Conclusions SOA exerted both trial-specific and block-wide effects on executive processing, providing a unique paradigm for functional investigations of the cognitive control network. PMID:23902451

A multinational clinical approach to assessing the effectiveness of catheter-based ultrasound renal denervation: The RADIANCE-HTN and REQUIRE clinical study designs.

PubMed

Mauri, Laura; Kario, Kazuomi; Basile, Jan; Daemen, Joost; Davies, Justin; Kirtane, Ajay J; Mahfoud, Felix; Schmieder, Roland E; Weber, Michael; Nanto, Shinsuke; Azizi, Michel

2018-01-01

Catheter-based renal denervation is a new approach to treat hypertension via modulation of the renal sympathetic nerves. Although nonrandomized and small, open-label randomized studies resulted in significant reductions in office blood pressure 6months after renal denervation with monopolar radiofrequency catheters, the first prospective, randomized, sham-controlled study (Symplicity HTN-3) failed to meet its blood pressure efficacy end point. New clinical trials with new catheters have since been designed to address the limitations of earlier studies. Accordingly, the RADIANCE-HTN and REQUIRE studies are multicenter, blinded, randomized, sham-controlled trials designed to assess the blood pressure-lowering efficacy of the ultrasound-based renal denervation system (Paradise) in patients with established hypertension either on or off antihypertensive medications, is designed to evaluate patients in 2 cohorts-SOLO and TRIO, in the United States and Europe. The SOLO cohort includes patients with essential hypertension, at low cardiovascular risk, and either controlled on 1 to 2 antihypertensive medications or uncontrolled on 0 to 2 antihypertensive medications. Patients undergo a 4-week medication washout period before randomization to renal denervation (treatment) or renal angiogram (sham). The TRIO cohort includes patients with hypertension resistant to at least 3 antihypertensive drugs including a diuretic. Patients will be stabilized on a single-pill, triple-antihypertensive-drug combination for 4weeks before randomization to treatment or sham. Reduction in daytime ambulatory systolic blood pressure (primary end point) will be assessed at 2months in both cohorts. A predefined medication escalation protocol, as needed for blood pressure control, is implemented between 2 and 6months in both cohorts by a study staff member blinded to the randomization process. At 6months, daytime ambulatory blood pressure and antihypertensive treatment score will be assessed. REQUIRE

High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia

PubMed Central

Plis, Sergey M; Sui, Jing; Lane, Terran; Roy, Sushmita; Clark, Vincent P; Potluru, Vamsi K; Huster, Rene J; Michael, Andrew; Sponheim, Scott R; Weisend, Michael P; Calhoun, Vince D

2013-01-01

Identifying the complex activity relationships present in rich, modern neuroimaging data sets remains a key challenge for neuroscience. The problem is hard because (a) the underlying spatial and temporal networks may be nonlinear and multivariate and (b) the observed data may be driven by numerous latent factors. Further, modern experiments often produce data sets containing multiple stimulus contexts or tasks processed by the same subjects. Fusing such multi-session data sets may reveal additional structure, but raises further statistical challenges. We present a novel analysis method for extracting complex activity networks from such multifaceted imaging data sets. Compared to previous methods, we choose a new point in the trade-off space, sacrificing detailed generative probability models and explicit latent variable inference in order to achieve robust estimation of multivariate, nonlinear group factors (“network clusters”). We apply our method to identify relationships of task-specific intrinsic networks in schizophrenia patients and control subjects from a large fMRI study. After identifying network-clusters characterized by within- and between-task interactions, we find significant differences between patient and control groups in interaction strength among networks. Our results are consistent with known findings of brain regions exhibiting deviations in schizophrenic patients. However, we also find high-order, nonlinear interactions that discriminate groups but that are not detected by linear, pair-wise methods. We additionally identify high-order relationships that provide new insights into schizophrenia but that have not been found by traditional univariate or second-order methods. Overall, our approach can identify key relationships that are missed by existing analysis methods, without losing the ability to find relationships that are known to be important. PMID:23876245

Task-Related Edge Density (TED)—A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

PubMed Central

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach “Task-related Edge Density” (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function. PMID:27341204

Task positive and default mode networks during a working memory in children with primary monosymptomatic nocturnal enuresis and healthy controls.

PubMed

Zhang, Kaihua; Ma, Jun; Lei, Du; Wang, Mengxing; Zhang, Jilei; Du, Xiaoxia

2015-10-01

Nocturnal enuresis is a common developmental disorder in children, and primary monosymptomatic nocturnal enuresis (PMNE) is the dominant subtype. This study investigated brain functional abnormalities that are specifically related to working memory in children with PMNE using function magnetic resonance imaging (fMRI) in combination with an n-back task. Twenty children with PMNE and 20 healthy children, group-matched for age and sex, participated in this experiment. Several brain regions exhibited reduced activation during the n-back task in children with PMNE, including the right precentral gyrus and the right inferior parietal lobule extending to the postcentral gyrus. Children with PMNE exhibited decreased cerebral activation in the task-positive network, increased task-related cerebral deactivation during a working memory task, and longer response times. Patients exhibited different brain response patterns to different levels of working memory and tended to compensate by greater default mode network deactivation to sustain normal working memory function. Our results suggest that children with PMNE have potential working memory dysfunction.

Mathematics anxiety reduces default mode network deactivation in response to numerical tasks.

PubMed

Pletzer, Belinda; Kronbichler, Martin; Nuerk, Hans-Christoph; Kerschbaum, Hubert H

2015-01-01

Mathematics anxiety is negatively related to mathematics performance, thereby threatening the professional success. Preoccupation with the emotional content of the stimuli may consume working memory resources, which may be reflected in decreased deactivation of areas associated with the default mode network (DMN) activated during self-referential and emotional processing. The common problem is that math anxiety is usually associated with poor math performance, so that any group differences are difficult to interpret. Here we compared the BOLD-response of 18 participants with high (HMAs) and 18 participants with low mathematics anxiety (LMAs) matched for their mathematical performance to two numerical tasks (number comparison, number bisection). During both tasks, we found stronger deactivation within the DMN in LMAs compared to HMAs, while BOLD-response in task-related activation areas did not differ between HMAs and LMAs. The difference in DMN deactivation between the HMA and LMA group was more pronounced in stimuli with additional requirement on inhibitory functions, but did not differ between number magnitude processing and arithmetic fact retrieval.

Mathematics anxiety reduces default mode network deactivation in response to numerical tasks

PubMed Central

Pletzer, Belinda; Kronbichler, Martin; Nuerk, Hans-Christoph; Kerschbaum, Hubert H.

2015-01-01

Mathematics anxiety is negatively related to mathematics performance, thereby threatening the professional success. Preoccupation with the emotional content of the stimuli may consume working memory resources, which may be reflected in decreased deactivation of areas associated with the default mode network (DMN) activated during self-referential and emotional processing. The common problem is that math anxiety is usually associated with poor math performance, so that any group differences are difficult to interpret. Here we compared the BOLD-response of 18 participants with high (HMAs) and 18 participants with low mathematics anxiety (LMAs) matched for their mathematical performance to two numerical tasks (number comparison, number bisection). During both tasks, we found stronger deactivation within the DMN in LMAs compared to HMAs, while BOLD-response in task-related activation areas did not differ between HMAs and LMAs. The difference in DMN deactivation between the HMA and LMA group was more pronounced in stimuli with additional requirement on inhibitory functions, but did not differ between number magnitude processing and arithmetic fact retrieval. PMID:25954179

Brain Activation for Language Dual-Tasking: Listening to Two People Speak at the Same Time and a Change in Network Timing

PubMed Central

Buchweitz, Augusto; Keller, Timothy A.; Meyler, Ann; Just, Marcel Adam

2011-01-01

The study used fMRI to investigate brain activation in participants who were able to listen to and successfully comprehend two people speaking at the same time (dual-tasking). The study identified brain mechanisms associated with high-level, concurrent dual-tasking, as compared to comprehending a single message. Results showed an increase in the functional connectivity among areas of the language network in the dual task. The increase in synchronization of brain activation for dual-tasking was brought about primarily by a change in the timing of left inferior frontal gyrus (LIFG) activation relative to posterior temporal activation, bringing the LIFG activation into closer correspondence with temporal activation. The results show that the change in LIFG timing was greater in participants with lower working memory capacity, and that recruitment of additional activation in the dual-task occurred only in the areas adjacent to the language network that was activated in the single task. The shift in LIFG activation may be a brain marker of how the brain adapts to high-level dual-tasking. PMID:21618666

Brain activation for language dual-tasking: listening to two people speak at the same time and a change in network timing.

PubMed

Buchweitz, Augusto; Keller, Timothy A; Meyler, Ann; Just, Marcel Adam

2012-08-01

The study used fMRI to investigate brain activation in participants who were able to listen to and successfully comprehend two people speaking at the same time (dual-tasking). The study identified brain mechanisms associated with high-level, concurrent dual-tasking, as compared with comprehending a single message. Results showed an increase in the functional connectivity among areas of the language network in the dual task. The increase in synchronization of brain activation for dual-tasking was brought about primarily by a change in the timing of left inferior frontal gyrus (LIFG) activation relative to posterior temporal activation, bringing the LIFG activation into closer correspondence with temporal activation. The results show that the change in LIFG timing was greater in participants with lower working memory capacity, and that recruitment of additional activation in the dual-task occurred only in the areas adjacent to the language network that was activated in the single task. The shift in LIFG activation may be a brain marker of how the brain adapts to high-level dual-tasking. Copyright © 2011 Wiley Periodicals, Inc.

A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

NASA Astrophysics Data System (ADS)

Tsumori, Kenji; Ozawa, Seiichi

When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

The functional neuroanatomy of multitasking: combining dual tasking with a short term memory task.

PubMed

Deprez, Sabine; Vandenbulcke, Mathieu; Peeters, Ron; Emsell, Louise; Amant, Frederic; Sunaert, Stefan

2013-09-01

Insight into the neural architecture of multitasking is crucial when investigating the pathophysiology of multitasking deficits in clinical populations. Presently, little is known about how the brain combines dual-tasking with a concurrent short-term memory task, despite the relevance of this mental operation in daily life and the frequency of complaints related to this process, in disease. In this study we aimed to examine how the brain responds when a memory task is added to dual-tasking. Thirty-three right-handed healthy volunteers (20 females, mean age 39.9 ± 5.8) were examined with functional brain imaging (fMRI). The paradigm consisted of two cross-modal single tasks (a visual and auditory temporal same-different task with short delay), a dual-task combining both single tasks simultaneously and a multi-task condition, combining the dual-task with an additional short-term memory task (temporal same-different visual task with long delay). Dual-tasking compared to both individual visual and auditory single tasks activated a predominantly right-sided fronto-parietal network and the cerebellum. When adding the additional short-term memory task, a larger and more bilateral frontoparietal network was recruited. We found enhanced activity during multitasking in components of the network that were already involved in dual-tasking, suggesting increased working memory demands, as well as recruitment of multitask-specific components including areas that are likely to be involved in online holding of visual stimuli in short-term memory such as occipito-temporal cortex. These results confirm concurrent neural processing of a visual short-term memory task during dual-tasking and provide evidence for an effective fMRI multitasking paradigm. © 2013 Elsevier Ltd. All rights reserved.

Task sharing within a managed clinical network to improve child health in Malawi.

PubMed

O'Hare, Bernadette; Phiri, Ajib; Lang, Hans-Joerg; Friesen, Hanny; Kennedy, Neil; Kawaza, Kondwani; Jana, Collins E; Chirambo, George; Mulwafu, Wakisa; Heikens, Geert T; Mipando, Mwapatsa

2015-07-21

Eighty per cent of Malawi's 8 million children live in rural areas, and there is an extensive tiered health system infrastructure from village health clinics to district hospitals which refers patients to one of the four central hospitals. The clinics and district hospitals are staffed by nurses, non-physician clinicians and recently qualified doctors. There are 16 paediatric specialists working in two of the four central hospitals which serve the urban population as well as accepting referrals from district hospitals. In order to provide expert paediatric care as close to home as possible, we describe our plan to task share within a managed clinical network and our hypothesis that this will improve paediatric care and child health. Managed clinical networks have been found to improve equity of care in rural districts and to ensure that the correct care is provided as close to home as possible. A network for paediatric care in Malawi with mentoring of non-physician clinicians based in a district hospital by paediatricians based at the central hospitals will establish and sustain clinical referral pathways in both directions. Ultimately, the plan envisages four managed paediatric clinical networks, each radiating from one of Malawi's four central hospitals and covering the entire country. This model of task sharing within four hub-and-spoke networks may facilitate wider dissemination of scarce expertise and improve child healthcare in Malawi close to the child's home. Funding has been secured to train sufficient personnel to staff all central and district hospitals in Malawi with teams of paediatric specialists in the central hospitals and specialist non-physician clinicians in each government district hospital. The hypothesis will be tested using a natural experiment model. Data routinely collected by the Ministry of Health will be corroborated at the district. This will include case fatality rates for common childhood illness, perinatal mortality and process

Analytical reasoning task reveals limits of social learning in networks

PubMed Central

Rahwan, Iyad; Krasnoshtan, Dmytro; Shariff, Azim; Bonnefon, Jean-François

2014-01-01

Social learning—by observing and copying others—is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an ‘unreflective copying bias’, which limits their social learning to the output, rather than the process, of their peers’ reasoning—even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning. PMID:24501275

Analytical reasoning task reveals limits of social learning in networks.

PubMed

Rahwan, Iyad; Krasnoshtan, Dmytro; Shariff, Azim; Bonnefon, Jean-François

2014-04-06

Social learning-by observing and copying others-is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias', which limits their social learning to the output, rather than the process, of their peers' reasoning-even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.

Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

2018-02-01

We propose a cross-domain, multi-task transfer learning framework to transfer knowledge learned from non-medical images by a deep convolutional neural network (DCNN) to medical image recognition task while improving the generalization by multi-task learning of auxiliary tasks. A first stage cross-domain transfer learning was initiated from ImageNet trained DCNN to mammography trained DCNN. 19,632 regions-of-interest (ROI) from 2,454 mass lesions were collected from two imaging modalities: digitized-screen film mammography (SFM) and full-field digital mammography (DM), and split into training and test sets. In the multi-task transfer learning, the DCNN learned the mass classification task simultaneously from the training set of SFM and DM. The best transfer network for mammography was selected from three transfer networks with different number of convolutional layers frozen. The performance of single-task and multitask transfer learning on an independent SFM test set in terms of the area under the receiver operating characteristic curve (AUC) was 0.78+/-0.02 and 0.82+/-0.02, respectively. In the second stage cross-domain transfer learning, a set of 12,680 ROIs from 317 mass lesions on DBT were split into validation and independent test sets. We first studied the data requirements for the first stage mammography trained DCNN by varying the mammography training data from 1% to 100% and evaluated its learning on the DBT validation set in inference mode. We found that the entire available mammography set provided the best generalization. The DBT validation set was then used to train only the last four fully connected layers, resulting in an AUC of 0.90+/-0.04 on the independent DBT test set.

Differential Contribution of Bilateral Supplementary Motor Area to the Effective Connectivity Networks Induced by Task Conditions Using Dynamic Causal Modeling

PubMed Central

Tao, Zhongping; Zhang, Mu

2014-01-01

Abstract Functional imaging studies have indicated hemispheric asymmetry of activation in bilateral supplementary motor area (SMA) during unimanual motor tasks. However, the hemispherically special roles of bilateral SMAs on primary motor cortex (M1) in the effective connectivity networks (ECN) during lateralized tasks remain unclear. Aiming to study the differential contribution of bilateral SMAs during the motor execution and motor imagery tasks, and the hemispherically asymmetric patterns of ECN among regions involved, the present study used dynamic causal modeling to analyze the functional magnetic resonance imaging data of the unimanual motor execution/imagery tasks in 12 right-handed subjects. Our results demonstrated that distributions of network parameters underlying motor execution and motor imagery were significantly different. The variation was mainly induced by task condition modulations of intrinsic coupling. Particularly, regardless of the performing hand, the task input modulations of intrinsic coupling from the contralateral SMA to contralateral M1 were positive during motor execution, while varied to be negative during motor imagery. The results suggested that the inhibitive modulation suppressed the overt movement during motor imagery. In addition, the left SMA also helped accomplishing left hand tasks through task input modulation of left SMA→right SMA connection, implying that hemispheric recruitment occurred when performing nondominant hand tasks. The results specified differential and altered contributions of bilateral SMAs to the ECN during unimanual motor execution and motor imagery, and highlighted the contributions induced by the task input of motor execution/imagery. PMID:24606178

Dynamic afferent synapses to decision-making networks improve performance in tasks requiring stimulus associations and discriminations

PubMed Central

Bourjaily, Mark A.

2012-01-01

Animals must often make opposing responses to similar complex stimuli. Multiple sensory inputs from such stimuli combine to produce stimulus-specific patterns of neural activity. It is the differences between these activity patterns, even when small, that provide the basis for any differences in behavioral response. In the present study, we investigate three tasks with differing degrees of overlap in the inputs, each with just two response possibilities. We simulate behavioral output via winner-takes-all activity in one of two pools of neurons forming a biologically based decision-making layer. The decision-making layer receives inputs either in a direct stimulus-dependent manner or via an intervening recurrent network of neurons that form the associative layer, whose activity helps distinguish the stimuli of each task. We show that synaptic facilitation of synapses to the decision-making layer improves performance in these tasks, robustly increasing accuracy and speed of responses across multiple configurations of network inputs. Conversely, we find that synaptic depression worsens performance. In a linearly nonseparable task with exclusive-or logic, the benefit of synaptic facilitation lies in its superlinear transmission: effective synaptic strength increases with presynaptic firing rate, which enhances the already present superlinearity of presynaptic firing rate as a function of stimulus-dependent input. In linearly separable single-stimulus discrimination tasks, we find that facilitating synapses are always beneficial because synaptic facilitation always enhances any differences between inputs. Thus we predict that for optimal decision-making accuracy and speed, synapses from sensory or associative areas to decision-making or premotor areas should be facilitating. PMID:22457467

Executive and arousal vigilance decrement in the context of the attentional networks: The ANTI-Vea task.

PubMed

Luna, Fernando Gabriel; Marino, Julián; Roca, Javier; Lupiáñez, Juan

2018-05-20

Vigilance is generally understood as the ability to detect infrequent critical events through long time periods. In tasks like the Sustained Attention to Response Task (SART), participants tend to detect fewer events across time, a phenomenon known as "vigilance decrement". However, vigilance might also involve sustaining a tonic arousal level. In the Psychomotor Vigilance Test (PVT), the vigilance decrement corresponds to an increment across time in both mean and variability of reaction time. The present study aimed to develop a single task -Attentional Networks Test for Interactions and Vigilance - executive and arousal components (ANTI-Vea)- to simultaneously assess both components of vigilance (i.e., the executive vigilance as in the SART, and the arousal vigilance as in the PVT), while measuring the classic attentional functions (phasic alertness, orienting, and executive control). In Experiment #1, the executive vigilance decrement was found as an increment in response bias. In Experiment #2, this result was replicated, and the arousal vigilance decrement was simultaneously observed as an increment in reaction time. The ANTI-Vea solves some issues observed in the previous ANTI-V task with the executive vigilance measure (e.g., a low hit rate and no vigilance decrement). Furthermore, the new ANTI-Vea task assesses both components of vigilance together with others typical attentional functions. The new attentional networks test developed here may be useful to provide a better understanding of the human attentional system. The role of sensitivity and response bias in the executive vigilance decrement are discussed. Copyright © 2018 Elsevier B.V. All rights reserved.

Modeling Network Interdiction Tasks

DTIC Science & Technology

2015-09-17

they may attack the flaw to cause widespread chaos. Attacks such as these are considered a form of network interdiction. Assessing the networks over...and forms a foundation for the techniques of the measures and models approaches of the research framework, which is depicted in Figure 2. The...ensures the distance of the shortest (i, j) path is computed. This insight is attributed to Warshall [62]. The algorithm’s present form is attributed

Queueing Network Models for Parallel Processing of Task Systems: an Operational Approach

NASA Technical Reports Server (NTRS)

Mak, Victor W. K.

1986-01-01

Computer performance modeling of possibly complex computations running on highly concurrent systems is considered. Earlier works in this area either dealt with a very simple program structure or resulted in methods with exponential complexity. An efficient procedure is developed to compute the performance measures for series-parallel-reducible task systems using queueing network models. The procedure is based on the concept of hierarchical decomposition and a new operational approach. Numerical results for three test cases are presented and compared to those of simulations.

Altered Distant Synchronization of Background Network in Mild Cognitive Impairment during an Executive Function Task.

PubMed

Wang, Pengyun; Li, Rui; Yu, Jing; Huang, Zirui; Yan, Zhixiong; Zhao, Ke; Li, Juan

2017-01-01

Few studies to date have investigated the background network in the cognitive state relying on executive function in mild cognitive impairment (MCI) patients. Using the index of degree of centrality (DC), we explored distant synchronization of background network in MCI during a hybrid delayed-match-to-sample task (DMST), which mainly relies on the working memory component of executive function. We observed significant interactions between group and cognitive state in the bilateral posterior cingulate cortex (PCC) and the ventral subregion of precuneus. For normal control (NC) group, the long distance functional connectivity (FC) of the PCC/precuneus with the other regions of the brain was higher in rest state than that working memory state. For MCI patients, however, this pattern altered. There was no significant difference between rest and working memory state. The similar pattern was observed in the other cluster located in the right angular gyrus. To examine whether abnormal DC in PCC/precuneus and angular gyrus partially resulted from the deficit of FC between these regions and the other parts in the whole brain, we conducted a seed-based correlation analysis with these regions as seeds. The results indicated that the FC between bilateral PCC/precuneus and the right inferior parietal lobule (IPL) increased from rest to working memory state for NC participants. For MCI patients, however, there was no significant change between rest and working memory state. The similar pattern was observed for the FC between right angular gyrus and right anterior insula. However, there was no difference between MCI and NC groups in global efficiency and modularity. It may indicate a lack of efficient reorganization from rest state to a working memory state in the brain network of MCI patients. The present study demonstrates the altered distant synchronization of background network in MCI during a task relying on executive function. The results provide a new perspective regarding

Assessing the risk of incident hypertension and chronic kidney disease after exposure to shockwave lithotripsy and ureteroscopy

PubMed Central

Denburg, Michelle R.; Jemielita, Thomas; Tasian, Gregory; Haynes, Kevin; Mucksavage, Phillip; Shults, Justine; Copelovitch, Lawrence

2015-01-01

In this study we sought to determine if among individuals with urolithiasis, extracorporeal shock wave lithotripsy (SWL) and ureteroscopy are associated with a higher risk of incident arterial hypertension (HTN) and/or chronic kidney disease (CKD). This was measured in a population-based retrospective study of 11,570 participants with incident urolithiasis and 127,464 without urolithiasis in The Health Improvement Network. Patients with pre-existing HTN and CKD were excluded. The study included 1319 and 919 urolithiasis patients with at least one SWL or URS procedure, respectively. Multivariable Cox regression was used to estimate the hazard ratio for incident CKD stage 3–5 and HTN in separate analyses. Over a median of 3.7 and 4.1 years, 1423 and 595 of urolithiasis participants developed HTN and CKD, respectively. Urolithiasis was associated with a significant hazard ratio each for HTN of 1.42 (95% CI: 1.35, 1.51) and for CKD of 1.82 (1.67, 1.98). SWL was associated with a significant increased risk of HTN 1.34 (1.15, 1.57), while ureteroscopy was not. When further stratified as SWL to the kidney or ureter, only SWL to the kidney was significantly and independently associated with HTN 1.40 (1.19, 1.66). Neither SWL nor ureteroscopy was associated with incident CKD. Since urolithiasis itself was associated with a hazard ratio of 1.42 for HTN, an individual who undergoes SWL to the kidney can be expected to have a significantly increased hazard ratio for HTN of 1.96 (1.67, 2.29) compared to an individual without urolithiasis. PMID:26509587

Shaped by our thoughts--a new task to assess spontaneous cognition and its associated neural correlates in the default network.

PubMed

O'Callaghan, Claire; Shine, James M; Lewis, Simon J G; Andrews-Hanna, Jessica R; Irish, Muireann

2015-02-01

Self-generated cognition, or mind wandering, refers to the quintessentially human tendency to withdraw from the immediate external environment and engage in internally driven mentation. This thought activity is suggested to be underpinned by a distributed set of regions in the brain, referred to as the default network. To date, experimental assessment of mind wandering has typically taken place during performance of a concurrent attention-demanding task. The attentional demands of concurrent tasks can influence the emergence of mind wandering, and their application to clinical disorders with reduced cognitive resources is limited. Furthermore, few paradigms have investigated the phenomenological content of mind wandering episodes. Here, we present data from a novel thought sampling task that measures both the frequency and qualitative content of mind wandering, in the absence of a concurrent task to reduce cognitive demand. The task was validated in a non-pathological cohort of 31 older controls and resting-state functional connectivity analyses in a subset of participants (n=18) was conducted to explore the neural bases of mind wandering. Overall, instances of mind wandering were found to occur in 37% of experimental trials. Resting state functional connectivity analyses confirmed that mind wandering frequency was associated with regional patterns of both increased and decreased default network connectivity, namely in the temporal lobe, posterior cingulate cortex and dorsal medial prefrontal cortex. Our findings demonstrate that the novel task provides a context of low cognitive demand, which is conducive to mind wandering. Furthermore, performance on the task is associated with specific patterns of functional connectivity in the default network. Together, this new paradigm offers an important avenue to investigate the frequency and content of mind wandering in the context of low cognitive demands, and has significant potential to be applied in clinical conditions

Dysfunctional default mode network and executive control network in people with Internet gaming disorder: Independent component analysis under a probability discounting task.

PubMed

Wang, L; Wu, L; Lin, X; Zhang, Y; Zhou, H; Du, X; Dong, G

2016-04-01

The present study identified the neural mechanism of risky decision-making in Internet gaming disorder (IGD) under a probability discounting task. Independent component analysis was used on the functional magnetic resonance imaging data from 19 IGD subjects (22.2 ± 3.08 years) and 21 healthy controls (HC, 22.8 ± 3.5 years). For the behavioral results, IGD subjects prefer the risky to the fixed options and showed shorter reaction time compared to HC. For the imaging results, the IGD subjects showed higher task-related activity in default mode network (DMN) and less engagement in the executive control network (ECN) than HC when making the risky decisions. Also, we found the activities of DMN correlate negatively with the reaction time and the ECN correlate positively with the probability discounting rates. The results suggest that people with IGD show altered modulation in DMN and deficit in executive control function, which might be the reason for why the IGD subjects continue to play online games despite the potential negative consequences. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Task-dependent activity and connectivity predict episodic memory network-based responses to brain stimulation in healthy aging.

PubMed

Vidal-Piñeiro, Dídac; Martin-Trias, Pablo; Arenaza-Urquijo, Eider M; Sala-Llonch, Roser; Clemente, Imma C; Mena-Sánchez, Isaias; Bargalló, Núria; Falcón, Carles; Pascual-Leone, Álvaro; Bartrés-Faz, David

2014-01-01

Transcranial magnetic stimulation (TMS) can affect episodic memory, one of the main cognitive hallmarks of aging, but the mechanisms of action remain unclear. To evaluate the behavioral and functional impact of excitatory TMS in a group of healthy elders. We applied a paradigm of repetitive TMS - intermittent theta-burst stimulation - over left inferior frontal gyrus in healthy elders (n = 24) and evaluated its impact on the performance of an episodic memory task with two levels of processing and the associated brain activity as captured by a pre and post fMRI scans. In the post-TMS fMRI we found TMS-related activity increases in left prefrontal and cerebellum-occipital areas specifically during deep encoding but not during shallow encoding or at rest. Furthermore, we found a task-dependent change in connectivity during the encoding task between cerebellum-occipital areas and the TMS-targeted left inferior frontal region. This connectivity change correlated with the TMS effects over brain networks. The results suggest that the aged brain responds to brain stimulation in a state-dependent manner as engaged by different tasks components and that TMS effect is related to inter-individual connectivity changes measures. These findings reveal fundamental insights into brain network dynamics in aging and the capacity to probe them with combined behavioral and stimulation approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

Task-dependent activity and connectivity predict episodic memory network-based responses to brain stimulation in healthy aging

PubMed Central

Vidal-Piñeiro, Dídac; Martin-Trias, Pablo; Arenaza-Urquijo, Eider M.; Sala-Llonch, Roser; Mena-Sánchez, Isaias; Bargalló, Núria; Falcón, Carles; Pascual-Leone, Álvaro; Bartrés-Faz, David

2015-01-01

Background Transcranial Magnetic Stimulation (TMS) can affect episodic memory, one of the main cognitive hallmarks of aging, but the mechanisms of action remain unclear. Objectives To evaluate the behavioral and functional impact of excitatory TMS in a group of healthy elders. Methods We applied a paradigm of repetitive TMS -intermittent theta-burst stimulation- over left inferior frontal gyrus in healthy elders (n=24) and evaluated its impact on the performance of an episodic memory task with two levels of processing and the associated brain activity as captured by a pre and post fMRI scans. Results In the post-TMS fMRI we found TMS-related activity increases in left prefrontal and cerebellum-occipital areas specifically during deep encoding but not during shallow encoding or at rest. Furthermore, we found a task-dependent change in connectivity during the encoding task between cerebellum-occipital areas and the TMS-targeted left inferior frontal region. This connectivity change correlated with the TMS effects over brain networks. Conclusions The results suggest that the aged brain responds to brain stimulation in a state-dependent manner as engaged by different tasks components and that TMS effect is related to inter-individual connectivity changes measures. These findings reveal fundamental insights into brain network dynamics in aging and the capacity to probe them with combined behavioral and stimulation approaches. PMID:24485466

Functional mapping of language networks in the normal brain using a word-association task.

PubMed

Ghosh, Shantanu; Basu, Amrita; Kumaran, Senthil S; Khushu, Subash

2010-08-01

Language functions are known to be affected in diverse neurological conditions, including ischemic stroke, traumatic brain injury, and brain tumors. Because language networks are extensive, interpretation of functional data depends on the task completed during evaluation. The aim was to map the hemodynamic consequences of word association using functional magnetic resonance imaging (fMRI) in normal human subjects. Ten healthy subjects underwent fMRI scanning with a postlexical access semantic association task vs lexical processing task. The fMRI protocol involved a T2*-weighted gradient-echo echo-planar imaging (GE-EPI) sequence (TR 4523 ms, TE 64 ms, flip angle 90°) with alternate baseline and activation blocks. A total of 78 scans were taken (interscan interval = 3 s) with a total imaging time of 587 s. Functional data were processed in Statistical Parametric Mapping software (SPM2) with 8-mm Gaussian kernel by convolving the blood oxygenation level-dependent (BOLD) signal with an hemodynamic response function estimated by general linear method to generate SPM{t} and SPM{F} maps. Single subject analysis of the functional data (FWE-corrected, P≤0.001) revealed extensive activation in the frontal lobes, with overlaps among middle frontal gyrus (MFG), superior, and inferior frontal gyri. BOLD activity was also found in the medial frontal gyrus, middle occipital gyrus (MOG), anterior fusiform gyrus, superior and inferior parietal lobules, and to a smaller extent, the thalamus and right anterior cerebellum. Group analysis (FWE-corrected, P≤0.001) revealed neural recruitment of bilateral lingual gyri, left MFG, bilateral MOG, left superior occipital gyrus, left fusiform gyrus, bilateral thalami, and right cerebellar areas. Group data analysis revealed a cerebellar-occipital-fusiform-thalamic network centered around bilateral lingual gyri for word association, thereby indicating how these areas facilitate language comprehension by activating a semantic association

Internet protocol network mapper

DOEpatents

Youd, David W.; Colon III, Domingo R.; Seidl, Edward T.

2016-02-23

A network mapper for performing tasks on targets is provided. The mapper generates a map of a network that specifies the overall configuration of the network. The mapper inputs a procedure that defines how the network is to be mapped. The procedure specifies what, when, and in what order the tasks are to be performed. Each task specifies processing that is to be performed for a target to produce results. The procedure may also specify input parameters for a task. The mapper inputs initial targets that specify a range of network addresses to be mapped. The mapper maps the network by, for each target, executing the procedure to perform the tasks on the target. The results of the tasks represent the mapping of the network defined by the initial targets.

Person re-identification over camera networks using multi-task distance metric learning.

PubMed

Ma, Lianyang; Yang, Xiaokang; Tao, Dacheng

2014-08-01

Person reidentification in a camera network is a valuable yet challenging problem to solve. Existing methods learn a common Mahalanobis distance metric by using the data collected from different cameras and then exploit the learned metric for identifying people in the images. However, the cameras in a camera network have different settings and the recorded images are seriously affected by variability in illumination conditions, camera viewing angles, and background clutter. Using a common metric to conduct person reidentification tasks on different camera pairs overlooks the differences in camera settings; however, it is very time-consuming to label people manually in images from surveillance videos. For example, in most existing person reidentification data sets, only one image of a person is collected from each of only two cameras; therefore, directly learning a unique Mahalanobis distance metric for each camera pair is susceptible to over-fitting by using insufficiently labeled data. In this paper, we reformulate person reidentification in a camera network as a multitask distance metric learning problem. The proposed method designs multiple Mahalanobis distance metrics to cope with the complicated conditions that exist in typical camera networks. We address the fact that these Mahalanobis distance metrics are different but related, and learned by adding joint regularization to alleviate over-fitting. Furthermore, by extending, we present a novel multitask maximally collapsing metric learning (MtMCML) model for person reidentification in a camera network. Experimental results demonstrate that formulating person reidentification over camera networks as multitask distance metric learning problem can improve performance, and our proposed MtMCML works substantially better than other current state-of-the-art person reidentification methods.

Task-dependent individual differences in prefrontal connectivity.

PubMed

Biswal, Bharat B; Eldreth, Dana A; Motes, Michael A; Rypma, Bart

2010-09-01

Recent advances in neuroimaging have permitted testing of hypotheses regarding the neural bases of individual differences, but this burgeoning literature has been characterized by inconsistent results. To test the hypothesis that differences in task demands could contribute to between-study variability in brain-behavior relationships, we had participants perform 2 tasks that varied in the extent of cognitive involvement. We examined connectivity between brain regions during a low-demand vigilance task and a higher-demand digit-symbol visual search task using Granger causality analysis (GCA). Our results showed 1) Significant differences in numbers of frontoparietal connections between low- and high-demand tasks 2) that GCA can detect activity changes that correspond with task-demand changes, and 3) faster participants showed more vigilance-related activity than slower participants, but less visual-search activity. These results suggest that relatively low-demand cognitive performance depends on spontaneous bidirectionally fluctuating network activity, whereas high-demand performance depends on a limited, unidirectional network. The nature of brain-behavior relationships may vary depending on the extent of cognitive demand. High-demand network activity may reflect the extent to which individuals require top-down executive guidance of behavior for successful task performance. Low-demand network activity may reflect task- and performance monitoring that minimizes executive requirements for guidance of behavior.

Task-Dependent Individual Differences in Prefrontal Connectivity

PubMed Central

Biswal, Bharat B.; Eldreth, Dana A.; Motes, Michael A.

2010-01-01

Recent advances in neuroimaging have permitted testing of hypotheses regarding the neural bases of individual differences, but this burgeoning literature has been characterized by inconsistent results. To test the hypothesis that differences in task demands could contribute to between-study variability in brain-behavior relationships, we had participants perform 2 tasks that varied in the extent of cognitive involvement. We examined connectivity between brain regions during a low-demand vigilance task and a higher-demand digit–symbol visual search task using Granger causality analysis (GCA). Our results showed 1) Significant differences in numbers of frontoparietal connections between low- and high-demand tasks 2) that GCA can detect activity changes that correspond with task-demand changes, and 3) faster participants showed more vigilance-related activity than slower participants, but less visual-search activity. These results suggest that relatively low-demand cognitive performance depends on spontaneous bidirectionally fluctuating network activity, whereas high-demand performance depends on a limited, unidirectional network. The nature of brain-behavior relationships may vary depending on the extent of cognitive demand. High-demand network activity may reflect the extent to which individuals require top-down executive guidance of behavior for successful task performance. Low-demand network activity may reflect task- and performance monitoring that minimizes executive requirements for guidance of behavior. PMID:20064942

PLANT - An experimental task for the study of human problem solving in process control. [Production Levels and Network Troubleshooting

NASA Technical Reports Server (NTRS)

Morris, N. M.; Rouse, W. B.; Fath, J. L.

1985-01-01

An experimental tool for the investigation of human problem-solving behavior is introduced. Production Levels and Network Troubleshooting (PLANT) is a computer-based process-control task which may be used to provide opportunities for subjects to control a dynamic system and diagnose, repair, and compensate for system failures. The task is described in detail, and experiments which have been conducted using PLANT are briefly discussed.

Task-Dependent Changes in Cross-Level Coupling between Single Neurons and Oscillatory Activity in Multiscale Networks

PubMed Central

Canolty, Ryan T.; Ganguly, Karunesh; Carmena, Jose M.

2012-01-01

Understanding the principles governing the dynamic coordination of functional brain networks remains an important unmet goal within neuroscience. How do distributed ensembles of neurons transiently coordinate their activity across a variety of spatial and temporal scales? While a complete mechanistic account of this process remains elusive, evidence suggests that neuronal oscillations may play a key role in this process, with different rhythms influencing both local computation and long-range communication. To investigate this question, we recorded multiple single unit and local field potential (LFP) activity from microelectrode arrays implanted bilaterally in macaque motor areas. Monkeys performed a delayed center-out reach task either manually using their natural arm (Manual Control, MC) or under direct neural control through a brain-machine interface (Brain Control, BC). In accord with prior work, we found that the spiking activity of individual neurons is coupled to multiple aspects of the ongoing motor beta rhythm (10–45 Hz) during both MC and BC, with neurons exhibiting a diversity of coupling preferences. However, here we show that for identified single neurons, this beta-to-rate mapping can change in a reversible and task-dependent way. For example, as beta power increases, a given neuron may increase spiking during MC but decrease spiking during BC, or exhibit a reversible shift in the preferred phase of firing. The within-task stability of coupling, combined with the reversible cross-task changes in coupling, suggest that task-dependent changes in the beta-to-rate mapping play a role in the transient functional reorganization of neural ensembles. We characterize the range of task-dependent changes in the mapping from beta amplitude, phase, and inter-hemispheric phase differences to the spike rates of an ensemble of simultaneously-recorded neurons, and discuss the potential implications that dynamic remapping from oscillatory activity to spike rate and

Scheduling for Emergency Tasks in Industrial Wireless Sensor Networks

PubMed Central

Xia, Changqing; Kong, Linghe; Zeng, Peng

2017-01-01

Wireless sensor networks (WSNs) are widely applied in industrial manufacturing systems. By means of centralized control, the real-time requirement and reliability can be provided by WSNs in industrial production. Furthermore, many approaches reserve resources for situations in which the controller cannot perform centralized resource allocation. The controller assigns these resources as it becomes aware of when and where accidents have occurred. However, the reserved resources are limited, and such incidents are low-probability events. In addition, resource reservation may not be effective since the controller does not know when and where accidents will actually occur. To address this issue, we improve the reliability of scheduling for emergency tasks by proposing a method based on a stealing mechanism. In our method, an emergency task is transmitted by stealing resources allocated to regular flows. The challenges addressed in our work are as follows: (1) emergencies occur only occasionally, but the industrial system must deliver the corresponding flows within their deadlines when they occur; (2) we wish to minimize the impact of emergency flows by reducing the number of stolen flows. The contributions of this work are two-fold: (1) we first define intersections and blocking as new characteristics of flows; and (2) we propose a series of distributed routing algorithms to improve the schedulability and to reduce the impact of emergency flows. We demonstrate that our scheduling algorithm and analysis approach are better than the existing ones by extensive simulations. PMID:28726738

Renal denervation in the era of HTN-3. Comprehensive review and glimpse into the future.

PubMed

Silva, Joana Delgado; Costa, Marco; Gersh, Bernard J; Gonçalves, Lino

2016-08-01

The pathophysiological role of sympathetic overactivity in conditions such as hypertension has been well documented. Catheter-based renal denervation (RDN) is a minimally invasive percutaneous procedure which aims to disrupt sympathetic nerve afferent and efferent activity through the application of radiofrequency energy directly within the renal artery wall. This technique has emerged as a very promising treatment with dramatic effects on refractory hypertension but also in other conditions in which a sympathetic influence is present. Several studies have evaluated the safety and efficacy of this procedure, presently surrounded by controversy since the recent outcome of Symplicity HTN-3, the first randomized, sham-control trial, which failed to confirm RDN previous reported benefits on BP and cardiovascular risk lowering. Consequently, although some centers halted their RDN programs, research continues and both the concept of denervation and treatment strategies are being redefined to identify patients who can drive the most benefit from this technology. In the United States, the Food and Drug Administration (FDA) has appropriately mandated that RDN remains an investigative procedure and a new generation of sham-controlled trials are ongoing and aimed to assess not only its efficacy against pharmacotherapy but also trials in drug free patients with the objective of demonstrating once and for all whether the procedure actually does lower BP in comparison to a placebo arm. In this article, we present an overview of the sympathetic nervous system and its role in hypertension, examine the current data on RDN, and share some insights and future expectations. Copyright © 2016 American Society of Hypertension. All rights reserved.

Errors on interrupter tasks presented during spatial and verbal working memory performance are linearly linked to large-scale functional network connectivity in high temporal resolution resting state fMRI.

PubMed

Magnuson, Matthew Evan; Thompson, Garth John; Schwarb, Hillary; Pan, Wen-Ju; McKinley, Andy; Schumacher, Eric H; Keilholz, Shella Dawn

2015-12-01

The brain is organized into networks composed of spatially separated anatomical regions exhibiting coherent functional activity over time. Two of these networks (the default mode network, DMN, and the task positive network, TPN) have been implicated in the performance of a number of cognitive tasks. To directly examine the stable relationship between network connectivity and behavioral performance, high temporal resolution functional magnetic resonance imaging (fMRI) data were collected during the resting state, and behavioral data were collected from 15 subjects on different days, exploring verbal working memory, spatial working memory, and fluid intelligence. Sustained attention performance was also evaluated in a task interleaved between resting state scans. Functional connectivity within and between the DMN and TPN was related to performance on these tasks. Decreased TPN resting state connectivity was found to significantly correlate with fewer errors on an interrupter task presented during a spatial working memory paradigm and decreased DMN/TPN anti-correlation was significantly correlated with fewer errors on an interrupter task presented during a verbal working memory paradigm. A trend for increased DMN resting state connectivity to correlate to measures of fluid intelligence was also observed. These results provide additional evidence of the relationship between resting state networks and behavioral performance, and show that such results can be observed with high temporal resolution fMRI. Because cognitive scores and functional connectivity were collected on nonconsecutive days, these results highlight the stability of functional connectivity/cognitive performance coupling.

Establishing the resting state default mode network derived from functional magnetic resonance imaging tasks as an endophenotype: A twins study.

PubMed

Korgaonkar, Mayuresh S; Ram, Kaushik; Williams, Leanne M; Gatt, Justine M; Grieve, Stuart M

2014-08-01

The resting state default mode network (DMN) has been shown to characterize a number of neurological and psychiatric disorders. Evidence suggests an underlying genetic basis for this network and hence could serve as potential endophenotype for these disorders. Heritability is a defining criterion for endophenotypes. The DMN is measured either using a resting-state functional magnetic resonance imaging (fMRI) scan or by extracting resting state activity from task-based fMRI. The current study is the first to evaluate heritability of this task-derived resting activity. 250 healthy adult twins (79 monozygotic and 46 dizygotic same sex twin pairs) completed five cognitive and emotion processing fMRI tasks. Resting state DMN functional connectivity was derived from these five fMRI tasks. We validated this approach by comparing connectivity estimates from task-derived resting activity for all five fMRI tasks, with those obtained using a dedicated task-free resting state scan in an independent cohort of 27 healthy individuals. Structural equation modeling using the classic twin design was used to estimate the genetic and environmental contributions to variance for the resting-state DMN functional connectivity. About 9-41% of the variance in functional connectivity between the DMN nodes was attributed to genetic contribution with the greatest heritability found for functional connectivity between the posterior cingulate and right inferior parietal nodes (P<0.001). Our data provide new evidence that functional connectivity measures from the intrinsic DMN derived from task-based fMRI datasets are under genetic control and have the potential to serve as endophenotypes for genetically predisposed psychiatric and neurological disorders. Copyright © 2014 Wiley Periodicals, Inc.

Learning the ideal observer for SKE detection tasks by use of convolutional neural networks (Cum Laude Poster Award)

NASA Astrophysics Data System (ADS)

Zhou, Weimin; Anastasio, Mark A.

2018-03-01

It has been advocated that task-based measures of image quality (IQ) should be employed to evaluate and optimize imaging systems. Task-based measures of IQ quantify the performance of an observer on a medically relevant task. The Bayesian Ideal Observer (IO), which employs complete statistical information of the object and noise, achieves the upper limit of the performance for a binary signal classification task. However, computing the IO performance is generally analytically intractable and can be computationally burdensome when Markov-chain Monte Carlo (MCMC) techniques are employed. In this paper, supervised learning with convolutional neural networks (CNNs) is employed to approximate the IO test statistics for a signal-known-exactly and background-known-exactly (SKE/BKE) binary detection task. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are compared to those produced by the analytically computed IO. The advantages of the proposed supervised learning approach for approximating the IO are demonstrated.

Age-related Multiscale Changes in Brain Signal Variability in Pre-task versus Post-task Resting-state EEG.

PubMed

Wang, Hongye; McIntosh, Anthony R; Kovacevic, Natasa; Karachalios, Maria; Protzner, Andrea B

2016-07-01

Recent empirical work suggests that, during healthy aging, the variability of network dynamics changes during task performance. Such variability appears to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into resting-state dynamics. We recorded EEG in young, middle-aged, and older adults during a "rest-task-rest" design and investigated if aging modifies the interaction between resting-state activity and external stimulus-induced activity. Using multiscale entropy as our measure of variability, we found that, with increasing age, resting-state dynamics shifts from distributed to more local neural processing, especially at posterior sources. In the young group, resting-state dynamics also changed from pre- to post-task, where fine-scale entropy increased in task-positive regions and coarse-scale entropy increased in the posterior cingulate, a key region associated with the default mode network. Lastly, pre- and post-task resting-state dynamics were linked to performance on the intervening task for all age groups, but this relationship became weaker with increasing age. Our results suggest that age-related changes in resting-state dynamics occur across different spatial and temporal scales and have consequences for information processing capacity.

Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS, fMRI, EEG Study.

PubMed

Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Heute, U; Deuschl, G; Raethjen, J; Muthuraman, Muthuraman

2016-09-01

Recently, interest has been growing to understand the underlying dynamic directional relationship between simultaneously activated regions of the brain during motor task performance. Such directionality analysis (or effective connectivity analysis), based on non-invasive electrophysiological (electroencephalography-EEG) and hemodynamic (functional near infrared spectroscopy-fNIRS; and functional magnetic resonance imaging-fMRI) neuroimaging modalities can provide an estimate of the motor task-related information flow from one brain region to another. Since EEG, fNIRS and fMRI modalities achieve different spatial and temporal resolutions of motor-task related activation in the brain, the aim of this study was to determine the effective connectivity of cortico-cortical sensorimotor networks during finger movement tasks measured by each neuroimaging modality. Nine healthy subjects performed right hand finger movement tasks of different complexity (simple finger tapping-FT, simple finger sequence-SFS, and complex finger sequence-CFS). We focused our observations on three cortical regions of interest (ROIs), namely the contralateral sensorimotor cortex (SMC), the contralateral premotor cortex (PMC) and the contralateral dorsolateral prefrontal cortex (DLPFC). We estimated the effective connectivity between these ROIs using conditional Granger causality (GC) analysis determined from the time series signals measured by fMRI (blood oxygenation level-dependent-BOLD), fNIRS (oxygenated-O2Hb and deoxygenated-HHb hemoglobin), and EEG (scalp and source level analysis) neuroimaging modalities. The effective connectivity analysis showed significant bi-directional information flow between the SMC, PMC, and DLPFC as determined by the EEG (scalp and source), fMRI (BOLD) and fNIRS (O2Hb and HHb) modalities for all three motor tasks. However the source level EEG GC values were significantly greater than the other modalities. In addition, only the source level EEG showed a

Changes in default mode network as automaticity develops in a categorization task.

PubMed

Shamloo, Farzin; Helie, Sebastien

2016-10-15

The default mode network (DMN) is a set of brain regions in which blood oxygen level dependent signal is suppressed during attentional focus on the external environment. Because automatic task processing requires less attention, development of automaticity in a rule-based categorization task may result in less deactivation and altered functional connectivity of the DMN when compared to the initial learning stage. We tested this hypothesis by re-analyzing functional magnetic resonance imaging data of participants trained in rule-based categorization for over 10,000 trials (Helie et al., 2010) [12,13]. The results show that some DMN regions are deactivated in initial training but not after automaticity has developed. There is also a significant decrease in DMN deactivation after extensive practice. Seed-based functional connectivity analyses with the precuneus, medial prefrontal cortex (two important DMN regions) and Brodmann area 6 (an important region in automatic categorization) were also performed. The results show increased functional connectivity with both DMN and non-DMN regions after the development of automaticity, and a decrease in functional connectivity between the medial prefrontal cortex and ventromedial orbitofrontal cortex. Together, these results further support the hypothesis of a strategy shift in automatic categorization and bridge the cognitive and neuroscientific conceptions of automaticity in showing that the reduced need for cognitive resources in automatic processing is accompanied by a disinhibition of the DMN and stronger functional connectivity between DMN and task-related brain regions. Copyright © 2016 Elsevier B.V. All rights reserved.

Physical exercise increases involvement of motor networks as a compensatory mechanism during a cognitively challenging task.

PubMed

Ji, Lanxin; Pearlson, Godfrey D; Zhang, Xue; Steffens, David C; Ji, Xiaoqing; Guo, Hua; Wang, Lihong

2018-05-31

Neuroimaging studies suggest that older adults may compensate for declines in cognitive function through neural compensation and reorganization of neural resources. While neural compensation as a key component of cognitive reserve is an important factor that mediates cognitive decline, the field lacks a quantitative measure of neural compensatory ability, and little is known about factors that may modify compensation, such as physical exercise. Twenty-five healthy older adults participated in a 6-week dance training exercise program. Gait speed, cognitive function, and functional magnetic resonance imaging during a challenging memory task were measured before and after the exercise program. In this study, we used a newly proposed data-driven independent component analysis approach to measure neural compensatory ability and tested the effect of physical exercise on neural compensation through a longitudinal study. After the exercise program, participants showed significantly improved memory performance in Logical Memory Test (WMS(LM)) (P < .001) and Rey Auditory Verbal Learning Test (P = .001) and increased gait speed measured by the 6-minute walking test (P = .01). Among all identified neural networks, only the motor cortices and cerebellum showed greater involvement during the memory task after exercise. Importantly, subjects who activated the motor network only after exercise (but not before exercise) showed WMS(LM) increases. We conclude that physical exercise improved gait speed, cognitive function, and compensatory ability through increased involvement of motor-related networks. Copyright © 2018 John Wiley & Sons, Ltd.

Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Cha, Kenny H.; Richter, Caleb D.

2017-12-01

Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the ‘knowledge’ learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p  =  0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.

The Persistence of Experience: Prior Attentional and Emotional State Affects Network Functioning in a Target Detection Task.

PubMed

Stern, Emily R; Muratore, Alexandra F; Taylor, Stephan F; Abelson, James L; Hof, Patrick R; Goodman, Wayne K

2015-09-01

Efficient, adaptive behavior relies on the ability to flexibly move between internally focused (IF) and externally focused (EF) attentional states. Despite evidence that IF cognitive processes such as event imagination comprise a significant amount of awake cognition, the consequences of internal absorption on the subsequent recruitment of brain networks during EF tasks are unknown. The present functional magnetic resonance imaging (fMRI) study employed a novel attentional state switching task. Subjects imagined positive and negative events (IF task) or performed a working memory task (EF task) before switching to a target detection (TD) task also requiring attention to external information, allowing for the investigation of neural functioning during external attention based on prior attentional state. There was a robust increase of activity in frontal, parietal, and temporal regions during TD when subjects were previously performing the EF compared with IF task, an effect that was most pronounced following negative IF. Additionally, dorsolateral prefrontal cortex was less negatively coupled with ventromedial prefrontal and posterior cingulate cortices during TD following IF compared with EF. These findings reveal the striking consequences for brain activity following immersion in an IF attentional state, which have strong implications for psychiatric disorders characterized by excessive internal focus. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Brain Modularity Mediates the Relation between Task Complexity and Performance

NASA Astrophysics Data System (ADS)

Ye, Fengdan; Yue, Qiuhai; Martin, Randi; Fischer-Baum, Simon; Ramos-Nuã+/-Ez, Aurora; Deem, Michael

Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases, and other tasks showing worse performance. A recent theoretical model suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of behavioral tasks. Complex and simple tasks were defined on the basis of whether they drew on executive attention. Consistent with predictions, we found a negative correlation between individuals' modularity and their performance on the complex tasks but a positive correlation with performance on the simple tasks. The results presented here provide a framework for linking measures of whole brain organization to cognitive processing.

Social representations and contextual adjustments as two distinct components of the Theory of Mind brain network: Evidence from the REMICS task.

PubMed

Lavoie, Marie-Audrey; Vistoli, Damien; Sutliff, Stephanie; Jackson, Philip L; Achim, Amélie M

2016-08-01

Theory of mind (ToM) refers to the ability to infer the mental states of others. Behavioral measures of ToM usually present information about both a character and the context in which this character is placed, and these different pieces of information can be used to infer the character's mental states. A set of brain regions designated as the ToM brain network is recognized to support (ToM) inferences. Different brain regions within that network could however support different ToM processes. This functional magnetic resonance imaging (fMRI) study aimed to distinguish the brain regions supporting two aspects inherent to many ToM tasks, i.e., the ability to infer or represent mental states and the ability to use the context to adjust these inferences. Nineteen healthy subjects were scanned during the REMICS task, a novel task designed to orthogonally manipulate mental state inferences (as opposed to physical inferences) and contextual adjustments of inferences (as opposed to inferences that do not require contextual adjustments). We observed that mental state inferences and contextual adjustments, which are important aspects of most behavioral ToM tasks, rely on distinct brain regions or subregions within the classical brain network activated in previous ToM research. Notably, an interesting dissociation emerged within the medial prefrontal cortex (mPFC) and temporo-parietal junctions (TPJ) such that the inferior part of these brain regions responded to mental state inferences while the superior part of these brain regions responded to the requirement for contextual adjustments. This study provides evidence that the overall set of brain regions activated during ToM tasks supports different processes, and highlights that cognitive processes related to contextual adjustments have an important role in ToM and should be further studied. Copyright © 2016 Elsevier Ltd. All rights reserved.

Achieving Educational Excellence by Increasing Access to Knowledge: Discussion Document. Report of the Task Force on Education Network Technology.

ERIC Educational Resources Information Center

National Education Goals Panel, Washington, DC.

Internetworked communications restructures relationships among educators, learners, and knowledge and information, and promises a systemic acceleration of the pace of educational change. In this report, the Task Force on Education Network Technology identifies the following rationales for deployment and utilization of such communications: to…

A Graph theoretical approach to study the organization of the cortical networks during different mathematical tasks.

PubMed

Klados, Manousos A; Kanatsouli, Kassia; Antoniou, Ioannis; Babiloni, Fabio; Tsirka, Vassiliki; Bamidis, Panagiotis D; Micheloyannis, Sifis

2013-01-01

The two core systems of mathematical processing (subitizing and retrieval) as well as their functionality are already known and published. In this study we have used graph theory to compare the brain network organization of these two core systems in the cortical layer during difficult calculations. We have examined separately all the EEG frequency bands in healthy young individuals and we found that the network organization at rest, as well as during mathematical tasks has the characteristics of Small World Networks for all the bands, which is the optimum organization required for efficient information processing. The different mathematical stimuli provoked changes in the graph parameters of different frequency bands, especially the low frequency bands. More specific, in Delta band the induced network increases it's local and global efficiency during the transition from subitizing to retrieval system, while results suggest that difficult mathematics provoke networks with higher cliquish organization due to more specific demands. The network of the Theta band follows the same pattern as before, having high nodal and remote organization during difficult mathematics. Also the spatial distribution of the network's weights revealed more prominent connections in frontoparietal regions, revealing the working memory load due to the engagement of the retrieval system. The cortical networks of the alpha brainwaves were also more efficient, both locally and globally, during difficult mathematics, while the fact that alpha's network was more dense on the frontparietal regions as well, reveals the engagement of the retrieval system again. Concluding, this study gives more evidences regarding the interaction of the two core systems, exploiting the produced functional networks of the cerebral cortex, especially for the difficult mathematics.

Analytic Networks in Music Task Definition.

ERIC Educational Resources Information Center

Piper, Richard M.

For a student to acquire the conceptual systems of a discipline, the designer must reflect that structure or analytic network in his curriculum. The four networks identified for music and used in the development of the Southwest Regional Laboratory (SWRL) Music Program are the variable-value, the whole-part, the process-stage, and the class-member…

Network Routing Using the Network Tasking Order, a Chron Approach

DTIC Science & Technology

2015-03-26

57  Figure 4.6: Maximum bandwidth possible. ..................................................................... 58  Figure 4.7: MikroTik Router...iv xii RouterOS MikroTik Router Operating System...traffic through the network. Within the ESXi platform, four MikroTik Router Operating Systems (RouterOS) are installed. Within ESXi, the network

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

PubMed Central

2017-01-01

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

Exploring adolescent cognitive control in a combined interference switching task.

PubMed

Mennigen, Eva; Rodehacke, Sarah; Müller, Kathrin U; Ripke, Stephan; Goschke, Thomas; Smolka, Michael N

2014-08-01

Cognitive control enables individuals to flexibly adapt to environmental challenges. In the present functional magnetic resonance imaging (fMRI) study, we investigated 185 adolescents at the age of 14 with a combined response interference switching task measuring behavioral responses (reaction time, RT and error rate, ER) and brain activity during the task. This task comprises two types of conflict which are co-occurring, namely, task switching and stimulus-response incongruence. Data indicated that already in adolescents an overlapping cognitive control network comprising the dorsal anterior cingulate cortex (dACC), dorsolateral prefrontal cortex (DLPFC), pre-supplementary motor area (preSMA) and posterior parietal cortex (PPC) is recruited by conflicts arising from task switching and response incongruence. Furthermore our study revealed higher blood oxygenation level dependent (BOLD) responses elicited by incongruent stimuli in participants with a pronounced incongruence effect, calculated as the RT difference between incongruent and congruent trials. No such correlation was observed for switch costs. Furthermore, increased activation of the default mode network (DMN) was only observed in congruent trials compared to incongruent trials, but not in task repetition relative to task switch trials. These findings suggest that even though the two processes of task switching and response incongruence share a common cognitive control network they might be processed differentially within the cognitive control network. Results are discussed in the context of a novel hypothesis concerning antagonistic relations between the DMN and the cognitive control network. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Graph Theoretical Approach to Study the Organization of the Cortical Networks during Different Mathematical Tasks

PubMed Central

Klados, Manousos A.; Kanatsouli, Kassia; Antoniou, Ioannis; Babiloni, Fabio; Tsirka, Vassiliki; Bamidis, Panagiotis D.; Micheloyannis, Sifis

2013-01-01

The two core systems of mathematical processing (subitizing and retrieval) as well as their functionality are already known and published. In this study we have used graph theory to compare the brain network organization of these two core systems in the cortical layer during difficult calculations. We have examined separately all the EEG frequency bands in healthy young individuals and we found that the network organization at rest, as well as during mathematical tasks has the characteristics of Small World Networks for all the bands, which is the optimum organization required for efficient information processing. The different mathematical stimuli provoked changes in the graph parameters of different frequency bands, especially the low frequency bands. More specific, in Delta band the induced network increases it’s local and global efficiency during the transition from subitizing to retrieval system, while results suggest that difficult mathematics provoke networks with higher cliquish organization due to more specific demands. The network of the Theta band follows the same pattern as before, having high nodal and remote organization during difficult mathematics. Also the spatial distribution of the network’s weights revealed more prominent connections in frontoparietal regions, revealing the working memory load due to the engagement of the retrieval system. The cortical networks of the alpha brainwaves were also more efficient, both locally and globally, during difficult mathematics, while the fact that alpha’s network was more dense on the frontparietal regions as well, reveals the engagement of the retrieval system again. Concluding, this study gives more evidences regarding the interaction of the two core systems, exploiting the produced functional networks of the cerebral cortex, especially for the difficult mathematics. PMID:23990992

Deep Multi-Task Learning for Tree Genera Classification

NASA Astrophysics Data System (ADS)

Ko, C.; Kang, J.; Sohn, G.

2018-05-01

The goal for our paper is to classify tree genera using airborne Light Detection and Ranging (LiDAR) data with Convolution Neural Network (CNN) - Multi-task Network (MTN) implementation. Unlike Single-task Network (STN) where only one task is assigned to the learning outcome, MTN is a deep learning architect for learning a main task (classification of tree genera) with other tasks (in our study, classification of coniferous and deciduous) simultaneously, with shared classification features. The main contribution of this paper is to improve classification accuracy from CNN-STN to CNN-MTN. This is achieved by introducing a concurrence loss (Lcd) to the designed MTN. This term regulates the overall network performance by minimizing the inconsistencies between the two tasks. Results show that we can increase the classification accuracy from 88.7 % to 91.0 % (from STN to MTN). The second goal of this paper is to solve the problem of small training sample size by multiple-view data generation. The motivation of this goal is to address one of the most common problems in implementing deep learning architecture, the insufficient number of training data. We address this problem by simulating training dataset with multiple-view approach. The promising results from this paper are providing a basis for classifying a larger number of dataset and number of classes in the future.

A network model of behavioural performance in a rule learning task.

PubMed

Hasselmo, Michael E; Stern, Chantal E

2018-04-19

Humans demonstrate differences in performance on cognitive rule learning tasks which could involve differences in properties of neural circuits. An example model is presented to show how gating of the spread of neural activity could underlie rule learning and the generalization of rules to previously unseen stimuli. This model uses the activity of gating units to regulate the pattern of connectivity between neurons responding to sensory input and subsequent gating units or output units. This model allows analysis of network parameters that could contribute to differences in cognitive rule learning. These network parameters include differences in the parameters of synaptic modification and presynaptic inhibition of synaptic transmission that could be regulated by neuromodulatory influences on neural circuits. Neuromodulatory receptors play an important role in cognitive function, as demonstrated by the fact that drugs that block cholinergic muscarinic receptors can cause cognitive impairments. In discussions of the links between neuromodulatory systems and biologically based traits, the issue of mechanisms through which these linkages are realized is often missing. This model demonstrates potential roles of neural circuit parameters regulated by acetylcholine in learning context-dependent rules, and demonstrates the potential contribution of variation in neural circuit properties and neuromodulatory function to individual differences in cognitive function.This article is part of the theme issue 'Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'. © 2018 The Author(s).

GEO Carbon and GHG Initiative Task 3: Optimizing in-situ measurements of essential carbon cycle variables across observational networks

NASA Astrophysics Data System (ADS)

Durden, D.; Muraoka, H.; Scholes, R. J.; Kim, D. G.; Loescher, H. W.; Bombelli, A.

2017-12-01

The development of an integrated global carbon cycle observation system to monitor changes in the carbon cycle, and ultimately the climate system, across the globe is of crucial importance in the 21stcentury. This system should be comprised of space and ground-based observations, in concert with modelling and analysis, to produce more robust budgets of carbon and other greenhouse gases (GHGs). A global initiative, the GEO Carbon and GHG Initiative, is working within the framework of Group on Earth Observations (GEO) to promote interoperability and provide integration across different parts of the system, particularly at domain interfaces. Thus, optimizing the efforts of existing networks and initiatives to reduce uncertainties in budgets of carbon and other GHGs. This is a very ambitious undertaking; therefore, the initiative is separated into tasks to provide actionable objectives. Task 3 focuses on the optimization of in-situ observational networks. The main objective of Task 3 is to develop and implement a procedure for enhancing and refining the observation system for identified essential carbon cycle variables (ECVs) that meets user-defined specifications at minimum total cost. This work focuses on the outline of the implementation plan, which includes a review of essential carbon cycle variables and observation technologies, mapping the ECVs performance, and analyzing gaps and opportunities in order to design an improved observing system. A description of the gap analysis of in-situ observations that will begin in the terrestrial domain to address issues of missing coordination and large spatial gaps, then extend to ocean and atmospheric observations in the future, will be outlined as the subsequent step to landscape mapping of existing observational networks.

The semantic distance task: Quantifying semantic distance with semantic network path length.

PubMed

Kenett, Yoed N; Levi, Effi; Anaki, David; Faust, Miriam

2017-09-01

Semantic distance is a determining factor in cognitive processes, such as semantic priming, operating upon semantic memory. The main computational approach to compute semantic distance is through latent semantic analysis (LSA). However, objections have been raised against this approach, mainly in its failure at predicting semantic priming. We propose a novel approach to computing semantic distance, based on network science methodology. Path length in a semantic network represents the amount of steps needed to traverse from 1 word in the network to the other. We examine whether path length can be used as a measure of semantic distance, by investigating how path length affect performance in a semantic relatedness judgment task and recall from memory. Our results show a differential effect on performance: Up to 4 steps separating between word-pairs, participants exhibit an increase in reaction time (RT) and decrease in the percentage of word-pairs judged as related. From 4 steps onward, participants exhibit a significant decrease in RT and the word-pairs are dominantly judged as unrelated. Furthermore, we show that as path length between word-pairs increases, success in free- and cued-recall decreases. Finally, we demonstrate how our measure outperforms computational methods measuring semantic distance (LSA and positive pointwise mutual information) in predicting participants RT and subjective judgments of semantic strength. Thus, we provide a computational alternative to computing semantic distance. Furthermore, this approach addresses key issues in cognitive theory, namely the breadth of the spreading activation process and the effect of semantic distance on memory retrieval. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A Validated Set of MIDAS V5 Task Network Model Scenarios to Evaluate Nextgen Closely Spaced Parallel Operations Concepts

NASA Technical Reports Server (NTRS)

Gore, Brian Francis; Hooey, Becky Lee; Haan, Nancy; Socash, Connie; Mahlstedt, Eric; Foyle, David C.

2013-01-01

The Closely Spaced Parallel Operations (CSPO) scenario is a complex, human performance model scenario that tested alternate operator roles and responsibilities to a series of off-nominal operations on approach and landing (see Gore, Hooey, Mahlstedt, Foyle, 2013). The model links together the procedures, equipment, crewstation, and external environment to produce predictions of operator performance in response to Next Generation system designs, like those expected in the National Airspaces NextGen concepts. The task analysis that is contained in the present report comes from the task analysis window in the MIDAS software. These tasks link definitions and states for equipment components, environmental features as well as operational contexts. The current task analysis culminated in 3300 tasks that included over 1000 Subject Matter Expert (SME)-vetted, re-usable procedural sets for three critical phases of flight; the Descent, Approach, and Land procedural sets (see Gore et al., 2011 for a description of the development of the tasks included in the model; Gore, Hooey, Mahlstedt, Foyle, 2013 for a description of the model, and its results; Hooey, Gore, Mahlstedt, Foyle, 2013 for a description of the guidelines that were generated from the models results; Gore, Hooey, Foyle, 2012 for a description of the models implementation and its settings). The rollout, after landing checks, taxi to gate and arrive at gate illustrated in Figure 1 were not used in the approach and divert scenarios exercised. The other networks in Figure 1 set up appropriate context settings for the flight deck.The current report presents the models task decomposition from the tophighest level and decomposes it to finer-grained levels. The first task that is completed by the model is to set all of the initial settings for the scenario runs included in the model (network 75 in Figure 1). This initialization process also resets the CAD graphic files contained with MIDAS, as well as the embedded

Prefrontal spatial working memory network predicts animal's decision making in a free choice saccade task

PubMed Central

Mochizuki, Kei

2015-01-01

While neurons in the lateral prefrontal cortex (PFC) encode spatial information during the performance of working memory tasks, they are also known to participate in subjective behavior such as spatial attention and action selection. In the present study, we analyzed the activity of primate PFC neurons during the performance of a free choice memory-guided saccade task in which the monkeys needed to choose a saccade direction by themselves. In trials when the receptive field location was subsequently chosen by the animal, PFC neurons with spatially selective visual response started to show greater activation before cue onset. This result suggests that the fluctuation of firing before cue presentation prematurely biased the representation of a certain spatial location and eventually encouraged the subsequent choice of that location. In addition, modulation of the activity by the animal's choice was observed only in neurons with high sustainability of activation and was also dependent on the spatial configuration of the visual cues. These findings were consistent with known characteristics of PFC neurons in information maintenance in spatial working memory function. These results suggest that precue fluctuation of spatial representation was shared and enhanced through the working memory network in the PFC and could finally influence the animal's free choice of saccade direction. The present study revealed that the PFC plays an important role in decision making in a free choice condition and that the dynamics of decision making are constrained by the network architecture embedded in this cortical area. PMID:26490287

Dim Networks: The Utility of Social Network Analysis for Illuminating Partner Security Force Networks

DTIC Science & Technology

2015-12-01

use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations and selectively target key elements...data to improve SC. 14. SUBJECT TERMS social network analysis, dark networks, light networks, dim networks, security cooperation, Southeast Asia...task may already exist. Recently, the use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations

Default Network Modulation and Large-Scale Network Interactivity in Healthy Young and Old Adults

PubMed Central

Schacter, Daniel L.

2012-01-01

We investigated age-related changes in default, attention, and control network activity and their interactions in young and old adults. Brain activity during autobiographical and visuospatial planning was assessed using multivariate analysis and with intrinsic connectivity networks as regions of interest. In both groups, autobiographical planning engaged the default network while visuospatial planning engaged the attention network, consistent with a competition between the domains of internalized and externalized cognition. The control network was engaged for both planning tasks. In young subjects, the control network coupled with the default network during autobiographical planning and with the attention network during visuospatial planning. In old subjects, default-to-control network coupling was observed during both planning tasks, and old adults failed to deactivate the default network during visuospatial planning. This failure is not indicative of default network dysfunction per se, evidenced by default network engagement during autobiographical planning. Rather, a failure to modulate the default network in old adults is indicative of a lower degree of flexible network interactivity and reduced dynamic range of network modulation to changing task demands. PMID:22128194

Alpha power indexes task-related networks on large and small scales: A multimodal ECoG study in humans and a non-human primate.

PubMed

de Pesters, A; Coon, W G; Brunner, P; Gunduz, A; Ritaccio, A L; Brunet, N M; de Weerd, P; Roberts, M J; Oostenveld, R; Fries, P; Schalk, G

2016-07-01

Performing different tasks, such as generating motor movements or processing sensory input, requires the recruitment of specific networks of neuronal populations. Previous studies suggested that power variations in the alpha band (8-12Hz) may implement such recruitment of task-specific populations by increasing cortical excitability in task-related areas while inhibiting population-level cortical activity in task-unrelated areas (Klimesch et al., 2007; Jensen and Mazaheri, 2010). However, the precise temporal and spatial relationships between the modulatory function implemented by alpha oscillations and population-level cortical activity remained undefined. Furthermore, while several studies suggested that alpha power indexes task-related populations across large and spatially separated cortical areas, it was largely unclear whether alpha power also differentially indexes smaller networks of task-related neuronal populations. Here we addressed these questions by investigating the temporal and spatial relationships of electrocorticographic (ECoG) power modulations in the alpha band and in the broadband gamma range (70-170Hz, indexing population-level activity) during auditory and motor tasks in five human subjects and one macaque monkey. In line with previous research, our results confirm that broadband gamma power accurately tracks task-related behavior and that alpha power decreases in task-related areas. More importantly, they demonstrate that alpha power suppression lags population-level activity in auditory areas during the auditory task, but precedes it in motor areas during the motor task. This suppression of alpha power in task-related areas was accompanied by an increase in areas not related to the task. In addition, we show for the first time that these differential modulations of alpha power could be observed not only across widely distributed systems (e.g., motor vs. auditory system), but also within the auditory system. Specifically, alpha power was

Effect of a pharmacist-managed hypertension program on health system costs: an evaluation of the Study of Cardiovascular Risk Intervention by Pharmacists-Hypertension (SCRIP-HTN).

PubMed

Houle, Sherilyn K D; Chuck, Anderson W; McAlister, Finlay A; Tsuyuki, Ross T

2012-06-01

To quantify the potential cost savings of a community pharmacy-based hypertension management program based on the results of the Study of Cardiovascular Risk Intervention by Pharmacists-Hypertension (SCRIP-HTN) study in terms of avoided cardiovascular events-myocardial infarction, stroke, and heart failure hospitalization, and to compare these cost savings with the cost of the pharmacist intervention program. An economic model was developed to estimate the potential cost avoidance in direct health care resources from reduced cardiovascular events over a 1-year period. The SCRIP-HTN study found that patients with diabetes mellitus and hypertension who were receiving the pharmacist intervention had a greater mean reduction in systolic blood pressure of 5.6 mm Hg than patients receiving usual care. For our model, published meta-analysis data were used to compute cardiovascular event absolute risk reductions associated with a 5.6-mm Hg reduction in systolic blood pressure over 6 months. Costs/event were obtained from administrative data, and probabilistic sensitivity analyses were performed to assess the robustness of the results. Two program scenarios were evaluated-one with monthly follow-up for a total of 1 year with sustained blood pressure reduction, and the other in which pharmacist care ended after the 6-month program but the effects on systolic blood pressure diminished over time. The cost saving results from the economic model were then compared with the costs of the program. Annual estimated cost savings (in 2011 Canadian dollars) from avoided cardiovascular events were $265/patient (95% confidence interval [CI] $63-467) if the program lasted 1 year or $221/patient (95%CI $72-371) if pharmacist care ceased after 6 months with an assumed loss of effect afterward. Estimated pharmacist costs were $90/patient for 6 months or $150/patient for 1 year, suggesting that pharmacist-managed programs are cost saving, with the annual net total cost savings

Investigation of automated task learning, decomposition and scheduling

NASA Technical Reports Server (NTRS)

Livingston, David L.; Serpen, Gursel; Masti, Chandrashekar L.

1990-01-01

The details and results of research conducted in the application of neural networks to task planning and decomposition are presented. Task planning and decomposition are operations that humans perform in a reasonably efficient manner. Without the use of good heuristics and usually much human interaction, automatic planners and decomposers generally do not perform well due to the intractable nature of the problems under consideration. The human-like performance of neural networks has shown promise for generating acceptable solutions to intractable problems such as planning and decomposition. This was the primary reasoning behind attempting the study. The basis for the work is the use of state machines to model tasks. State machine models provide a useful means for examining the structure of tasks since many formal techniques have been developed for their analysis and synthesis. It is the approach to integrate the strong algebraic foundations of state machines with the heretofore trial-and-error approach to neural network synthesis.

Trust Maximization in Social Networks

NASA Astrophysics Data System (ADS)

Zhan, Justin; Fang, Xing

Trust is a human-related phenomenon in social networks. Trust research on social networks has gained much attention on its usefulness, and on modeling propagations. There is little focus on finding maximum trust in social networks which is particularly important when a social network is oriented by certain tasks. In this paper, we propose a trust maximization algorithm based on the task-oriented social networks.

Social Network Analysis as an Analytic Tool for Task Group Research: A Case Study of an Interdisciplinary Community of Practice

ERIC Educational Resources Information Center

Lockhart, Naorah C.

2017-01-01

Group counselors commonly collaborate in interdisciplinary settings in health care, substance abuse, and juvenile justice. Social network analysis is a methodology rarely used in counseling research yet has potential to examine task group dynamics in new ways. This case study explores the scholarly relationships among 36 members of an…

BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.

PubMed

Rinaldi, Fabio; Ellendorff, Tilia Renate; Madan, Sumit; Clematide, Simon; van der Lek, Adrian; Mevissen, Theo; Fluck, Juliane

2016-01-01

Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text. © The Author(s) 2016. Published by Oxford University Press.

Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

PubMed Central

Wang, Xiao-Jing

2016-01-01

The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity

APHiD: Hierarchical Task Placement to Enable a Tapered Fat Tree Topology for Lower Power and Cost in HPC Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Michelogiannakis, George; Ibrahim, Khaled Z.; Shalf, John

The power and procurement cost of bandwidth in system-wide networks has forced a steady drop in the byte/flop ratio. This trend of computation becoming faster relative to the network is expected to hold. In this paper, we explore how cost-oriented task placement enables reducing the cost of system-wide networks by enabling high performance even on tapered topologies where more bandwidth is provisioned at lower levels. We describe APHiD, an efficient hierarchical placement algorithm that uses new techniques to improve the quality of heuristic solutions and reduces the demand on high-level, expensive bandwidth in hierarchical topologies. We apply APHiD to amore » tapered fat-tree, demonstrating that APHiD maintains application scalability even for severely tapered network configurations. Using simulation, we show that for tapered networks APHiD improves performance by more than 50% over random placement and even 15% in some cases over costlier, state-of-the-art placement algorithms.« less

Robot Task Commander with Extensible Programming Environment

NASA Technical Reports Server (NTRS)

Hart, Stephen W (Inventor); Wightman, Brian J (Inventor); Dinh, Duy Paul (Inventor); Yamokoski, John D. (Inventor); Gooding, Dustin R (Inventor)

2014-01-01

A system for developing distributed robot application-level software includes a robot having an associated control module which controls motion of the robot in response to a commanded task, and a robot task commander (RTC) in networked communication with the control module over a network transport layer (NTL). The RTC includes a script engine(s) and a GUI, with a processor and a centralized library of library blocks constructed from an interpretive computer programming code and having input and output connections. The GUI provides access to a Visual Programming Language (VPL) environment and a text editor. In executing a method, the VPL is opened, a task for the robot is built from the code library blocks, and data is assigned to input and output connections identifying input and output data for each block. A task sequence(s) is sent to the control module(s) over the NTL to command execution of the task.

Prefrontal spatial working memory network predicts animal's decision making in a free choice saccade task.

PubMed

Mochizuki, Kei; Funahashi, Shintaro

2016-01-01

While neurons in the lateral prefrontal cortex (PFC) encode spatial information during the performance of working memory tasks, they are also known to participate in subjective behavior such as spatial attention and action selection. In the present study, we analyzed the activity of primate PFC neurons during the performance of a free choice memory-guided saccade task in which the monkeys needed to choose a saccade direction by themselves. In trials when the receptive field location was subsequently chosen by the animal, PFC neurons with spatially selective visual response started to show greater activation before cue onset. This result suggests that the fluctuation of firing before cue presentation prematurely biased the representation of a certain spatial location and eventually encouraged the subsequent choice of that location. In addition, modulation of the activity by the animal's choice was observed only in neurons with high sustainability of activation and was also dependent on the spatial configuration of the visual cues. These findings were consistent with known characteristics of PFC neurons in information maintenance in spatial working memory function. These results suggest that precue fluctuation of spatial representation was shared and enhanced through the working memory network in the PFC and could finally influence the animal's free choice of saccade direction. The present study revealed that the PFC plays an important role in decision making in a free choice condition and that the dynamics of decision making are constrained by the network architecture embedded in this cortical area. Copyright © 2016 the American Physiological Society.

Specific default mode subnetworks support mentalizing as revealed through opposing network recruitment by social and semantic FMRI tasks.

PubMed

Hyatt, Christopher J; Calhoun, Vince D; Pearlson, Godfrey D; Assaf, Michal

2015-08-01

The ability to attribute mental states to others, or "mentalizing," is posited to involve specific subnetworks within the overall default mode network (DMN), but this question needs clarification. To determine which default mode (DM) subnetworks are engaged by mentalizing processes, we assessed task-related recruitment of DM subnetworks. Spatial independent component analysis (sICA) applied to fMRI data using relatively high-order model (75 components). Healthy participants (n = 53, ages 17-60) performed two fMRI tasks: an interactive game involving mentalizing (Domino), a semantic memory task (SORT), and a resting state fMRI scan. sICA of the two tasks split the DMN into 10 subnetworks located in three core regions: medial prefrontal cortex (mPFC; five subnetworks), posterior cingulate/precuneus (PCC/PrC; three subnetworks), and bilateral temporoparietal junction (TPJ). Mentalizing events increased recruitment in five of 10 DM subnetworks, located in all three core DMN regions. In addition, three of these five DM subnetworks, one dmPFC subnetwork, one PCC/PrC subnetwork, and the right TPJ subnetwork, showed reduced recruitment by semantic memory task events. The opposing modulation by the two tasks suggests that these three DM subnetworks are specifically engaged in mentalizing. Our findings, therefore, suggest the unique involvement of mentalizing processes in only three of 10 DM subnetworks, and support the importance of the dmPFC, PCC/PrC, and right TPJ in mentalizing as described in prior studies. © 2015 Wiley Periodicals, Inc.

Hypertension and cardiac arrhythmias: executive summary of a consensus document from the European Heart Rhythm Association (EHRA) and ESC Council on Hypertension, endorsed by the Heart Rhythm Society (HRS), Asia-Pacific Heart Rhythm Society (APHRS), and Sociedad Latinoamericana de Estimulación Cardíaca y Electrofisiología (SOLEACE).

PubMed

Lip, Gregory Y H; Coca, Antonio; Kahan, Thomas; Boriani, Giuseppe; Manolis, Antonis S; Olsen, Michael Hecht; Oto, Ali; Potpara, Tatjana S; Steffel, Jan; Marín, Francisco; de Oliveira Figueiredo, Márcio Jansen; de Simone, Giovanni; Tzou, Wendy S; En Chiang, Chern; Williams, Bryan

2017-10-01

Hypertension (HTN) is a common cardiovascular risk factor leading to heart failure (HF), coronary artery disease (CAD), stroke, peripheral artery disease and chronic renal failure. Hypertensive heart disease can manifest as many types of cardiac arrhythmias, most commonly being atrial fibrillation (AF). Both supraventricular and ventricular arrhythmias may occur in HTN patients, especially in those with left ventricular hypertrophy (LVH), CAD, or HF. In addition, high doses of thiazide diuretics commonly used to treat HTN, may result in electrolyte abnormalities (e.g. hypokalaemia, hypomagnesaemia), contributing further to arrhythmias, while effective blood pressure control may prevent the development of the arrhythmias such as AF. In recognizing this close relationship between HTN and arrhythmias, the European Heart Rhythm Association (EHRA) and the European Society of Cardiology (ESC) Council on Hypertension convened a Task Force, with representation from the Heart Rhythm Society (HRS), Asia-Pacific Heart Rhythm Society (APHRS), and Sociedad Latinoamericana de Estimulación Cardíaca y Electrofisiología (SOLEACE), with the remit of comprehensively reviewing the available evidence and publishing a joint consensus document on HTN and cardiac arrhythmias, and providing up-to-date consensus recommendations for use in clinical practice. The ultimate judgment on the care of a specific patient must be made by the healthcare provider and the patient in light of all individual factors presented. This is an executive summary of the full document co-published by EHRA in EP-Europace. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For Permissions, please email: journals.permissions@oup.com.

Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks: personal trait or task-specific challenge?

NASA Astrophysics Data System (ADS)

Fels, Meike; Bauer, Robert; Gharabaghi, Alireza

2015-08-01

Objective. Novel rehabilitation strategies apply robot-assisted exercises and neurofeedback tasks to facilitate intensive motor training. We aimed to disentangle task-specific and subject-related contributions to the perceived workload of these interventions and the related cortical activation patterns. Approach. We assessed the perceived workload with the NASA Task Load Index in twenty-one subjects who were exposed to two different feedback tasks in a cross-over design: (i) brain-robot interface (BRI) with haptic/proprioceptive feedback of sensorimotor oscillations related to motor imagery, and (ii) control of neuromuscular activity with feedback of the electromyography (EMG) of the same hand. We also used electroencephalography to examine the cortical activation patterns beforehand in resting state and during the training session of each task. Main results. The workload profile of BRI feedback differed from EMG feedback and was particularly characterized by the experience of frustration. The frustration level was highly correlated across tasks, suggesting subject-related relevance of this workload component. Those subjects who were specifically challenged by the respective tasks could be detected by an interhemispheric alpha-band network in resting state before the training and by their sensorimotor theta-band activation pattern during the exercise. Significance. Neurophysiological profiles in resting state and during the exercise may provide task-independent workload markers for monitoring and matching participants’ ability and task difficulty of neurofeedback interventions.

Thinking About a Task Is Associated with Increased Connectivity in Regions Activated by Task Performance

PubMed Central

Robertson, Edwin M.; Manoach, Dara S.; Stickgold, Robert

2016-01-01

Abstract We investigated whether functional neuroimaging of quiet “rest” can reveal the neural correlates of conscious thought. Using resting-state functional MRI, we measured functional connectivity during a resting scan that immediately followed performance of a finger tapping motor sequence task. Self-reports of the amount of time spent thinking about the task during the resting scan correlated with connectivity between regions of the motor network activated during task performance. Thus, thinking about a task is associated with coordinated activity in brain regions responsible for that task's performance. More generally, this study demonstrates the feasibility of using the combination of functional connectivity MRI and self-reports to examine the neural correlates of thought. PMID:26650337

Hypertensive crisis: an update on clinical approach and management.

PubMed

Ipek, Emrah; Oktay, Ahmet Afşin; Krim, Selim R

2017-07-01

Here, we review current concepts on hypertensive crisis (HTN-C) with a focus on epidemiology, causes, pathophysiology and prognosis. We also offer a practical approach to the management of HTN-C. HTN-C is characterized by a severe and abrupt increase in blood pressure (BP) with impending or progressive acute end-organ damage (EOD). HTN-C can be divided into hypertensive emergency (HTN-E) and hypertensive urgency (HTN-U) based on the presence or absence of acute EOD, respectively. Recent retrospective studies have demonstrated that emergency department (ED) referrals from an outpatient clinic or rapid BP-lowering strategies in the ED do not lead to improved outcomes in patients with HTN-U. HTN-C can be a de-novo manifestation or a complication of essential or secondary HTN. The presence of acute EOD is a major poor prognostic indicator in HTN-C. The main objectives of the management of HTN-C are distinction of HTN-E from HTN-U and appropriate risk stratification, prevention or regression of acute EOD due to severely elevated BP, prevention of recurrence of HTN-C with an effective long-term management plan and avoidance of rapid lowering of BP except in some special circumstances. The majority of patients with asymptomatic HTN-U can be safely managed in the outpatient setting without exposing them to the risks of aggressive BP lowering. However, patients with HTN-E require hospitalization, prompt treatment and close monitoring.

An analysis of the blood pressure and safety outcomes to renal denervation in African Americans and Non-African Americans in the SYMPLICITY HTN-3 trial.

PubMed

Flack, John M; Bhatt, Deepak L; Kandzari, David E; Brown, David; Brar, Sandeep; Choi, James W; D'Agostino, Ralph; East, Cara; Katzen, Barry T; Lee, Lilian; Leon, Martin B; Mauri, Laura; O'Neill, William W; Oparil, Suzanne; Rocha-Singh, Krishna; Townsend, Raymond R; Bakris, George

2015-10-01

SYMPLICITY HTN-3, the first trial of renal denervation (RDN) versus sham, enrolled 26% African Americans, a prospectively stratified cohort. Although the 6-month systolic blood pressure (SBP) reduction in African Americans (AAs) was similar in the RDN group (-15.5 ± 25.4 mm Hg, n = 85 vs. -17.8 ± 29.2, n = 49, P = .641), the sham SBP response was 9.2 mm Hg greater (P = .057) in AAs than non-AAs. In multivariate analyses, sham SBP response was predicted by an interaction between AA and a complex antihypertensive regimen (at least one antihypertensive medication prescribed ≥3 times daily), while in the RDN group, SBP response was predicted by an interaction between AA race and baseline BP ≥ 180 mm Hg. AA race did not independently predict SBP response in either sham or RDN. There appears to be effect modification by race with individual-level patient characteristics in both treatment arms that affect the observed pattern of SBP responses. Copyright © 2015 American Society of Hypertension. Published by Elsevier Inc. All rights reserved.

A common neural hub resolves syntactic and non-syntactic conflict through cooperation with task-specific networks

PubMed Central

Hsu, Nina S.; Jaeggi, Susanne M.; Novick, Jared M.

2017-01-01

Regions within the left inferior frontal gyrus (LIFG) have simultaneously been implicated in syntactic processing and cognitive control. Accounts attempting to unify LIFG’s function hypothesize that, during comprehension, cognitive control resolves conflict between incompatible representations of sentence meaning. Some studies demonstrate co-localized activity within LIFG for syntactic and non-syntactic conflict resolution, suggesting domain-generality, but others show non-overlapping activity, suggesting domain-specific cognitive control and/or regions that respond uniquely to syntax. We propose however that examining exclusive activation sites for certain contrasts creates a false dichotomy: both domain-general and domain-specific neural machinery must coordinate to facilitate conflict resolution across domains. Here, subjects completed four diverse tasks involving conflict —one syntactic, three non-syntactic— while undergoing fMRI. Though LIFG consistently activated within individuals during conflict processing, functional connectivity analyses revealed task-specific coordination with distinct brain networks. Thus, LIFG may function as a conflict-resolution “hub” that cooperates with specialized neural systems according to information content. PMID:28110105

Trust-based Task Assignment in Military Tactical Networks

DTIC Science & Technology

2012-06-01

Decentralized   Auctions  for  Robust   Task   Allocation ,“  IEEE Trans. on Robotics, vol. 25, no. 4, pp. 912‐926, Aug. 2009.  [10] B.B. Choudhury, B.B. Biswal, and D...Choi et al. [9]  and Choudhury et al. [10] investigated market‐ based   task   allocation  algorithms for multi‐robot systems. Choi et  al. [9] proposed a... consensus ‐ based  bundle algorithm for rapid conflict‐free matching between  tasks  and robots.  Choudhury et al. [10] conducted an empirical study on a

Glyph-based generic network visualization

NASA Astrophysics Data System (ADS)

Erbacher, Robert F.

2002-03-01

Network managers and system administrators have an enormous task set before them in this day of growing network usage. This is particularly true of e-commerce companies and others dependent on a computer network for their livelihood. Network managers and system administrators must monitor activity for intrusions and misuse while at the same time monitoring performance of the network. In this paper, we describe our visualization techniques for assisting in the monitoring of networks for both of these tasks. The goal of these visualization techniques is to integrate the visual representation of both network performance/usage as well as data relevant to intrusion detection. The main difficulties arise from the difference in the intrinsic data and layout needs of each of these tasks. Glyph based techniques are additionally used to indicate the representative values of the necessary data parameters over time. Additionally, our techniques are geared towards providing an environment that can be used continuously for constant real-time monitoring of the network environment.

Accuracy of Blood Pressure-to-Height Ratio to Define Elevated Blood Pressure in Children and Adolescents: The CASPIAN-IV Study.

PubMed

Kelishadi, Roya; Bahreynian, Maryam; Heshmat, Ramin; Motlagh, Mohammad Esmail; Djalalinia, Shirin; Naji, Fatemeh; Ardalan, Gelayol; Asayesh, Hamid; Qorbani, Mostafa

2016-02-01

The aim of this study was to propose a simple practical diagnostic criterion for pre-hypertension (pre-HTN) and hypertension (HTN) in the pediatric age group. This study was conducted on a nationally representative sample of 14,880 students, aged 6-18 years. HTN and pre-HTN were defined as systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) ≥ 95 and 90-95th percentile for age, gender, and height, respectively. By using the area under the curve (AUC) of the receiver operator characteristic curves, we estimated the diagnostic accuracy of two indexes of SBP-to-height ratio (SBPHR) and DBP-to-height (DBPHR) to define pre-HTN and HTN. Overall, SBPHR performed relatively well in classifying subjects to HTN (AUC 0.80-0.85) and pre-HTN (AUC 0.84-0.90). Likewise, DBPHR performed relatively well in classifying subjects to HTN (AUC 0.90-0.97) and pre-HTN (AUC 0.70-0.83). Two indexes of SBPHR and DBPHR are considered as valid, simple, inexpensive, and accurate tools to diagnose pre-HTN and HTN in pediatric age group.

Therapy of Acute Hypertension in Hospitalized Children and Adolescents

PubMed Central

Webb, Tennille N.; Shatat, Ibrahim F.

2014-01-01

Acute hypertension (HTN) in hospitalized children and adolescents occurs relatively frequently and in some cases, if not recognized and treated promptly, it can lead to hypertensive crisis with potentially significant morbidity and mortality. In contrast to adults, where acute HTN is most likely due to uncontrolled primary HTN, children and adolescents with acute HTN are more likely to have secondary HTN. This review will briefly cover evaluation of acute HTN and various age specific etiologies of secondary HTN and provide more in-depth discussion on treatment target, potential risks of acute HTN therapy, available pediatric data on intravenous and oral antihypertensive agents, and propose treatment schema including unique therapy of specific secondary HTN scenarios. PMID:24522943

Policy Transfer via Markov Logic Networks

NASA Astrophysics Data System (ADS)

Torrey, Lisa; Shavlik, Jude

We propose using a statistical-relational model, the Markov Logic Network, for knowledge transfer in reinforcement learning. Our goal is to extract relational knowledge from a source task and use it to speed up learning in a related target task. We show that Markov Logic Networks are effective models for capturing both source-task Q-functions and source-task policies. We apply them via demonstration, which involves using them for decision making in an initial stage of the target task before continuing to learn. Through experiments in the RoboCup simulated-soccer domain, we show that transfer via Markov Logic Networks can significantly improve early performance in complex tasks, and that transferring policies is more effective than transferring Q-functions.

Emotional task management: neural correlates of switching between affective and non-affective task-sets

PubMed Central

Reeck, Crystal

2015-01-01

Although task-switching has been investigated extensively, its interaction with emotionally salient task content remains unclear. Prioritized processing of affective stimulus content may enhance accessibility of affective task-sets and generate increased interference when switching between affective and non-affective task-sets. Previous research has demonstrated that more dominant task-sets experience greater switch costs, as they necessitate active inhibition during performance of less entrenched tasks. Extending this logic to the affective domain, the present experiment examined (a) whether affective task-sets are more dominant than non-affective ones, and (b) what neural mechanisms regulate affective task-sets, so that weaker, non-affective task-sets can be executed. While undergoing functional magnetic resonance imaging, participants categorized face stimuli according to either their gender (non-affective task) or their emotional expression (affective task). Behavioral results were consistent with the affective task dominance hypothesis: participants were slower to switch to the affective task, and cross-task interference was strongest when participants tried to switch from the affective to the non-affective task. These behavioral costs of controlling the affective task-set were mirrored in the activation of a right-lateralized frontostriatal network previously implicated in task-set updating and response inhibition. Connectivity between amygdala and right ventrolateral prefrontal cortex was especially pronounced during cross-task interference from affective features. PMID:25552571

A computational study of whole-brain connectivity in resting state and task fMRI

PubMed Central

Goparaju, Balaji; Rana, Kunjan D.; Calabro, Finnegan J.; Vaina, Lucia Maria

2014-01-01

Background We compared the functional brain connectivity produced during resting-state in which subjects were not actively engaged in a task with that produced while they actively performed a visual motion task (task-state). Material/Methods In this paper we employed graph-theoretical measures and network statistics in novel ways to compare, in the same group of human subjects, functional brain connectivity during resting-state fMRI with brain connectivity during performance of a high level visual task. We performed a whole-brain connectivity analysis to compare network statistics in resting and task states among anatomically defined Brodmann areas to investigate how brain networks spanning the cortex changed when subjects were engaged in task performance. Results In the resting state, we found strong connectivity among the posterior cingulate cortex (PCC), precuneus, medial prefrontal cortex (MPFC), lateral parietal cortex, and hippocampal formation, consistent with previous reports of the default mode network (DMN). The connections among these areas were strengthened while subjects actively performed an event-related visual motion task, indicating a continued and strong engagement of the DMN during task processing. Regional measures such as degree (number of connections) and betweenness centrality (number of shortest paths), showed that task performance induces stronger inter-regional connections, leading to a denser processing network, but that this does not imply a more efficient system as shown by the integration measures such as path length and global efficiency, and from global measures such as small-worldness. Conclusions In spite of the maintenance of connectivity and the “hub-like” behavior of areas, our results suggest that the network paths may be rerouted when performing the task condition. PMID:24947491

Identification of Resting State Networks Involved in Executive Function.

PubMed

Connolly, Joanna; McNulty, Jonathan P; Boran, Lorraine; Roche, Richard A P; Delany, David; Bokde, Arun L W

2016-06-01

The structural networks in the human brain are consistent across subjects, and this is reflected also in that functional networks across subjects are relatively consistent. These findings are not only present during performance of a goal oriented task but there are also consistent functional networks during resting state. It suggests that goal oriented activation patterns may be a function of component networks identified using resting state. The current study examines the relationship between resting state networks measured and patterns of neural activation elicited during a Stroop task. The association between the Stroop-activated networks and the resting state networks was quantified using spatial linear regression. In addition, we investigated if the degree of spatial association of resting state networks with the Stroop task may predict performance on the Stroop task. The results of this investigation demonstrated that the Stroop activated network can be decomposed into a number of resting state networks, which were primarily associated with attention, executive function, visual perception, and the default mode network. The close spatial correspondence between the functional organization of the resting brain and task-evoked patterns supports the relevance of resting state networks in cognitive function.

Training Working Memory in Childhood Enhances Coupling between Frontoparietal Control Network and Task-Related Regions.

PubMed

Barnes, Jessica J; Nobre, Anna Christina; Woolrich, Mark W; Baker, Kate; Astle, Duncan E

2016-08-24

Working memory is a capacity upon which many everyday tasks depend and which constrains a child's educational progress. We show that a child's working memory can be significantly enhanced by intensive computer-based training, relative to a placebo control intervention, in terms of both standardized assessments of working memory and performance on a working memory task performed in a magnetoencephalography scanner. Neurophysiologically, we identified significantly increased cross-frequency phase amplitude coupling in children who completed training. Following training, the coupling between the upper alpha rhythm (at 16 Hz), recorded in superior frontal and parietal cortex, became significantly coupled with high gamma activity (at ∼90 Hz) in inferior temporal cortex. This altered neural network activity associated with cognitive skill enhancement is consistent with a framework in which slower cortical rhythms enable the dynamic regulation of higher-frequency oscillatory activity related to task-related cognitive processes. Whether we can enhance cognitive abilities through intensive training is one of the most controversial topics of cognitive psychology in recent years. This is particularly controversial in childhood, where aspects of cognition, such as working memory, are closely related to school success and are implicated in numerous developmental disorders. We provide the first neurophysiological account of how working memory training may enhance ability in childhood, using a brain recording technique called magnetoencephalography. We borrowed an analysis approach previously used with intracranial recordings in adults, or more typically in other animal models, called "phase amplitude coupling." Copyright © 2016 Barnes et al.

Training Working Memory in Childhood Enhances Coupling between Frontoparietal Control Network and Task-Related Regions

PubMed Central

Barnes, Jessica J.; Nobre, Anna Christina; Woolrich, Mark W.; Baker, Kate

2016-01-01

Working memory is a capacity upon which many everyday tasks depend and which constrains a child's educational progress. We show that a child's working memory can be significantly enhanced by intensive computer-based training, relative to a placebo control intervention, in terms of both standardized assessments of working memory and performance on a working memory task performed in a magnetoencephalography scanner. Neurophysiologically, we identified significantly increased cross-frequency phase amplitude coupling in children who completed training. Following training, the coupling between the upper alpha rhythm (at 16 Hz), recorded in superior frontal and parietal cortex, became significantly coupled with high gamma activity (at ∼90 Hz) in inferior temporal cortex. This altered neural network activity associated with cognitive skill enhancement is consistent with a framework in which slower cortical rhythms enable the dynamic regulation of higher-frequency oscillatory activity related to task-related cognitive processes. SIGNIFICANCE STATEMENT Whether we can enhance cognitive abilities through intensive training is one of the most controversial topics of cognitive psychology in recent years. This is particularly controversial in childhood, where aspects of cognition, such as working memory, are closely related to school success and are implicated in numerous developmental disorders. We provide the first neurophysiological account of how working memory training may enhance ability in childhood, using a brain recording technique called magnetoencephalography. We borrowed an analysis approach previously used with intracranial recordings in adults, or more typically in other animal models, called “phase amplitude coupling.” PMID:27559180

Long-Term Effects of Attentional Performance on Functional Brain Network Topology

PubMed Central

Breckel, Thomas P. K.; Thiel, Christiane M.; Bullmore, Edward T.; Zalesky, Andrew; Patel, Ameera X.; Giessing, Carsten

2013-01-01

Individuals differ in their cognitive resilience. Less resilient people demonstrate a greater tendency to vigilance decrements within sustained attention tasks. We hypothesized that a period of sustained attention is followed by prolonged changes in the organization of “resting state” brain networks and that individual differences in cognitive resilience are related to differences in post-task network reorganization. We compared the topological and spatial properties of brain networks as derived from functional MRI data (N = 20) recorded for 6 mins before and 12 mins after the performance of an attentional task. Furthermore we analysed changes in brain topology during task performance and during the switches between rest and task conditions. The cognitive resilience of each individual was quantified as the rate of increase in response latencies over the 32-minute time course of the attentional paradigm. On average, functional networks measured immediately post-task demonstrated significant and prolonged changes in network organization compared to pre-task networks with higher connectivity strength, more clustering, less efficiency, and shorter distance connections. Individual differences in cognitive resilience were significantly correlated with differences in the degree of recovery of some network parameters. Changes in network measures were still present in less resilient individuals in the second half of the post-task period (i.e. 6–12 mins after task completion), while resilient individuals already demonstrated significant reductions of functional connectivity and clustering towards pre-task levels. During task performance brain topology became more integrated with less clustering and higher global efficiency, but linearly decreased with ongoing time-on-task. We conclude that sustained attentional task performance has prolonged, “hang-over” effects on the organization of post-task resting-state brain networks; and that more cognitively resilient

Emotional task management: neural correlates of switching between affective and non-affective task-sets.

PubMed

Reeck, Crystal; Egner, Tobias

2015-08-01

Although task-switching has been investigated extensively, its interaction with emotionally salient task content remains unclear. Prioritized processing of affective stimulus content may enhance accessibility of affective task-sets and generate increased interference when switching between affective and non-affective task-sets. Previous research has demonstrated that more dominant task-sets experience greater switch costs, as they necessitate active inhibition during performance of less entrenched tasks. Extending this logic to the affective domain, the present experiment examined (a) whether affective task-sets are more dominant than non-affective ones, and (b) what neural mechanisms regulate affective task-sets, so that weaker, non-affective task-sets can be executed. While undergoing functional magnetic resonance imaging, participants categorized face stimuli according to either their gender (non-affective task) or their emotional expression (affective task). Behavioral results were consistent with the affective task dominance hypothesis: participants were slower to switch to the affective task, and cross-task interference was strongest when participants tried to switch from the affective to the non-affective task. These behavioral costs of controlling the affective task-set were mirrored in the activation of a right-lateralized frontostriatal network previously implicated in task-set updating and response inhibition. Connectivity between amygdala and right ventrolateral prefrontal cortex was especially pronounced during cross-task interference from affective features. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

An overview of the heterogeneous telescope network system: Concept, scalability and operation

NASA Astrophysics Data System (ADS)

White, R. R.; Allan, A.

2008-03-01

In the coming decade there will be an avalanche of data streams devoted to astronomical exploration opening new windows of scientific discovery. The shear volume of data and the diversity of event types (Kantor 2006; Kaiser 2004; Vestrand & Theiler & Wozniak 2004) will necessitate; the move to a common language for the communication of event data, and enabling telescope systems with the ability to not just simply respond, but to act independently in order to take full advantage of available resources in a timely manner. Developed over the past three years, the Virtual Observatory Event (VOEvent) provides the best format for carrying these diverse event messages (White et al. 2006a; Seaman & Warner 2006). However, in order for the telescopes to be able to act independently, a system of interoperable network nodes must be in place, that will allow the astronomical assets to not only issue event notifications, but to coordinate and request specific observations. The Heterogeneous Telescope Network (HTN) is a network architecture that can achieve the goals set forth and provide a scalable design to match both fully autonomous and manual telescope system needs (Allan et al. 2006a; White et al. 2006b; Hessman 2006b). In this paper we will show the design concept of this meta-network and nodes, their scalable architecture and complexity, and how this concept can meet the needs of institutions in the near future.

Cardiovascular dysfunctions and sympathovagal imbalance in hypertension and prehypertension: physiological perspectives.

PubMed

Pal, Gopal Krushna; Pal, Pravati; Nanda, Nivedita; Amudharaj, Dharmalingam; Adithan, Chandrasekaran

2013-01-01

Hypertension (HTN) and prehypertension (pre-HTN) have been identified as independent risk factors for adverse cardiovascular events. Recently, increased psychosocial stress and work stress have contributed to the increased prevalence of HTN and pre-HTN, in addition to the contribution of obesity, diabetes, poor food habits and physical inactivity. Irrespective of the etiology, sympathetic overactivity has been recognized as the main pathophysiologic mechanism in the genesis of HTN and pre-HTN. Sympathovagal imbalance owing to sympathetic overactivity and vagal withdrawal is reported to be the basis of many clinical disorders. However, the role played by vagal withdrawal has been under-reported. In this review, we have analyzed the pathophysiologic involvement of sympathovagal imbalance in the development of HTN and pre-HTN, and the link of sympathovagal imbalance to cardiovascular dysfunctions. We have emphasized that adaptation to a healthier lifestyle will help improve sympathovagal homeostasis and prevent the occurrence of HTN and pre-HTN.

Cortical subnetwork dynamics during human language tasks.

PubMed

Collard, Maxwell J; Fifer, Matthew S; Benz, Heather L; McMullen, David P; Wang, Yujing; Milsap, Griffin W; Korzeniewska, Anna; Crone, Nathan E

2016-07-15

Language tasks require the coordinated activation of multiple subnetworks-groups of related cortical interactions involved in specific components of task processing. Although electrocorticography (ECoG) has sufficient temporal and spatial resolution to capture the dynamics of event-related interactions between cortical sites, it is difficult to decompose these complex spatiotemporal patterns into functionally discrete subnetworks without explicit knowledge of each subnetwork's timing. We hypothesized that subnetworks corresponding to distinct components of task-related processing could be identified as groups of interactions with co-varying strengths. In this study, five subjects implanted with ECoG grids over language areas performed word repetition and picture naming. We estimated the interaction strength between each pair of electrodes during each task using a time-varying dynamic Bayesian network (tvDBN) model constructed from the power of high gamma (70-110Hz) activity, a surrogate for population firing rates. We then reduced the dimensionality of this model using principal component analysis (PCA) to identify groups of interactions with co-varying strengths, which we term functional network components (FNCs). This data-driven technique estimates both the weight of each interaction's contribution to a particular subnetwork, and the temporal profile of each subnetwork's activation during the task. We found FNCs with temporal and anatomical features consistent with articulatory preparation in both tasks, and with auditory and visual processing in the word repetition and picture naming tasks, respectively. These FNCs were highly consistent between subjects with similar electrode placement, and were robust enough to be characterized in single trials. Furthermore, the interaction patterns uncovered by FNC analysis correlated well with recent literature suggesting important functional-anatomical distinctions between processing external and self-produced speech. Our

The contributions of resting state and task-based functional connectivity studies to our understanding of adolescent brain network maturation.

PubMed

Stevens, Michael C

2016-11-01

This review summarizes functional magnetic resonance imaging (fMRI) research done over the past decade that examined changes in the function and organization of brain networks across human adolescence. Its over-arching goal is to highlight how both resting state functional connectivity (rs-fcMRI) and task-based functional connectivity (t-fcMRI) have jointly contributed - albeit in different ways - to our understanding of the scope and types of network organization changes that occur from puberty until young adulthood. These two approaches generally have tested different types of hypotheses using different analysis techniques. This has hampered the convergence of findings. Although much has been learned about system-wide changes to adolescents' neural network organization, if both rs-fcMRI and t-fcMRI approaches draw upon each other's methodology and ask broader questions, it will produce a more detailed connectome-informed theory of adolescent neurodevelopment to guide physiological, clinical, and other lines of research. Copyright © 2016 Elsevier Ltd. All rights reserved.

A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task.

PubMed

Jangraw, David C; Gonzalez-Castillo, Javier; Handwerker, Daniel A; Ghane, Merage; Rosenberg, Monica D; Panwar, Puja; Bandettini, Peter A

2018-02-01

Sustaining attention to the task at hand is a crucial part of everyday life, from following a lecture at school to maintaining focus while driving. Lapses in sustained attention are frequent and often problematic, with conditions such as attention deficit hyperactivity disorder affecting millions of people worldwide. Recent work has had some success in finding signatures of sustained attention in whole-brain functional connectivity (FC) measures during basic tasks, but since FC can be dynamic and task-dependent, it remains unclear how fully these signatures would generalize to a more complex and naturalistic scenario. To this end, we used a previously defined whole-brain FC network - a marker of attention that was derived from a sustained attention task - to predict the ability of participants to recall material during a free-viewing reading task. Though the predictive network was trained on a different task and set of participants, the strength of FC in the sustained attention network predicted reading recall significantly better than permutation tests where behavior was scrambled to simulate chance performance. To test the generalization of the method used to derive the sustained attention network, we applied the same method to our reading task data to find a new FC network whose strength specifically predicts reading recall. Even though the sustained attention network provided significant prediction of recall, the reading network was more predictive of recall accuracy. The new reading network's spatial distribution indicates that reading recall is highest when temporal pole regions have higher FC with left occipital regions and lower FC with bilateral supramarginal gyrus. Right cerebellar to right frontal connectivity is also indicative of poor reading recall. We examine these and other differences between the two predictive FC networks, providing new insight into the task-dependent nature of FC-based performance metrics. Published by Elsevier Inc.

A common neural hub resolves syntactic and non-syntactic conflict through cooperation with task-specific networks.

PubMed

Hsu, Nina S; Jaeggi, Susanne M; Novick, Jared M

2017-03-01

Regions within the left inferior frontal gyrus (LIFG) have simultaneously been implicated in syntactic processing and cognitive control. Accounts attempting to unify LIFG's function hypothesize that, during comprehension, cognitive control resolves conflict between incompatible representations of sentence meaning. Some studies demonstrate co-localized activity within LIFG for syntactic and non-syntactic conflict resolution, suggesting domain-generality, but others show non-overlapping activity, suggesting domain-specific cognitive control and/or regions that respond uniquely to syntax. We propose however that examining exclusive activation sites for certain contrasts creates a false dichotomy: both domain-general and domain-specific neural machinery must coordinate to facilitate conflict resolution across domains. Here, subjects completed four diverse tasks involving conflict -one syntactic, three non-syntactic- while undergoing fMRI. Though LIFG consistently activated within individuals during conflict processing, functional connectivity analyses revealed task-specific coordination with distinct brain networks. Thus, LIFG may function as a conflict-resolution "hub" that cooperates with specialized neural systems according to information content. Copyright © 2016 Elsevier Inc. All rights reserved.

Utilizing Novel Non-traditional Sensor Tasking Approaches to Enhance the Space Situational Awareness Picture Maintained by the Space Surveillance Network

NASA Astrophysics Data System (ADS)

Herz, A.; Herz, E.; Center, K.; George, P.; Axelrad, P.; Mutschler, S.; Jones, B.

2016-09-01

The Space Surveillance Network (SSN) is tasked with the increasingly difficult mission of detecting, tracking, cataloging and identifying artificial objects orbiting the Earth, including active and inactive satellites, spent rocket bodies, and fragmented debris. Much of the architecture and operations of the SSN are limited and outdated. Efforts are underway to modernize some elements of the systems. Even so, the ability to maintain the best current Space Situational Awareness (SSA) picture and identify emerging events in a timely fashion could be significantly improved by leveraging non-traditional sensor sites. Orbit Logic, the University of Colorado and the University of Texas at Austin are developing an innovative architecture and operations concept to coordinate the tasking and observation information processing of non - traditional assets based on information-theoretic approaches. These confirmed tasking schedules and the resulting data can then be used to "inform" the SSN tasking process. The 'Heimdall Web' system is comprised of core tasking optimization components and accompanying Web interfaces within a secure, split architecture that will for the first time allow non-traditional sensors to support SSA and improve SSN tasking. Heimdall Web application components appropriately score/prioritize space catalog objects based on covariance, priority, observability, expected information gain, and probability of detect - then coordinate an efficient sensor observation schedule for non-SSN sensors contributing to the overall SSA picture maintained by the Joint Space Operations Center (JSpOC). The Heimdall Web Ops concept supports sensor participation levels of "Scheduled", "Tasked" and "Contributing". Scheduled and Tasked sensors are provided optimized observation schedules or object tracking lists from central algorithms, while Contributing sensors review and select from a list of "desired track objects". All sensors are "Web Enabled" for tasking and feedback

Tera-node Network Technology (Task 3) Scalable Personal Telecommunications

DTIC Science & Technology

2000-03-14

Simulation results of this work may be found in http://north.east.isi.edu/spt/ audio.html. 6. Internet Research Task Force Reliable Multicast...Adaptation, 4. Multimedia Proxy Caching, 5. Experiments with the Rate Adaptation Protocol (RAP) 6. Providing leadership and innovation to the Internet ... Research Task Force (IRTF) Reliable Multicast Research Group (RMRG) 1. End-to-end Architecture for Quality-adaptive Streaming Applications over the

Face-name association task reveals memory networks in patients with left and right hippocampal sclerosis.

PubMed

Klamer, Silke; Milian, Monika; Erb, Michael; Rona, Sabine; Lerche, Holger; Ethofer, Thomas

2017-01-01

We aimed to identify reorganization processes of episodic memory networks in patients with left and right temporal lobe epilepsy (TLE) due to hippocampal sclerosis as well as their relations to neuropsychological memory performance. We investigated 28 healthy subjects, 12 patients with left TLE (LTLE) and 9 patients with right TLE (RTLE) with hippocampal sclerosis by means of functional magnetic resonance imaging (fMRI) using a face-name association task, which combines verbal and non-verbal memory functions. Regions-of-interest (ROIs) were defined based on the group results of the healthy subjects. In each ROI, fMRI activations were compared across groups and correlated with verbal and non-verbal memory scores. The face-name association task yielded activations in bilateral hippocampus (HC), left inferior frontal gyrus (IFG), left superior frontal gyrus (SFG), left superior temporal gyrus, bilateral angular gyrus (AG), bilateral medial prefrontal cortex and right anterior temporal lobe (ATL). LTLE patients demonstrated significantly less activation in the left HC and left SFG, whereas RTLE patients showed significantly less activation in the HC bilaterally, the left SFG and right AG. Verbal memory scores correlated with activations in the left and right HC, left SFG and right ATL and non-verbal memory scores with fMRI activations in the left and right HC and left SFG. The face-name association task can be employed to examine functional alterations of hippocampal activation during encoding of both verbal and non-verbal material in one fMRI paradigm. Further, the left SFG seems to be a convergence region for encoding of verbal and non-verbal material.

Comparison of continuously acquired resting state and extracted analogues from active tasks.

PubMed

Ganger, Sebastian; Hahn, Andreas; Küblböck, Martin; Kranz, Georg S; Spies, Marie; Vanicek, Thomas; Seiger, René; Sladky, Ronald; Windischberger, Christian; Kasper, Siegfried; Lanzenberger, Rupert

2015-10-01

Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting-state data, the application to task-specific fMRI has received growing attention. Three major methods for extraction of resting-state data from task-related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in-between task blocks. Despite widespread application in current research, consensus on which method best resembles resting-state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting-state, two different task paradigms were assessed (emotion discrimination and right finger-tapping) and five well-described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting-state (Dice, Intraclass correlation coefficient (ICC), R(2) ) showed that regression against task effects yields functional connectivity networks most alike to resting-state. However, all methods exhibited significant differences when compared to continuous resting-state and similarity metrics were lower than test-retest of two resting-state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting-state when extracting signals from task designs, although functional connectivity computed from task-specific data may indeed yield interesting information. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Neurometaplasticity: Glucoallostasis control of plasticity of the neural networks of error commission, detection, and correction modulates neuroplasticity to influence task precision

NASA Astrophysics Data System (ADS)

Welcome, Menizibeya O.; Dane, Şenol; Mastorakis, Nikos E.; Pereverzev, Vladimir A.

2017-12-01

The term "metaplasticity" is a recent one, which means plasticity of synaptic plasticity. Correspondingly, neurometaplasticity simply means plasticity of neuroplasticity, indicating that a previous plastic event determines the current plasticity of neurons. Emerging studies suggest that neurometaplasticity underlie many neural activities and neurobehavioral disorders. In our previous work, we indicated that glucoallostasis is essential for the control of plasticity of the neural network that control error commission, detection and correction. Here we review recent works, which suggest that task precision depends on the modulatory effects of neuroplasticity on the neural networks of error commission, detection, and correction. Furthermore, we discuss neurometaplasticity and its role in error commission, detection, and correction.

Formal Models of the Network Co-occurrence Underlying Mental Operations.

PubMed

Bzdok, Danilo; Varoquaux, Gaël; Grisel, Olivier; Eickenberg, Michael; Poupon, Cyril; Thirion, Bertrand

2016-06-01

Systems neuroscience has identified a set of canonical large-scale networks in humans. These have predominantly been characterized by resting-state analyses of the task-unconstrained, mind-wandering brain. Their explicit relationship to defined task performance is largely unknown and remains challenging. The present work contributes a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two extensive datasets (n = 500 and n = 81) by model-based generation of synthetic activity maps from recombination of shared network topographies. To study a use case, we formally revisited the poorly understood difference between neural activity underlying idling versus goal-directed behavior. We demonstrate that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks. The possibility of decomposing a mental task into the relative contributions of major brain networks, the "network co-occurrence architecture" of a given task, opens an alternative access to the neural substrates of human cognition.

Shared and disorder-specific task-positive and default mode network dysfunctions during sustained attention in paediatric Attention-Deficit/Hyperactivity Disorder and obsessive/compulsive disorder.

PubMed

Norman, Luke J; Carlisi, Christina O; Christakou, Anastasia; Cubillo, Ana; Murphy, Clodagh M; Chantiluke, Kaylita; Simmons, Andrew; Giampietro, Vincent; Brammer, Michael; Mataix-Cols, David; Rubia, Katya

2017-01-01

Patients with Attention-Deficit/Hyperactivity Disorder (ADHD) and obsessive/compulsive disorder (OCD) share problems with sustained attention, and are proposed to share deficits in switching between default mode and task positive networks. The aim of this study was to investigate shared and disorder-specific brain activation abnormalities during sustained attention in the two disorders. Twenty boys with ADHD, 20 boys with OCD and 20 age-matched healthy controls aged between 12 and 18 years completed a functional magnetic resonance imaging (fMRI) version of a parametrically modulated sustained attention task with a progressively increasing sustained attention load. Performance and brain activation were compared between groups. Only ADHD patients were impaired in performance. Group by sustained attention load interaction effects showed that OCD patients had disorder-specific middle anterior cingulate underactivation relative to controls and ADHD patients, while ADHD patients showed disorder-specific underactivation in left dorsolateral prefrontal cortex/dorsal inferior frontal gyrus (IFG). ADHD and OCD patients shared left insula/ventral IFG underactivation and increased activation in posterior default mode network relative to controls, but had disorder-specific overactivation in anterior default mode regions, in dorsal anterior cingulate for ADHD and in anterior ventromedial prefrontal cortex for OCD. In sum, ADHD and OCD patients showed mostly disorder-specific patterns of brain abnormalities in both task positive salience/ventral attention networks with lateral frontal deficits in ADHD and middle ACC deficits in OCD, as well as in their deactivation patterns in medial frontal DMN regions. The findings suggest that attention performance in the two disorders is underpinned by disorder-specific activation patterns.

Research in Network Management Techniques for Tactical Data Communications Network.

DTIC Science & Technology

1982-09-01

the control period. Research areas include Packet Network modelling, adaptive network routing, network design algorithms, network design techniques...contro!lers are designed to perform their limited tasks optimally. For the dynamic routing problem considered here, the local controllers are node...feedback to finding in optimum stead-o-state routing (static strategies) under non - control which can be easily implemented in real time. congested

Network mechanisms of intentional learning

PubMed Central

Hampshire, Adam; Hellyer, Peter J.; Parkin, Beth; Hiebert, Nole; MacDonald, Penny; Owen, Adrian M.; Leech, Robert; Rowe, James

2016-01-01

The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flexible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple distinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus–response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated. PMID:26658925

Task-Rest Modulation of Basal Ganglia Connectivity in Mild to Moderate Parkinson’s Disease

PubMed Central

Müller-Oehring, Eva M.; Sullivan, Edith V.; Pfefferbaum, Adolf; Huang, Neng C.; Poston, Kathleen L.; Bronte-Stewart, Helen M.; Schulte, Tilman

2014-01-01

Parkinson’s disease (PD) is associated with abnormal synchronization in basal ganglia-thalamo-cortical loops. We tested whether early PD patients without demonstrable cognitive impairment exhibit abnormal modulation of functional connectivity at rest, while engaged in a task, or both. PD and healthy controls underwent two functional MRI scans: a resting-state scan and a Stroop Match-to-Sample task scan. Rest-task modulation of basal ganglia (BG) connectivity was tested using seed-to-voxel connectivity analysis with task and rest time series as conditions. Despite substantial overlap of BG–cortical connectivity patterns in both groups, connectivity differences between groups had clinical and behavioral correlates. During rest, stronger putamen–medial parietal and pallidum–occipital connectivity in PD than controls was associated with worse task performance and more severe PD symptoms suggesting that abnormalities in resting-state connectivity denote neural network dedifferentiation. During the executive task, PD patients showed weaker BG-cortical connectivity than controls, i.e., between caudate–supramarginal gyrus and pallidum–inferior prefrontal regions, that was related to more severe PD symptoms and worse task performance. Yet, task processing also evoked stronger striatal–cortical connectivity, specifically between caudate–prefrontal, caudate–precuneus, and putamen–motor/premotor regions in PD relative to controls, which was related to less severe PD symptoms and better performance on the Stroop task. Thus, stronger task-evoked striatal connectivity in PD demonstrated compensatory neural network enhancement to meet task demands and improve performance levels. fMRI-based network analysis revealed that despite resting-state BG network compromise in PD, BG connectivity to prefrontal, premotor, and precuneus regions can be adequately invoked during executive control demands enabling near normal task performance. PMID:25280970

An examination of the relationship between athlete leadership and cohesion using social network analysis.

PubMed

Loughead, Todd M; Fransen, Katrien; Van Puyenbroeck, Stef; Hoffmann, Matt D; De Cuyper, Bert; Vanbeselaere, Norbert; Boen, Filip

2016-11-01

Two studies investigated the structure of different athlete leadership networks and its relationship to cohesion using social network analysis. In Study 1, we examined the relationship between a general leadership quality network and task and social cohesion as measured by the Group Environment Questionnaire (GEQ). In Study 2, we investigated the leadership networks for four different athlete leadership roles (task, motivational, social and external) and their association with task and social cohesion networks. In Study 1, the results demonstrated that the general leadership quality network was positively related to task and social cohesion. The results from Study 2 indicated positive correlations between the four leadership networks and task and social cohesion networks. Further, the motivational leadership network emerged as the strongest predictor of the task cohesion network, while the social leadership network was the strongest predictor of the social cohesion network. The results complement a growing body of research indicating that athlete leadership has a positive association with cohesion.

Engineering technology for networks

NASA Technical Reports Server (NTRS)

Paul, Arthur S.; Benjamin, Norman

1991-01-01

Space Network (SN) modeling and evaluation are presented. The following tasks are included: Network Modeling (developing measures and metrics for SN, modeling of the Network Control Center (NCC), using knowledge acquired from the NCC to model the SNC, and modeling the SN); and Space Network Resource scheduling.

Algorithmic Coordination in Robotic Networks

DTIC Science & Technology

2010-11-29

appropriate performance, robustness and scalability properties for various task allocation , surveillance, and information gathering applications is...networking, we envision designing and analyzing algorithms with appropriate performance, robustness and scalability properties for various task ...distributed algorithms for target assignments; based on the classic auction algorithms in static networks, we intend to design efficient algorithms in worst

Increased engagement of the cognitive control network associated with music training in children during an fMRI Stroop task.

PubMed

Sachs, Matthew; Kaplan, Jonas; Der Sarkissian, Alissa; Habibi, Assal

2017-01-01

Playing a musical instrument engages various sensorimotor processes and draws on cognitive capacities collectively termed executive functions. However, while music training is believed to associated with enhancements in certain cognitive and language abilities, studies that have explored the specific relationship between music and executive function have yielded conflicting results. As part of an ongoing longitudinal study, we investigated the effects of music training on executive function using fMRI and several behavioral tasks, including the Color-Word Stroop task. Children involved in ongoing music training (N = 14, mean age = 8.67) were compared with two groups of comparable general cognitive abilities and socioeconomic status, one involved in sports ("sports" group, N = 13, mean age = 8.85) and another not involved in music or sports ("control" group, N = 17, mean age = 9.05). During the Color-Word Stroop task, children with music training showed significantly greater bilateral activation in the pre-SMA/SMA, ACC, IFG, and insula in trials that required cognitive control compared to the control group, despite no differences in performance on behavioral measures of executive function. No significant differences in brain activation or in task performance were found between the music and sports groups. The results suggest that systematic extracurricular training, particularly music-based training, is associated with changes in the cognitive control network in the brain even in the absence of changes in behavioral performance.

Reduced dual-task gait speed is associated with visual Go/No-Go brain network activation in children and adolescents with concussion.

PubMed

Howell, David R; Meehan, William P; Barber Foss, Kim D; Reches, Amit; Weiss, Michal; Myer, Gregory D

2018-05-31

To investigate the association between dual-task gait performance and brain network activation (BNA) using an electroencephalography (EEG)-based Go/No-Go paradigm among children and adolescents with concussion. Participants with a concussion completed a visual Go/No-Go task with collection of electroencephalogram brain activity. Data were treated with BNA analysis, which involves an algorithmic approach to EEG-ERP activation quantification. Participants also completed a dual-task gait assessment. The relationship between dual-task gait speed and BNA was assessed using multiple linear regression models. Participants (n = 20, 13.9 ± 2.3 years of age, 50% female) were tested at a mean of 7.0 ± 2.5 days post-concussion and were symptomatic at the time of testing (post-concussion symptom scale = 40.4 ± 21.9). Slower dual-task average gait speed (mean = 82.2 ± 21.0 cm/s) was significantly associated with lower relative time BNA scores (mean = 39.6 ± 25.8) during the No-Go task (β = 0.599, 95% CI = 0.214, 0.985, p = 0.005, R 2  = 0.405), while controlling for the effect of age and gender. Among children and adolescents with a concussion, slower dual-task gait speed was independently associated with lower BNA relative time scores during a visual Go/No-Go task. The relationship between abnormal gait behaviour and brain activation deficits may be reflective of disruption to multiple functional abilities after concussion.

Functional brain and age-related changes associated with congruency in task switching

PubMed Central

Eich, Teal S.; Parker, David; Liu, Dan; Oh, Hwamee; Razlighi, Qolamreza; Gazes, Yunglin; Habeck, Christian; Stern, Yaakov

2016-01-01

Alternating between completing two simple tasks, as opposed to completing only one task, has been shown to produce costs to performance and changes to neural patterns of activity, effects which are augmented in old age. Cognitive conflict may arise from factors other than switching tasks, however. Sensorimotor congruency (whether stimulus-response mappings are the same or different for the two tasks) has been shown to behaviorally moderate switch costs in older, but not younger adults. In the current study, we used fMRI to investigate the neurobiological mechanisms of response-conflict congruency effects within a task switching paradigm in older (N=75) and younger (N=62) adults. Behaviorally, incongruency moderated age-related differences in switch costs. Neurally, switch costs were associated with greater activation in the dorsal attention network for older relative to younger adults. We also found that older adults recruited an additional set of brain areas in the ventral attention network to a greater extent than did younger adults to resolve congruency-related response-conflict. These results suggest both a network and an age-based dissociation between congruency and switch costs in task switching. PMID:27520472

Visual Network Asymmetry and Default Mode Network Function in ADHD: An fMRI Study

PubMed Central

Hale, T. Sigi; Kane, Andrea M.; Kaminsky, Olivia; Tung, Kelly L.; Wiley, Joshua F.; McGough, James J.; Loo, Sandra K.; Kaplan, Jonas T.

2014-01-01

Background: A growing body of research has identified abnormal visual information processing in attention-deficit hyperactivity disorder (ADHD). In particular, slow processing speed and increased reliance on visuo-perceptual strategies have become evident. Objective: The current study used recently developed fMRI methods to replicate and further examine abnormal rightward biased visual information processing in ADHD and to further characterize the nature of this effect; we tested its association with several large-scale distributed network systems. Method: We examined fMRI BOLD response during letter and location judgment tasks, and directly assessed visual network asymmetry and its association with large-scale networks using both a voxelwise and an averaged signal approach. Results: Initial within-group analyses revealed a pattern of left-lateralized visual cortical activity in controls but right-lateralized visual cortical activity in ADHD children. Direct analyses of visual network asymmetry confirmed atypical rightward bias in ADHD children compared to controls. This ADHD characteristic was atypically associated with reduced activation across several extra-visual networks, including the default mode network (DMN). We also found atypical associations between DMN activation and ADHD subjects’ inattentive symptoms and task performance. Conclusion: The current study demonstrated rightward VNA in ADHD during a simple letter discrimination task. This result adds an important novel consideration to the growing literature identifying abnormal visual processing in ADHD. We postulate that this characteristic reflects greater perceptual engagement of task-extraneous content, and that it may be a basic feature of less efficient top-down task-directed control over visual processing. We additionally argue that abnormal DMN function may contribute to this characteristic. PMID:25076915

Formal Models of the Network Co-occurrence Underlying Mental Operations

PubMed Central

Bzdok, Danilo; Varoquaux, Gaël; Grisel, Olivier; Eickenberg, Michael; Poupon, Cyril; Thirion, Bertrand

2016-01-01

Systems neuroscience has identified a set of canonical large-scale networks in humans. These have predominantly been characterized by resting-state analyses of the task-unconstrained, mind-wandering brain. Their explicit relationship to defined task performance is largely unknown and remains challenging. The present work contributes a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two extensive datasets (n = 500 and n = 81) by model-based generation of synthetic activity maps from recombination of shared network topographies. To study a use case, we formally revisited the poorly understood difference between neural activity underlying idling versus goal-directed behavior. We demonstrate that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks. The possibility of decomposing a mental task into the relative contributions of major brain networks, the "network co-occurrence architecture" of a given task, opens an alternative access to the neural substrates of human cognition. PMID:27310288

Mapping lexical-semantic networks and determining hemispheric language dominance: Do task design, sex, age, and language performance make a difference?

PubMed

Chang, Yu-Hsuan A; Javadi, Sogol S; Bahrami, Naeim; Uttarwar, Vedang S; Reyes, Anny; McDonald, Carrie R

2018-04-01

Blocked and event-related fMRI designs are both commonly used to localize language networks and determine hemispheric dominance in research and clinical settings. We compared activation profiles on a semantic monitoring task using one of the two designs in a total of 43 healthy individual to determine whether task design or subject-specific factors (i.e., age, sex, or language performance) influence activation patterns. We found high concordance between the two designs within core language regions, including the inferior frontal, posterior temporal, and basal temporal region. However, differences emerged within inferior parietal cortex. Subject-specific factors did not influence activation patterns, nor did they interact with task design. These results suggest that despite high concordance within perisylvian regions that are robust to subject-specific factors, methodological differences between blocked and event-related designs may contribute to parietal activations. These findings provide important information for researchers incorporating fMRI results into meta-analytic studies, as well as for clinicians using fMRI to guide pre-surgical planning. Copyright © 2018 Elsevier Inc. All rights reserved.

Reconfiguration of brain network architecture to support executive control in aging.

PubMed

Gallen, Courtney L; Turner, Gary R; Adnan, Areeba; D'Esposito, Mark

2016-08-01

Aging is accompanied by declines in executive control abilities and changes in underlying brain network architecture. Here, we examined brain networks in young and older adults during a task-free resting state and an N-back task and investigated age-related changes in the modular network organization of the brain. Compared with young adults, older adults showed larger changes in network organization between resting state and task. Although young adults exhibited increased connectivity between lateral frontal regions and other network modules during the most difficult task condition, older adults also exhibited this pattern of increased connectivity during less-demanding task conditions. Moreover, the increase in between-module connectivity in older adults was related to faster task performance and greater fractional anisotropy of the superior longitudinal fasciculus. These results demonstrate that older adults who exhibit more pronounced network changes between a resting state and task have better executive control performance and greater structural connectivity of a core frontal-posterior white matter pathway. Copyright © 2016 Elsevier Inc. All rights reserved.

Racism and hypertension: a review of the empirical evidence and implications for clinical practice.

PubMed

Brondolo, Elizabeth; Love, Erica E; Pencille, Melissa; Schoenthaler, Antoinette; Ogedegbe, Gbenga

2011-05-01

Despite improved hypertension (HTN) awareness and treatment, racial disparities in HTN prevalence persist. An understanding of the biopsychosocial determinants of HTN is necessary to address racial disparities in the prevalence of HTN. This review examines the evidence directly and indirectly linking multiple levels of racism to HTN. Published empirical research in EBSCO databases investigating the relationships of three levels of racism (individual/interpersonal, internalized, and institutional racism) to HTN was reviewed. Direct evidence linking individual/interpersonal racism to HTN diagnosis is weak. However, the relationship of individual/interpersonal racism to ambulatory blood pressure (ABP) is more consistent, with all published studies reporting a positive relationship of interpersonal racism to ABP. There is no direct evidence linking internalized racism to BP. Population-based studies provide some evidence linking institutional racism, in the forms of residential racial segregation (RRS) and incarceration, to HTN incidence. Racism shows associations to stress exposure and reactivity as well as associations to established HTN-related risk factors including obesity, low levels of physical activity and alcohol use. The effects vary by level of racism. Overall the findings suggest that racism may increase risk for HTN; these effects emerge more clearly for institutional racism than for individual level racism. All levels of racism may influence the prevalence of HTN via stress exposure and reactivity and by fostering conditions that undermine health behaviors, raising the barriers to lifestyle change.

The Emerging Infrastructure of Autonomous Astronomy

NASA Astrophysics Data System (ADS)

Seaman, R.; Allan, A.; Axelrod, T.; Cook, K.; White, R.; Williams, R.

2007-10-01

Advances in the understanding of cosmic processes demand that sky transient events be confronted with statistical techniques honed on static phenomena. Time domain data sets require vast surveys such as LSST {http://www.lsst.org/lsst_home.shtml} and Pan-STARRS {http://www.pan-starrs.ifa.hawaii.edu}. A new autonomous infrastructure must close the loop from the scheduling of survey observations, through data archiving and pipeline processing, to the publication of transient event alerts and automated follow-up, and to the easy analysis of resulting data. The IVOA VOEvent {http://voevent.org} working group leads efforts to characterize sky transient alerts published through VOEventNet {http://voeventnet.org}. The Heterogeneous Telescope Networks (HTN {http://www.telescope-networks.org}) consortium are observatories and robotic telescope projects seeking interoperability with a long-term goal of creating an e-market for telescope time. Two projects relying on VOEvent and HTN are eSTAR {http://www.estar.org.uk} and the Thinking Telescope {http://www.thinkingtelescopes.lanl.gov} Project.

QoS for Real Time Applications over Next Generation Data Networks

NASA Technical Reports Server (NTRS)

Ivancic, William; Atiquzzaman, Mohammed; Bai, Haowei; Su, Hongjun; Chitri, Jyotsna; Ahamed, Faruque

2001-01-01

Viewgraphs on Qualtity of Service (QOS) for real time applications over next generation data networks are presented. The progress to date include: Task 1: QoS in Integrated Services over DiffServ networks (UD); Task 2: Interconnecting ATN with the next generation Internet (UD); Task 3: QoS in DiffServ over ATM (UD); Task 4: Improving Explicit Congestion Notification with the Mark-Front Strategy (OSU); Task 5: Multiplexing VBR over VBR (OSU); and Task 6: Achieving QoS for TCP traffic in Satellite Networks with Differentiated Services (OSU).

First report on simplified diagnostic criteria for pre-hypertension and hypertension in a national sample of adolescents from the Middle East and North Africa: the CASPIAN-III study.

PubMed

Kelishadi, Roya; Heshmat, Ramin; Ardalan, Gelayol; Qorbani, Mostafa; Taslimi, Mahnaz; Poursafa, Parinaz; Keramatian, Kasra; Taheri, Majzoubeh; Motlagh, Mohammad-Esmaeil

2014-01-01

This study aimed to simplify the diagnostic criteria of pre-hypertension (pre-HTN) and hypertension (HTN) in the pediatric age group, and to determine the accuracy of these simple indexes in a nationally-representative sample of Iranian children and adolescents. The diagnostic accuracy of the indexes of systolic blood pressure-to-height ratio (SBPHR) and diastolic BPHR (DBPHR) to define pre-HTN and HTN was determined by the area under the curve of the receiver operator characteristic curves. The study population consisted of 5,738 Iranian students (2,875 females) with mean (SD) age of 14.7 (2.4) years. The prevalences of pre-HTN and HTN were 6.9% and 5.6%. The optimal thresholds for defining pre-HTN were 0.73 in males and 0.71 in females for SBPHR, and 0.47 in males and 0.45 in females for DBPHR, respectively. The corresponding figures for HTN were 0.73, 0.71, 0.48, and 0.46, respectively. In both genders, the accuracies of SBPHR and DBPHR in diagnosing pre-HTN and HTN were approximately 80%. BPHR is a valid, simple, inexpensive, and accurate tool to diagnose pre-HTN and HTN in adolescents. The optimal thresholds of SBPHR and DBPHR were consistent with the corresponding figures in other populations of children and adolescents with different racial and ethnic backgrounds. Thus, it is suggested that the use of these indexes can be generalized in programs aiming to screen elevated blood pressure in the pediatric age group. Copyright © 2013 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

Brain Network Changes and Memory Decline in Aging

PubMed Central

Beason-Held, Lori L.; Hohman, Timothy J.; Venkatraman, Vijay; An, Yang; Resnick, Susan M.

2016-01-01

One theory of age-related cognitive decline proposes that changes within the default mode network (DMN) of the brain impact the ability to successfully perform cognitive operations. To investigate this theory, we examined functional covariance within brain networks using regional cerebral blood flow data, measured by 15O-water PET, from 99 participants (mean baseline age 68.6 ±7.5) in the Baltimore Longitudinal Study of Aging collected over a 7.4 year period. The sample was divided in tertiles based on longitudinal performance on a verbal recognition memory task administered during scanning, and functional covariance was compared between the upper (improvers) and lower (decliners) tertile groups. The DMN and verbal memory networks (VMN) were then examined during the verbal memory scan condition. For each network, group differences in node-to-network coherence and individual node-to-node covariance relationships were assessed at baseline and in change over time. Compared with improvers, decliners showed differences in node-to-network coherence and in node-to-node relationships in the DMN but not the VMN during verbal memory. These DMN differences reflected greater covariance with better task performance at baseline and both increasing and declining covariance with declining task performance over time for decliners. When examined during the resting state alone, the direction of change in DMN covariance was similar to that seen during task performance, but node-to-node relationships differed from those observed during the task condition. These results suggest that disengagement of DMN components during task performance is not essential for successful cognitive performance as previously proposed. Instead, a proper balance in network processes may be needed to support optimal task performance. PMID:27319002

Hypertension: diagnosis, control status and its predictors in general population aged between 15 and 75 years: a community-based study in southeastern Iran.

PubMed

Najafipour, Hamid; Nasri, Hamid Reza; Afshari, Mahdi; Moazenzadeh, Mansoor; Shokoohi, Mostafa; Foroud, Afsaneh; Etemad, Koorosh; Sadeghi, Behnam; Mirzazadeh, Ali

2014-12-01

Hypertension (HTN) is an important cause of cardiovascular related morbidity and mortality. This study aimed at providing the prevalence of pre-HTN, diagnosed and undiagnosed HTN, along with its control and associated factors in an adult population. 5,900 participants aged 15-75 years took part in the study. HTN was verified by examination, self-reported history or using anti-hypertensive drug(s). Pre-hypertension and hypertension were defined as 120-139/80-89 mmHg and >140/>90 mmHg for systolic/diastolic BP, respectively. The prevalence of hypertension was 18.4 % from which 10.5 %were diagnosed and 7.9 % were undiagnosed. The prevalence of pre-HTN was 35.5 %. HTN increased by age (2.4 % in 15-24 to 49 % in 55-64 years). The men had higher pre-HTN (42.7 vs. 28.1 %) and undiagnosed HTN (11.3 vs. 4.6 %). Of those diagnosed, 56.3 % had uncontrolled BP levels. Smoking, anxiety, obesity, and positive family history of HTN were the most significant predictors for HTN. Hypertension affected almost one-fifth of the population. Given the poor control in diagnosed hypertensive patients, it is alarming that the current health system in urban areas in Iran is not effective enough to control the epidemic spread of non-communicable diseases.

What Makes Patient Navigation Most Effective: Defining Useful Tasks and Networks.

PubMed

Gunn, Christine; Battaglia, Tracy A; Parker, Victoria A; Clark, Jack A; Paskett, Electra D; Calhoun, Elizabeth; Snyder, Frederick R; Bergling, Emily; Freund, Karen M

2017-01-01

Given the momentum in adopting patient navigation into cancer care, there is a need to understand the contribution of specific navigator activities to improved clinical outcomes. A mixed-methods study combined direct observations of patient navigators within the Patient Navigation Research Program and outcome data from the trial. We correlated the frequency of navigator tasks with the outcome of rate of diagnostic resolution within 365 days among patients who received the intervention relative to controls. A focused content analysis examined those tasks with the strongest correlations between navigator tasks and patient outcomes. Navigating directly with specific patients (r = 0.679), working with clinical providers to facilitate patient care (r = 0.643), and performing tasks not directly related to their diagnostic evaluation for patients were positively associated with more timely diagnosis (r = 0.714). Using medical records for non-navigation tasks had a negative association (r = -0.643). Content analysis revealed service provision directed at specific patients improved care while systems-focused activities did not.

Canceled connections: Lesion-derived network mapping helps explain differences in performance on a complex decision-making task

PubMed Central

Sutterer, Matthew J.; Bruss, Joel; Boes, Aaron D.; Voss, Michelle W.; Bechara, Antoine; Tranel, Daniel

2016-01-01

Studies of patients with brain damage have highlighted a broad neural network of limbic and prefrontal areas as important for adaptive decision-making. However, some patients with damage outside these regions have impaired decision-making behavior, and the behavioral impairments observed in these cases are often attributed to the general variability in behavior following brain damage, rather than a deficit in a specific brain-behavior relationship. A novel approach, lesion-derived network mapping, uses healthy subject resting-state functional connectivity (RSFC) data to infer the areas that would be connected with each patient’s lesion area in healthy adults. Here, we used this approach to investigate whether there was a systematic pattern of connectivity associated with decision-making performance in patients with focal damage in areas not classically associated with decision-making. These patients were categorized a priori into “impaired” or “unimpaired” groups based on their performance on the Iowa Gambling Task (IGT). Lesion-derived network maps based on the impaired patients showed overlap in somatosensory, motor and insula cortices, to a greater extent than patients who showed unimpaired IGT performance. Akin to the classic concept of “diaschisis” (von Monakow, 1914), this focus on the remote effects that focal damage can have on large-scale distributed brain networks has the potential to inform not only differences in decision-making behavior, but also other cognitive functions or neurological syndromes where a distinct phenotype has eluded neuroanatomical classification and brain-behavior relationships appear highly heterogeneous. PMID:26994344

Increased engagement of the cognitive control network associated with music training in children during an fMRI Stroop task

PubMed Central

2017-01-01

Playing a musical instrument engages various sensorimotor processes and draws on cognitive capacities collectively termed executive functions. However, while music training is believed to associated with enhancements in certain cognitive and language abilities, studies that have explored the specific relationship between music and executive function have yielded conflicting results. As part of an ongoing longitudinal study, we investigated the effects of music training on executive function using fMRI and several behavioral tasks, including the Color-Word Stroop task. Children involved in ongoing music training (N = 14, mean age = 8.67) were compared with two groups of comparable general cognitive abilities and socioeconomic status, one involved in sports (“sports” group, N = 13, mean age = 8.85) and another not involved in music or sports (“control” group, N = 17, mean age = 9.05). During the Color-Word Stroop task, children with music training showed significantly greater bilateral activation in the pre-SMA/SMA, ACC, IFG, and insula in trials that required cognitive control compared to the control group, despite no differences in performance on behavioral measures of executive function. No significant differences in brain activation or in task performance were found between the music and sports groups. The results suggest that systematic extracurricular training, particularly music-based training, is associated with changes in the cognitive control network in the brain even in the absence of changes in behavioral performance. PMID:29084283

A Neural Network Approach to fMRI Binocular Visual Rivalry Task Analysis

PubMed Central

Bertolino, Nicola; Ferraro, Stefania; Nigri, Anna; Bruzzone, Maria Grazia; Ghielmetti, Francesco; Leonardi, Matilde; Agostino Parati, Eugenio; Grazia Bruzzone, Maria; Franceschetti, Silvana; Caldiroli, Dario; Sattin, Davide; Giovannetti, Ambra; Pagani, Marco; Covelli, Venusia; Ciaraffa, Francesca; Vela Gomez, Jesus; Reggiori, Barbara; Ferraro, Stefania; Nigri, Anna; D'Incerti, Ludovico; Minati, Ludovico; Andronache, Adrian; Rosazza, Cristina; Fazio, Patrik; Rossi, Davide; Varotto, Giulia; Panzica, Ferruccio; Benti, Riccardo; Marotta, Giorgio; Molteni, Franco

2014-01-01

The purpose of this study was to investigate whether artificial neural networks (ANN) are able to decode participants’ conscious experience perception from brain activity alone, using complex and ecological stimuli. To reach the aim we conducted pattern recognition data analysis on fMRI data acquired during the execution of a binocular visual rivalry paradigm (BR). Twelve healthy participants were submitted to fMRI during the execution of a binocular non-rivalry (BNR) and a BR paradigm in which two classes of stimuli (faces and houses) were presented. During the binocular rivalry paradigm, behavioral responses related to the switching between consciously perceived stimuli were also collected. First, we used the BNR paradigm as a functional localizer to identify the brain areas involved the processing of the stimuli. Second, we trained the ANN on the BNR fMRI data restricted to these regions of interest. Third, we applied the trained ANN to the BR data as a ‘brain reading’ tool to discriminate the pattern of neural activity between the two stimuli. Fourth, we verified the consistency of the ANN outputs with the collected behavioral indicators of which stimulus was consciously perceived by the participants. Our main results showed that the trained ANN was able to generalize across the two different tasks (i.e. BNR and BR) and to identify with high accuracy the cognitive state of the participants (i.e. which stimulus was consciously perceived) during the BR condition. The behavioral response, employed as control parameter, was compared with the network output and a statistically significant percentage of correspondences (p-value <0.05) were obtained for all subjects. In conclusion the present study provides a method based on multivariate pattern analysis to investigate the neural basis of visual consciousness during the BR phenomenon when behavioral indicators lack or are inconsistent, like in disorders of consciousness or sedated patients. PMID:25121595

Long-range functional interactions of anterior insula and medial frontal cortex are differently modulated by visuospatial and inductive reasoning tasks.

PubMed

Ebisch, Sjoerd J H; Mantini, Dante; Romanelli, Roberta; Tommasi, Marco; Perrucci, Mauro G; Romani, Gian Luca; Colom, Roberto; Saggino, Aristide

2013-09-01

The brain is organized into functionally specific networks as characterized by intrinsic functional relationships within discrete sets of brain regions. However, it is poorly understood whether such functional networks are dynamically organized according to specific task-states. The anterior insular cortex (aIC)-dorsal anterior cingulate cortex (dACC)/medial frontal cortex (mFC) network has been proposed to play a central role in human cognitive abilities. The present functional magnetic resonance imaging (fMRI) study aimed at testing whether functional interactions of the aIC-dACC/mFC network in terms of temporally correlated patterns of neural activity across brain regions are dynamically modulated by transitory, ongoing task demands. For this purpose, functional interactions of the aIC-dACC/mFC network are compared during two distinguishable fluid reasoning tasks, Visualization and Induction. The results show an increased functional coupling of bilateral aIC with visual cortices in the occipital lobe during the Visualization task, whereas coupling of mFC with right anterior frontal cortex was enhanced during the Induction task. These task-specific modulations of functional interactions likely reflect ability related neural processing. Furthermore, functional connectivity strength between right aIC and right dACC/mFC reliably predicts general task performance. The findings suggest that the analysis of long-range functional interactions may provide complementary information about brain-behavior relationships. On the basis of our results, it is proposed that the aIC-dACC/mFC network contributes to the integration of task-common and task-specific information based on its within-network as well as its between-network dynamic functional interactions. Copyright © 2013 Elsevier Inc. All rights reserved.

Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

PubMed

Orhan, A Emin; Ma, Wei Ji

2017-07-26

Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

Anterior medial prefrontal cortex exhibits activation during task preparation but deactivation during task execution.

PubMed

Koshino, Hideya; Minamoto, Takehiro; Ikeda, Takashi; Osaka, Mariko; Otsuka, Yuki; Osaka, Naoyuki

2011-01-01

The anterior prefrontal cortex (PFC) exhibits activation during some cognitive tasks, including episodic memory, reasoning, attention, multitasking, task sets, decision making, mentalizing, and processing of self-referenced information. However, the medial part of anterior PFC is part of the default mode network (DMN), which shows deactivation during various goal-directed cognitive tasks compared to a resting baseline. One possible factor for this pattern is that activity in the anterior medial PFC (MPFC) is affected by dynamic allocation of attentional resources depending on task demands. We investigated this possibility using an event related fMRI with a face working memory task. Sixteen students participated in a single fMRI session. They were asked to form a task set to remember the faces (Face memory condition) or to ignore them (No face memory condition), then they were given 6 seconds of preparation period before the onset of the face stimuli. During this 6-second period, four single digits were presented one at a time at the center of the display, and participants were asked to add them and to remember the final answer. When participants formed a task set to remember faces, the anterior MPFC exhibited activation during a task preparation period but deactivation during a task execution period within a single trial. The results suggest that the anterior MPFC plays a role in task set formation but is not involved in execution of the face working memory task. Therefore, when attentional resources are allocated to other brain regions during task execution, the anterior MPFC shows deactivation. The results suggest that activation and deactivation in the anterior MPFC are affected by dynamic allocation of processing resources across different phases of processing.

Cognitive and default-mode resting state networks: do male and female brains "rest" differently?

PubMed

Weissman-Fogel, Irit; Moayedi, Massieh; Taylor, Keri S; Pope, Geoff; Davis, Karen D

2010-11-01

Variability in human behavior related to sex is supported by neuroimaging studies showing differences in brain activation patterns during cognitive task performance. An emerging field is examining the human connectome, including networks of brain regions that are not only temporally-correlated during different task conditions, but also networks that show highly correlated spontaneous activity during a task-free state. Both task-related and task-free network activity has been associated with individual task performance and behavior under certain conditions. Therefore, our aim was to determine whether sex differences exist during a task-free resting state for two networks associated with cognitive task performance (executive control network (ECN), salience network (SN)) and the default mode network (DMN). Forty-nine healthy subjects (26 females, 23 males) underwent a 5-min task-free fMRI scan in a 3T MRI. An independent components analysis (ICA) was performed to identify the best-fit IC for each network based on specific spatial nodes defined in previous studies. To determine the consistency of these networks across subjects we performed self-organizing group-level ICA analyses. There were no significant differences between sexes in the functional connectivity of the brain areas within the ECN, SN, or the DMN. These important findings highlight the robustness of intrinsic connectivity of these resting state networks and their similarity between sexes. Furthermore, our findings suggest that resting state fMRI studies do not need to be controlled for sex. © 2010 Wiley-Liss, Inc.

Inter-subject phase synchronization for exploratory analysis of task-fMRI.

PubMed

Bolt, Taylor; Nomi, Jason S; Vij, Shruti G; Chang, Catie; Uddin, Lucina Q

2018-08-01

Analysis of task-based fMRI data is conventionally carried out using a hypothesis-driven approach, where blood-oxygen-level dependent (BOLD) time courses are correlated with a hypothesized temporal structure. In some experimental designs, this temporal structure can be difficult to define. In other cases, experimenters may wish to take a more exploratory, data-driven approach to detecting task-driven BOLD activity. In this study, we demonstrate the efficiency and power of an inter-subject synchronization approach for exploratory analysis of task-based fMRI data. Combining the tools of instantaneous phase synchronization and independent component analysis, we characterize whole-brain task-driven responses in terms of group-wise similarity in temporal signal dynamics of brain networks. We applied this framework to fMRI data collected during performance of a simple motor task and a social cognitive task. Analyses using an inter-subject phase synchronization approach revealed a large number of brain networks that dynamically synchronized to various features of the task, often not predicted by the hypothesized temporal structure of the task. We suggest that this methodological framework, along with readily available tools in the fMRI community, provides a powerful exploratory, data-driven approach for analysis of task-driven BOLD activity. Copyright © 2018 Elsevier Inc. All rights reserved.

Task network models in the prediction of workload imposed by extravehicular activities during the Hubble Space Telescope servicing mission

NASA Technical Reports Server (NTRS)

Diaz, Manuel F.; Takamoto, Neal; Woolford, Barbara

1994-01-01

In a joint effort with Brooks AFB, Texas, the Flight Crew Support Division at JSC has begun a computer simulation and performance modeling program directed at establishing the predictive validity of software tools for modeling human performance during spaceflight. This paper addresses the utility of task network modeling for predicting the workload that astronauts are likely to encounter in extravehicular activities (EVA) during the Hubble Space Telescope (HST) repair mission. The intent of the study was to determine whether two EVA crewmembers and one intravehicular activity (IVA) crewmember could reasonably be expected to complete HST Wide Field/Planetary Camera (WFPC) replacement in the allotted time. Ultimately, examination of the points during HST servicing that may result in excessive workload will lead to recommendations to the HST Flight Systems and Servicing Project concerning (1) expectation of degraded performance, (2) the need to change task allocation across crewmembers, (3) the need to expand the timeline, and (4) the need to increase the number of EVA's.

Social Insects: A Model System for Network Dynamics

NASA Astrophysics Data System (ADS)

Charbonneau, Daniel; Blonder, Benjamin; Dornhaus, Anna

Social insect colonies (ants, bees, wasps, and termites) show sophisticated collective problem-solving in the face of variable constraints. Individuals exchange information and materials such as food. The resulting network structure and dynamics can inform us about the mechanisms by which the insects achieve particular collective behaviors and these can be transposed to man-made and social networks. We discuss how network analysis can answer important questions about social insects, such as how effective task allocation or information flow is realized. We put forward the idea that network analysis methods are under-utilized in social insect research, and that they can provide novel ways to view the complexity of collective behavior, particularly if network dynamics are taken into account. To illustrate this, we present an example of network tasks performed by ant workers, linked by instances of workers switching from one task to another. We show how temporal network analysis can propose and test new hypotheses on mechanisms of task allocation, and how adding temporal elements to static networks can drastically change results. We discuss the benefits of using social insects as models for complex systems in general. There are multiple opportunities emergent technologies and analysis methods in facilitating research on social insect network. The potential for interdisciplinary work could significantly advance diverse fields such as behavioral ecology, computer sciences, and engineering.

Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning.

PubMed

Hearne, Luke J; Cocchi, Luca; Zalesky, Andrew; Mattingley, Jason B

2017-08-30

Our capacity for higher cognitive reasoning has a measurable limit. This limit is thought to arise from the brain's capacity to flexibly reconfigure interactions between spatially distributed networks. Recent work, however, has suggested that reconfigurations of task-related networks are modest when compared with intrinsic "resting-state" network architecture. Here we combined resting-state and task-driven functional magnetic resonance imaging to examine how flexible, task-specific reconfigurations associated with increasing reasoning demands are integrated within a stable intrinsic brain topology. Human participants (21 males and 28 females) underwent an initial resting-state scan, followed by a cognitive reasoning task involving different levels of complexity, followed by a second resting-state scan. The reasoning task required participants to deduce the identity of a missing element in a 4 × 4 matrix, and item difficulty was scaled parametrically as determined by relational complexity theory. Analyses revealed that external task engagement was characterized by a significant change in functional brain modules. Specifically, resting-state and null-task demand conditions were associated with more segregated brain-network topology, whereas increases in reasoning complexity resulted in merging of resting-state modules. Further increments in task complexity did not change the established modular architecture, but affected selective patterns of connectivity between frontoparietal, subcortical, cingulo-opercular, and default-mode networks. Larger increases in network efficiency within the newly established task modules were associated with higher reasoning accuracy. Our results shed light on the network architectures that underlie external task engagement, and highlight selective changes in brain connectivity supporting increases in task complexity. SIGNIFICANCE STATEMENT Humans have clear limits in their ability to solve complex reasoning problems. It is thought that

Sparse dictionary learning of resting state fMRI networks.

PubMed

Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C

2012-07-02

Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.

Hypertension risk and clinical care in patients with bipolar disorder or schizophrenia; a systematic review and meta-analysis.

PubMed

Ayerbe, Luis; Forgnone, Ivo; Addo, Juliet; Siguero, Ana; Gelati, Stefano; Ayis, Salma

2018-01-01

A higher cardiovascular morbidity and mortality has been observed in patients with bipolar disorder (BPD) or schizophrenia, partly due to an increased risk of hypertension (HTN), or a less effective care of it. This systematic review and meta-analysis, presents a critical appraisal and summary of the studies addressing the risk of HTN, or the differences in its care, for those with schizophrenia or BPD. Prospective studies were searched in PubMed, Embase, PsycINFO, Scopus, and the Web of Science, from database inception to June 2017. A meta-analysis was undertaken to obtain pooled estimates of the risk of HTN. Five studies reporting the risk of HTN, and five studies presenting differences in its clinical care, were identified. An increased risk of HTN was observed for BPD patients, with an overall Incidence Rate Ratio 1.27(1.15-1.40). The pooled Incidence Rate Ratio of HTN for those with schizophrenia was 0.94 (0.75 - 1.14). A poorer care of HTN (lower rates of screening, prescription, and adherence) was reported in four studies of schizophrenia, and two of BPD patients, compared to people without these conditions. reduced number of studies on risk and care of HTN on patients with BPD or schizophrenia. Limited evidence suggests that patients with BPD have a higher risk of HTN. Patients with schizophrenia and BPD receive poor care of HTN. Understanding the risk of HTN, and the differences in its care, is essential for clinicians to reduce the cardiovascular morbidity and overall mortality of these patients. Copyright © 2017 Elsevier B.V. All rights reserved.

The neural implementation of task rule activation in the task-cuing paradigm: an event-related fMRI study.

PubMed

Shi, Yiquan; Zhou, Xiaolin; Müller, Hermann J; Schubert, Torsten

2010-07-01

To isolate the neural correlates for task rule activation from those related to general task preparation, the effect of a cue explicitly specifying the S-R correspondences (rule-cue) was contrasted with the effects of a cue specifying only the task to performed (task-cue). While the task-cue provides merely information about the type of task, the rule-cue is explicit about both the task type and the task rule (i.e., the set of S-R correspondences). The rule-cue was expected to activate the task rule more efficiently in the preparation period (prior to target presentation); by contrast, in the task-cue condition, part of the task rule activation was expected to be postponed into the task execution period (following the presentation of the target). In an event-related fMRI experiment, we found the right anterior and middle parts of the middle frontal and superior frontal gyri, the right inferior frontal junction, the pre-SMA, as well as the right superior and inferior parietal lobes to show larger activation elicited by the rule-cue than by the task-cue prior to target presentation. Conversely, the results revealed larger activations in these regions in the task-cue than in the rule-cue condition during the task execution period. In summary, this study identified some of the neural correlates of task rule activation and showed that these are a subset of the general task preparation network. Copyright (c) 2010 Elsevier Inc. All rights reserved.

A reliability analysis tool for SpaceWire network

NASA Astrophysics Data System (ADS)

Zhou, Qiang; Zhu, Longjiang; Fei, Haidong; Wang, Xingyou

2017-04-01

A SpaceWire is a standard for on-board satellite networks as the basis for future data-handling architectures. It is becoming more and more popular in space applications due to its technical advantages, including reliability, low power and fault protection, etc. High reliability is the vital issue for spacecraft. Therefore, it is very important to analyze and improve the reliability performance of the SpaceWire network. This paper deals with the problem of reliability modeling and analysis with SpaceWire network. According to the function division of distributed network, a reliability analysis method based on a task is proposed, the reliability analysis of every task can lead to the system reliability matrix, the reliability result of the network system can be deduced by integrating these entire reliability indexes in the matrix. With the method, we develop a reliability analysis tool for SpaceWire Network based on VC, where the computation schemes for reliability matrix and the multi-path-task reliability are also implemented. By using this tool, we analyze several cases on typical architectures. And the analytic results indicate that redundancy architecture has better reliability performance than basic one. In practical, the dual redundancy scheme has been adopted for some key unit, to improve the reliability index of the system or task. Finally, this reliability analysis tool will has a directive influence on both task division and topology selection in the phase of SpaceWire network system design.

Is performance in task-cuing experiments mediated by task set selection or associative compound retrieval?

PubMed

Forrest, Charlotte L D; Monsell, Stephen; McLaren, Ian P L

2014-07-01

Task-cuing experiments are usually intended to explore control of task set. But when small stimulus sets are used, they plausibly afford learning of the response associated with a combination of cue and stimulus, without reference to tasks. In 3 experiments we presented the typical trials of a task-cuing experiment: a cue (colored shape) followed, after a short or long interval, by a digit to which 1 of 2 responses was required. In a tasks condition, participants were (as usual) directed to interpret the cue as an instruction to perform either an odd/even or a high/low classification task. In a cue + stimulus → response (CSR) condition, to induce learning of mappings between cue-stimulus compound and response, participants were, in Experiment 1, given standard task instructions and additionally encouraged to learn the CSR mappings; in Experiment 2, informed of all the CSR mappings and asked to learn them, without standard task instructions; in Experiment 3, required to learn the mappings by trial and error. The effects of a task switch, response congruence, preparation, and transfer to a new set of stimuli differed substantially between the conditions in ways indicative of classification according to task rules in the tasks condition, and retrieval of responses specific to stimulus-cue combinations in the CSR conditions. Qualitative features of the latter could be captured by an associative learning network. Hence associatively based compound retrieval can serve as the basis for performance with a small stimulus set. But when organization by tasks is apparent, control via task set selection is the natural and efficient strategy. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Overcoming catastrophic forgetting in neural networks

PubMed Central

Kirkpatrick, James; Pascanu, Razvan; Rabinowitz, Neil; Veness, Joel; Desjardins, Guillaume; Rusu, Andrei A.; Milan, Kieran; Quan, John; Ramalho, Tiago; Grabska-Barwinska, Agnieszka; Hassabis, Demis; Clopath, Claudia; Kumaran, Dharshan; Hadsell, Raia

2017-01-01

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially. PMID:28292907

Prototype-Incorporated Emotional Neural Network.

PubMed

Oyedotun, Oyebade K; Khashman, Adnan

2017-08-15

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

Is the failure of SYMPLICITY HTN-3 trial to meet its efficacy endpoint the "end of the road" for renal denervation?

PubMed

Epstein, Murray; de Marchena, Eduardo

2015-02-01

Resistant hypertension is a common medical problem that is increasing with the advent of an increasingly older and heavier population. The etiology of resistant hypertension is almost always multifactorial, but the results of numerous studies indicate that renal sympathetic activation is a particularly common cause of resistance to antihypertensive treatment. Consistent with the belief in a pivotal role of renal sympathetic stimulation, there has been a growing interest in renal denervation (RDN) treatment strategies. The long-awaited results of SYMPLICITY HTN-3 study disclosed that the reduction in blood pressure by the SYMPLICITY device did not differ from that in the sham-procedure arm of the study. In the present article, we identify several factors that explain why the study failed to demonstrate any benefit from the intervention. The reasons are multifactorial and include inadequate screening at entry and frequent medication changes during the study. Additional problems include the lack of experience of many operators with the SYMPLICITY device and procedure variability, as attested to by a diminished number of ablation "quadrants." Also a factor was the inability of the first generation Medtronic device to allow four ablations to be performed simultaneously. We recommend that future RDN studies adhere to more rigorous screening procedures, and utilize newer multi-site denervation systems that facilitate four ablations simultaneously. Drug optimization should be achieved by monitoring adherence throughout the study. Nevertheless, we are optimistic about a future role of RDN. To optimize chances of success, increased efforts are necessary to identify the appropriate patients for RDN and investigators must use second and third generation denervation devices and techniques. Copyright © 2015 American Society of Hypertension. Published by Elsevier Inc. All rights reserved.

Anterior Medial Prefrontal Cortex Exhibits Activation during Task Preparation but Deactivation during Task Execution

PubMed Central

Koshino, Hideya; Minamoto, Takehiro; Ikeda, Takashi; Osaka, Mariko; Otsuka, Yuki; Osaka, Naoyuki

2011-01-01

Background The anterior prefrontal cortex (PFC) exhibits activation during some cognitive tasks, including episodic memory, reasoning, attention, multitasking, task sets, decision making, mentalizing, and processing of self-referenced information. However, the medial part of anterior PFC is part of the default mode network (DMN), which shows deactivation during various goal-directed cognitive tasks compared to a resting baseline. One possible factor for this pattern is that activity in the anterior medial PFC (MPFC) is affected by dynamic allocation of attentional resources depending on task demands. We investigated this possibility using an event related fMRI with a face working memory task. Methodology/Principal Findings Sixteen students participated in a single fMRI session. They were asked to form a task set to remember the faces (Face memory condition) or to ignore them (No face memory condition), then they were given 6 seconds of preparation period before the onset of the face stimuli. During this 6-second period, four single digits were presented one at a time at the center of the display, and participants were asked to add them and to remember the final answer. When participants formed a task set to remember faces, the anterior MPFC exhibited activation during a task preparation period but deactivation during a task execution period within a single trial. Conclusions/Significance The results suggest that the anterior MPFC plays a role in task set formation but is not involved in execution of the face working memory task. Therefore, when attentional resources are allocated to other brain regions during task execution, the anterior MPFC shows deactivation. The results suggest that activation and deactivation in the anterior MPFC are affected by dynamic allocation of processing resources across different phases of processing. PMID:21829668

Increased extracellular volume and altered mechanics are associated with LVH in hypertensive heart disease, not hypertension alone.

PubMed

Kuruvilla, Sujith; Janardhanan, Rajesh; Antkowiak, Patrick; Keeley, Ellen C; Adenaw, Nebiyu; Brooks, Jeremy; Epstein, Frederick H; Kramer, Christopher M; Salerno, Michael

2015-02-01

The goal of this study was to assess the relationship among extracellular volume (ECV), native T1, and systolic strain in hypertensive patients with left ventricular hypertrophy (HTN LVH), hypertensive patients without LVH (HTN non-LVH), and normotensive controls. Diffuse myocardial fibrosis in HTN LVH patients, as reflected by increased ECV and native T1, may be an underlying mechanism contributing to increased cardiovascular risk compared with HTN non-LVH subjects and controls. Furthermore, increased diffuse fibrosis in HTN LVH subjects may be associated with reduced peak systolic and early diastolic strain rate compared with the other 2 groups. T1 mapping was performed in 20 HTN LVH (mean age, 55 ± 11 years), 23 HTN non-LVH (mean age, 61 ± 12 years), and 22 control subjects (mean age, 54 ± 7 years) on a Siemens 1.5-T Avanto (Siemens Healthcare, Erlangen, Germany) using a previously validated modified look-locker inversion-recovery pulse sequence. T1 was measured pre-contrast and 10, 15, and 20 min after injection of 0.15 mmol/kg gadopentetate dimeglumine, and the mean ECV and native T1 were determined for each subject. Measurement of circumferential strain parameters were performed using cine displacement encoding with stimulated echoes. HTN LVH subjects had higher native T1 compared with controls (p < 0.05). HTN LVH subjects had higher ECV compared with HTN non-LVH subjects and controls (p < 0.05). Peak systolic circumferential strain and early diastolic strain rates were reduced in HTN LVH subjects compared with HTN non-LVH subjects and controls (p < 0.05). Increased levels of ECV and native T1 were associated with reduced peak systolic and early diastolic circumferential strain rate across all subjects. HTN LVH patients had higher ECV, longer native T1 and associated reduction in peak systolic circumferential strain, and early diastolic strain rate compared with HTN non-LVH and control subjects. Measurement of ECV and native T1 provide a noninvasive

Treatment-emergent hypertension and efficacy in the phase 3 Study of (E7080) lenvatinib in differentiated cancer of the thyroid (SELECT).

PubMed

Wirth, Lori J; Tahara, Makoto; Robinson, Bruce; Francis, Sanjeev; Brose, Marcia S; Habra, Mouhammed Amir; Newbold, Kate; Kiyota, Naomi; Dutcus, Corina E; Mathias, Elton; Guo, Matthew; Sherman, Steven I; Schlumberger, Martin

2018-06-01

Hypertension (HTN) is an established class effect of vascular endothelial growth factor receptor (VEGFR) inhibition. In the phase 3 Study of (E7080) Lenvatinib in Differentiated Cancer of the Thyroid (SELECT) trial, HTN was the most frequent adverse event of lenvatinib, an inhibitor of VEGFR1, VEGFR2, VEGFR3, fibroblast growth factor receptor 1 (FGFR1), FGFR2, FGFR3, FGFR4, platelet-derived growth factor receptor α (PDGFRα), ret proto-oncogene (RET), and stem cell factor receptor (KIT). This exploratory analysis examined treatment-emergent hypertension (TE-HTN) and its relation with lenvatinib efficacy and safety in SELECT. In the multicenter, double-blind SELECT trial, 392 patients with progressive radioiodine-refractory differentiated thyroid cancer (RR-DTC) were randomized 2:1 to lenvatinib (24 mg/d on a 28-day cycle) or placebo. Survival endpoints were assessed with Kaplan-Meier estimates and log-rank tests. The influence of TE-HTN on progression-free survival (PFS) and overall survival (OS) was analyzed with univariate and multivariate Cox proportional hazards models. Overall, 73% of lenvatinib-treated patients and 15% of placebo-treated patients experienced TE-HTN. The median PFS for lenvatinib-treated patients with (n = 190) and without TE-HTN (n = 71) was 18.8 and 12.9 months, respectively (hazard ratio [HR], 0.59; 95% confidence interval [CI], 0.39-0.88; P = .0085). For lenvatinib-treated patients, the objective response rate was 69% with TE-HTN and 56% without TE-HTN (odds ratio, 1.72; 95% CI, 0.98-3.01). The median change in tumor size for patients with and without TE-HTN was -45% and -40%, respectively (P = .2). The median OS was not reached for patients with TE-HTN; for those without TE-HTN, it was 21.7 months (HR, 0.43; 95% CI, 0.27-0.69; P = .0003). Although HTN is a clinically significant adverse event that warrants monitoring and management, TE-HTN was significantly correlated with improved outcomes in patients with RR-DTC, indicating that HTN

Progression rate from new-onset pre-hypertension to hypertension in Korean adults.

PubMed

Kim, Soo Jeong; Lee, Jakyoung; Nam, Chung Mo; Jee, Sun Ha; Park, Il Soo; Lee, Kyung Jong; Lee, Soon Young

2011-01-01

There are limited studies conducted in Asia to investigate the progression rate to hypertension (HTN). This study was done to estimate the progression rate of new-onset pre-HTN (PreHTN) to HTN during an 8-year follow-up period, and to compare the impact of PreHTN on progression to HTN. A total of 49,228 participants, aged 30 to 54 years with new-onset PreHTN at baseline (1994-1996) from a biennial national medical exam were enrolled and followed up every 2 years until 2004. The incidence rate recorded at each interval and the cumulative incidence rate of HTN were analyzed. Hazard ratio of high-normal and high blood pressure (BP) in men and women was calculated. The cumulative incidence rate for high-normal BP was 27.6% and 26.4% at 2-year follow-up, increased to respectively 64.1% and 55.8% in men and women at the 8-year follow-up. Compared to optimal BP, hazard ratios for men with high-normal BP across all age groups were 3- to 4-fold higher at 2-year, and 2- to 3-fold higher at 8-year follow-up. Hazard ratios for women were about 6-fold higher at 2-year and around 4-fold higher at 8-year follow-up. New PreHTN was a significant predisposing factor for future HTN, in young adults and the effect is more prominent in women.

Is Rest Really Rest? Resting State Functional Connectivity during Rest and Motor Task Paradigms.

PubMed

Jurkiewicz, Michael T; Crawley, Adrian P; Mikulis, David J

2018-04-18

Numerous studies have identified the default mode network (DMN) within the brain of healthy individuals, which has been attributed to the ongoing mental activity of the brain during the wakeful resting-state. While engaged during specific resting-state fMRI paradigms, it remains unclear as to whether traditional block-design simple movement fMRI experiments significantly influence the default mode network or other areas. Using blood-oxygen level dependent (BOLD) fMRI we characterized the pattern of functional connectivity in healthy subjects during a resting-state paradigm and compared this to the same resting-state analysis performed on motor task data residual time courses after regressing out the task paradigm. Using seed-voxel analysis to define the DMN, the executive control network (ECN), and sensorimotor, auditory and visual networks, the resting-state analysis of the residual time courses demonstrated reduced functional connectivity in the motor network and reduced connectivity between the insula and the ECN compared to the standard resting-state datasets. Overall, performance of simple self-directed motor tasks does little to change the resting-state functional connectivity across the brain, especially in non-motor areas. This would suggest that previously acquired fMRI studies incorporating simple block-design motor tasks could be mined retrospectively for assessment of the resting-state connectivity.

Distributed intelligent control and status networking

NASA Technical Reports Server (NTRS)

Fortin, Andre; Patel, Manoj

1993-01-01

Over the past two years, the Network Control Systems Branch (Code 532) has been investigating control and status networking technologies. These emerging technologies use distributed processing over a network to accomplish a particular custom task. These networks consist of small intelligent 'nodes' that perform simple tasks. Containing simple, inexpensive hardware and software, these nodes can be easily developed and maintained. Once networked, the nodes can perform a complex operation without a central host. This type of system provides an alternative to more complex control and status systems which require a central computer. This paper will provide some background and discuss some applications of this technology. It will also demonstrate the suitability of one particular technology for the Space Network (SN) and discuss the prototyping activities of Code 532 utilizing this technology.

Routing Protocols to Minimize the Number of Route Disconnections for Communication in Mobile Ad Hoc Networks

DTIC Science & Technology

2009-09-01

Wireless Sensor Network (WSN) Simulator Research Personnel: Dr. Ali Abu-El Humos Task No. Task Current Status 1 Literature review and problem definition...networks.com/ [2] S. Dulman, P. Havinga, "A Simulation Template for Wireless Sensor Networks ," Supplement of the Sixth International Symposium on Autonomous... Sensor Network (WSN) Simulator 76 I Breakdown of the Research Activity to Tasks 76 II Description of the Tasks 76 Task 1 Literature Review and

Specific Transcriptome Changes Associated with Blood Pressure Reduction in Hypertensive Patients After Relaxation Response Training.

PubMed

Bhasin, Manoj K; Denninger, John W; Huffman, Jeff C; Joseph, Marie G; Niles, Halsey; Chad-Friedman, Emma; Goldman, Roberta; Buczynski-Kelley, Beverly; Mahoney, Barbara A; Fricchione, Gregory L; Dusek, Jeffery A; Benson, Herbert; Zusman, Randall M; Libermann, Towia A

2018-05-01

Mind-body practices that elicit the relaxation response (RR) have been demonstrated to reduce blood pressure (BP) in essential hypertension (HTN) and may be an adjunct to antihypertensive drug therapy. However, the molecular mechanisms by which the RR reduces BP remain undefined. Genomic determinants associated with responsiveness to an 8-week RR-based mind-body intervention for lowering HTN in 13 stage 1 hypertensive patients classified as BP responders and 11 as nonresponders were identified. Transcriptome analysis in peripheral blood mononuclear cells identified 1771 genes regulated by the RR in responders. Biological process- and pathway-based analysis of transcriptome data demonstrated enrichment in the following gene categories: immune regulatory pathways and metabolism (among downregulated genes); glucose metabolism, cardiovascular system development, and circadian rhythm (among upregulated genes). Further in silico estimation of cell abundance from the microarray data showed enrichment of the anti-inflammatory M2 subtype of macrophages in BP responders. Nuclear factor-κB, vascular endothelial growth factor, and insulin were critical molecules emerging from interactive network analysis. These findings provide the first insights into the molecular mechanisms that are associated with the beneficial effects of the RR on HTN.

Specific Transcriptome Changes Associated with Blood Pressure Reduction in Hypertensive Patients After Relaxation Response Training

PubMed Central

Bhasin, Manoj K.; Denninger, John W.; Huffman, Jeff C.; Joseph, Marie G.; Niles, Halsey; Chad-Friedman, Emma; Goldman, Roberta; Buczynski-Kelley, Beverly; Mahoney, Barbara A.; Fricchione, Gregory L.; Dusek, Jeffery A.; Benson, Herbert; Zusman, Randall M.

2018-01-01

Abstract Objective: Mind–body practices that elicit the relaxation response (RR) have been demonstrated to reduce blood pressure (BP) in essential hypertension (HTN) and may be an adjunct to antihypertensive drug therapy. However, the molecular mechanisms by which the RR reduces BP remain undefined. Design: Genomic determinants associated with responsiveness to an 8-week RR-based mind–body intervention for lowering HTN in 13 stage 1 hypertensive patients classified as BP responders and 11 as nonresponders were identified. Results: Transcriptome analysis in peripheral blood mononuclear cells identified 1771 genes regulated by the RR in responders. Biological process- and pathway-based analysis of transcriptome data demonstrated enrichment in the following gene categories: immune regulatory pathways and metabolism (among downregulated genes); glucose metabolism, cardiovascular system development, and circadian rhythm (among upregulated genes). Further in silico estimation of cell abundance from the microarray data showed enrichment of the anti-inflammatory M2 subtype of macrophages in BP responders. Nuclear factor-κB, vascular endothelial growth factor, and insulin were critical molecules emerging from interactive network analysis. Conclusions: These findings provide the first insights into the molecular mechanisms that are associated with the beneficial effects of the RR on HTN. PMID:29616846

Changes in task-based effective connectivity in language networks following rehabilitation in post-stroke patients with aphasia

PubMed Central

Kiran, Swathi; Meier, Erin L.; Kapse, Kushal J.; Glynn, Peter A.

2015-01-01

In this study, we examined regions in the left and right hemisphere language network that were altered in terms of the underlying neural activation and effective connectivity subsequent to language rehabilitation. Eight persons with chronic post-stroke aphasia and eight normal controls participated in the current study. Patients received a 10 week semantic feature-based rehabilitation program to improve their skills. Therapy was provided on atypical examples of one trained category while two control categories were monitored; the categories were counterbalanced across patients. In each fMRI session, two experimental tasks were conducted: (a) picture naming and (b) semantic feature verification of trained and untrained categories. Analysis of treatment effect sizes revealed that all patients showed greater improvements on the trained category relative to untrained categories. Results from this study show remarkable patterns of consistency despite the inherent variability in lesion size and activation patterns across patients. Across patients, activation that emerged as a function of rehabilitation on the trained category included bilateral IFG, bilateral SFG, LMFG, and LPCG for picture naming; and bilateral IFG, bilateral MFG, LSFG, and bilateral MTG for semantic feature verification. Analysis of effective connectivity using Dynamic Causal Modeling (DCM) indicated that LIFG was the consistently significantly modulated region after rehabilitation across participants. These results indicate that language networks in patients with aphasia resemble normal language control networks and that this similarity is accentuated by rehabilitation. PMID:26106314

Statistical modelling of networked human-automation performance using working memory capacity.

PubMed

Ahmed, Nisar; de Visser, Ewart; Shaw, Tyler; Mohamed-Ameen, Amira; Campbell, Mark; Parasuraman, Raja

2014-01-01

This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity. Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

Pathophysiology and Potential Non-Pharmacologic Treatments of Obesity or Kidney Disease Associated Refractory Hypertension.

PubMed

Le Jemtel, Thierry H; Richardson, William; Samson, Rohan; Jaiswal, Abhishek; Oparil, Suzanne

2017-02-01

The review assesses the role of non-pharmacologic therapy for obesity and chronic kidney disease (CKD) associated refractory hypertension (rf HTN). Hypertensive patients with markedly heightened sympathetic nervous system (SNS) activity are prone to develop refractory hypertension (rfHTN). Patients with obesity and chronic kidney disease (CKD)-associated HTN have particularly heightened SNS activity and are at high risk of rfHTN. The role of bariatric surgery is increasingly recognized in treatment of obesity. Current evidence advocates for a greater role of bariatric surgery in the management of obesity-associated HTN. In contrast, renal denervation does not appear have a role in the management of obesity or CKD-associated HTN. The role of baroreflex activation as adjunctive anti-hypertensive therapy remains to be defined.

Conducting indirect-treatment-comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 2.

PubMed

Hoaglin, David C; Hawkins, Neil; Jansen, Jeroen P; Scott, David A; Itzler, Robbin; Cappelleri, Joseph C; Boersma, Cornelis; Thompson, David; Larholt, Kay M; Diaz, Mireya; Barrett, Annabel

2011-06-01

Evidence-based health care decision making requires comparison of all relevant competing interventions. In the absence of randomized controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and network meta-analysis provide useful evidence for judiciously selecting the best treatment(s). Mixed treatment comparisons, a special case of network meta-analysis, combine direct evidence and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than traditional meta-analysis. This report from the International Society for Pharmacoeconomics and Outcomes Research Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on technical aspects of conducting network meta-analyses (our use of this term includes most methods that involve meta-analysis in the context of a network of evidence). We start with a discussion of strategies for developing networks of evidence. Next we briefly review assumptions of network meta-analysis. Then we focus on the statistical analysis of the data: objectives, models (fixed-effects and random-effects), frequentist versus Bayesian approaches, and model validation. A checklist highlights key components of network meta-analysis, and substantial examples illustrate indirect treatment comparisons (both frequentist and Bayesian approaches) and network meta-analysis. A further section discusses eight key areas for future research. Copyright © 2011 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Supervised dictionary learning for inferring concurrent brain networks.

PubMed

Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

2015-10-01

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

Distributed and cooperative task processing: Cournot oligopolies on a graph.

PubMed

Pavlic, Theodore P; Passino, Kevin M

2014-06-01

This paper introduces a novel framework for the design of distributed agents that must complete externally generated tasks but also can volunteer to process tasks encountered by other agents. To reduce the computational and communication burden of coordination between agents to perfectly balance load around the network, the agents adjust their volunteering propensity asynchronously within a fictitious trading economy. This economy provides incentives for nontrivial levels of volunteering for remote tasks, and thus load is shared. Moreover, the combined effects of diminishing marginal returns and network topology lead to competitive equilibria that have task reallocations that are qualitatively similar to what is expected in a load-balancing system with explicit coordination between nodes. In the paper, topological and algorithmic conditions are given that ensure the existence and uniqueness of a competitive equilibrium. Additionally, a decentralized distributed gradient-ascent algorithm is given that is guaranteed to converge to this equilibrium while not causing any node to over-volunteer beyond its maximum task-processing rate. The framework is applied to an autonomous-air-vehicle example, and connections are drawn to classic studies of the evolution of cooperation in nature.

Diabetes mellitus, hypertension and albuminuria in rural Zambia: a hospital-based survey.

PubMed

Rasmussen, Jon B; Thomsen, Jakúp A; Rossing, Peter; Parkinson, Shelagh; Christensen, Dirk L; Bygbjerg, Ib C

2013-09-01

To assess albuminuria in rural Zambia among patients with diabetes mellitus only (DM group), hypertension only (HTN group) and patients with combined DM and HTN (DM/HTN group). A cross-sectional survey was conducted at St. Francis Hospital in the Eastern province of Zambia. Albumin-creatinine ratio in one urine sample was used to assess albuminuria. Other information obtained included age, sex, body mass index (BMI), waist circumference (WC), blood pressure (BP), glycosylated haemoglobin (HbA1c ), random capillary glucose, time since diagnosis, medication and family history of DM or HTN. A total of 193 participants were included (DM group: n = 33; HTN group: n = 92; DM/HTN group: n = 68). The participants in the DM group used insulin more frequently as diabetes medication than the DM/HTN group (P < 0.05). Furthermore, the DM group was younger and had lower BMI, WC and BP than the two other groups. In the DM group, HTN group and DM/HTN group, microalbuminuria was found in 12.1%, 19.6% and 29.4% (P = 0.11), and macroalbuminuria was found in 0.0%, 3.3% and 13.2% (P = 0.014), respectively. The urine albumin (P = 0.014) and albumin-creatinine ratio (P = 0.0006) differed between the three groups. This hospital-based survey in rural Zambia found a lower frequency of albuminuria among the participants than in previous studies of patients with DM or HTN in urban sub-Saharan Africa. © 2013 John Wiley & Sons Ltd.

MET network in PubMed: a text-mined network visualization and curation system.

PubMed

Dai, Hong-Jie; Su, Chu-Hsien; Lai, Po-Ting; Huang, Ming-Siang; Jonnagaddala, Jitendra; Rose Jue, Toni; Rao, Shruti; Chou, Hui-Jou; Milacic, Marija; Singh, Onkar; Syed-Abdul, Shabbir; Hsu, Wen-Lian

2016-01-01

Metastasis is the dissemination of a cancer/tumor from one organ to another, and it is the most dangerous stage during cancer progression, causing more than 90% of cancer deaths. Improving the understanding of the complicated cellular mechanisms underlying metastasis requires investigations of the signaling pathways. To this end, we developed a METastasis (MET) network visualization and curation tool to assist metastasis researchers retrieve network information of interest while browsing through the large volume of studies in PubMed. MET can recognize relations among genes, cancers, tissues and organs of metastasis mentioned in the literature through text-mining techniques, and then produce a visualization of all mined relations in a metastasis network. To facilitate the curation process, MET is developed as a browser extension that allows curators to review and edit concepts and relations related to metastasis directly in PubMed. PubMed users can also view the metastatic networks integrated from the large collection of research papers directly through MET. For the BioCreative 2015 interactive track (IAT), a curation task was proposed to curate metastatic networks among PubMed abstracts. Six curators participated in the proposed task and a post-IAT task, curating 963 unique metastatic relations from 174 PubMed abstracts using MET.Database URL: http://btm.tmu.edu.tw/metastasisway. © The Author(s) 2016. Published by Oxford University Press.

Information Network Development.

ERIC Educational Resources Information Center

Mahon, F. V.

The International Bureau of Education (IBE) and Unesco, together with their member states, are faced with the task of implementing a proposed network--the International Network for Educational Information (INED)--for the better use of information resources for educational development. This review of issues that need to be considered in the…

Comparison of continuously acquired resting state and extracted analogues from active tasks

PubMed Central

Ganger, Sebastian; Hahn, Andreas; Küblböck, Martin; Kranz, Georg S.; Spies, Marie; Vanicek, Thomas; Seiger, René; Sladky, Ronald; Windischberger, Christian; Kasper, Siegfried

2015-01-01

Abstract Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting‐state data, the application to task‐specific fMRI has received growing attention. Three major methods for extraction of resting‐state data from task‐related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in‐between task blocks. Despite widespread application in current research, consensus on which method best resembles resting‐state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting‐state, two different task paradigms were assessed (emotion discrimination and right finger‐tapping) and five well‐described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting‐state (Dice, Intraclass correlation coefficient (ICC), R 2) showed that regression against task effects yields functional connectivity networks most alike to resting‐state. However, all methods exhibited significant differences when compared to continuous resting‐state and similarity metrics were lower than test‐retest of two resting‐state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting‐state when extracting signals from task designs, although functional connectivity computed from task‐specific data may indeed yield interesting information. Hum Brain Mapp 36:4053–4063, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals

Describing functional diversity of brain regions and brain networks

PubMed Central

Anderson, Michael L.; Kinnison, Josh; Pessoa, Luiz

2013-01-01

Despite the general acceptance that functional specialization plays an important role in brain function, there is little consensus about its extent in the brain. We sought to advance the understanding of this question by employing a data-driven approach that capitalizes on the existence of large databases of neuroimaging data. We quantified the diversity of activation in brain regions as a way to characterize the degree of functional specialization. To do so, brain activations were classified in terms of task domains, such as vision, attention, and language, which determined a region’s functional fingerprint. We found that the degree of diversity varied considerably across the brain. We also quantified novel properties of regions and of networks that inform our understanding of several task-positive and task-negative networks described in the literature, including defining functional fingerprints for entire networks and measuring their functional assortativity, namely the degree to which they are composed of regions with similar functional fingerprints. Our results demonstrate that some brain networks exhibit strong assortativity, whereas other networks consist of relatively heterogeneous parts. In sum, rather than characterizing the contributions of individual brain regions using task-based functional attributions, we instead quantified their dispositional tendencies, and related those to each region’s affiliative properties in both task-positive and task-negative contexts. PMID:23396162

Bilingualism Aids Conflict Resolution: Evidence from the ANT Task

ERIC Educational Resources Information Center

Costa, Albert; Hernandez, Mirea; Sebastian-Galles, Nuria

2008-01-01

The need of bilinguals to continuously control two languages during speech production may exert general effects on their attentional networks. To explore this issue we compared the performance of bilinguals and monolinguals in the attentional network task (ANT) developed by Fan et al. [Fan, J., McCandliss, B.D. Sommer, T., Raz, A., Posner, M.I.…

Incident Hypertension in Older Women and Men with or at-risk for HIV Infection

PubMed Central

Factor, SH; Lo, Y; Schoenbaum, EE; Klein, RS

2015-01-01

Objectives Antiretroviral (ARV) therapy has prolonged the life expectancy of HIV-infected persons, increasing their risk of age-associated diseases including atherosclerosis (AS). Decreased risk of AS has been associated with the prevention and control of hypertension (HTN). We conducted a cohort study of perimenopausal women and older men with or at-risk of HIV infection to identify risk factors for incident HTN. Methods Standardized interviews, physical examinations, and laboratory examinations were scheduled at 6-month intervals. Interview data included demographics, medical, family, sexual behavior, and drug use histories, and physical activity. Results There were 330 women and 329 men eligible persons; 27% and 35% of participants developed HTN during a median follow-up of 1080 and 1071 days, respectively. In gender-stratified analysis, adjusting for traditional HTN risk factors (age, race, BMI, smoking, diabetes, family history of HTN, alcohol dependence, physical activity, and high cholesterol), HIV infection was not associated with incident HTN in women or men [HR= 1.31, 95%CI (0.56, 3.06); HR = 1.67, 95%CI (0.75, 3.74), respectively]. Among HIV-infected women, although exposure to ARV's was not significantly associated with incident HTN [HR=0.72, 95%CI (0.26, 1.99)], CD4+ T-cell count was positively associated with incident HTN [HR=1.15 per 100 cells, 95%CI (1.03, 1.28)]. Among physically active HIV-infected men, exposure to ARV's was negatively associated with incident HTN [HR=0.15, 95%CI (0.03, 0.78)]. Conclusions HIV infection was not associated with incident HTN in older men or women. This study provides additional evidence supporting a causal relationship between immune function and incident HTN, which warrants further study. PMID:23294666

Association of aldosterone and cortisol with cardiovascular risk factors in prehypertension stage.

PubMed

Syed, Sadiqa Badar; Qureshi, Masood Anwar

2012-01-01

Background. The Pakistani population has higher incidence of cardiovascular (CV) diseases at younger ages, due to undiagnosed, uncontrolled hypertension (HTN). A variety of associated HTN stressors is also reported. The study plans to understand the variables associated with initiation of HTN in this population. Objective. To find plasma aldosterone and cortisol relationship with some CV risk factors (obesity, dyslipidemia, hyperglycemia, sodium and potassium) in different stages of HTN particularly prehypertension. Subjects and Methods. The study conducted on 276 subjects (25-60 years), classified into prehypertensive (n = 55), HTN stage-1 (n = 70) and II (n = 76) according to 7th JNC report and compared with normotensive controls (n = 75). The anthropometric profiles (height, weight, waist circumference, Body Mass index) and BP recorded. Serum cortisol, aldosterone, total cholesterol, Low density lipoproteins, blood glucose, Na(+) and K(+), using standard laboratory techniques, were determined in fasting blood samples. Results. Subjects were mostly overweight and obese (80%, 90%, and 76% in pre-HTN, stage-I and II versus 69% in controls). The aldosterone level (ng/dl) was in higher normal range (9.17-12.41) and significantly correlated to BMI (0.587) in controls, and to TC (0.726) and LDL (0.620) in pre-HTN stage-I. The cortisol level was positively correlated (P < 0.01) to BMI (0.538), Na(+) (0.690) and K(+) (0.578) in control, and to BMI (0.628) and WC (0.679) in pre-HTN group, showing its association with BMI > 25. Conclusion. Pre-HTN stage among Pakistani population with successive increase in various risk factors of HTN in relation to aldosterone and cortisol has been identified. Interaction of the risk factors with endogenous levels of these hormones may initiate stages of HTN.

Association of Aldosterone and Cortisol with Cardiovascular Risk Factors in Prehypertension Stage

PubMed Central

Syed, Sadiqa Badar; Qureshi, Masood Anwar

2012-01-01

Background. The Pakistani population has higher incidence of cardiovascular (CV) diseases at younger ages, due to undiagnosed, uncontrolled hypertension (HTN). A variety of associated HTN stressors is also reported. The study plans to understand the variables associated with initiation of HTN in this population. Objective. To find plasma aldosterone and cortisol relationship with some CV risk factors (obesity, dyslipidemia, hyperglycemia, sodium and potassium) in different stages of HTN particularly prehypertension. Subjects and Methods. The study conducted on 276 subjects (25–60 years), classified into prehypertensive (n = 55), HTN stage-1 (n = 70) and II (n = 76) according to 7th JNC report and compared with normotensive controls (n = 75). The anthropometric profiles (height, weight, waist circumference, Body Mass index) and BP recorded. Serum cortisol, aldosterone, total cholesterol, Low density lipoproteins, blood glucose, Na+ and K+, using standard laboratory techniques, were determined in fasting blood samples. Results. Subjects were mostly overweight and obese (80%, 90%, and 76% in pre-HTN, stage-I and II versus 69% in controls). The aldosterone level (ng/dl) was in higher normal range (9.17–12.41) and significantly correlated to BMI (0.587) in controls, and to TC (0.726) and LDL (0.620) in pre-HTN stage-I. The cortisol level was positively correlated (P < 0.01) to BMI (0.538), Na+ (0.690) and K+ (0.578) in control, and to BMI (0.628) and WC (0.679) in pre-HTN group, showing its association with BMI > 25. Conclusion. Pre-HTN stage among Pakistani population with successive increase in various risk factors of HTN in relation to aldosterone and cortisol has been identified. Interaction of the risk factors with endogenous levels of these hormones may initiate stages of HTN. PMID:22957211

Episodic Memory Retrieval Benefits from a Less Modular Brain Network Organization

PubMed Central

2017-01-01

Most complex cognitive tasks require the coordinated interplay of multiple brain networks, but the act of retrieving an episodic memory may place especially heavy demands for communication between the frontoparietal control network (FPCN) and the default mode network (DMN), two networks that do not strongly interact with one another in many task contexts. We applied graph theoretical analysis to task-related fMRI functional connectivity data from 20 human participants and found that global brain modularity—a measure of network segregation—is markedly reduced during episodic memory retrieval relative to closely matched analogical reasoning and visuospatial perception tasks. Individual differences in modularity were correlated with memory task performance, such that lower modularity levels were associated with a lower false alarm rate. Moreover, the FPCN and DMN showed significantly elevated coupling with each other during the memory task, which correlated with the global reduction in brain modularity. Both networks also strengthened their functional connectivity with the hippocampus during the memory task. Together, these results provide a novel demonstration that reduced modularity is conducive to effective episodic retrieval, which requires close collaboration between goal-directed control processes supported by the FPCN and internally oriented self-referential processing supported by the DMN. SIGNIFICANCE STATEMENT Modularity, an index of the degree to which nodes of a complex system are organized into discrete communities, has emerged as an important construct in the characterization of brain connectivity dynamics. We provide novel evidence that the modularity of the human brain is reduced when individuals engage in episodic memory retrieval, relative to other cognitive tasks, and that this state of lower modularity is associated with improved memory performance. We propose a neural systems mechanism for this finding where the nodes of the frontoparietal

Episodic Memory Retrieval Benefits from a Less Modular Brain Network Organization.

PubMed

Westphal, Andrew J; Wang, Siliang; Rissman, Jesse

2017-03-29

Most complex cognitive tasks require the coordinated interplay of multiple brain networks, but the act of retrieving an episodic memory may place especially heavy demands for communication between the frontoparietal control network (FPCN) and the default mode network (DMN), two networks that do not strongly interact with one another in many task contexts. We applied graph theoretical analysis to task-related fMRI functional connectivity data from 20 human participants and found that global brain modularity-a measure of network segregation-is markedly reduced during episodic memory retrieval relative to closely matched analogical reasoning and visuospatial perception tasks. Individual differences in modularity were correlated with memory task performance, such that lower modularity levels were associated with a lower false alarm rate. Moreover, the FPCN and DMN showed significantly elevated coupling with each other during the memory task, which correlated with the global reduction in brain modularity. Both networks also strengthened their functional connectivity with the hippocampus during the memory task. Together, these results provide a novel demonstration that reduced modularity is conducive to effective episodic retrieval, which requires close collaboration between goal-directed control processes supported by the FPCN and internally oriented self-referential processing supported by the DMN. SIGNIFICANCE STATEMENT Modularity, an index of the degree to which nodes of a complex system are organized into discrete communities, has emerged as an important construct in the characterization of brain connectivity dynamics. We provide novel evidence that the modularity of the human brain is reduced when individuals engage in episodic memory retrieval, relative to other cognitive tasks, and that this state of lower modularity is associated with improved memory performance. We propose a neural systems mechanism for this finding where the nodes of the frontoparietal control

Changes in dynamic resting state network connectivity following aphasia therapy.

PubMed

Duncan, E Susan; Small, Steven L

2017-10-24

Resting state magnetic resonance imaging (rsfMRI) permits observation of intrinsic neural networks produced by task-independent correlations in low frequency brain activity. Various resting state networks have been described, with each thought to reflect common engagement in some shared function. There has been limited investigation of the plasticity in these network relationships after stroke or induced by therapy. Twelve individuals with language disorders after stroke (aphasia) were imaged at multiple time points before (baseline) and after an imitation-based aphasia therapy. Language assessment using a narrative production task was performed at the same time points. Group independent component analysis (ICA) was performed on the rsfMRI data to identify resting state networks. A sliding window approach was then applied to assess the dynamic nature of the correlations among these networks. Network correlations during each 30-second window were used to cluster the data into ten states for each window at each time point for each subject. Correlation was performed between changes in time spent in each state and therapeutic gains on the narrative task. The amount of time spent in a single one of the (ten overall) dynamic states was positively associated with behavioral improvement on the narrative task at the 6-week post-therapy maintenance interval, when compared with either baseline or assessment immediately following therapy. This particular state was characterized by minimal correlation among the task-independent resting state networks. Increased functional independence and segregation of resting state networks underlies improvement on a narrative production task following imitation-based aphasia treatment. This has important clinical implications for the targeting of noninvasive brain stimulation in post-stroke remediation.

Mid-Task Break Improves Global Integration of Functional Connectivity in Lower Alpha Band

PubMed Central

Li, Junhua; Lim, Julian; Chen, Yu; Wong, Kianfoong; Thakor, Nitish; Bezerianos, Anastasios; Sun, Yu

2016-01-01

Numerous efforts have been devoted to revealing neurophysiological mechanisms of mental fatigue, aiming to find an effective way to reduce the undesirable fatigue-related outcomes. Until recently, mental fatigue is thought to be related to functional dysconnectivity among brain regions. However, the topological representation of brain functional connectivity altered by mental fatigue is only beginning to be revealed. In the current study, we applied a graph theoretical approach to analyse such topological alterations in the lower alpha band (8~10 Hz) of EEG data from 20 subjects undergoing a two-session experiment, in which one session includes four successive blocks with visual oddball tasks (session 1) whereas a mid-task break was introduced in the middle of four task blocks in the other session (session 2). Phase lag index (PLI) was then employed to measure functional connectivity strengths for all pairs of EEG channels. Behavior and connectivity maps were compared between the first and last task blocks in both sessions. Inverse efficiency scores (IES = reaction time/response accuracy) were significantly increased in the last task block, showing a clear effect of time-on-task in participants. Furthermore, a significant block-by-session interaction was revealed in the IES, suggesting the effectiveness of the mid-task break on maintaining task performance. More importantly, a significant session-independent deficit of global integration and an increase of local segregation were found in the last task block across both sessions, providing further support for the presence of a reshaped topology in functional brain connectivity networks under fatigue state. Moreover, a significant block-by-session interaction was revealed in the characteristic path length, small-worldness, and global efficiency, attributing to the significantly disrupted network topology in session 1 in comparison of the maintained network structure in session 2. Specifically, we found increased

Frontal networks associated with command following after hemorrhagic stroke.

PubMed

Mikell, Charles B; Banks, Garrett P; Frey, Hans-Peter; Youngerman, Brett E; Nelp, Taylor B; Karas, Patrick J; Chan, Andrew K; Voss, Henning U; Connolly, E Sander; Claassen, Jan

2015-01-01

Level of consciousness is frequently assessed by command-following ability in the clinical setting. However, it is unclear what brain circuits are needed to follow commands. We sought to determine what networks differentiate command following from noncommand following patients after hemorrhagic stroke. Structural MRI, resting-state functional MRI, and electroencephalography were performed on 25 awake and unresponsive patients with acute intracerebral and subarachnoid hemorrhage. Structural injury was assessed via volumetric T1-weighted MRI analysis. Functional connectivity differences were analyzed against a template of standard resting-state networks. The default mode network (DMN) and the task-positive network were investigated using seed-based functional connectivity. Networks were interrogated by pairwise coherence of electroencephalograph leads in regions of interest defined by functional MRI. Functional imaging of unresponsive patients identified significant differences in 6 of 16 standard resting-state networks. Significant voxels were found in premotor cortex, dorsal anterior cingulate gyrus, and supplementary motor area. Direct interrogation of the DMN and task-positive network revealed loss of connectivity between the DMN and the orbitofrontal cortex and new connections between the task-positive network and DMN. Coherence between electrodes corresponding to right executive network and visual networks was also decreased in unresponsive patients. Resting-state functional MRI and electroencephalography coherence data support a model in which multiple, chiefly frontal networks are required for command following. Loss of DMN anticorrelation with task-positive network may reflect a loss of inhibitory control of the DMN by motor-executive regions. Frontal networks should thus be a target for future investigations into the mechanism of responsiveness in the intensive care unit environment. © 2014 American Heart Association, Inc.

Modeling gene regulatory networks: A network simplification algorithm

NASA Astrophysics Data System (ADS)

Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.

2016-12-01

Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.

Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions

PubMed Central

Wang, Xiang; Öngür, Dost; Auerbach, Randy P.; Yao, Shuqiao

2016-01-01

Abstract Although it is generally accepted that cognitive factors contribute to the pathogenesis of major depressive disorder (MDD), there are missing links between behavioral and biological models of depression. Nevertheless, research employing neuroimaging technologies has elucidated some of the neurobiological mechanisms related to cognitive-vulnerability factors, especially from a whole-brain, dynamic perspective. In this review, we integrate well-established cognitive-vulnerability factors for MDD and corresponding neural mechanisms in intrinsic networks using a dual-process framework. We propose that the dynamic alteration and imbalance among the intrinsic networks, both in the resting-state and the rest-task transition stages, contribute to the development of cognitive vulnerability and MDD. Specifically, we propose that abnormally increased resting-state default mode network (DMN) activity and connectivity (mainly in anterior DMN regions) contribute to the development of cognitive vulnerability. Furthermore, when subjects confront negative stimuli in the period of rest-to-task transition, the following three kinds of aberrant network interactions have been identified as facilitators of vulnerability and dysphoric mood, each through a different cognitive mechanism: DMN dominance over the central executive network (CEN), an impaired salience network–mediated switching between the DMN and CEN, and ineffective CEN modulation of the DMN. This focus on interrelated networks and brain-activity changes between rest and task states provides a neural-system perspective for future research on cognitive vulnerability and resilience, and may potentially guide the development of new intervention strategies for MDD. PMID:27148911

Examining a supramodal network for conflict processing: a systematic review and novel functional magnetic resonance imaging data for related visual and auditory stroop tasks.

PubMed

Roberts, Katherine L; Hall, Deborah A

2008-06-01

Cognitive control over conflicting information has been studied extensively using tasks such as the color-word Stroop, flanker, and spatial conflict task. Neuroimaging studies typically identify a fronto-parietal network engaged in conflict processing, but numerous additional regions are also reported. Ascribing putative functional roles to these regions is problematic because some may have less to do with conflict processing per se, but could be engaged in specific processes related to the chosen stimulus modality, stimulus feature, or type of conflict task. In addition, some studies contrast activation on incongruent and congruent trials, even though a neutral baseline is needed to separate the effect of inhibition from that of facilitation. In the first part of this article, we report a systematic review of 34 neuroimaging publications, which reveals that conflict-related activity is reliably reported in the anterior cingulate cortex and bilaterally in the lateral prefrontal cortex, the anterior insula, and the parietal lobe. In the second part, we further explore these candidate "conflict" regions through a novel functional magnetic resonance imaging experiment, in which the same group of subjects perform related visual and auditory Stroop tasks. By carefully controlling for the same task (Stroop), the same to-be-ignored stimulus dimension (word meaning), and by separating out inhibitory processes from those of facilitation, we attempt to minimize the potential differences between the two tasks. The results provide converging evidence that the regions identified by the systematic review are reliably engaged in conflict processing. Despite carefully matching the Stroop tasks, some regions of differential activity remained, particularly in the parietal cortex. We discuss some of the task-specific processes which might account for this finding.

Spectral properties of the temporal evolution of brain network structure.

PubMed

Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

2015-12-01

The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

Spectral properties of the temporal evolution of brain network structure

NASA Astrophysics Data System (ADS)

Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

2015-12-01

The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

Regional GABA Concentrations Modulate Inter-network Resting-state Functional Connectivity.

PubMed

Chen, Xi; Fan, Xiaoying; Hu, Yuzheng; Zuo, Chun; Whitfield-Gabrieli, Susan; Holt, Daphne; Gong, Qiyong; Yang, Yihong; Pizzagalli, Diego A; Du, Fei; Ongur, Dost

2018-03-28

Coordinated activity within and differential activity between large-scale neuronal networks such as the default mode network (DMN) and the control network (CN) is a critical feature of brain organization. The CN usually exhibits activations in response to cognitive tasks while the DMN shows deactivations; in addition, activity between the two networks is anti-correlated at rest. To address this issue, we used functional MRI to measure whole-brain BOLD signal during resting-state and task-evoked conditions, and MR spectroscopy (MRS) to quantify GABA and glutamate concentrations, in nodes within the DMN and CN (MPFC and DLPFC, respectively) in 19 healthy individuals at 3 Tesla. We found that GABA concentrations in the MPFC were significantly associated with DMN deactivation during a working memory task and with anti-correlation between DMN and CN at rest and during task performance, while GABA concentrations in the DLPFC weakly modulated DMN-CN anti-correlation in the opposite direction. Highlighting specificity, glutamate played a less significant role related to brain activity. These findings indicate that GABA in the MPFC is potentially involved in orchestrating between-network brain activity at rest and during task performance.

Information flow through threespine stickleback networks without social transmission

PubMed Central

Atton, N.; Hoppitt, W.; Webster, M. M.; Galef, B. G.; Laland, K. N.

2012-01-01

Social networks can result in directed social transmission of learned information, thus influencing how innovations spread through populations. Here we presented shoals of threespine sticklebacks (Gasterosteous aculeatus) with two identical foraging tasks and applied network-based diffusion analysis (NBDA) to determine whether the order in which individuals in a social group contacted and solved the tasks was affected by the group's network structure. We found strong evidence for a social effect on discovery of the foraging tasks with individuals tending to discover a task sooner when others in their group had previously done so, and with the spread of discovery of the foraging tasks influenced by groups' social networks. However, the same patterns of association did not reliably predict spread of solution to the tasks, suggesting that social interactions affected the time at which the tasks were discovered, but not the latency to its solution following discovery. The present analysis, one of the first applications of NBDA to a natural animal system, illustrates how NBDA can lead to insight into the mechanisms supporting behaviour acquisition that more conventional statistical approaches might miss. Importantly, we provide the first compelling evidence that the spread of novel behaviours can result from social learning in the absence of social transmission, a phenomenon that we refer to as an untransmitted social effect on learning. PMID:22896644

Transition of the functional brain network related to increasing cognitive demands.

PubMed

Finc, Karolina; Bonna, Kamil; Lewandowska, Monika; Wolak, Tomasz; Nikadon, Jan; Dreszer, Joanna; Duch, Włodzisław; Kühn, Simone

2017-04-22

Network neuroscience provides tools that can easily be used to verify main assumptions of the global workspace theory (GWT), such as the existence of highly segregated information processing during effortless tasks performance, engagement of multiple distributed networks during effortful tasks and the critical role of long-range connections in workspace formation. A number of studies support the assumptions of GWT by showing the reorganization of the whole-brain functional network during cognitive task performance; however, the involvement of specific large scale networks in the formation of workspace is still not well-understood. (1) to examine changes in the whole-brain functional network under increased cognitive demands of working memory during an n-back task, and their relationship with behavioral outcomes; and (2) to provide a comprehensive description of local changes that may be involved in the formation of the global workspace, using hub detection and network-based statistic. Our results show that network modularity decreased with increasing cognitive demands, and this change allowed us to predict behavioral performance. The number of connector hubs increased, whereas the number of provincial hubs decreased when the task became more demanding. We also found that the default mode network (DMN) increased its connectivity to other networks while decreasing connectivity between its own regions. These results, apart from replicating previous findings, provide a valuable insight into the mechanisms of the formation of the global workspace, highlighting the role of the DMN in the processes of network integration. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Associations of rotational shift work and night shift status with hypertension: a systematic review and meta-analysis.

PubMed

Manohar, Sandhya; Thongprayoon, Charat; Cheungpasitporn, Wisit; Mao, Michael A; Herrmann, Sandra M

2017-10-01

The reported risks of hypertension (HTN) in rotating shift and night shift workers are controversial. The objective of this meta-analysis was to assess the association between shift work status and HTN. A literature search was performed using MEDLINE, EMBASE and Cochrane Database from inception through October 2016. Studies that reported odds ratios (OR) comparing the risk of HTN in shift workers were included. A prespecified subgroup analysis by rotating shift and night shift statuses were also performed. Pooled OR and 95% confidence interval (CI) were calculated using a random-effect, generic inverse variance method. The protocol for this study is registered with International Prospective Register of Systematic Reviews; no. CRD42016051843. Twenty-seven observational studies (nine cohort and 18 cross-sectional studies) with a total of 394 793 individuals were enrolled. The pooled ORs of HTN in shift workers in cohort and cross-sectional studies were 1.31 (95% CI, 1.07-1.60) and 1.10 (95% CI, 1.00-1.20), respectively. When meta-analysis was restricted only to cohort studies in rotating shift, the pooled OR of HTN in rotating shift workers was 1.34 (95% CI, 1.08-1.67). The data regarding night shift and HTN in cohort studies was limited. The pooled OR of HTN in night shift workers in cross-sectional studies was 1.07 (95% CI, 0.85-1.35). Based on the findings of our meta-analysis, shiftwork status may play an important role in HTN, as there is a significant association between rotating shift work and HTN. However, there is no significant association between night shift status and risk of HTN.

Angiotensin-converting enzyme-inhibitor therapy in adolescents with type 1 diabetes in a regional cohort: Auckland, New Zealand from 2006 to 2016.

PubMed

Hornung, Rosalie J; Reed, Peter W; Mouat, Fran; Jefferies, Craig; Gunn, Alistair J; Hofman, Paul L

2018-05-01

To review indications and use of angiotensin-converting enzyme-inhibitor (ACEI) therapy for the treatment of persistent microalbuminuria (MA) and/or hypertension (HTN) in adolescents with type 1 diabetes mellitus (T1DM). Retrospective chart review of adolescent patients with T1DM seen within the paediatric diabetes service in Auckland, New Zealand, from 2006 to 2016. MA, HTN, patient demographic characteristics and ACEI prescribing and monitoring indices were examined. Five hundred adolescents with T1DM were included. There were 26 patients (5%) with MA and/or HTN. MA alone was present in 16, HTN alone in 3 and both HTN and MA in 7. The 5-year MA/HTN-free rate was 98%, and the 10-year MA/HTN-free rate was 93%. Longer disease duration and earlier diagnosis were predictors of MA/HTN. There was no significant difference in standard clinical indices between study patients and others. ACEI was prescribed for 17 of 26 patients for either HTN or MA. Within 6 weeks of ACEI commencement, less than half of the subjects had repeat serum creatinine and MA screens and no record of repeat blood pressure measurement. Despite this, all patients had 3-monthly reviews within outpatient clinics where adjustments of ACEI doses were made. In our regional adolescent population with T1DM, there were low rates of both MA and/or HTN. In those who required treatment with ACEI, clinical monitoring post-commencement of therapy was inconsistent. Local consensus guidelines for the management of persistent MA in children and adolescents with diabetes mellitus were developed in response to this study. © 2017 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).

Functional connectivity of task context representations in prefrontal nodes of the multiple demand network.

PubMed

Stiers, Peter; Goulas, Alexandros

2018-06-01

A subset of regions in the lateral and medial prefrontal cortex and the anterior insula increase their activity level whenever a cognitive task becomes more demanding, regardless of the specific nature of this demand. During execution of a task, these areas and the surrounding cortex temporally encode aspects of the task context in spatially distributed patterns of activity. It is not clear whether these patterns reflect underlying anatomical subnetworks that still exist when task execution has finished. We use fMRI in 12 participants performing alternating blocks of three cognitive tasks to address this question. A first data set is used to define multiple demand regions in each participant. A second dataset from the same participants is used to determine multiple demand voxel assemblies with a preference for one task over the others. We then show that these voxels remain functionally coupled during execution of non-preferred tasks and that they exhibit stronger functional connectivity during rest. This indicates that the assemblies of task preference sharing voxels reflect patterns of underlying anatomical connections. Moreover, we show that voxels preferring the same task have more similar whole brain functional connectivity profiles that are consistent across participants. This suggests that voxel assemblies differ in patterns of input-output connections, most likely reflecting task demand-specific information exchange.

Obesity-hypertension and its relation to other diseases in dogs.

PubMed

Pérez-Sánchez, Alicia Pamela; Del-Angel-Caraza, Javier; Quijano-Hernández, Israel Alejandro; Barbosa-Mireles, Marco Antonio

2015-03-01

Obesity is a chronic disease in which adipose tissue accumulates in such a way that it affects the health of the patient and is associated with a myriad of alterations such as systemic hypertension (HTN). The mechanisms by which obesity causes HTN are complex and involve several organic mechanisms. The objective of this study was to determine the correlation between obesity to HTN in dogs in accordance with recent international protocols (systolic blood pressure >160 mmHg) relating to age, genre, gonadal status, breed and other diseases commonly associated with HTN. A total of 244 dogs were studied, 105 non-obese controls and 139 in the obese group. For both groups, healthy and a variety of diseased dogs were observed; the correlations between pathologies and obesity were studied, paying special attention to diseases whose pathophysiologies could lead to HTN. We conclude that obesity is not a risk factor for dogs to develop HTN, and that HTN present in these patients was related to comorbidities such as chronic kidney disease, cardiopathies and endocrinopathies.

Catheter-based renal denervation for resistant hypertension: Twenty-four month results of the EnligHTN I first-in-human study using a multi-electrode ablation system.

PubMed

Tsioufis, Costas P; Papademetriou, Vasilios; Dimitriadis, Kyriakos S; Kasiakogias, Alexandros; Tsiachris, Dimitrios; Worthley, Matthew I; Sinhal, Ajay R; Chew, Derek P; Meredith, Ian T; Malaiapan, Yuvi; Thomopoulos, Costas; Kallikazaros, Ioannis; Tousoulis, Dimitrios; Worthley, Stephen G

2015-12-15

Long term safety and efficacy data of multi-electrode ablation system for renal denervation (RDN) in patients with drug resistant hypertension (dRHT) are limited. We studied 46 patients (age: 60 ± 10 years, 4.7 ± 1.0 antihypertensive drugs) with drug resistant hypertension (dRHT). Reduction in office BP at 24 months from baseline was -29/-13 mmHg, while the reduction in 24-hour ambulatory BP and in home BP at 24 months were -13/-7 mmHg and -11/-6 mmHg respectively (p<0.05 for all). A correlation analysis revealed that baseline office and ambulatory BP were related to the extent of office and ambulatory BP drop. Apart from higher body mass index (33.3 ± 4.7 vs 29.5 ± 6.2 kg/m(2), p<0.05), there were no differences in patients that were RDN responders defined as ≥10 mmHg decrease (74%, n=34) compared to non-responders. Stepwise logistic regression analysis revealed no prognosticators of RDN response (p=NS for all). At 24 months there were no new serious device or procedure related adverse events. The EnligHTN I study shows that the multi-electrode ablation system provides a safe method of RDN in dRHT accompanied by a clinically relevant and sustained BP reduction. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Expression and methylation of circulating microRNA-510 in essential hypertension.

PubMed

Krishnan, Ramalingam; Mani, Panagal; Sivakumar, Pethanen; Gopinath, Vincent; Sekar, Durairaj

2017-04-01

Hypertension (HTN) is one of the most common emerging disease in developing countries. It alters endothelial cell structure and function, resulting in several diseases, such as cardiovascular disease, peripheral vasculopathy, cerebrovascular disease and nephropathy. Although much progress has been made in researching HTN in recent years, early diagnosis and treatment of HTN are not yet satisfactory, and progression control/treatment is still poor. MicroRNAs are well-known regulators of the physiological and developmental processes of HTN. Our results revealed that miR-510 was upregulated in blood samples from HTN patients, whereas no significant differences were observed in the control samples. Methylation analyses corroborated the miR-510 upregulation in patient samples. These results suggested that miR-510 can be used as a novel biomarker for diagnosis and as a new therapeutic target for HTN.

Preparatory neural activity predicts performance on a conflict task.

PubMed

Stern, Emily R; Wager, Tor D; Egner, Tobias; Hirsch, Joy; Mangels, Jennifer A

2007-10-24

Advance preparation has been shown to improve the efficiency of conflict resolution. Yet, with little empirical work directly linking preparatory neural activity to the performance benefits of advance cueing, it is not clear whether this relationship results from preparatory activation of task-specific networks, or from activity associated with general alerting processes. Here, fMRI data were acquired during a spatial Stroop task in which advance cues either informed subjects of the upcoming relevant feature of conflict stimuli (spatial or semantic) or were neutral. Informative cues decreased reaction time (RT) relative to neutral cues, and cues indicating that spatial information would be task-relevant elicited greater activity than neutral cues in multiple areas, including right anterior prefrontal and bilateral parietal cortex. Additionally, preparatory activation in bilateral parietal cortex and right dorsolateral prefrontal cortex predicted faster RT when subjects responded to spatial location. No regions were found to be specific to semantic cues at conventional thresholds, and lowering the threshold further revealed little overlap between activity associated with spatial and semantic cueing effects, thereby demonstrating a single dissociation between activations related to preparing a spatial versus semantic task-set. This relationship between preparatory activation of spatial processing networks and efficient conflict resolution suggests that advance information can benefit performance by leading to domain-specific biasing of task-relevant information.

Dynamic reorganization of human resting-state networks during visuospatial attention.

PubMed

Spadone, Sara; Della Penna, Stefania; Sestieri, Carlo; Betti, Viviana; Tosoni, Annalisa; Perrucci, Mauro Gianni; Romani, Gian Luca; Corbetta, Maurizio

2015-06-30

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 connectivity of hippocampal and prefrontal networks during episodic and spatial memory based on real-world environments.

PubMed

Robin, Jessica; Hirshhorn, Marnie; Rosenbaum, R Shayna; Winocur, Gordon; Moscovitch, Morris; Grady, Cheryl L

2015-01-01

Several recent studies have compared episodic and spatial memory in neuroimaging paradigms in order to understand better the contribution of the hippocampus to each of these tasks. In the present study, we build on previous findings showing common neural activation in default network areas during episodic and spatial memory tasks based on familiar, real-world environments (Hirshhorn et al. (2012) Neuropsychologia 50:3094-3106). Following previous demonstrations of the presence of functionally connected sub-networks within the default network, we performed seed-based functional connectivity analyses to determine how, depending on the task, the hippocampus and prefrontal cortex differentially couple with one another and with distinct whole-brain networks. We found evidence for a medial prefrontal-parietal network and a medial temporal lobe network, which were functionally connected to the prefrontal and hippocampal seeds, respectively, regardless of the nature of the memory task. However, these two networks were functionally connected with one another during the episodic memory task, but not during spatial memory tasks. Replicating previous reports of fractionation of the default network into stable sub-networks, this study also shows how these sub-networks may flexibly couple and uncouple with one another based on task demands. These findings support the hypothesis that episodic memory and spatial memory share a common medial temporal lobe-based neural substrate, with episodic memory recruiting additional prefrontal sub-networks. © 2014 Wiley Periodicals, Inc.

Dynamics of brain networks in the aesthetic appreciation

PubMed Central

Cela-Conde, Camilo J.; García-Prieto, Juan; Ramasco, José J.; Mirasso, Claudio R.; Bajo, Ricardo; Munar, Enric; Flexas, Albert; del-Pozo, Francisco; Maestú, Fernando

2013-01-01

Neuroimage experiments have been essential for identifying active brain networks. During cognitive tasks as in, e.g., aesthetic appreciation, such networks include regions that belong to the default mode network (DMN). Theoretically, DMN activity should be interrupted during cognitive tasks demanding attention, as is the case for aesthetic appreciation. Analyzing the functional connectivity dynamics along three temporal windows and two conditions, beautiful and not beautiful stimuli, here we report experimental support for the hypothesis that aesthetic appreciation relies on the activation of two different networks, an initial aesthetic network and a delayed aesthetic network, engaged within distinct time frames. Activation of the DMN might correspond mainly to the delayed aesthetic network. We discuss adaptive and evolutionary explanations for the relationships existing between the DMN and aesthetic networks and offer unique inputs to debates on the mind/brain interaction. PMID:23754437

a Task-Oriented Disaster Information Correlation Method

NASA Astrophysics Data System (ADS)

Linyao, Q.; Zhiqiang, D.; Qing, Z.

2015-07-01

With the rapid development of sensor networks and Earth observation technology, a large quantity of disaster-related data is available, such as remotely sensed data, historic data, case data, simulated data, and disaster products. However, the efficiency of current data management and service systems has become increasingly difficult due to the task variety and heterogeneous data. For emergency task-oriented applications, the data searches primarily rely on artificial experience based on simple metadata indices, the high time consumption and low accuracy of which cannot satisfy the speed and veracity requirements for disaster products. In this paper, a task-oriented correlation method is proposed for efficient disaster data management and intelligent service with the objectives of 1) putting forward disaster task ontology and data ontology to unify the different semantics of multi-source information, 2) identifying the semantic mapping from emergency tasks to multiple data sources on the basis of uniform description in 1), and 3) linking task-related data automatically and calculating the correlation between each data set and a certain task. The method goes beyond traditional static management of disaster data and establishes a basis for intelligent retrieval and active dissemination of disaster information. The case study presented in this paper illustrates the use of the method on an example flood emergency relief task.

a Task-Driven Disaster Data Link Approach

NASA Astrophysics Data System (ADS)

Qiu, L. Y.; Zhu, Q.; Gu, J. Y.; Du, Z. Q.

2015-08-01

With the rapid development of sensor networks and Earth observation technology, a large quantity of disaster-related data is available, such as remotely sensed data, historic data, cases data, simulation data, disaster products and so on. However, the efficiency of current data management and service systems has become increasingly serious due to the task variety and heterogeneous data. For emergency task-oriented applications, data searching mainly relies on artificial experience based on simple metadata index, whose high time-consuming and low accuracy cannot satisfy the requirements of disaster products on velocity and veracity. In this paper, a task-oriented linking method is proposed for efficient disaster data management and intelligent service, with the objectives of 1) putting forward ontologies of disaster task and data to unify the different semantics of multi-source information, 2) identifying the semantic mapping from emergency tasks to multiple sources on the basis of uniform description in 1), 3) linking task-related data automatically and calculating the degree of correlation between each data and a target task. The method breaks through traditional static management of disaster data and establishes a base for intelligent retrieval and active push of disaster information. The case study presented in this paper illustrates the use of the method with a flood emergency relief task.

Dependency Network Analysis (DEPNA) Reveals Context Related Influence of Brain Network Nodes

PubMed Central

Jacob, Yael; Winetraub, Yonatan; Raz, Gal; Ben-Simon, Eti; Okon-Singer, Hadas; Rosenberg-Katz, Keren; Hendler, Talma; Ben-Jacob, Eshel

2016-01-01

Communication between and within brain regions is essential for information processing within functional networks. The current methods to determine the influence of one region on another are either based on temporal resolution, or require a predefined model for the connectivity direction. However these requirements are not always achieved, especially in fMRI studies, which have poor temporal resolution. We thus propose a new graph theory approach that focuses on the correlation influence between selected brain regions, entitled Dependency Network Analysis (DEPNA). Partial correlations are used to quantify the level of influence of each node during task performance. As a proof of concept, we conducted the DEPNA on simulated datasets and on two empirical motor and working memory fMRI tasks. The simulations revealed that the DEPNA correctly captures the network’s hierarchy of influence. Applying DEPNA to the functional tasks reveals the dynamics between specific nodes as would be expected from prior knowledge. To conclude, we demonstrate that DEPNA can capture the most influencing nodes in the network, as they emerge during specific cognitive processes. This ability opens a new horizon for example in delineating critical nodes for specific clinical interventions. PMID:27271458

Network structure exploration in networks with node attributes

NASA Astrophysics Data System (ADS)

Chen, Yi; Wang, Xiaolong; Bu, Junzhao; Tang, Buzhou; Xiang, Xin

2016-05-01

Complex networks provide a powerful way to represent complex systems and have been widely studied during the past several years. One of the most important tasks of network analysis is to detect structures (also called structural regularities) embedded in networks by determining group number and group partition. Most of network structure exploration models only consider network links. However, in real world networks, nodes may have attributes that are useful for network structure exploration. In this paper, we propose a novel Bayesian nonparametric (BNP) model to explore structural regularities in networks with node attributes, called Bayesian nonparametric attribute (BNPA) model. This model does not only take full advantage of both links between nodes and node attributes for group partition via shared hidden variables, but also determine group number automatically via the Bayesian nonparametric theory. Experiments conducted on a number of real and synthetic networks show that our BNPA model is able to automatically explore structural regularities in networks with node attributes and is competitive with other state-of-the-art models.

Aging-related changes in the default mode network and its anti-correlated networks: a resting-state fMRI study.

PubMed

Wu, Jing-Tao; Wu, Hui-Zhen; Yan, Chao-Gan; Chen, Wen-Xin; Zhang, Hong-Ying; He, Yong; Yang, Hai-Shan

2011-10-17

Intrinsic brain activity in a resting state incorporates components of the task negative network called default mode network (DMN) and task-positive networks called attentional networks. In the present study, the reciprocal neuronal networks in the elder group were compared with the young group to investigate the differences of the intrinsic brain activity using a method of temporal correlation analysis based on seed regions of posterior cingulate cortex (PCC) and ventromedial prefrontal cortex (vmPFC). We found significant decreased positive correlations and negative correlations with the seeds of PCC and vmPFC in the old group. The decreased coactivations in the DMN network components and their negative networks in the old group may reflect age-related alterations in various brain functions such as attention, motor control and inhibition modulation in cognitive processing. These alterations in the resting state anti-correlative networks could provide neuronal substrates for the aging brain. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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

PubMed

Tommasin, Silvia; Mascali, Daniele; Moraschi, Marta; Gili, Tommaso; Assan, Ibrahim Eid; Fratini, Michela; DiNuzzo, Mauro; Wise, Richard G; Mangia, Silvia; Macaluso, Emiliano; Giove, Federico

2018-06-14

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 be not 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. Copyright © 2018. Published by Elsevier Inc.

Variations of response time in a selective attention task are linked to variations of functional connectivity in the attentional network.

PubMed

Prado, Jérôme; Carp, Joshua; Weissman, Daniel H

2011-01-01

Although variations of response time (RT) within a particular experimental condition are typically ignored, they may sometimes reflect meaningful changes in the efficiency of cognitive and neural processes. In the present study, we investigated whether trial-by-trial variations of response time (RT) in a cross-modal selective attention task were associated with variations of functional connectivity between brain regions that are thought to underlie attention. Sixteen healthy young adults performed an audiovisual selective attention task, which involved attending to a relevant visual letter while ignoring an irrelevant auditory letter, as we recorded their brain activity using functional magnetic resonance imaging (fMRI). In line with predictions, variations of RT were associated with variations of functional connectivity between the anterior cingulate cortex and various other brain regions that are posited to underlie attentional control, such as the right dorsolateral prefrontal cortex and bilateral regions of the posterior parietal cortex. They were also linked to variations of functional connectivity between anatomically early and anatomically late regions of the relevant-modality visual cortex whose communication is thought to be modulated by attentional control processes. By revealing that variations of RT in a selective attention task are linked to variations of functional connectivity in the attentional network, the present findings suggest that variations of attention may contribute to trial-by-trial fluctuations of behavioral performance. Copyright © 2010 Elsevier Inc. All rights reserved.

Social networks predict selective observation and information spread in ravens

PubMed Central

Rubenstein, Daniel I.; Bugnyar, Thomas; Hoppitt, William; Mikus, Nace; Schwab, Christine

2016-01-01

Animals are predicted to selectively observe and learn from the conspecifics with whom they share social connections. Yet, hardly anything is known about the role of different connections in observation and learning. To address the relationships between social connections, observation and learning, we investigated transmission of information in two raven (Corvus corax) groups. First, we quantified social connections in each group by constructing networks on affiliative interactions, aggressive interactions and proximity. We then seeded novel information by training one group member on a novel task and allowing others to observe. In each group, an observation network based on who observed whose task-solving behaviour was strongly correlated with networks based on affiliative interactions and proximity. Ravens with high social centrality (strength, eigenvector, information centrality) in the affiliative interaction network were also central in the observation network, possibly as a result of solving the task sooner. Network-based diffusion analysis revealed that the order that ravens first solved the task was best predicted by connections in the affiliative interaction network in a group of subadult ravens, and by social rank and kinship (which influenced affiliative interactions) in a group of juvenile ravens. Our results demonstrate that not all social connections are equally effective at predicting the patterns of selective observation and information transmission. PMID:27493780

Supply network configuration—A benchmarking problem

NASA Astrophysics Data System (ADS)

Brandenburg, Marcus

2018-03-01

Managing supply networks is a highly relevant task that strongly influences the competitiveness of firms from various industries. Designing supply networks is a strategic process that considerably affects the structure of the whole network. In contrast, supply networks for new products are configured without major adaptations of the existing structure, but the network has to be configured before the new product is actually launched in the marketplace. Due to dynamics and uncertainties, the resulting planning problem is highly complex. However, formal models and solution approaches that support supply network configuration decisions for new products are scant. The paper at hand aims at stimulating related model-based research. To formulate mathematical models and solution procedures, a benchmarking problem is introduced which is derived from a case study of a cosmetics manufacturer. Tasks, objectives, and constraints of the problem are described in great detail and numerical values and ranges of all problem parameters are given. In addition, several directions for future research are suggested.

Functional connectivity of default mode network components: correlation, anticorrelation, and causality

PubMed Central

Uddin, Lucina Q.; Clare Kelly, A. M.; Biswal, Bharat B.; Castellanos, F. Xavier; Milham, Michael P.

2013-01-01

The default mode network (DMN), based in ventromedial prefrontal cortex (vmPFC) and posterior cingulate cortex (PCC), exhibits higher metabolic activity at rest than during performance of externally-oriented cognitive tasks. Recent studies have suggested that competitive relationships between the DMN and various task-positive networks involved in task performance are intrinsically represented in the brain in the form of strong negative correlations (anticorrelations) between spontaneous fluctuations in these networks. Most neuroimaging studies characterize the DMN as a homogenous network, thus few have examined the differential contributions of DMN components to such competitive relationships. Here we examined functional differentiation within the default mode network, with an emphasis on understanding competitive relationships between this and other networks. We used a seed correlation approach on resting-state data to assess differences in functional connectivity between these two regions and their anticorrelated networks. While the positively correlated networks for the vmPFC and PCC seeds largely overlapped, the anticorrelated networks for each showed striking differences. Activity in vmPFC negatively predicted activity in parietal visual spatial and temporal attention networks, whereas activity in PCC negatively predicted activity in prefrontal-based motor control circuits. Granger causality analyses suggest that vmPFC and PCC exert greater influence on their anticorrelated networks than the other way around, suggesting that these two default mode nodes may directly modulate activity in task-positive networks. Thus, the two major nodes comprising the default mode network are differentiated with respect to the specific brain systems with which they interact, suggesting greater heterogeneity within this network than is commonly appreciated. PMID:18219617

An evolutionary algorithm that constructs recurrent neural networks.

PubMed

Angeline, P J; Saunders, G M; Pollack, J B

1994-01-01

Standard methods for simultaneously inducing the structure and weights of recurrent neural networks limit every task to an assumed class of architectures. Such a simplification is necessary since the interactions between network structure and function are not well understood. Evolutionary computations, which include genetic algorithms and evolutionary programming, are population-based search methods that have shown promise in many similarly complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. GNARL's empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods.

Time-Frequency Cross Mutual Information Analysis of the Brain Functional Networks Underlying Multiclass Motor Imagery.

PubMed

Gong, Anmin; Liu, Jianping; Chen, Si; Fu, Yunfa

2018-01-01

To study the physiologic mechanism of the brain during different motor imagery (MI) tasks, the authors employed a method of brain-network modeling based on time-frequency cross mutual information obtained from 4-class (left hand, right hand, feet, and tongue) MI tasks recorded as brain-computer interface (BCI) electroencephalography data. The authors explored the brain network revealed by these MI tasks using statistical analysis and the analysis of topologic characteristics, and observed significant differences in the reaction level, reaction time, and activated target during 4-class MI tasks. There was a great difference in the reaction level between the execution and resting states during different tasks: the reaction level of the left-hand MI task was the greatest, followed by that of the right-hand, feet, and tongue MI tasks. The reaction time required to perform the tasks also differed: during the left-hand and right-hand MI tasks, the brain networks of subjects reacted promptly and strongly, but there was a delay during the feet and tongue MI task. Statistical analysis and the analysis of network topology revealed the target regions of the brain network during different MI processes. In conclusion, our findings suggest a new way to explain the neural mechanism behind MI.

Transcranial direct current stimulation facilitates cognitive multi-task performance differentially depending on anode location and subtask

PubMed Central

Scheldrup, Melissa; Greenwood, Pamela M.; McKendrick, Ryan; Strohl, Jon; Bikson, Marom; Alam, Mahtab; McKinley, R. Andy; Parasuraman, Raja

2014-01-01

There is a need to facilitate acquisition of real world cognitive multi-tasks that require long periods of training (e.g., air traffic control, intelligence analysis, medicine). Non-invasive brain stimulation—specifically transcranial Direct Current Stimulation (tDCS)—has promise as a method to speed multi-task training. We hypothesized that during acquisition of the complex multi-task Space Fortress, subtasks that require focused attention on ship control would benefit from tDCS aimed at the dorsal attention network while subtasks that require redirection of attention would benefit from tDCS aimed at the right hemisphere ventral attention network. We compared effects of 30 min prefrontal and parietal stimulation to right and left hemispheres on subtask performance during the first 45 min of training. The strongest effects both overall and for ship flying (control and velocity subtasks) were seen with a right parietal (C4, reference to left shoulder) montage, shown by modeling to induce an electric field that includes nodes in both dorsal and ventral attention networks. This is consistent with the re-orienting hypothesis that the ventral attention network is activated along with the dorsal attention network if a new, task-relevant event occurs while visuospatial attention is focused (Corbetta et al., 2008). No effects were seen with anodes over sites that stimulated only dorsal (C3) or only ventral (F10) attention networks. The speed subtask (update memory for symbols) benefited from an F9 anode over left prefrontal cortex. These results argue for development of tDCS as a training aid in real world settings where multi-tasking is critical. PMID:25249958

Transcranial direct current stimulation facilitates cognitive multi-task performance differentially depending on anode location and subtask.

PubMed

Scheldrup, Melissa; Greenwood, Pamela M; McKendrick, Ryan; Strohl, Jon; Bikson, Marom; Alam, Mahtab; McKinley, R Andy; Parasuraman, Raja

2014-01-01

There is a need to facilitate acquisition of real world cognitive multi-tasks that require long periods of training (e.g., air traffic control, intelligence analysis, medicine). Non-invasive brain stimulation-specifically transcranial Direct Current Stimulation (tDCS)-has promise as a method to speed multi-task training. We hypothesized that during acquisition of the complex multi-task Space Fortress, subtasks that require focused attention on ship control would benefit from tDCS aimed at the dorsal attention network while subtasks that require redirection of attention would benefit from tDCS aimed at the right hemisphere ventral attention network. We compared effects of 30 min prefrontal and parietal stimulation to right and left hemispheres on subtask performance during the first 45 min of training. The strongest effects both overall and for ship flying (control and velocity subtasks) were seen with a right parietal (C4, reference to left shoulder) montage, shown by modeling to induce an electric field that includes nodes in both dorsal and ventral attention networks. This is consistent with the re-orienting hypothesis that the ventral attention network is activated along with the dorsal attention network if a new, task-relevant event occurs while visuospatial attention is focused (Corbetta et al., 2008). No effects were seen with anodes over sites that stimulated only dorsal (C3) or only ventral (F10) attention networks. The speed subtask (update memory for symbols) benefited from an F9 anode over left prefrontal cortex. These results argue for development of tDCS as a training aid in real world settings where multi-tasking is critical.

Association between blood pressure and magnesium and uric acid levels in indigenous Argentinean children at high altitude.

PubMed

Hirschler, Valeria; González, Claudio; Maccallini, Gustavo; Molinari, Claudia; Castano, Luis

2017-07-08

To determine the association between nontraditional risk factors such as magnesium and uric acid with blood pressure (BP) in Indigenous children. A total of 263 school-aged indigenous children living at high altitude were enrolled in a cross-sectional study in November 2011. Prehypertension (preHTN) and hypertension (HTN) were defined by systolic and/or diastolic BP ≥ 90th to <95th percentile or ≥95th percentile respectively, according to age, sex, and height. The prevalence of preHTN and HTN was 13.7 and 8.3%, respectively. Low magnesium levels were identified in 21.7% (57/263): 28.1% (16/57) of the children with low magnesium levels had preHTN versus 9.7% (20/206) with normal magnesium values. Furthermore, 21.8% (12/57) of the children with low magnesium levels had HTN versus 4.5% (20/206) with normal magnesium values. There was a significant association between mean arterial pressure and magnesium (r = -026), uric acid (r = 0.20), phosphorus (r = -0.17), z-BMI (r = 0.22), potassium (r = -0.10), HOMA-IR (r = 0.17), calcium (r = -0.10), and sodium (r = -0.13). Multiple linear regression analysis showed that mean arterial pressure was associated significantly and directly with BMI, age, gender, and uric acid; and inversely with magnesium, adjusted for sodium, calcium, phosphorus, potassium, and HOMA-IR (R 2  = 0.43). Furthermore, multiple logistic regression analyses showed that magnesium (OR = 0.015) and uric acid (OR = 2.95) were significantly associated with preHTN. Similar results were obtained when preHTN was replaced by HTN. Our results indicate that HTN was associated inversely with magnesium and positively with uric acid in indigenous school children. © 2017 Wiley Periodicals, Inc.

Neural Correlates of Task Cost for Stance Control with an Additional Motor Task: Phase-Locked Electroencephalogram Responses

PubMed Central

Hwang, Ing-Shiou; Huang, Cheng-Ya

2016-01-01

With appropriate reallocation of central resources, the ability to maintain an erect posture is not necessarily degraded by a concurrent motor task. This study investigated the neural control of a particular postural-suprapostural procedure involving brain mechanisms to solve crosstalk between posture and motor subtasks. Participants completed a single posture task and a dual-task while concurrently conducting force-matching and maintaining a tilted stabilometer stance at a target angle. Stabilometer movements and event-related potentials (ERPs) were recorded. The added force-matching task increased the irregularity of postural response rather than the size of postural response prior to force-matching. In addition, the added force-matching task during stabilometer stance led to marked topographic ERP modulation, with greater P2 positivity in the frontal and sensorimotor-parietal areas of the N1-P2 transitional phase and in the sensorimotor-parietal area of the late P2 phase. The time-frequency distribution of the ERP primary principal component revealed that the dual-task condition manifested more pronounced delta (1–4 Hz) and beta (13–35 Hz) synchronizations but suppressed theta activity (4–8 Hz) before force-matching. The dual-task condition also manifested coherent fronto-parietal delta activity in the P2 period. In addition to a decrease in postural regularity, this study reveals spatio-temporal and temporal-spectral reorganizations of ERPs in the fronto-sensorimotor-parietal network due to the added suprapostural motor task. For a particular set of postural-suprapostural task, the behavior and neural data suggest a facilitatory role of autonomous postural response and central resource expansion with increasing interregional interactions for task-shift and planning the motor-suprapostural task. PMID:27010634

Network Analysis Tools: from biological networks to clusters and pathways.

PubMed

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Vanderstocken, Gilles; van Helden, Jacques

2008-01-01

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.

Behavioral Interpretations of Intrinsic Connectivity Networks

ERIC Educational Resources Information Center

Laird, Angela R.; Fox, P. Mickle; Eickhoff, Simon B.; Turner, Jessica A.; Ray, Kimberly L.; McKay, D. Reese; Glahn, David C.; Beckmann, Christian F.; Smith, Stephen M.; Fox, Peter T.

2011-01-01

An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific…

Muscular strength and incident hypertension in normotensive and prehypertensive men.

PubMed

Maslow, Andréa L; Sui, Xuemei; Colabianchi, Natalie; Hussey, Jim; Blair, Steven N

2010-02-01

The protective effects of cardiorespiratory fitness (CRF) on hypertension (HTN) are well known; however, the association between muscular strength and incidence of HTN has yet to be examined. This study evaluated the strength-HTN association with and without accounting for CRF. Participants were 4147 men (age = 20-82 yr) in the Aerobics Center Longitudinal Study for whom an age-specific composite muscular strength score was computed from measures of a one-repetition maximal leg and a one-repetition maximal bench press. CRF was quantified by maximal treadmill exercise test time in minutes. Cox proportional hazards regression analysis was used to estimate hazard ratios (HR) and 95% confidence intervals of incident HTN events according to exposure categories. During a mean follow-up of 19 yr, there were 503 incident HTN cases. Multivariable-adjusted (excluding CRF) HR of HTN in normotensive men comparing middle- and high-strength thirds to the lowest third were not significant at 1.17 and 0.84, respectively. Multivariable-adjusted (excluding CRF) HR of HTN in baseline prehypertensive men comparing middle- and high-strength thirds to the lowest third were significant at 0.73 and 0.72 (P = 0.01 each), respectively. The association between muscular strength and incidence of HTN in baseline prehypertensive men was no longer significant after control for CRF (P = 0.26). The study indicated that middle and high levels of muscular strength were associated with a reduced risk of HTN in prehypertensive men only. However, this relationship was no longer significant after controlling for CRF.

Dynamic Sensor Tasking for Space Situational Awareness via Reinforcement Learning

NASA Astrophysics Data System (ADS)

Linares, R.; Furfaro, R.

2016-09-01

This paper studies the Sensor Management (SM) problem for optical Space Object (SO) tracking. The tasking problem is formulated as a Markov Decision Process (MDP) and solved using Reinforcement Learning (RL). The RL problem is solved using the actor-critic policy gradient approach. The actor provides a policy which is random over actions and given by a parametric probability density function (pdf). The critic evaluates the policy by calculating the estimated total reward or the value function for the problem. The parameters of the policy action pdf are optimized using gradients with respect to the reward function. Both the critic and the actor are modeled using deep neural networks (multi-layer neural networks). The policy neural network takes the current state as input and outputs probabilities for each possible action. This policy is random, and can be evaluated by sampling random actions using the probabilities determined by the policy neural network's outputs. The critic approximates the total reward using a neural network. The estimated total reward is used to approximate the gradient of the policy network with respect to the network parameters. This approach is used to find the non-myopic optimal policy for tasking optical sensors to estimate SO orbits. The reward function is based on reducing the uncertainty for the overall catalog to below a user specified uncertainty threshold. This work uses a 30 km total position error for the uncertainty threshold. This work provides the RL method with a negative reward as long as any SO has a total position error above the uncertainty threshold. This penalizes policies that take longer to achieve the desired accuracy. A positive reward is provided when all SOs are below the catalog uncertainty threshold. An optimal policy is sought that takes actions to achieve the desired catalog uncertainty in minimum time. This work trains the policy in simulation by letting it task a single sensor to "learn" from its performance

Irrelevant stimulus processing in ADHD: catecholamine dynamics and attentional networks

PubMed Central

Aboitiz, Francisco; Ossandón, Tomás; Zamorano, Francisco; Palma, Bárbara; Carrasco, Ximena

2014-01-01

A cardinal symptom of attention deficit and hyperactivity disorder (ADHD) is a general distractibility where children and adults shift their attentional focus to stimuli that are irrelevant to the ongoing behavior. This has been attributed to a deficit in dopaminergic signaling in cortico-striatal networks that regulate goal-directed behavior. Furthermore, recent imaging evidence points to an impairment of large scale, antagonistic brain networks that normally contribute to attentional engagement and disengagement, such as the task-positive networks and the default mode network (DMN). Related networks are the ventral attentional network (VAN) involved in attentional shifting, and the salience network (SN) related to task expectancy. Here we discuss the tonic–phasic dynamics of catecholaminergic signaling in the brain, and attempt to provide a link between this and the activities of the large-scale cortical networks that regulate behavior. More specifically, we propose that a disbalance of tonic catecholamine levels during task performance produces an emphasis of phasic signaling and increased excitability of the VAN, yielding distractibility symptoms. Likewise, immaturity of the SN may relate to abnormal tonic signaling and an incapacity to build up a proper executive system during task performance. We discuss different lines of evidence including pharmacology, brain imaging and electrophysiology, that are consistent with our proposal. Finally, restoring the pharmacodynamics of catecholaminergic signaling seems crucial to alleviate ADHD symptoms; however, the possibility is open to explore cognitive rehabilitation strategies to top-down modulate network dynamics compensating the pharmacological deficits. PMID:24723897

Irrelevant stimulus processing in ADHD: catecholamine dynamics and attentional networks.

PubMed

Aboitiz, Francisco; Ossandón, Tomás; Zamorano, Francisco; Palma, Bárbara; Carrasco, Ximena

2014-01-01

A cardinal symptom of attention deficit and hyperactivity disorder (ADHD) is a general distractibility where children and adults shift their attentional focus to stimuli that are irrelevant to the ongoing behavior. This has been attributed to a deficit in dopaminergic signaling in cortico-striatal networks that regulate goal-directed behavior. Furthermore, recent imaging evidence points to an impairment of large scale, antagonistic brain networks that normally contribute to attentional engagement and disengagement, such as the task-positive networks and the default mode network (DMN). Related networks are the ventral attentional network (VAN) involved in attentional shifting, and the salience network (SN) related to task expectancy. Here we discuss the tonic-phasic dynamics of catecholaminergic signaling in the brain, and attempt to provide a link between this and the activities of the large-scale cortical networks that regulate behavior. More specifically, we propose that a disbalance of tonic catecholamine levels during task performance produces an emphasis of phasic signaling and increased excitability of the VAN, yielding distractibility symptoms. Likewise, immaturity of the SN may relate to abnormal tonic signaling and an incapacity to build up a proper executive system during task performance. We discuss different lines of evidence including pharmacology, brain imaging and electrophysiology, that are consistent with our proposal. Finally, restoring the pharmacodynamics of catecholaminergic signaling seems crucial to alleviate ADHD symptoms; however, the possibility is open to explore cognitive rehabilitation strategies to top-down modulate network dynamics compensating the pharmacological deficits.

Greater preference consistency during the Willingness-to-Pay task is related to higher resting state connectivity between the ventromedial prefrontal cortex and the ventral striatum.

PubMed

Mackey, Scott; Olafsson, Valur; Aupperle, Robin L; Lu, Kun; Fonzo, Greg A; Parnass, Jason; Liu, Thomas; Paulus, Martin P

2016-09-01

The significance of why a similar set of brain regions are associated with the default mode network and value-related neural processes remains to be clarified. Here, we examined i) whether brain regions exhibiting willingness-to-pay (WTP) task-related activity are intrinsically connected when the brain is at rest, ii) whether these regions overlap spatially with the default mode network, and iii) whether individual differences in choice behavior during the WTP task are reflected in functional brain connectivity at rest. Blood-oxygen-level dependent (BOLD) signal was measured by functional magnetic resonance imaging while subjects performed the WTP task and at rest with eyes open. Brain regions that tracked the value of bids during the WTP task were used as seed regions in an analysis of functional connectivity in the resting state data. The seed in the ventromedial prefrontal cortex was functionally connected to core regions of the WTP task-related network. Brain regions within the WTP task-related network, namely the ventral precuneus, ventromedial prefrontal and posterior cingulate cortex overlapped spatially with publically available maps of the default mode network. Also, those individuals with higher functional connectivity during rest between the ventromedial prefrontal cortex and the ventral striatum showed greater preference consistency during the WTP task. Thus, WTP task-related regions are an intrinsic network of the brain that corresponds spatially with the default mode network, and individual differences in functional connectivity within the WTP network at rest may reveal a priori biases in choice behavior.

Greater preference consistency during the Willingness-to-Pay task is related to higher resting state connectivity between the ventromedial prefrontal cortex and the ventral striatum

PubMed Central

Mackey, Scott; Olafsson, Valur; Aupperle, Robin; Lu, Kun; Fonzo, Greg; Parnass, Jason; Liu, Thomas; Paulus, Martin P.

2015-01-01

The significance of why a similar set of brain regions are associated with the default mode network and value-related neural processes remains to be clarified. Here, we examined i) whether brain regions exhibiting willingness-to-pay (WTP) task-related activity are intrinsically connected when the brain is at rest, ii) whether these regions overlap spatially with the default mode network, and iii) whether individual differences in choice behavior during the WTP task are reflected in functional brain connectivity at rest. Blood-oxygen-level dependent (BOLD) signal was measured by functional magnetic resonance imaging while subjects performed the WTP task and at rest with eyes open. Brain regions that tracked the value of bids during the WTP task were used as seed regions in an analysis of functional connectivity in the resting state data. The seed in the ventromedial prefrontal cortex was functionally connected to core regions of the WTP task-related network. Brain regions within the WTP task-related network, namely the ventral precuneus, ventromedial prefrontal and posterior cingulate cortex overlapped spatially with publically available maps of the default mode network. Also, those individuals with higher functional connectivity during rest between the ventromedial prefrontal cortex and the ventral striatum showed greater preference consistency during the WTP task. Thus, WTP task-related regions are an intrinsic network of the brain that corresponds spatially with the default mode network, and individual differences in functional connectivity within the WTP network at rest may reveal a priori biases in choice behavior. PMID:26271206

The Effects of Divided Attention on Speech Motor, Verbal Fluency, and Manual Task Performance

ERIC Educational Resources Information Center

Dromey, Christopher; Shim, Erin

2008-01-01

Purpose: The goal of this study was to evaluate aspects of the "functional distance hypothesis," which predicts that tasks regulated by brain networks in closer anatomic proximity will interfere more with each other than tasks controlled by spatially distant regions. Speech, verbal fluency, and manual motor tasks were examined to ascertain whether…

Disentangling the brain networks supporting affective speech comprehension.

PubMed

Hervé, Pierre-Yves; Razafimandimby, Annick; Vigneau, Mathieu; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie

2012-07-16

Areas involved in social cognition, such as the medial prefrontal cortex (mPFC) and the left temporo-parietal junction (TPJ) appear to be active during the classification of sentences according to emotional criteria (happy, angry or sad, [Beaucousin et al., 2007]). These two regions are frequently co-activated in studies about theory of mind (ToM). To confirm that these regions constitute a coherent network during affective speech comprehension, new event-related functional magnetic resonance imaging data were acquired, using the emotional and grammatical-person sentence classification tasks on a larger sample of 51 participants. The comparison of the emotional and grammatical tasks confirmed the previous findings. Functional connectivity analyses established a clear demarcation between a "Medial" network, including the mPFC and TPJ regions, and a bilateral "Language" network, which gathered inferior frontal and temporal areas. These findings suggest that emotional speech comprehension results from interactions between language, ToM and emotion processing networks. The language network, active during both tasks, would be involved in the extraction of lexical and prosodic emotional cues, while the medial network, active only during the emotional task, would drive the making of inferences about the sentences' emotional content, based on their meanings. The left and right amygdalae displayed a stronger response during the emotional condition, but were seldom correlated with the other regions, and thus formed a third entity. Finally, distinct regions belonging to the Language and Medial networks were found in the left angular gyrus, where these two systems could interface. Copyright © 2012 Elsevier Inc. All rights reserved.

Distributed computation of graphics primitives on a transputer network

NASA Technical Reports Server (NTRS)

Ellis, Graham K.

1988-01-01

A method is developed for distributing the computation of graphics primitives on a parallel processing network. Off-the-shelf transputer boards are used to perform the graphics transformations and scan-conversion tasks that would normally be assigned to a single transputer based display processor. Each node in the network performs a single graphics primitive computation. Frequently requested tasks can be duplicated on several nodes. The results indicate that the current distribution of commands on the graphics network shows a performance degradation when compared to the graphics display board alone. A change to more computation per node for every communication (perform more complex tasks on each node) may cause the desired increase in throughput.

Theta-Modulated Gamma-Band Synchronization Among Activated Regions During a Verb Generation Task

PubMed Central

Doesburg, Sam M.; Vinette, Sarah A.; Cheung, Michael J.; Pang, Elizabeth W.

2012-01-01

Expressive language is complex and involves processing within a distributed network of cortical regions. Functional MRI and magnetoencephalography (MEG) have identified brain areas critical for expressive language, but how these regions communicate across the network remains poorly understood. It is thought that synchronization of oscillations between neural populations, particularly at a gamma rate (>30 Hz), underlies functional integration within cortical networks. Modulation of gamma rhythms by theta-band oscillations (4–8 Hz) has been proposed as a mechanism for the integration of local cell coalitions into large-scale networks underlying cognition and perception. The present study tested the hypothesis that these oscillatory mechanisms of functional integration were present within the expressive language network. We recorded MEG while subjects performed a covert verb generation task. We localized activated cortical regions using beamformer analysis, calculated inter-regional phase locking between activated areas, and measured modulation of inter-regional gamma synchronization by theta phase. The results show task-dependent gamma-band synchronization among regions activated during the performance of the verb generation task, and we provide evidence that these transient and periodic instances of high-frequency connectivity were modulated by the phase of cortical theta oscillations. These findings suggest that oscillatory synchronization and cross-frequency interactions are mechanisms for functional integration among distributed brain areas supporting expressive language processing. PMID:22707946

Informal networks: the company behind the chart.

PubMed

Krackhardt, D; Hanson, J R

1993-01-01

A glance at an organizational chart can show who's the boss and who reports to whom. But this formal chart won't reveal which people confer on technical matters or discuss office politics over lunch. Much of the real work in any company gets done through this informal organization with its complex networks of relationships that cross functions and divisions. According to consultants David Krackhardt and Jeffrey Hanson, managers can harness the true power in their companies by diagramming three types of networks: the advice network, which reveals the people to whom others turn to get work done; the trust network, which uncovers who shares delicate information; and the communication network, which shows who talks about work-related matters. Using employee questionnaires, managers can generate network maps that will get to the root of many organizational problems. When a task force in a computer company, for example, was not achieving its goals, the CEO turned to network maps to find out why. He discovered that the task force leader was central in the advice network but marginal in the trust network. Task force members did not believe he would look out for their interests, so the CEO used the trust map to find someone to share responsibility for the group. And when a bank manager saw in the network map that there was little communication between tellers and supervisors, he looked for ways to foster interaction among employees of all levels. As companies continue to flatten and rely on teams, managers must rely less on their authority and more on understanding these informal networks. Managers who can use maps to identify, leverage, and revamp informal networks will have the key to success.

Neuronal Substrates Underlying Performance Variability in Well-Trained Skillful Motor Task in Humans.

PubMed

Mizuguchi, Nobuaki; Uehara, Shintaro; Hirose, Satoshi; Yamamoto, Shinji; Naito, Eiichi

2016-01-01

Motor performance fluctuates trial by trial even in a well-trained motor skill. Here we show neural substrates underlying such behavioral fluctuation in humans. We first scanned brain activity with functional magnetic resonance imaging while healthy participants repeatedly performed a 10 s skillful sequential finger-tapping task. Before starting the experiment, the participants had completed intensive training. We evaluated task performance per trial (number of correct sequences in 10 s) and depicted brain regions where the activity changes in association with the fluctuation of the task performance across trials. We found that the activity in a broader range of frontoparietocerebellar network, including the bilateral dorsolateral prefrontal cortex (DLPFC), anterior cingulate and anterior insular cortices, and left cerebellar hemisphere, was negatively correlated with the task performance. We further showed in another transcranial direct current stimulation (tDCS) experiment that task performance deteriorated, when we applied anodal tDCS to the right DLPFC. These results indicate that fluctuation of brain activity in the nonmotor frontoparietocerebellar network may underlie trial-by-trial performance variability even in a well-trained motor skill, and its neuromodulation with tDCS may affect the task performance.

Frontal parietal control network regulates the anti-correlated default and dorsal attention networks.

PubMed

Gao, Wei; Lin, Weili

2012-01-01

Recent reports demonstrate the anti-correlated behaviors between the default (DF) and the dorsal attention (DA) networks. We aimed to investigate the roles of the frontal parietal control (FPC) network in regulating the two anti-correlated networks through three experimental conditions, including resting, continuous self-paced/attended sequential finger tapping (FT), and natural movie watching (MW), respectively. The two goal-directed tasks were chosen to engage either one of the two competing networks-FT for DA whereas MW for default. We hypothesized that FPC will selectively augment/suppress either network depending on how the task targets the specific network; FPC will positively correlate with the target network, but negatively correlate with the network anti-correlated with the target network. We further hypothesized that significant causal links from FPC to both DA and DF are present during all three experimental conditions, supporting the initiative regulating role of FPC over the two opposing systems. Consistent with our hypotheses, FPC exhibited a significantly higher positive correlation with DA (P = 0.0095) whereas significantly more negative correlation with default (P = 0.0025) during FT when compared to resting. Completely opposite to that observed during FT, the FPC was significantly anti-correlated with DA (P = 2.1e-6) whereas positively correlated with default (P = 0.0035) during MW. Furthermore, extensive causal links from FPC to both DA and DF were observed across all three experimental states. Together, our results strongly support the notion that the FPC regulates the anti-correlated default and DA networks. Copyright © 2011 Wiley Periodicals, Inc.

Modeling User Behavior in Computer Learning Tasks.

ERIC Educational Resources Information Center

Mantei, Marilyn M.

Model building techniques from Artifical Intelligence and Information-Processing Psychology are applied to human-computer interface tasks to evaluate existing interfaces and suggest new and better ones. The model is in the form of an augmented transition network (ATN) grammar which is built by applying grammar induction heuristics on a sequential…

Mathematically gifted adolescents use more extensive and more bilateral areas of the fronto-parietal network than controls during executive functioning and fluid reasoning tasks.

PubMed

Desco, Manuel; Navas-Sanchez, Francisco J; Sanchez-González, Javier; Reig, Santiago; Robles, Olalla; Franco, Carolina; Guzmán-De-Villoria, Juan A; García-Barreno, Pedro; Arango, Celso

2011-07-01

The main goal of this study was to investigate the neural substrates of fluid reasoning and visuospatial working memory in adolescents with precocious mathematical ability. The study population comprised two groups of adolescents: 13 math-gifted adolescents and 14 controls with average mathematical skills. Patterns of activation specific to reasoning tasks in math-gifted subjects were examined using functional magnetic resonance images acquired while the subjects were performing Raven's Advanced Progressive Matrices (RAPM) and the Tower of London (TOL) tasks. During the tasks, both groups showed significant activations in the frontoparietal network. In the math-gifted group, clusters of activation were always bilateral and more regions were recruited, especially in the right hemisphere. In the TOL task, math-gifted adolescents showed significant hyper-activations relative to controls in the precuneus, superior occipital lobe (BA 19), and medial temporal lobe (BA 39). The maximum differences between the groups were detected during RAPM tasks at the highest level of difficulty, where math-gifted subjects showed significant activations relative to controls in the right inferior parietal lobule (BA 40), anterior cingulated gyrus (BA 32), and frontal (BA 9, and BA 6) areas. Our results support the hypothesis that greater ability for complex mathematical reasoning may be related to more bilateral patterns of activation and that increased activation in the parietal and frontal regions of math-gifted adolescents is associated with enhanced skills in visuospatial processing and logical reasoning. Copyright © 2011 Elsevier Inc. All rights reserved.

The prevalence of hypertension in relation with the normal albuminuria range in type 2 diabetes mellitus within the South Korean population: The Korean National Health and Nutrition Examination Survey, 2011-2012.

PubMed

Shin, Koh-Eun; Roh, Yong-Kyun; Cho, Kyung-Hwan; Han, Kyung-Do; Park, Yong-Gyu; Kim, Do-Hoon; Kim, Yang-Hyun

2017-06-01

The coexistence of hypertension (HTN) and diabetes mellitus (DM) increases the risk of cardiovascular disease. In some studies, normal albuminuria has also been associated with cardiovascular disease and HTN. Therefore, we examined the relationships between albuminuria and the prevalence of HTN and its control rate in type 2 DM patients. We analyzed data from the 2011-2012 Korea National Health and Nutrition Examination Survey, and 1188 subjects with type 2 DM were included in the study. We divided albuminuria into 3 albuminuria tertiles (T): T1: <4.82mg/g; T2: 4.82-17.56mg/g; and T3: ≥17.56mg/g. The systolic and diastolic blood pressure were positively correlated with the albumin to creatinine ratio (ACR) after adjusting for all covariates (P<0.001). Type 2 DM subjects with hypertension had more ACR T3 (odds ratio=2.018, 95% confidence interval=1.445-2.818) than subjects without HTN. Subjects with controlled HTN had less ACR T3 than subjects without controlled HTN (odds ratio=0.566, 95% confidence interval=0.384-0.836). When, we redivided albuminuria by <10, 10-30 (high normal albuminuria), 30-300mg/g (microalbuminuria), and 300mg/g≤(macroalbuminuria), the odds ratio for high normal albuminuria and microalbuminuria was 1.52 and 2.24, respectively in the presence of HTN, however, high normal albuminuria was not associated with HTN control. In conclusion, albuminuria within the high normal range was associated with the prevalence of HTN in South Korean patients with type 2 DM. Copyright © 2017 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

Evolving Digital Ecological Networks

PubMed Central

Wagner, Aaron P.; Ofria, Charles

2013-01-01

“It is hard to realize that the living world as we know it is just one among many possibilities” [1]. Evolving digital ecological networks are webs of interacting, self-replicating, and evolving computer programs (i.e., digital organisms) that experience the same major ecological interactions as biological organisms (e.g., competition, predation, parasitism, and mutualism). Despite being computational, these programs evolve quickly in an open-ended way, and starting from only one or two ancestral organisms, the formation of ecological networks can be observed in real-time by tracking interactions between the constantly evolving organism phenotypes. These phenotypes may be defined by combinations of logical computations (hereafter tasks) that digital organisms perform and by expressed behaviors that have evolved. The types and outcomes of interactions between phenotypes are determined by task overlap for logic-defined phenotypes and by responses to encounters in the case of behavioral phenotypes. Biologists use these evolving networks to study active and fundamental topics within evolutionary ecology (e.g., the extent to which the architecture of multispecies networks shape coevolutionary outcomes, and the processes involved). PMID:23533370

Real-Time Network Management

DTIC Science & Technology

1998-07-01

Report No. WH97JR00-A002 Sponsored by REAL-TIME NETWORK MANAGEMENT FINAL TECHNICAL REPORT K CD July 1998 CO CO O W O Defense Advanced...Approved for public release; distribution unlimited. t^GquALmmsPEami Report No. WH97JR00-A002 REAL-TIME NETWORK MANAGEMENT Synectics Corporation...2.1.2.1 WAN-class Networks 12 2.1.2.2 IEEE 802.3-class Networks 13 2.2 Task 2 - Object Modeling for Architecture 14 2.2.1 Managed Objects 14 2.2.2

Functional Disconnectivity during Inter-Task Resting State in Dementia with Lewy Bodies.

PubMed

Chabran, Eléna; Roquet, Daniel; Gounot, Daniel; Sourty, Marion; Armspach, Jean-Paul; Blanc, Frédéric

2018-01-01

Limited research has been done on the functional connectivity in visuoperceptual regions in dementia with Lewy bodies (DLB) patients. This study aimed to investigate the functional connectivity differences between a task condition and an inter-task resting state condition within a visuoperceptual paradigm, in DLB patients compared with Alzheimer disease (AD) patients and healthy elderly control subjects. Twenty-six DLB, 29 AD, and 22 healthy subjects underwent a detailed clinical and neuropsychological examination along with a functional MRI during the different conditions of a visuoperceptual paradigm. Functional images were analyzed using group-level spatial independent component analysis and seed-based connectivity analyses. While the DLB patients scored well and did not differ from the control and AD groups in terms of functional activity and connectivity during the task conditions, they showed decreased functional connectivity in visuoperceptual regions during the resting state condition, along with a temporal impairment of the default-mode network activity. Functional connectivity disturbances were also found within two attentional-executive networks and between these networks and visuoperceptual regions. We found a specific functional profile in the switching between task and resting state conditions in DLB patients. This result could help better characterize functional impairments in DLB and their contribution to several core symptoms of this pathology such as visual hallucinations and cognitive fluctuations. © 2018 S. Karger AG, Basel.

Perceptions of family history and genetic testing and feasibility of pedigree development among African Americans with hypertension

PubMed Central

Pettey, Christina M; McSweeney, Jean C; Stewart, Katharine E; Price, Elvin T; Cleves, Mario A; Heo, Seongkum; Souder, Elaine

2016-01-01

Background Pedigree development, family history, and genetic testing are thought to be useful in improving outcomes of chronic illnesses such as hypertension (HTN). However, the clinical utility of pedigree development is still unknown. Further, little is known about African Americans’ (AAs’) perceptions of family history and genetic testing. Aims This study examined the feasibility of developing pedigrees for AAs with HTN and explored perceptions of family history and genetic research among AAs with HTN. Methods The US Surgeon General’s My Family Health Portrait was administered, and 30–60 minute in-person individual interviews were conducted. Descriptive statistics were used to analyze pedigree data. Interview transcripts were analyzed with content analysis and constant comparison. Results Twenty-nine AAs with HTN were recruited from one free clinic (15 women, 14 men; mean age 49 years, SD 9.6). Twenty-six (90%) reported their family history in sufficient detail to develop a pedigree. Perceptions of family history included knowledge of HTN in the family, culturally influenced family teaching about HTN, and response to family history of HTN. Most participants agreed to future genetic testing and DNA collection because they wanted to help others; some said they needed more information and others expressed a concern for privacy. Conclusion The majority of AAs in this sample possessed extensive knowledge of HTN within their family and were able to develop a three generation pedigree with assistance. The majority were willing to participate in future genetic research. PMID:25322748

Perceptions of family history and genetic testing and feasibility of pedigree development among African Americans with hypertension.

PubMed

Pettey, Christina M; McSweeney, Jean C; Stewart, Katharine E; Price, Elvin T; Cleves, Mario A; Heo, Seongkum; Souder, Elaine

2015-02-01

Pedigree development, family history, and genetic testing are thought to be useful in improving outcomes of chronic illnesses such as hypertension (HTN). However, the clinical utility of pedigree development is still unknown. Further, little is known about the perceptions of African Americans (AAs) of family history and genetic testing. This study examined the feasibility of developing pedigrees for AAs with HTN and explored perceptions of family history and genetic research among AAs with HTN. The US Surgeon General's My Family Health Portrait was administered, and 30-60 min in-person individual interviews were conducted. Descriptive statistics were used to analyze pedigree data. Interview transcripts were analyzed with content analysis and constant comparison. Twenty-nine AAs with HTN were recruited from one free clinic (15 women, 14 men; mean age 49 years, standard deviation (SD) 9.6). Twenty-six (90%) reported their family history in sufficient detail to develop a pedigree. Perceptions of family history included knowledge of HTN in the family, culturally influenced family teaching about HTN, and response to family history of HTN. Most participants agreed to future genetic testing and DNA collection because they wanted to help others; some said they needed more information and others expressed a concern for privacy. The majority of AAs in this sample possessed extensive knowledge of HTN within their family and were able to develop a three-generation pedigree with assistance. The majority were willing to participate in future genetic research. © The European Society of Cardiology 2014.

Renal sympathetic denervation in the treatment of resistant hypertension.

PubMed

Sánchez-Álvarez, Catalina; González-Vélez, Miguel; Stilp, Erik; Ward, Charisse; Mena-Hurtado, Carlos

2014-12-01

Arterial hypertension (HTN) is a major health problem worldwide. Treatment-resistant hypertension (trHTN) is defined as the failure to achieve target blood pressure despite the concomitant use of maximally tolerated doses of three different antihypertensive medications, including a diuretic. trHTN is associated with considerable morbidity and mortality. Renal sympathetic denervation (RDn) is available and implemented abroad as a strategy for the treatment of trHTN and is currently under clinical investigation in the United States. Selective renal sympathectomy via an endovascular approach effectively decreases renal sympathetic nerve hyperactivity leading to a decrease in blood pressure. The Symplicity catheter, currently under investigation in the United States, is a 6-French compatible system advanced under fluoroscopic guidance via percutaneous access of the common femoral artery to the distal lumen of each of the main renal arteries. Radiofrequency (RF) energy is then applied to the endoluminal surface of the renal arteries via an electrode located at the tip of the catheter. Two clinical trials (Symplicity HTN 1 and Symplicity HTN 2) have shown the efficacy of RDn with a post-procedure decline of 27/17 mmHg at 12 months and 32/12 mmHg at 6 months, respectively, with few minor adverse events. Symplicity HTN-3 study is a, multi-center, prospective, single-blind, randomized, controlled study currently under way and will provide further insights about the safety and efficacy of renal denervation in patients with trHTN.

Renal Sympathetic Denervation in the Treatment of Resistant Hypertension

PubMed Central

Sánchez-Álvarez, Catalina; González-Vélez, Miguel; Stilp, Erik; Ward, Charisse; Mena-Hurtado, Carlos

2014-01-01

Arterial hypertension (HTN) is a major health problem worldwide. Treatment-resistant hypertension (trHTN) is defined as the failure to achieve target blood pressure despite the concomitant use of maximally tolerated doses of three different antihypertensive medications, including a diuretic. trHTN is associated with considerable morbidity and mortality. Renal sympathetic denervation (RDn) is available and implemented abroad as a strategy for the treatment of trHTN and is currently under clinical investigation in the United States. Selective renal sympathectomy via an endovascular approach effectively decreases renal sympathetic nerve hyperactivity leading to a decrease in blood pressure. The Symplicity catheter, currently under investigation in the United States, is a 6-French compatible system advanced under fluoroscopic guidance via percutaneous access of the common femoral artery to the distal lumen of each of the main renal arteries. Radiofrequency (RF) energy is then applied to the endoluminal surface of the renal arteries via an electrode located at the tip of the catheter. Two clinical trials (Symplicity HTN 1 and Symplicity HTN 2) have shown the efficacy of RDn with a post-procedure decline of 27/17mmHg at 12 months and 32/12 mmHg at 6 months, respectively, with few minor adverse events. Symplicity HTN-3 study is a, multi-center, prospective, single-blind, randomized, controlled study currently under way and will provide further insights about the safety and efficacy of renal denervation in patients with trHTN. PMID:25506285

Therapy of Treatment-Related Hypertension in Metastatic Renal-Cell Cancer Patients Receiving Sunitinib.

PubMed

Ivanyi, Philipp; Beutel, Gernot; Drewes, Nicole; Pirr, Jens; Kielstein, Jan T; Morgan, Michael; Ganser, Arnold; Grünwald, Viktor

2017-04-01

Treatment-related hypertension (tHTN) is frequent during sunitinib treatment. However, data on risk factors and treatment of tHTN remain scarce. Patients with metastatic renal-cell carcinoma treated with sunitinib from June 2004 to December 2011 were included. Medical records were retrospectively analyzed for tHTN risk factors and antihypertensive treatments (AHT). Descriptive statistics, Cox regression, and competitive risk models were applied. A total of 51 (70.8%) of 72 patients developed tHTN after a median sunitinib treatment of 28 days. Mean blood pressure increased from 130/75 (range, 90 to 190/58 to 101) mm Hg on day 1 to 140/80 (range, 90 to 190/60 to 120, P < .001) mm Hg on day 28. Standard dose of sunitinib, age > 50 years, and prehypertension were identified as independent risk factors for tHTN. Thirty-eight patients (72.5%) in the tHTN subgroup received modification of AHT. Calcium channel blockers (CCB) were identified as the best at controlling tHTN compared to other drugs (P = .045). The combination of AHT was more potent than a dose increase of a single-drug AHT, and early AHT intervention was more efficacious than delayed start of therapy. Patients at risk for tHTN require more rigorous blood pressure measurement. CCB seemed to be most potent and efficient, and an early combination of different classes of AHT was more efficacious than full-dose, single-agent AHT. Copyright © 2016 Elsevier Inc. All rights reserved.

Salience network integrity predicts default mode network function after traumatic brain injury

PubMed Central

Bonnelle, Valerie; Ham, Timothy E.; Leech, Robert; Kinnunen, Kirsi M.; Mehta, Mitul A.; Greenwood, Richard J.; Sharp, David J.

2012-01-01

Efficient behavior involves the coordinated activity of large-scale brain networks, but the way in which these networks interact is uncertain. One theory is that the salience network (SN)—which includes the anterior cingulate cortex, presupplementary motor area, and anterior insulae—regulates dynamic changes in other networks. If this is the case, then damage to the structural connectivity of the SN should disrupt the regulation of associated networks. To investigate this hypothesis, we studied a group of 57 patients with cognitive impairments following traumatic brain injury (TBI) and 25 control subjects using the stop-signal task. The pattern of brain activity associated with stop-signal task performance was studied by using functional MRI, and the structural integrity of network connections was quantified by using diffusion tensor imaging. Efficient inhibitory control was associated with rapid deactivation within parts of the default mode network (DMN), including the precuneus and posterior cingulate cortex. TBI patients showed a failure of DMN deactivation, which was associated with an impairment of inhibitory control. TBI frequently results in traumatic axonal injury, which can disconnect brain networks by damaging white matter tracts. The abnormality of DMN function was specifically predicted by the amount of white matter damage in the SN tract connecting the right anterior insulae to the presupplementary motor area and dorsal anterior cingulate cortex. The results provide evidence that structural integrity of the SN is necessary for the efficient regulation of activity in the DMN, and that a failure of this regulation leads to inefficient cognitive control. PMID:22393019

Hierarchical Control Using Networks Trained with Higher-Level Forward Models

PubMed Central

Wayne, Greg; Abbott, L.F.

2015-01-01

We propose and develop a hierarchical approach to network control of complex tasks. In this approach, a low-level controller directs the activity of a “plant,” the system that performs the task. However, the low-level controller may only be able to solve fairly simple problems involving the plant. To accomplish more complex tasks, we introduce a higher-level controller that controls the lower-level controller. We use this system to direct an articulated truck to a specified location through an environment filled with static or moving obstacles. The final system consists of networks that have memorized associations between the sensory data they receive and the commands they issue. These networks are trained on a set of optimal associations that are generated by minimizing cost functions. Cost function minimization requires predicting the consequences of sequences of commands, which is achieved by constructing forward models, including a model of the lower-level controller. The forward models and cost minimization are only used during training, allowing the trained networks to respond rapidly. In general, the hierarchical approach can be extended to larger numbers of levels, dividing complex tasks into more manageable sub-tasks. The optimization procedure and the construction of the forward models and controllers can be performed in similar ways at each level of the hierarchy, which allows the system to be modified to perform other tasks, or to be extended for more complex tasks without retraining lower-levels. PMID:25058706

Altered intrinsic organisation of brain networks implicated in attentional processes in adult attention-deficit/hyperactivity disorder: a resting-state study of attention, default mode and salience network connectivity.

PubMed

Sidlauskaite, Justina; Sonuga-Barke, Edmund; Roeyers, Herbert; Wiersema, Jan R

2016-06-01

Deficits in task-related attentional engagement in attention-deficit/hyperactivity disorder (ADHD) have been hypothesised to be due to altered interrelationships between attention, default mode and salience networks. We examined the intrinsic connectivity during rest within and between these networks. Six-minute resting-state scans were obtained. Using a network-based approach, connectivity within and between the dorsal and ventral attention, the default mode and the salience networks was compared between the ADHD and control group. The ADHD group displayed hyperconnectivity between the two attention networks and within the default mode and ventral attention network. The salience network was hypoconnected to the dorsal attention network. There were trends towards hyperconnectivity within the dorsal attention network and between the salience and ventral attention network in ADHD. Connectivity within and between other networks was unrelated to ADHD. Our findings highlight the altered connectivity within and between attention networks, and between them and the salience network in ADHD. One hypothesis to be tested in future studies is that individuals with ADHD are affected by an imbalance between ventral and dorsal attention systems with the former playing a dominant role during task engagement, making individuals with ADHD highly susceptible to distraction by salient task-irrelevant stimuli.

Classification of complex networks based on similarity of topological network features

NASA Astrophysics Data System (ADS)

Attar, Niousha; Aliakbary, Sadegh

2017-09-01

Over the past few decades, networks have been widely used to model real-world phenomena. Real-world networks exhibit nontrivial topological characteristics and therefore, many network models are proposed in the literature for generating graphs that are similar to real networks. Network models reproduce nontrivial properties such as long-tail degree distributions or high clustering coefficients. In this context, we encounter the problem of selecting the network model that best fits a given real-world network. The need for a model selection method reveals the network classification problem, in which a target-network is classified into one of the candidate network models. In this paper, we propose a novel network classification method which is independent of the network size and employs an alignment-free metric of network comparison. The proposed method is based on supervised machine learning algorithms and utilizes the topological similarities of networks for the classification task. The experiments show that the proposed method outperforms state-of-the-art methods with respect to classification accuracy, time efficiency, and robustness to noise.