Bilingualism as a gradient measure modulates functional connectivity of language and control networks

Objective:To examine the utility of resting-state functional connectivity MRI (rs-fcMRI) measurements of network integrity as a predictor of future cognitive decline in preclinical Alzheimer disease (AD).Methods:A total of 237 clinically normal older adults (aged 63–90 years, Clinical Dementia Rating 0) underwent baseline β-amyloid (Aβ) imaging with Pittsburgh compound B PET and structural and rs-fcMRI. We identified 7 networks for analysis, including 4 cognitive networks (default, salience, dorsal attention, and frontoparietal control) and 3 noncognitive networks (primary visual, extrastriate visual, motor). Using linear and curvilinear mixed models, we used baseline connectivity in these networks to predict longitudinal changes in preclinical Alzheimer cognitive composite (PACC) performance, both alone and interacting with Aβ burden. Median neuropsychological follow-up was 3 years.Results:Baseline connectivity in the default, salience, and control networks predicted longitudinal PACC decline, unlike connectivity in the dorsal attention and all noncognitive networks. Default, salience, and control network connectivity was also synergistic with Aβ burden in predicting decline, with combined higher Aβ and lower connectivity predicting the steepest curvilinear decline in PACC performance.Conclusions:In clinically normal older adults, lower functional connectivity predicted more rapid decline in PACC scores over time, particularly when coupled with increased Aβ burden. Among examined networks, default, salience, and control networks were the strongest predictors of rate of change in PACC scores, with the inflection point of greatest decline beyond the fourth year of follow-up. These results suggest that rs-fcMRI may be a useful predictor of early, AD-related cognitive decline in clinical research settings.

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Altered functional connectivity among default, attention, and control networks in idiopathic generalized epilepsy

Epilepsy & Behavior ◽

10.1016/j.yebeh.2015.03.031 ◽

2015 ◽

Vol 46 ◽

pp. 118-125 ◽

Cited By ~ 20

Author(s):

H.L. Wei ◽

J. An ◽

L.L. Zeng ◽

H. Shen ◽

S.J. Qiu ◽

...

Keyword(s):

Functional Connectivity ◽

Idiopathic Generalized Epilepsy ◽

Control Networks ◽

Generalized Epilepsy ◽

And Control

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Co-scheduling of Communication and Control of Multi-Hop Control Networks

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e96.a.878 ◽

2013 ◽

Vol E96.A (5) ◽

pp. 878-885 ◽

Cited By ~ 3

Author(s):

Yasuki NANAMORI ◽

Toshimitsu USHIO

Keyword(s):

Control Networks ◽

And Control

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Toward a Neural Model of the Openness-Psychoticism Dimension: Functional Connectivity in the Default and Frontoparietal Control Networks

10.31234/osf.io/7uzmj ◽

2019 ◽

Author(s):

Scott D. Blain ◽

Rachael Grazioplene ◽

Yizhou Ma ◽

Colin G. DeYoung

Keyword(s):

Functional Connectivity ◽

Structural Equation ◽

Psychotic Disorders ◽

Neural Model ◽

Equation Modeling ◽

Control Networks ◽

Psychosis Proneness ◽

Human Connectome Project ◽

Intrinsic Connectivity Networks ◽

Personality Psychopathology

Psychosis proneness has been linked to heightened Openness to Experience and to cognitive deficits. Openness and psychotic disorders are associated with the default and frontoparietal networks, and the latter network is also robustly associated with intelligence. We tested the hypothesis that functional connectivity of the default and frontoparietal networks is a neural correlate of the openness-psychoticism dimension. Participants in the Human Connectome Project (N = 1003) completed measures of psychoticism, openness, and intelligence. Resting state functional magnetic resonance imaging was used to identify intrinsic connectivity networks. Structural equation modeling revealed relations among personality, intelligence, and network coherence. Psychoticism, openness, and especially their shared variance, were related positively to default network coherence and negatively to frontoparietal coherence. These associations remained after controlling for intelligence. Intelligence was positively related to frontoparietal coherence. Research suggests psychoticism and openness are linked in part through their association with connectivity in networks involving experiential simulation and cognitive control. We propose a model of psychosis risk that highlights roles of the default and frontoparietal networks. Findings echo research on functional connectivity in psychosis patients, suggesting shared mechanisms across the personality-psychopathology continuum.

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A Conceptual Model of Measurement Uncertainty in IoT Sensor Networks

Sensors ◽

10.3390/s21051827 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1827

Author(s):

Piotr Cofta ◽

Kostas Karatzas ◽

Cezary Orłowski

Keyword(s):

Sensor Networks ◽

Internet Of Things ◽

Reference Model ◽

Primary Objective ◽

Theory And Practice ◽

Fitness For Purpose ◽

Industrial Practice ◽

Control Networks ◽

And Control ◽

Measurement And Control

The growing popularity of inexpensive IoT (Internet of Things) sensor networks makes their uncertainty an important aspect of their adoption. The uncertainty determines their fitness for purpose, their perceived quality and the usefulness of information they provide. Nevertheless, neither the theory nor the industrial practice of uncertainty offer a coherent answer on how to address uncertainty of networks of this type and their components. The primary objective of this paper is to facilitate the discussion of what progress should be made regarding the theory and the practice of uncertainty of IoT sensor networks to satisfy current needs. This paper provides a structured overview of uncertainty, specifically focusing on IoT sensor networks. It positions IoT sensor networks as contrasted with professional measurement and control networks and presents their conceptual sociotechnical reference model. The reference model advises on the taxonomy of uncertainty proposed in this paper that demonstrates semantic differences between various views on uncertainty. This model also allows for identifying key challenges that should be addressed to improve the theory and practice of uncertainty in IoT sensor networks.

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Distinct network topology in Alzheimer’s disease and behavioral variant frontotemporal dementia

Alzheimer s Research & Therapy ◽

10.1186/s13195-020-00752-w ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Adeline Su Lyn Ng ◽

Juan Wang ◽

Kwun Kei Ng ◽

Joanna Su Xian Chong ◽

Xing Qian ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Frontotemporal Dementia ◽

Network Topology ◽

Neuropsychiatric Symptoms ◽

Brain Network ◽

Salience Network ◽

Behavioral Variant Frontotemporal Dementia ◽

Control Networks ◽

And Control

Abstract Background Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) cause distinct atrophy and functional disruptions within two major intrinsic brain networks, namely the default network and the salience network, respectively. It remains unclear if inter-network relationships and whole-brain network topology are also altered and underpin cognitive and social–emotional functional deficits. Methods In total, 111 participants (50 AD, 14 bvFTD, and 47 age- and gender-matched healthy controls) underwent resting-state functional magnetic resonance imaging (fMRI) and neuropsychological assessments. Functional connectivity was derived among 144 brain regions of interest. Graph theoretical analysis was applied to characterize network integration, segregation, and module distinctiveness (degree centrality, nodal efficiency, within-module degree, and participation coefficient) in AD, bvFTD, and healthy participants. Group differences in graph theoretical measures and empirically derived network community structures, as well as the associations between these indices and cognitive performance and neuropsychiatric symptoms, were subject to general linear models, with age, gender, education, motion, and scanner type controlled. Results Our results suggested that AD had lower integration in the default and control networks, while bvFTD exhibited disrupted integration in the salience network. Interestingly, AD and bvFTD had the highest and lowest degree of integration in the thalamus, respectively. Such divergence in topological aberration was recapitulated in network segregation and module distinctiveness loss, with AD showing poorer modular structure between the default and control networks, and bvFTD having more fragmented modules in the salience network and subcortical regions. Importantly, aberrations in network topology were related to worse attention deficits and greater severity in neuropsychiatric symptoms across syndromes. Conclusions Our findings underscore the reciprocal relationships between the default, control, and salience networks that may account for the cognitive decline and neuropsychiatric symptoms in dementia.

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Real-Time Detection of Dictionary DGA Network Traffic Using Deep Learning

SN Computer Science ◽

10.1007/s42979-021-00507-w ◽

2021 ◽

Vol 2 (2) ◽

Author(s):

Kate Highnam ◽

Domenic Puzio ◽

Song Luo ◽

Nicholas R. Jennings

Keyword(s):

Neural Network ◽

Deep Learning ◽

Real Time ◽

Network Traffic ◽

Short Term Memory ◽

Domain Names ◽

Control Networks ◽

Detection Techniques ◽

Lstm Network ◽

And Control

AbstractBotnets and malware continue to avoid detection by static rule engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the “bagging” model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, $$F_1$$ F 1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large enterprise. In 4 h of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.

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MicroRNAs in Autoimmunity and Hematological Malignancies

International Journal of Molecular Sciences ◽

10.3390/ijms19103139 ◽

2018 ◽

Vol 19 (10) ◽

pp. 3139 ◽

Cited By ~ 7

Author(s):

Mirco Marco ◽

Alice Ramassone ◽

Sara Pagotto ◽

Eleni Anastasiadou ◽

Angelo Veronese ◽

...

Keyword(s):

Molecular Mechanisms ◽

Hematological Malignancies ◽

Immune Cell ◽

Immunological Tolerance ◽

Considerable Effort ◽

Control Networks ◽

Immune Cell Subsets ◽

Bidirectional Relationship ◽

Relevant Role ◽

And Control

Autoimmunity and hematological malignancies are often concomitant in patients. A causal bidirectional relationship exists between them. Loss of immunological tolerance with inappropriate activation of the immune system, likely due to environmental and genetic factors, can represent a breeding ground for the appearance of cancer cells and, on the other hand, blood cancers are characterized by imbalanced immune cell subsets that could support the development of the autoimmune clone. Considerable effort has been made for understanding the proteins that have a relevant role in both processes; however, literature advances demonstrate that microRNAs (miRNAs) surface as the epigenetic regulators of those proteins and control networks linked to both autoimmunity and hematological malignancies. Here we review the most up-to-date findings regarding the miRNA-based molecular mechanisms that underpin autoimmunity and hematological malignancies.

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