Insights into Dynamic Network States Using Metabolomic Data

2019 ◽  
pp. 243-258
Author(s):  
Reihaneh Mostolizadeh ◽  
Andreas Dräger ◽  
Neema Jamshidi
Keyword(s):  
Dynamic Network ◽  
Metabolomic Data ◽  
Network States

scholarly journals Neurocraft: software for microscale brain network dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dimitris Fotis Sakellariou ◽  
Angeliki Vakrinou ◽  
Michalis Koutroumanidis ◽  
Mark Phillip Richardson
Keyword(s):  
Focal Epilepsy ◽  
Network Dynamics ◽  
Dynamic Network ◽  
Brain Network ◽  
Frequency Resolution ◽  
Rapid Progression ◽  
Learning Tools ◽  
Epileptiform Discharges ◽  
Network States ◽  
User Friendly

AbstractThe brain operates at millisecond timescales but despite of that, the study of its functional networks is approached with time invariant methods. Equally, for a variety of brain conditions treatment is delivered with fixed temporal protocols unable to monitor and follow the rapid progression and therefore the cycles of a disease. To facilitate the understanding of brain network dynamics we developed Neurocraft, a user friendly software suite. Neurocraft features a highly novel signal processing engine fit for tracking evolving network states with superior time and frequency resolution. A variety of analytics like dynamic connectivity maps, force-directed representations and propagation models, allow for the highly selective investigation of transient pathophysiological dynamics. In addition, machine-learning tools enable the unsupervised investigation and selection of key network features at individual and group-levels. For proof of concept, we compared six seizure-free and non seizure-free focal epilepsy patients after resective surgery using Neurocraft. The network features were calculated using 50 intracranial electrodes on average during at least 120 epileptiform discharges lasting less than one second, per patient. Powerful network differences were detected in the pre-operative data of the two patient groups (effect size = 1.27), suggesting the predictive value of dynamic network features. More than one million patients are treated with cardiac and neuro modulation devices that are unable to track the hourly or daily changes in a subject’s disease. Decoding the dynamics of transition from normal to abnormal states may be crucial in the understanding, tracking and treatment of neurological conditions. Neurocraft provides a user-friendly platform for the research of microscale brain dynamics and a stepping stone for the personalised device-based adaptive neuromodulation in real-time.

scholarly journals A Tensor Decomposition-Based Approach for Detecting Dynamic Network States From EEG

2017 ◽  
Vol 64 (1) ◽  
pp. 225-237 ◽  
Author(s):  
Arash Golibagh Mahyari ◽  
David M. Zoltowski ◽  
Edward M. Bernat ◽  
Selin Aviyente
Keyword(s):  
Dynamic Network ◽  
Tensor Decomposition ◽  
Network States

scholarly journals Neuroimaging evidence for a network sampling theory of individual differences in human intelligence test performance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Eyal Soreq ◽  
Ines R. Violante ◽  
Richard E. Daws ◽  
Adam Hampshire
Keyword(s):  
Test Performance ◽  
Cognitive Task ◽  
Dynamic Network ◽  
Individual Variability ◽  
Intelligence Test ◽  
Human Intelligence ◽  
Network Sampling ◽  
Dimensional Network ◽  
Network States ◽  
The Brain

AbstractDespite a century of research, it remains unclear whether human intelligence should be studied as one dominant, several major, or many distinct abilities, and how such abilities relate to the functional organisation of the brain. Here, we combine psychometric and machine learning methods to examine in a data-driven manner how factor structure and individual variability in cognitive-task performance relate to dynamic-network connectomics. We report that 12 sub-tasks from an established intelligence test can be accurately multi-way classified (74%, chance 8.3%) based on the network states that they evoke. The proximities of the tasks in behavioural-psychometric space correlate with the similarities of their network states. Furthermore, the network states were more accurately classified for higher relative to lower performing individuals. These results suggest that the human brain uses a high-dimensional network-sampling mechanism to flexibly code for diverse cognitive tasks. Population variability in intelligence test performance relates to the fidelity of expression of these task-optimised network states.

A Comparative study on secure routing algorithms SAODV and A-SAODV in Mobile AdHoc Networks (MANET) – The Enhancements of AODV

2012 ◽  
Vol 3 (2) ◽  
pp. 419-423
Author(s):  
JARUPULA RAJESHWAR ◽  
Dr G NARSIMHA
Keyword(s):  
Routing Protocol ◽  
Routing Protocols ◽  
Dynamic Network ◽  
Secure Routing ◽  
Mobile Adhoc Networks ◽  
Security Challenges ◽  
Basic Protocol ◽  
Freely Moving ◽  
Adhoc Networks ◽  
Secure Routing Protocols

A freely moving nodes forming as group to communicate among themselves are called as Mobile AdHoc Networks (MANET). Many applications are choosing this MANET for effective commutation due to its flexible nature in forming a network. But due to its openness characteristics it is posing many security challenges. As it has highly dynamic network topology security for routing is playing a major role. We have very good routing protocols for route discovery as well as for transporting data packers but most of them lack the feature of security like AODV. In this paper we are studying the basic protocol AODV and identify how it can be made secure. We are studying a protocol S-AODV which is a security extension of AODV which is called Secure AODV (S-AODV) and we are studying enhanced version of S-AODV routing protocol a Adaptive Secure AODV (A-SAODV). Finally we have described about the parameter to be taken for performance evaluation of different secure routing protocols

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