Transfer Entropy and Information Flow Patterns in Functional Brain Networks during Cognitive Activity

Shovon, Md. Hedayetul Islam; Nandagopal, Nanda; Ramasamy, Vijayalakshmi; Du, Jia Tina; Cocks, Bernadine

doi:10.1007/978-3-319-12637-1_1

Cited by 12 publications

(3 citation statements)

References 14 publications

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“…To model the effective connectivity, we used a directional measure called normalized transfer entropy (

), proposed by Shovon et al [ 30 ]. This measure builds upon the transfer entropy concept created by Schreiber [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, the finite size and nonstationarity of EEG data introduces uncertainty on the

measurement. To obtain a suitable estimate, Shovon et al [ 30 ] proposed two additional steps that increase accuracy. These two steps consist of subtracting the mean value of

from

(where

is a surrogate randomization of the Y signal) and normalizing the measure by the conditional entropy of

and

, where

is the joint probability of

and

.…”

Section: Methodsmentioning

confidence: 99%

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Connective Core Structures in Cognitive Networks: The Role of Hubs

Baltazar

Guinle

Caron

et al. 2019

Entropy

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Complex network analysis applied to the resting brain has shown that sets of highly interconnected networks with coherent activity may support a default mode of brain function within a global workspace. Perceptual processing of environmental stimuli induces architectural changes in network topology with higher specialized modules. Evidence shows that during cognitive tasks, network topology is reconfigured and information is broadcast from modular processors to a connective core, promoting efficient information integration. In this paper, we explored how the brain adapts its effective connectivity within the connective core and across behavioral states. We used complex network metrics to identify hubs and proposed a method of classification based on the effective connectivity patterns of information flow. Finally, we interpreted the role of the connective core and each type of hub on the network effectiveness. We also calculated the complexity of electroencephalography microstate sequences across different tasks. We observed that divergent hubs contribute significantly to the network effectiveness and that part of this contribution persists across behavioral states, forming an invariant structure. Moreover, we found that a large quantity of multiple types of hubs may be associated with transitions of functional networks.

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“…To model the effective connectivity, we used a directional measure called normalized transfer entropy (

), proposed by Shovon et al [ 30 ]. This measure builds upon the transfer entropy concept created by Schreiber [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, the finite size and nonstationarity of EEG data introduces uncertainty on the

measurement. To obtain a suitable estimate, Shovon et al [ 30 ] proposed two additional steps that increase accuracy. These two steps consist of subtracting the mean value of

from

(where

is a surrogate randomization of the Y signal) and normalizing the measure by the conditional entropy of

and

, where

is the joint probability of

and

.…”

Section: Methodsmentioning

confidence: 99%

Connective Core Structures in Cognitive Networks: The Role of Hubs

Baltazar

Guinle

Caron

et al. 2019

Entropy

View full text Add to dashboard Cite

show abstract

“…An EEG signal contains information from a complex and dense network of billions of interconnected neurons. Graph-based methods have been applied by researchers to successfully study these complex brain networks in recent years [3] and more specifically using both directional and undirected functional brain networks (FBN) [4,5]. In research, directional FBN is preferred because it provides more prominent topological features by estimating the direction of information transfer between nodes (EEG electrodes) and thereby enabling more detailed analysis [6].…”

Section: Introductionmentioning

confidence: 99%

Connectivity Analysis Using Functional Brain Networks to Evaluate Cognitive Activity during 3D Modelling

Baig

Kavakli

2019

Brain Sciences

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Modelling 3D objects in CAD software requires special skills which require a novice user to undergo a series of training exercises to obtain. To minimize the training time for a novice user, the user-dependent factors must be studied. we have presented a comparative analysis of novice/expert information flow patterns. We have used Normalized Transfer Entropy (NTE) and Electroencephalogram (EEG) to investigate the differences. The experiment was divided into three cognitive states i.e., rest, drawing, and manipulation. We applied classification algorithms on NTE matrices and graph theory measures to see the effectiveness of NTE. The results revealed that the experts show approximately the same cognitive activation in drawing and manipulation states, whereas for novices the brain activation is more in manipulation state than drawing state. The hemisphere- and lobe-wise analysis showed that expert users have developed an ability to control the information flow in various brain regions. On the other hand, novice users have shown a continuous increase in information flow activity in almost all regions when doing drawing and manipulation tasks. A classification accuracy of more than 90% was achieved with a simple K-nearest neighbors (k-NN) to classify novice and expert users. The results showed that the proposed technique can be used to develop adaptive 3D modelling systems.

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