Directed Connectivity Analysis of Functional Brain Networks during Cognitive Activity Using Transfer Entropy

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

doi:10.1007/s11063-016-9506-1

Cited by 35 publications

(18 citation statements)

References 32 publications

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“…The cluster-based permutation test was then computed to look for differences within the adjacency matrices [ 22 , 38 , 63 ]. The results are visualized within the cortex using EEGNET version 1 [ 64 ]; where statistically significant results are illustrated as red dots representing the brain region and connecting lines representing statistical dependencies from the functional connectivity metrics between brain regions [ 64 , 65 ].…”

Section: Methodsmentioning

confidence: 99%

Functional Connectivity Analysis on Resting-State Electroencephalography Signals Following Chiropractic Spinal Manipulation in Stroke Patients

et al. 2020

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Stroke impairments often present as cognitive and motor deficits, leading to a decline in quality of life. Recovery strategy and mechanisms, such as neuroplasticity, are important factors, as these can help improve the effectiveness of rehabilitation. The present study investigated chiropractic spinal manipulation (SM) and its effects on resting-state functional connectivity in 24 subacute to chronic stroke patients monitored by electroencephalography (EEG). Functional connectivity of both linear and non-linear coupling was estimated by coherence and phase lag index (PLI), respectively. Non-parametric cluster-based permutation tests were used to assess the statistical significance of the changes in functional connectivity following SM. Results showed a significant increase in functional connectivity from the PLI metric in the alpha band within the default mode network (DMN). The functional connectivity between the posterior cingulate cortex and parahippocampal regions increased following SM, t (23) = 10.45, p = 0.005. No significant changes occurred following the sham control procedure. These findings suggest that SM may alter functional connectivity in the brain of stroke patients and highlights the potential of EEG for monitoring neuroplastic changes following SM. Furthermore, the altered connectivity was observed between areas which may be affected by factors such as decreased pain perception, episodic memory, navigation, and space representation in the brain. However, these factors were not directly monitored in this study. Therefore, further research is needed to elucidate the underlying mechanisms and clinical significance of the observed changes.

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Section: Methodsmentioning

confidence: 99%

Functional Connectivity Analysis on Resting-State Electroencephalography Signals Following Chiropractic Spinal Manipulation in Stroke Patients

et al. 2020

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“…Eventually, dominant influences of the distribution of estimated strengths of interaction on network properties of interest [192] can be minimized by assigning weights to the edges using ranks of the entries of W [193]. Directed functional brain networks have been investigated only rarely (see, e.g., [194][195][196]). Expanding a binary network to a directed binary network appears intuitive.…”

Section: From Pairwise Interactions To Functional Brain Networkmentioning

confidence: 99%

Capturing time-varying brain dynamics

Lehnertz

Geier

Rings

et al. 2017

EPJ Nonlinear Biomed. Phys.

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The human brain is a complex network of interacting nonstationary subsystems, whose complicated spatial-temporal dynamics is still poorly understood. Deeper insights can be gained from recent improvements of time-series-analysis techniques to assess strength and direction of interactions together with methodologies for deriving and characterizing evolving networks from empirical time series. We here review these developments, and by taking the example of evolving epileptic brain networks, we discuss the progress that has been made in capturing and understanding brain dynamics that varies on time scales ranging from seconds to years.

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“…The complex nature of the brain makes its non-linear causal dynamics unknown, and how the brain matches its rhythm as well as its metabolic processes and a causal relationship is still under investigation. The brain might be attacked with many psychosomatic diseases such as schizophrenia (SCZ), leading to deteriorated brain network, which eventually affects its cognitive functions (Shovon et al, 2017;Li et al, 2018). Researchers have explored the EEG non-linearity in multiple psychiatric disorders, for example, in epileptic patients probably due to low dimensional chaos during a seizure (Lee et al, 2001;Henderson et al, 2011;Liu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Even though much has been achieved with the GCA, a different data-driven approach which involves information theoretic measures like Transfer entropy (TE) may play a critical role in elucidating the effective connectivity of non-linear complex systems that the GCA may fail to unearth (Schreiber, 2006;Madulara et al, 2012;Dejman et al, 2017). Mathematically, the TE uses its entropy to quantitatively infer the coupling strength between two variables (Liu and Aviyente, 2012;Shovon et al, 2017) and has the potential for capturing both the linear and non-linear causal interactions effectively.…”

Section: Introductionmentioning

confidence: 99%

Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy

Harmah

et al. 2020

Front. Comput. Neurosci.

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People living with schizophrenia (SCZ) experience severe brain network deterioration. The brain is constantly fizzling with non-linear causal activities measured by electroencephalogram (EEG) and despite the variety of effective connectivity methods, only few approaches can quantify the direct non-linear causal interactions. To circumvent this problem, we are motivated to quantitatively measure the effective connectivity by multivariate transfer entropy (MTE) which has been demonstrated to be able to capture both linear and non-linear causal relationships effectively. In this work, we propose to construct the EEG effective network by MTE and further compare its performance with the Granger causal analysis (GCA) and Bivariate transfer entropy (BVTE). The simulation results quantitatively show that MTE outperformed GCA and BVTE under varied signal-to-noise conditions, edges recovered, sensitivity, and specificity. Moreover, its applications to the P300 task EEG of healthy controls (HC) and SCZ patients further clearly show the deteriorated network interactions of SCZ, compared to that of the HC. The MTE provides a novel tool to potentially deepen our knowledge of the brain network deterioration of the SCZ.

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Directed Connectivity Analysis of Functional Brain Networks during Cognitive Activity Using Transfer Entropy

Cited by 35 publications

References 32 publications

Functional Connectivity Analysis on Resting-State Electroencephalography Signals Following Chiropractic Spinal Manipulation in Stroke Patients

Functional Connectivity Analysis on Resting-State Electroencephalography Signals Following Chiropractic Spinal Manipulation in Stroke Patients

Capturing time-varying brain dynamics

Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy

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