Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy

Ekhlasi, Ali; Nasrabadi, Ali Motie; Mohammadi, Mohammad Reza

doi:10.1007/s11571-021-09680-3

Cited by 34 publications

(19 citation statements)

References 45 publications

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“…PTE is a measure for determining causal relationships between pairs of time series that can indicate the direction and strength of information transfer between two signals. In other articles, directed PTE (dPTE) was used to examine differences in connectivity between brain regions in healthy children and children with ADHD ( 26 ) and to classify the two groups ( 27 ). Due to limitation of dPTE in determining only one value as the information transferred between time series, in the connectivity matrix (formed by the dPTE), the upper part of the triangle and the lower part of the triangle of the matrix do not provide more information ( 39 ).…”

Section: Discussionmentioning

confidence: 99%

“…Statistical testing showed that the areas related to this electrode had more power in transmitting information in the healthy group compared to the ADHD group, which can probably be due to receiving more information in the prefrontal and posterior regions. Previous studies using various modalities such as fMRI and EEG have also reported the decreased function of brain networks in the prefrontal and frontal regions (often in the beta band) of the brain ( 6 - 10 , 26 ).…”

Section: Discussionmentioning

confidence: 99%

“…Due to the use of instantaneous phase of time series, PTE is more robust to noise and linear mixing ( 25 ). Recently, studies were conducted on the causal relationships between EEG signals of healthy children and children with ADHD based on directed PTE (dPTE) ( 26 , 27 ). The dPTE is the normalized value of the PTE estimate introduced by Hillebrand ( 28 ).…”

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confidence: 99%

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Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy

Ekhlasi

Nasrabadi

Mohammadi

2021

IJPS

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Objective: This study aimed to investigate differences in brain networks between healthy children and children with attention deficit hyperactivity disorder (ADHD) during an attention test. Method: To fulfill this, we constructed weighted directed graphs based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children with the same age. Nodes of graphs were 19 EEG electrodes, and the edges were phase transfer entropy (PTE) between each pair of electrodes. PTE is a measure for directed connectivity that determines the effective relationship between signals in linear and nonlinear coupling. Connectivity graphs of each sample were constructed using PTE in the five frequency bands as follows: delta, theta, alpha, beta, and gamma. To investigate the differences in connectivity strength of each node after the sparsification process with two values (0.5 and 0.25), the permutation statistical test was used with the statistical significance level of p<0.01. Results: The results indicate stronger inter-regional connectivity in the prefrontal brain regions of the control group compared to the ADHD group. However, the strength of inter-regional connectivity in the central regions of the ADHD group was higher. A comparison of the prefrontal regions between the two groups revealed that the areas of the Fp1 electrode (left prefrontal) in healthy individuals play stronger transmission roles. Conclusion: Our research can provide new insights into the strength and direction of connectivity in ADHD and healthy individuals during an attention task.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy

Ekhlasi

Nasrabadi

Mohammadi

2021

IJPS

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“…However, insufficient investigations committed to reveal and characterize the asymmetries of cortical information flow in depressed patients during sleep, the topic of which was considered to provide valuable information for the abnormal cerebral function in patients. Moreover, previous studies have tried to explore different inter-regional features to quantify different patterns of cortical information flow, such as left–right index and anterior–posterior rate ( Zhou et al, 2020 ; Ekhlasi et al, 2021 ; Pan et al, 2021 ). To discover the difference in sleep-dependent information flow patterns in patients, analysis on various EC asymmetry patterns was included in this research.…”

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confidence: 99%

Sleep-Dependent Anomalous Cortical Information Interaction in Patients With Depression

et al. 2022

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Depression is a prevalent mental illness with high morbidity and is considered the main cause of disability worldwide. Brain activity while sleeping is reported to be affected by such mental illness. To explore the change of cortical information flow during sleep in depressed patients, a delay symbolic phase transfer entropy of scalp electroencephalography signals was used to measure effective connectivity between cortical regions in various frequency bands and sleep stages. The patient group and the control group shared similar patterns of information flow between channels during sleep. Obvious information flows to the left hemisphere and to the anterior cortex were found. Moreover, the occiput tended to be the information driver, whereas the frontal regions played the role of the receiver, and the right hemispheric regions showed a stronger information drive than the left ones. Compared with healthy controls, such directional tendencies in information flow and the definiteness of role division in cortical regions were both weakened in patients in most frequency bands and sleep stages, but the beta band during the N1 stage was an exception. The computable sleep-dependent cortical interaction may provide clues to characterize cortical abnormalities in depressed patients and should be helpful for the diagnosis of depression.

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“…Karmaşıklık parametresi ise işaretin sinüs işaretinden sapmasının bir ölçüsü olup, eşitlik 16 kullanılarak elde edilmektedir. işaretin ikinci dereceden türevini ifade etmektedir[25],[26].…”

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Performance Analysis of Robust Local Mean Decomposition and Empirical Mode Decomposition Methods in the Detection of Epilepsy

ÇATALKAYA¹,

HAZMAN²,

TURTUROVA³

et al. 2022

EJOSAT

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Electroencephalography (EEG) data, which provides information about the electrical activity of the brain, are widely used in the diagnosis of neurological diseases, EEG signals also provide important information in the detection of epilepsy, which is one of the diseases affecting approximately 1% of the world’s population. In this study, it was aimed to detect the epileptic seizure before the seizure by using EEG signals. For this purpose, after preprocessing steps were performed by using EEG signals in different situations from epilepsy and healthy individuals, features were extracted from EEG signals from subband signals obtained by using Robust Local Mean Decomposition (RLMD) and Empirical Mode Decomposition (AKA) methods. Classification studies were carried out with the obtained features and Artificial Neural Networks (ANN). In line with the studies, the classification results of the different states of the EEG signals were revealed using the performance parameters of accuracy, sensitivity, specificity, precision and f1 score.

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Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy

Cited by 34 publications

References 45 publications

Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy

Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy

Sleep-Dependent Anomalous Cortical Information Interaction in Patients With Depression

Performance Analysis of Robust Local Mean Decomposition and Empirical Mode Decomposition Methods in the Detection of Epilepsy

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