Investigating the discrimination of linear and nonlinear effective connectivity patterns of EEG signals in children with Attention-Deficit/Hyperactivity Disorder and Typically Developing children

Talebi, Nasibeh; Nasrabadi, Ali Motie

doi:10.1016/j.compbiomed.2022.105791

Cited by 16 publications

(8 citation statements)

References 38 publications

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“…Modified from DTF, dDTF estimates the direct causal relationship only between the given pair of channels, and dDTF makes no assumptions about the underlying neural mechanism (Korzeniewska et al, 2003). dDTF has been used for effective connectivity estimation in many neural and cognitive studies (Bagherzadeh et al, 2022; Ghahari et al, 2020; Talebi & Motie Nasrabadi, 2022).…”

Section: Discussionmentioning

confidence: 99%

Enhanced electroencephalography effective connectivity in frontal low‐gamma band correlates of emotional regulation after mindfulness training

Huang

et al. 2023

J of Neuroscience Research

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Practicing mindfulness, focusing attention on the internal and external experiences occurring in the present moment with open and nonjudgement stance, can lead to the development of emotional regulation skills. Yet, the effective connectivity of brain regions during mindfulness has been largely unexplored. Studies have shown that mindfulness practice promotes functional connectivity in practitioners, potentially due to improved emotional regulation abilities and increased connectivity in the lateral prefrontal areas. To examine the changes in effective connectivity due to mindfulness training, we analyzed electroencephalogram (EEG) signals taken before and after mindfulness training, focusing on training‐related effective connectivity changes in the frontal area. The mindfulness training group participated in an 8‐week mindfulness‐based stress reduction (MBSR) program. The control group did not take part. Regardless of the specific mindfulness practice used, low‐gamma band effective connectivity increased globally after the mindfulness training. High‐beta band effective connectivity increased globally only during Breathing. Moreover, relatively higher outgoing effective connectivity strength was seen during Resting and Breathing and Body‐scan. By analyzing the changes in outgoing and incoming connectivity edges, both F7 and F8 exhibited strong parietal connectivity during Resting and Breathing. Multiple regression analysis revealed that the changes in effective connectivity of the right lateral prefrontal area predicted mindfulness and emotional regulation abilities. These results partially support the theory that the lateral prefrontal areas have top‐down modulatory control, as these areas have high outflow effective connectivity, implying that mindfulness training cultivates better emotional regulation.

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Section: Discussionmentioning

confidence: 99%

Enhanced electroencephalography effective connectivity in frontal low‐gamma band correlates of emotional regulation after mindfulness training

Huang

et al. 2023

J of Neuroscience Research

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“…Notably, the reduction in connectivity was particularly evident around the T5 region, which is closely linked to memory, language, and emotion processing. Previous studies have suggested that decreased connectivity in this region is associated with cognitive changes observed in conditions such as attention de cit hyperactivity disorder (ADHD) and mild cognitive impairment (MCI) 52,53 . Our ndings suggest that SARS-CoV-2 infection could potentially lead to noticeable cognitive decline.…”

Section: Spatial Biomarkers: Source Connectivity Analysismentioning

confidence: 99%

EEG Signatures of Cognitive Decline after Mild SARS- CoV-2 Infection: An Age-Dependent Study

Sun,

Chen

et al. 2024

Preprint

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Background and Objectives: Current research on the neurological impact of SARS-CoV-2 primarily focuses on the elderly or severely ill individuals. This study aims to explore the diverse neurological consequences of SARS-CoV-2 infection, with a particular focus on mildly affected children and adolescents. Methods A cohort study was conducted to collect pre- and post-infection resting-state EEG data from 185 participants and 181 structured questionnaires of long-term symptoms across four distinct age groups. The goal was to comprehensively evaluate the impact of SARS-CoV-2 infection on these different age demographics. The study analyzed EEG changes of SARS-CoV-2 by potential biomarkers across age groups using both spatial and temporal approaches. Results Spatial analysis indicated that children and adolescents exhibit smaller changes in brain network and microstate patterns post-infection, implying a milder cognitive impact. Sequential linear analyses showed that SARS-CoV-2 infection is associated with a marked rise in low-complexity, synchronized neural activity within low-frequency EEG bands. This is evidenced by a significant increase in Hjorth Activity within the theta band and Hjorth Mobility in the delta band. Sequential nonlinear analysis indicated a significant reduction in the Hurst exponent across all age groups, pointing to increased chaos and complexity within the cognitive system following infection. Furthermore, linear regression analysis based on questionnaires established a significant positive relationship between the magnitude of changes in these neural indicators and the persistence of long-term symptoms post-infection. Discussion The findings underscore the enduring neurological impacts of SARS-CoV-2 infection, marked by cognitive decline and increased EEG disarray. Although children and adolescents experienced milder effects, cognitive decline and heightened low-frequency electrical activity were evident. These observations might contribute to understanding potential anxiety, insomnia, and neurodevelopmental implications.

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“…the scalp using the standard 10-20 electrode placement system with a sampling frequency of 128Hz and a 16-bit quantizer [18].…”

Section: A Data Descriptionmentioning

confidence: 99%

“…This approach is based on the application of graph theory and various connectivity measures are investigated to be used as neural markers such as phase lag index (PLI), directed phase transfer entropy, etc. [15]- [18].…”

Section: Introductionmentioning

confidence: 99%

Automatic identification of ADHD children during visual attention task using variable length EEG

2023

1st International Conference on Modern and Advanced Research Icmar 2023

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neuropsychiatric disorders affect millions of people of all ages worldwide. Attention deficit/Hyperactivity Disorder (ADHD), a typical neurodevelopmental disorder, deteriorates the performance of children in family and school settings thereby, hindering typical brain development. ADHD children, in most cases, are predominantly inattentive. In this research, automatic identification of neuronal patterns in ADHD children undergoing visual continuous performance tasks (CPT) is used to successfully differentiate between ADHD and typically developing children. The proposed methodology utilizes wavelet packet decomposition to extract relative energy from different EEG sub-bands, namely-delta (0.5-4 Hz) theta (4-8 Hz), alpha (8-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz) and gamma (>30 Hz). The obtained feature from all bands is then passed to the Support Vector Machine (SVM) for the classification of children as normal and those with ADHD. The performance of the algorithm is assessed by following performance parameters, accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve. While testing the classification performance keeping the relative energy of individual bands in feature space, it is observed that between groups difference in normal and ADHD is much higher in high-frequency bands i.e., beta and gamma compared to low-frequency bands. However, all other bands are interacting features that performed well along with the relevant features (beta and gamma energy bands, here). The area under the curve obtained with a subject-independent approach and combined EEG sub-band's relative energy in feature space is found to be 0.99.

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Investigating the discrimination of linear and nonlinear effective connectivity patterns of EEG signals in children with Attention-Deficit/Hyperactivity Disorder and Typically Developing children

Cited by 16 publications

References 38 publications

Enhanced electroencephalography effective connectivity in frontal low‐gamma band correlates of emotional regulation after mindfulness training

Enhanced electroencephalography effective connectivity in frontal low‐gamma band correlates of emotional regulation after mindfulness training

EEG Signatures of Cognitive Decline after Mild SARS- CoV-2 Infection: An Age-Dependent Study

Automatic identification of ADHD children during visual attention task using variable length EEG

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