AI Article Synopsis
- The study focuses on analyzing effective connectivity in children with ADHD using a novel method called nCREANN, which estimates both linear and nonlinear connectivity from EEG signals.
- The research involves comparing ADHD children with Typically Developing (TD) peers during a visual attention task, revealing distinct connectivity patterns between groups.
- Findings indicate reduced connectivity in certain brain regions for ADHD children and highlight nCREANN's potential for accurately classifying neurological conditions, achieving a 99% classification accuracy.
Article Abstract
Background: Analysis of effective connectivity among brain regions is an important key to decipher the mechanisms underlying neural disorders such as Attention Deficit Hyperactivity Disorder (ADHD). We previously introduced a new method, called nCREANN (nonlinear Causal Relationship Estimation by Artificial Neural Network), for estimating linear and nonlinear components of effective connectivity, and provided novel findings about effective connectivity of EEG signals of children with autism. Using the nCREANN method in the present study, we assessed effective connectivity patterns of ADHD children based on their EEG signals recorded during a visual attention task, and compared them with the aged-matched Typically Developing (TD) subjects.
Method: In addition to the nCREANN method for estimating linear and nonlinear aspects of effective connectivity, the direct Directed Transfer Function (dDTF) was utilized to extract the spectral information of connectivity patterns.
Results: The dDTF results did not suggest a specific frequency band for distinguishing between the two groups, and different patterns of effective connectivity were observed in all bands. Both nCREANN and dDTF methods showed decreased connectivity between temporal/frontal and temporal/occipital regions, and increased connection between frontal/parietal regions in ADHDs than TDs. Furthermore, the nCREANN results showed more left-lateralized connections in ADHDs compared to the symmetric bilateral inter-hemispheric interactions in TDs. In addition, by fusion of linear and nonlinear connectivity measures of nCREANN method, we achieved an accuracy of 99% in classification of the two groups.
Conclusion: These findings emphasize the capability of nCREANN method to investigate the brain functioning of neural disorders and its strength in preciously distinguish between healthy and disordered subjects.
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| http://dx.doi.org/10.1016/j.compbiomed.2022.105791 | DOI Listing |
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