Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy
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Abstract
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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27. Ekhlasi A, Nasrabadi AM, Mohammadi M. Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals. Frontiers in Biomedical Technologies. 2021;8(2):115-22.
28. Hillebrand A, Tewarie P, van Dellen E, Yu M, Carbo EW, Douw L, et al. Direction of information flow in large-scale resting-state networks is frequency-dependent. Proc Natl Acad Sci U S A. 2016;113(14):3867-72.
29. Friston KJ. Functional and effective connectivity in neuroimaging: a synthesis. Human brain mapping. 1994;2(1‐2):56-78.
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31. Goldenberg D, Galván A. The use of functional and effective connectivity techniques to understand the developing brain. Dev Cogn Neurosci. 2015;12:155-64.
32. Allahverdy A, Moghadam AK, Mohammadi MR, Nasrabadi AM. Detecting adhd children using the attention continuity as nonlinear feature of eeg. Frontiers in Biomedical Technologies. 2016;3(1-2):28-33.
33. Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. EEG classification of ADHD and normal children using non-linear features and neural network. Biomedical Engineering Letters. 2016;6(2):66-73.
34. American Psychiatric Association. Diagnostic and Statistical 1. Manual of Mental Disorders, Fourth Edition, Text Revision. 1987.
35. Nasrabadi AM, Allahverdy A, Samavati M, Mohammadi MR. EEG data for ADHD / Control children 2020 [Available from: http://dx.doi.org/10.21227/rzfh-zn36.]
36. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9-21.
37. Engels MMA, Yu M, Stam CJ, Gouw AA, van der Flier WM, Scheltens P, et al. Directional information flow in patients with Alzheimer's disease. A source-space resting-state MEG study. Neuroimage Clin. 2017;15:673-81.
38. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological). 1995;57(1):289-300.
39. Demuru M, Gouw AA, Hillebrand A, Stam CJ, van Dijk BW, Scheltens P, et al. Functional and effective whole brain connectivity using magnetoencephalography to identify monozygotic twin pairs. Sci Rep. 2017;7(1):9685.
40. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008;6(7):e159.
2. Lenzi F, Cortese S, Harris J, Masi G. Pharmacotherapy of emotional dysregulation in adults with ADHD: A systematic review and meta-analysis. Neurosci Biobehav Rev. 2018;84:359-67.
3. Fonseca LC, Tedrus GM, Moraes C, Vicente Machado A, Almeida MP, Oliveira DO. Epileptiform abnormalities and quantitative EEG in children with attention-deficit/hyperactivity disorder. Arq Neuropsiquiatr. 2008;66(3a):462-7.
4. Mohammadi MR, Zarafshan H, Khaleghi A, Ahmadi N, Hooshyari Z, Mostafavi SA, et al. Prevalence of ADHD and Its Comorbidities in a Population-Based Sample. J Atten Disord. 2021;25(8):1058-67.
5. Sayal K, Prasad V, Daley D, Ford T, Coghill D. ADHD in children and young people: prevalence, care pathways, and service provision. Lancet Psychiatry. 2018;5(2):175-86.
6. Nestler EJ, Hyman SE, Malenka RC. Molecular neuropharmacology: a foundation for clinical neuroscience: McGraw-Hill Medical; 2001.
7. Krain AL, Castellanos FX. Brain development and ADHD. Clin Psychol Rev. 2006;26(4):433-44.
8. Rubia K, Cubillo A, Smith AB, Woolley J, Heyman I, Brammer MJ. Disorder-specific dysfunction in right inferior prefrontal cortex during two inhibition tasks in boys with attention-deficit hyperactivity disorder compared to boys with obsessive-compulsive disorder. Hum Brain Mapp. 2010;31(2):287-99.
9. Rubia K, Halari R, Smith AB, Mohammad M, Scott S, Brammer MJ. Shared and disorder-specific prefrontal abnormalities in boys with pure attention-deficit/hyperactivity disorder compared to boys with pure CD during interference inhibition and attention allocation. J Child Psychol Psychiatry. 2009;50(6):669-78.
10. Booth JR, Burman DD, Meyer JR, Lei Z, Trommer BL, Davenport ND, et al. Larger deficits in brain networks for response inhibition than for visual selective attention in attention deficit hyperactivity disorder (ADHD). J Child Psychol Psychiatry. 2005;46(1):94-111.
11. Barry RJ, Clarke AR. Resting state brain oscillations and symptom profiles in attention deficit/hyperactivity disorder. Suppl Clin Neurophysiol. 2013;62:275-87.
12. Lenartowicz A, Loo SK. Use of EEG to diagnose ADHD. Curr Psychiatry Rep. 2014;16(11):498.
13. González JJ, Alba G, Mañas S, González A, Pereda E. Assessment of ADHD through electroencephalographic measures of functional connectivity. ADHD-New Dir. Diagnosis Treat. 2017:35-54.
14. Alvarez JA, Emory E. Executive function and the frontal lobes: a meta-analytic review. Neuropsychol Rev. 2006;16(1):17-42.
15. Price JL. Prefrontal cortical networks related to visceral function and mood. Ann N Y Acad Sci. 1999;877:383-96.
16. Sporns O. Brain connectivity. Scholarpedia. 2007;2(10):4695.
17. Ahmadlou M, Adeli H. Fuzzy synchronization likelihood with application to attention-deficit/hyperactivity disorder. Clin EEG Neurosci. 2011;42(1):6-13.
18. Ahmadlou M, Adeli H. Functional community analysis of brain: a new approach for EEG-based investigation of the brain pathology. Neuroimage. 2011;58(2):401-8.
19. Mazaheri A, Coffey-Corina S, Mangun GR, Bekker EM, Berry AS, Corbett BA. Functional disconnection of frontal cortex and visual cortex in attention-deficit/hyperactivity disorder. Biol Psychiatry. 2010;67(7):617-23.
20. Kiiski H, Rueda-Delgado LM, Bennett M, Knight R, Rai L, Roddy D, et al. Functional EEG connectivity is a neuromarker for adult attention deficit hyperactivity disorder symptoms. Clin Neurophysiol. 2020;131(1):330-42.
21. Furlong S, Cohen JR, Hopfinger J, Snyder J, Robertson MM, Sheridan MA. Resting-state EEG Connectivity in Young Children with ADHD. J Clin Child Adolesc Psychol. 2020:1-17.
22. Vicente R, Wibral M, Lindner M, Pipa G. Transfer entropy--a model-free measure of effective connectivity for the neurosciences. J Comput Neurosci. 2011;30(1):45-67.
23. Wibral M, Vicente R, Lindner M. Transfer entropy in neuroscience. Directed information measures in neuroscience: Springer; 2014. p. 3-36.
24. Schreiber T. Measuring information transfer. Phys Rev Lett. 2000;85(2):461-4.
25. Lobier M, Siebenhühner F, Palva S, Palva JM. Phase transfer entropy: a novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions. Neuroimage. 2014;85 Pt 2:853-72.
26. Ekhlasi A, Nasrabadi AM, Mohammadi MR. Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy. Cognitive Neurodynamics. 2021:1-12.
27. Ekhlasi A, Nasrabadi AM, Mohammadi M. Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals. Frontiers in Biomedical Technologies. 2021;8(2):115-22.
28. Hillebrand A, Tewarie P, van Dellen E, Yu M, Carbo EW, Douw L, et al. Direction of information flow in large-scale resting-state networks is frequency-dependent. Proc Natl Acad Sci U S A. 2016;113(14):3867-72.
29. Friston KJ. Functional and effective connectivity in neuroimaging: a synthesis. Human brain mapping. 1994;2(1‐2):56-78.
30. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059-69.
31. Goldenberg D, Galván A. The use of functional and effective connectivity techniques to understand the developing brain. Dev Cogn Neurosci. 2015;12:155-64.
32. Allahverdy A, Moghadam AK, Mohammadi MR, Nasrabadi AM. Detecting adhd children using the attention continuity as nonlinear feature of eeg. Frontiers in Biomedical Technologies. 2016;3(1-2):28-33.
33. Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. EEG classification of ADHD and normal children using non-linear features and neural network. Biomedical Engineering Letters. 2016;6(2):66-73.
34. American Psychiatric Association. Diagnostic and Statistical 1. Manual of Mental Disorders, Fourth Edition, Text Revision. 1987.
35. Nasrabadi AM, Allahverdy A, Samavati M, Mohammadi MR. EEG data for ADHD / Control children 2020 [Available from: http://dx.doi.org/10.21227/rzfh-zn36.]
36. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9-21.
37. Engels MMA, Yu M, Stam CJ, Gouw AA, van der Flier WM, Scheltens P, et al. Directional information flow in patients with Alzheimer's disease. A source-space resting-state MEG study. Neuroimage Clin. 2017;15:673-81.
38. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological). 1995;57(1):289-300.
39. Demuru M, Gouw AA, Hillebrand A, Stam CJ, van Dijk BW, Scheltens P, et al. Functional and effective whole brain connectivity using magnetoencephalography to identify monozygotic twin pairs. Sci Rep. 2017;7(1):9685.
40. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008;6(7):e159.
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| Issue | Vol 16 No 4 (2021) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijps.v16i4.7224 | |
| Keywords | ||
| Attention Deficit Hyperactivity Disorder (ADHD) Electroencephalography (EEG) Signal Processing | ||
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How to Cite
1.
Ekhlasi A, Motie Nasrabadi A, Mohammadi M. Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy. Iran J Psychiatry. 2021;16(4):374-382.
