Optimal Multivariate Transfer Entropy to Determine Differences in Short and Long-Range EEG Connectivity in Children with ADHD and Healthy Children
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Abstract
The investigation of brain connectivity using electroencephalogram (EEG) is a valuable method for studying mental disorders, such as Attention deficit/hyperactivity disorder (ADHD), and optimizing and developing measures of effective connectivity can provide new insights into differences in brain communication in such disorders. Multivariate transfer entropy (MuTE) is a measure of causal connectivity that quantifies the influence of multiple variables on each other in a system. In this study, the MuTE measure was modified by incorporating an interaction delay parameter in connectivity calculations to create a measure with self-prediction optimality, which we named . We applied to investigate EEG effective connectivity in healthy and ADHD children performing an attention task across five frequency bands and to compare brain connectivity differences between the two groups using statistical analysis. Our analysis revealed that children with ADHD exhibited excessive short-distance connections in all frequency bands while healthy children demonstrated stronger long-range connections in the alpha and gamma frequency bands. Moreover, excessive short-distance connectivity was observed in the delta and theta frequency bands in all brain regions, as well as in the alpha, beta, and gamma frequency bands between central and parietal regions in children with ADHD. These connectivity patterns may contribute to impaired attention functions by impeding effective information transmission and reducing information processing speed in the brains of children with ADHD. Our analysis presents a novel methodology for measuring effective connectivity and elucidates the differences in EEG brain connectivity between children with ADHD and healthy children.
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[3] A. O. Diaconescu et al., "Aberrant effective connectivity in schizophrenia patients during appetitive conditioning," Frontiers in human neuroscience, vol. 4, p. 239, 2011.
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[6] F. Salehi, M. Jaloli, R. Coben, and A. M. Nasrabadi, "Estimating brain effective connectivity from EEG signals of patients with autism disorder and healthy individuals by reducing volume conduction effect," Cognitive Neurodynamics, vol. 16, no. 3, pp. 519-529, 2022.
[7] N. Talebi and A. M. Nasrabadi, "Investigating the discrimination of linear and nonlinear effective connectivity patterns of EEG signals in children with Attention-Deficit/Hyperactivity Disorder and Typically Developing children," Computers in Biology and Medicine, vol. 148, p. 105791, 2022.
[8] A. Ekhlasi, A. M. Nasrabadi, and M. R. Mohammadi, "Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy," Cognitive Neurodynamics, vol. 15, no. 6, pp. 975-986, 2021.
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[10] A. Association, "Diagnostic and statistical manual of mental disorders (DSM-IV-TR), text revision," (No Title), 2000.
[11] W. H. Organization, The ICD-10 classification of mental and behavioural disorders: diagnostic criteria for research. World Health Organization, 1993.
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[13] R. Kessler et al., "The effects of temporally secondary co-morbid mental disorders on the associations of DSM-IV ADHD with adverse outcomes in the US National Comorbidity Survey Replication Adolescent Supplement (NCS-A)," Psychological medicine, vol. 44, no. 8, pp. 1779-1792, 2014.
[14] P. L. Nunez and R. Srinivasan, Electric fields of the brain: the neurophysics of EEG. Oxford University Press, USA, 2006.
[15] J. J. González, G. Alba, S. Mañas, A. González, and E. Pereda, "Assessment of ADHD through electroencephalographic measures of functional connectivity," ADHD-New Dir. Diagnosis Treat, pp. 35-54, 2017.
[16] R. J. Barry and A. R. Clarke, "Resting state brain oscillations and symptom profiles in attention deficit/hyperactivity disorder," in Supplements to clinical neurophysiology, vol. 62: Elsevier, 2013, pp. 275-287.
[17] R. J. Barry, A. R. Clarke, and S. J. Johnstone, "A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography," Clinical neurophysiology, vol. 114, no. 2, pp. 171-183, 2003.
[18] F. E. Dupuy, A. R. Clarke, R. J. Barry, R. McCarthy, and M. Selikowitz, "EEG coherence in children with attention-deficit/hyperactivity disorder: differences between good and poor responders to methylphenidate," Psychiatry Research, vol. 180, no. 2-3, pp. 114-119, 2010.
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[22] T. Schreiber, "Measuring information transfer," Physical review letters, vol. 85, no. 2, p. 461, 2000.
[23] A. K. Abbas, G. Azemi, S. Amiri, S. Ravanshadi, and A. Omidvarnia, "Effective connectivity in brain networks estimated using EEG signals is altered in children with ADHD," Computers in Biology and Medicine, vol. 134, p. 104515, 2021.
[24] A. Ekhlasi, A. M. Nasrabadi, and M. Mohammadi, "Classification of the children with ADHD and healthy children based on the directed phase transfer entropy of EEG signals," Frontiers in Biomedical Technologies, vol. 8, no. 2, pp. 115-122, 2021.
[25] A. Ekhlasi, A. M. Nasrabadi, and M. Mohammadi, "Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer," Biomedical Engineering/Biomedizinische Technik, vol. 68, no. 2, pp. 133-146, 2023.
[26] A. Montalto, L. Faes, and D. Marinazzo, "MuTE: a MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy," PloS one, vol. 9, no. 10, p. e109462, 2014.
[27] A. M. Nasrabadi, A. Allahverdy, M. Samavati, and M. R. Mohammadi, "EEG data for ADHD/Control children," IEEE Dataport, 2020.
[28] M. Wibral, R. Vicente, and J. T. Lizier, Directed information measures in neuroscience. Springer, 2014.
[29] N. Wiener, "The theory of prediction," Modern mathematics for engineers, 1956.
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[31] M. Paluš, V. Komárek, Z. Hrnčíř, and K. Štěrbová, "Synchronization as adjustment of information rates: Detection from bivariate time series," Physical Review E, vol. 63, no. 4, p. 046211, 2001.
[32] M. Wibral et al., "Measuring information-transfer delays," PloS one, vol. 8, no. 2, p. e55809, 2013.
[33] A. Khadem, G.-A. Hossein-Zadeh, and A. Khorrami, "Long-range reduced predictive information transfers of autistic youths in EEG sensor-space during face processing," Brain topography, vol. 29, pp. 283-295, 2016.
[34] A. Papana, C. Kyrtsou, D. Kugiumtzis, and C. Diks, "Detecting causality in non-stationary time series using partial symbolic transfer entropy: Evidence in financial data," Computational economics, vol. 47, no. 3, pp. 341-365, 2016.
[35] D. Kugiumtzis, "Partial transfer entropy on rank vectors," The European Physical Journal Special Topics, vol. 222, no. 2, pp. 401-420, 2013.
[36] A. Ekhlasi, A. M. Nasrabadi, and M. R. Mohammadi, "Improving Transfer Entropy and Partial Transfer Entropy for Relative Detection of Effective Connectivity Strength between Time Series," Available at SSRN 4388493.
[37] G. Gómez-Herrero, W. Wu, K. Rutanen, M. C. Soriano, G. Pipa, and R. Vicente, "Assessing coupling dynamics from an ensemble of time series," Entropy, vol. 17, no. 4, pp. 1958-1970, 2015.
[38] A. Kraskov, H. Stögbauer, and P. Grassberger, "Estimating mutual information," Physical review E, vol. 69, no. 6, p. 066138, 2004.
[39] D. W. Hahs and S. D. Pethel, "Distinguishing anticipation from causality: Anticipatory bias in the estimation of information flow," Physical review letters, vol. 107, no. 12, p. 128701, 2011.
[40] D. Marinazzo, M. Pellicoro, and S. Stramaglia, "Causal information approach to partial conditioning in multivariate data sets," Computational and mathematical methods in medicine, vol. 2012, 2012.
[41] M. Ragwitz and H. Kantz, "Markov models from data by simple nonlinear time series predictors in delay embedding spaces," Physical Review E, vol. 65, no. 5, p. 056201, 2002.
[42] L. Cao, "Practical method for determining the minimum embedding dimension of a scalar time series," Physica D: Nonlinear Phenomena, vol. 110, no. 1-2, pp. 43-50, 1997.
[43] N. Luo et al., "Aberrant brain dynamics and spectral power in children with ADHD and its subtypes," European Child & Adolescent Psychiatry, pp. 1-12, 2022.
[44] M. Shen, P. Wen, B. Song, and Y. Li, "ADHD children identification based on EEG using effective connectivity techniques," in Health Information Science: 10th International Conference, HIS 2021, Melbourne, VIC, Australia, October 25–28, 2021, Proceedings 10, 2021, pp. 71-81: Springer.
[45] M. Ahmadlou and H. Adeli, "Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD," Clinical EEG and neuroscience, vol. 41, no. 1, pp. 1-10, 2010.
[46] M. Ahmadlou and H. Adeli, "Functional community analysis of brain: A new approach for EEG-based investigation of the brain pathology," Neuroimage, vol. 58, no. 2, pp. 401-408, 2011.
[47] R. Beare et al., "Altered structural connectivity in ADHD: a network based analysis," Brain imaging and behavior, vol. 11, no. 3, pp. 846-858, 2017.
[48] T. R. Henry and J. R. Cohen, "Dysfunctional brain network organization in neurodevelopmental disorders," in Connectomics: Elsevier, 2019, pp. 83-100.
[49] A. Khadmaoui et al., "MEG analysis of neural interactions in attention-deficit/hyperactivity disorder," Computational intelligence and neuroscience, vol. 2016, 2016.
[2] J. Huang, J.-Y. Jung, and C. S. Nam, "Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study," Frontiers in Human Neuroscience, 2022.
[3] A. O. Diaconescu et al., "Aberrant effective connectivity in schizophrenia patients during appetitive conditioning," Frontiers in human neuroscience, vol. 4, p. 239, 2011.
[4] N. Moharamzadeh and A. Motie Nasrabadi, "A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection," Biomedical Engineering/Biomedizinische Technik, vol. 67, no. 1, pp. 19-32, 2022.
[5] X. Geng et al., "Abnormalities of EEG Functional Connectivity and Effective Connectivity in Children with Autism Spectrum Disorder," Brain Sciences, vol. 13, no. 1, p. 130, 2023.
[6] F. Salehi, M. Jaloli, R. Coben, and A. M. Nasrabadi, "Estimating brain effective connectivity from EEG signals of patients with autism disorder and healthy individuals by reducing volume conduction effect," Cognitive Neurodynamics, vol. 16, no. 3, pp. 519-529, 2022.
[7] N. Talebi and A. M. Nasrabadi, "Investigating the discrimination of linear and nonlinear effective connectivity patterns of EEG signals in children with Attention-Deficit/Hyperactivity Disorder and Typically Developing children," Computers in Biology and Medicine, vol. 148, p. 105791, 2022.
[8] A. Ekhlasi, A. M. Nasrabadi, and M. R. Mohammadi, "Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy," Cognitive Neurodynamics, vol. 15, no. 6, pp. 975-986, 2021.
[9] P. H. Wender, "Attention-deficit hyperactivity disorder in adults," Psychiatric Clinics of North America, vol. 21, no. 4, pp. 761-774, 1998.
[10] A. Association, "Diagnostic and statistical manual of mental disorders (DSM-IV-TR), text revision," (No Title), 2000.
[11] W. H. Organization, The ICD-10 classification of mental and behavioural disorders: diagnostic criteria for research. World Health Organization, 1993.
[12] P. Asherson, J. Buitelaar, S. V. Faraone, and L. A. Rohde, "Adult attention-deficit hyperactivity disorder: key conceptual issues," The Lancet Psychiatry, vol. 3, no. 6, pp. 568-578, 2016.
[13] R. Kessler et al., "The effects of temporally secondary co-morbid mental disorders on the associations of DSM-IV ADHD with adverse outcomes in the US National Comorbidity Survey Replication Adolescent Supplement (NCS-A)," Psychological medicine, vol. 44, no. 8, pp. 1779-1792, 2014.
[14] P. L. Nunez and R. Srinivasan, Electric fields of the brain: the neurophysics of EEG. Oxford University Press, USA, 2006.
[15] J. J. González, G. Alba, S. Mañas, A. González, and E. Pereda, "Assessment of ADHD through electroencephalographic measures of functional connectivity," ADHD-New Dir. Diagnosis Treat, pp. 35-54, 2017.
[16] R. J. Barry and A. R. Clarke, "Resting state brain oscillations and symptom profiles in attention deficit/hyperactivity disorder," in Supplements to clinical neurophysiology, vol. 62: Elsevier, 2013, pp. 275-287.
[17] R. J. Barry, A. R. Clarke, and S. J. Johnstone, "A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography," Clinical neurophysiology, vol. 114, no. 2, pp. 171-183, 2003.
[18] F. E. Dupuy, A. R. Clarke, R. J. Barry, R. McCarthy, and M. Selikowitz, "EEG coherence in children with attention-deficit/hyperactivity disorder: differences between good and poor responders to methylphenidate," Psychiatry Research, vol. 180, no. 2-3, pp. 114-119, 2010.
[19] C. Sripada, D. Kessler, Y. Fang, R. C. Welsh, K. Prem Kumar, and M. Angstadt, "Disrupted network architecture of the resting brain in attention‐deficit/hyperactivity disorder," Human brain mapping, vol. 35, no. 9, pp. 4693-4705, 2014.
[20] R. J. Chabot, H. Merkin, L. M. Wood, T. L. Davenport, and G. Serfontein, "Sensitivity and specificity of QEEG in children with attention deficit or specific developmental learning disorders," Clinical Electroencephalography, vol. 27, no. 1, pp. 26-34, 1996.
[21] R. J. Chabot and G. Serfontein, "Quantitative electroencephalographic profiles of children with attention deficit disorder," Biological psychiatry, vol. 40, no. 10, pp. 951-963, 1996.
[22] T. Schreiber, "Measuring information transfer," Physical review letters, vol. 85, no. 2, p. 461, 2000.
[23] A. K. Abbas, G. Azemi, S. Amiri, S. Ravanshadi, and A. Omidvarnia, "Effective connectivity in brain networks estimated using EEG signals is altered in children with ADHD," Computers in Biology and Medicine, vol. 134, p. 104515, 2021.
[24] A. Ekhlasi, A. M. Nasrabadi, and M. Mohammadi, "Classification of the children with ADHD and healthy children based on the directed phase transfer entropy of EEG signals," Frontiers in Biomedical Technologies, vol. 8, no. 2, pp. 115-122, 2021.
[25] A. Ekhlasi, A. M. Nasrabadi, and M. Mohammadi, "Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer," Biomedical Engineering/Biomedizinische Technik, vol. 68, no. 2, pp. 133-146, 2023.
[26] A. Montalto, L. Faes, and D. Marinazzo, "MuTE: a MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy," PloS one, vol. 9, no. 10, p. e109462, 2014.
[27] A. M. Nasrabadi, A. Allahverdy, M. Samavati, and M. R. Mohammadi, "EEG data for ADHD/Control children," IEEE Dataport, 2020.
[28] M. Wibral, R. Vicente, and J. T. Lizier, Directed information measures in neuroscience. Springer, 2014.
[29] N. Wiener, "The theory of prediction," Modern mathematics for engineers, 1956.
[30] K. Hlaváčková-Schindler, M. Paluš, M. Vejmelka, and J. Bhattacharya, "Causality detection based on information-theoretic approaches in time series analysis," Physics Reports, vol. 441, no. 1, pp. 1-46, 2007.
[31] M. Paluš, V. Komárek, Z. Hrnčíř, and K. Štěrbová, "Synchronization as adjustment of information rates: Detection from bivariate time series," Physical Review E, vol. 63, no. 4, p. 046211, 2001.
[32] M. Wibral et al., "Measuring information-transfer delays," PloS one, vol. 8, no. 2, p. e55809, 2013.
[33] A. Khadem, G.-A. Hossein-Zadeh, and A. Khorrami, "Long-range reduced predictive information transfers of autistic youths in EEG sensor-space during face processing," Brain topography, vol. 29, pp. 283-295, 2016.
[34] A. Papana, C. Kyrtsou, D. Kugiumtzis, and C. Diks, "Detecting causality in non-stationary time series using partial symbolic transfer entropy: Evidence in financial data," Computational economics, vol. 47, no. 3, pp. 341-365, 2016.
[35] D. Kugiumtzis, "Partial transfer entropy on rank vectors," The European Physical Journal Special Topics, vol. 222, no. 2, pp. 401-420, 2013.
[36] A. Ekhlasi, A. M. Nasrabadi, and M. R. Mohammadi, "Improving Transfer Entropy and Partial Transfer Entropy for Relative Detection of Effective Connectivity Strength between Time Series," Available at SSRN 4388493.
[37] G. Gómez-Herrero, W. Wu, K. Rutanen, M. C. Soriano, G. Pipa, and R. Vicente, "Assessing coupling dynamics from an ensemble of time series," Entropy, vol. 17, no. 4, pp. 1958-1970, 2015.
[38] A. Kraskov, H. Stögbauer, and P. Grassberger, "Estimating mutual information," Physical review E, vol. 69, no. 6, p. 066138, 2004.
[39] D. W. Hahs and S. D. Pethel, "Distinguishing anticipation from causality: Anticipatory bias in the estimation of information flow," Physical review letters, vol. 107, no. 12, p. 128701, 2011.
[40] D. Marinazzo, M. Pellicoro, and S. Stramaglia, "Causal information approach to partial conditioning in multivariate data sets," Computational and mathematical methods in medicine, vol. 2012, 2012.
[41] M. Ragwitz and H. Kantz, "Markov models from data by simple nonlinear time series predictors in delay embedding spaces," Physical Review E, vol. 65, no. 5, p. 056201, 2002.
[42] L. Cao, "Practical method for determining the minimum embedding dimension of a scalar time series," Physica D: Nonlinear Phenomena, vol. 110, no. 1-2, pp. 43-50, 1997.
[43] N. Luo et al., "Aberrant brain dynamics and spectral power in children with ADHD and its subtypes," European Child & Adolescent Psychiatry, pp. 1-12, 2022.
[44] M. Shen, P. Wen, B. Song, and Y. Li, "ADHD children identification based on EEG using effective connectivity techniques," in Health Information Science: 10th International Conference, HIS 2021, Melbourne, VIC, Australia, October 25–28, 2021, Proceedings 10, 2021, pp. 71-81: Springer.
[45] M. Ahmadlou and H. Adeli, "Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD," Clinical EEG and neuroscience, vol. 41, no. 1, pp. 1-10, 2010.
[46] M. Ahmadlou and H. Adeli, "Functional community analysis of brain: A new approach for EEG-based investigation of the brain pathology," Neuroimage, vol. 58, no. 2, pp. 401-408, 2011.
[47] R. Beare et al., "Altered structural connectivity in ADHD: a network based analysis," Brain imaging and behavior, vol. 11, no. 3, pp. 846-858, 2017.
[48] T. R. Henry and J. R. Cohen, "Dysfunctional brain network organization in neurodevelopmental disorders," in Connectomics: Elsevier, 2019, pp. 83-100.
[49] A. Khadmaoui et al., "MEG analysis of neural interactions in attention-deficit/hyperactivity disorder," Computational intelligence and neuroscience, vol. 2016, 2016.
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How to Cite
1.
Ekhlasi A, Motie Nasrabadi A, Mohammadi M. Optimal Multivariate Transfer Entropy to Determine Differences in Short and Long-Range EEG Connectivity in Children with ADHD and Healthy Children. Frontiers Biomed Technol. 2024;.
