Correlation of the brain compartments in the attention deficit and hyperactivity disorder calculated by the method of virtual electrodes from magnetic encephalography data

M. N. Ustinin ; S. D. Rykunov ; A. I. Boyko

Matematičeskaâ biologiâ i bioinformatika, Tome 15 (2020) no. 2, pp. 471-486.

Résumé

New method to study the correlation of the human brain compartments based on the magnetic encephalography data analysis was proposed. The time series for the correlation analysis are generated by the method of virtual electrodes. First, the multichannel time series of the subject with confirmed attention deficit and hyperactivity disorder are transformed into the functional tomogram - spatial distribution of the magnetic field sources structure on the discrete grid. This structure is provided by the inverse problem solution for all elementary oscillations, found by the Fourier transform. Each frequency produces the elementary current dipole located in the node of the 3D grid. The virtual electrode includes the part of space, producing the activity under study. The time series for this activity is obtained by the summation of the spectral power of all sources, covered by the virtual electrode. To test the method, in this article we selected ten basic compartments of the brain, including frontal lobe, parietal lobe, occipital lobe and others. Each compartment was included in the virtual electrode, obtained from the subjects' MRI. We studied the correlation between compartments in the frequency bands, corresponding to four brain rhythms: theta, alpha, beta, and gamma. The time series for each electrode were calculated for the period of 300 seconds. The correlation coefficient between power series was calculated on the 1 second epoch and then averaged. The results were represented as matrices. The method can be used to study correlations of the arbitrary parts of the brain in any spectral band.

Bibliographie

[1] J. F. Saad, K. R. Griffiths, M. S. Korgaonkar, “A Systematic Review of Imaging Studies in the Combined and Inattentive Subtypes of Attention Deficit Hyperactivity Disorder”, Front. Integr. Neurosci, 14 (2020) | DOI

[2] J. R. Sato, M. Q. Hoexter, X. F. Castellanos, L. A. Rohde, “Abnormal brain connectivity patterns in adults with ADHD: a coherence study”, PLoS One, 7:9 (2012) | DOI

[3] P. J. Brockwell, R. A. Davis, Time Series: Theory and Methods, 2nd edition, Springer-Verlag, New York, 1991 | MR | Zbl

[4] P. Sörös, E. Hoxhaj, P. Borel, C. Sadohara, B. Feige, S. Matthies, H. H.O. Müller, K. Bachmann, M. Schulze, A. Philipsen, “Hyperactivity/restlessness is associated with increased functional connectivity in adults with ADHD: a dimensional analysis of resting state fMRI”, BMC Psychiatry, 19 (2019) | DOI

[5] G. Sudre, E. Szekely, W. Sharp, S. Kasparek, P. Shaw, “Multimodal mapping of the brain's functional connectivity and the adult outcome of attention deficit hyperactivity disorder”, PNAS, 114:44 (2017), 11787–11792 | DOI | MR

[6] S. E. Robinson, J. Vrba, “Functional neuroimaging by synthetic aperture magnetometry (SAM)”, Recent Advances in Biomagnetism, Proceedings of the 11th International Conference on Biomagnetism, eds. T. Yoshimoto, M. Kotani, S. Kuriki, H. Karibe, N. Nakasato, Tohoku Univ. Press, Sendai, 1998, 302–305

[7] V. Litvak, A. Eusebio, A. Jha, R. Oostenveld, G. R. Barnes, W. D. Penny, L. Zrinzo, M. I. Hariz, P. Limousin, K. J. Friston et al, “Optimized beamforming for simultaneous MEG and intracranial local field potential recordings in deep brain stimulation patients”, Neuroimage, 50:43 (2010), 1578–1588 | DOI

[8] M. Glasser, S. Smith, D. Marcus, J. L.R. Andersson, E. J. Auerbach, T. E.J. Behrens, T. S. Coalson, M. P. Harms, M. Jenkinson, S. Moeller et al, “The Human Connectome Project's neuroimaging approach”, Nat. Neurosci., 19 (2016), 1175–1187 | DOI

[9] I. Mohammad-Rezazadeh, “Atypical functional connectivity in adolescents and adults with persistent and remitted ADHD during a cognitive control task”, Transl. Psychiatry, 9 (2019), 137 | DOI

[10] P. L. Nunez, R. Srinivasan, A. F. Westdorp, R. S. Wijesinghe, D. M. Tucker, R. B. Silberstein, P. J. Cadusch, “EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales”, Electroencephalogr. Clin. Neurophysiol., 103 (1997), 499–515 | DOI

[11] S. Palva, J. M. Palva, “Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs”, Trends Cogn. Sci., 16 (2012), 219–230 | DOI

[12] K. M. Jang, M. S. Kim, D. W. Kim, “The Dynamic Properties of a Brain Network During Spatial Working Memory Tasks in College Students With ADHD Traits”, Front. Hum. Neurosci, 14 (2020), 580813 | DOI

[13] J. P. Lachaux, E. Rodriguez, J. Martinerie, F. J. Varela, “Measuring phase synchrony in brain signals”, Hum. Brain Mapp., 8 (1999), 194–208 | 3.0.CO;2-C class='badge bg-secondary rounded-pill ref-badge extid-badge'>DOI

[14] J. W. Choi, K. M. Jang, K. Y. Jung, M. S. Kim, K. H. Kim, “Reduced theta-band power and phase synchrony during explicit verbal memory tasks in female, non-clinical individuals with schizotypal traits”, PLoS One, 11 (2016), e0148272 | DOI

[15] Pankratova N.M., Rykunov S.D., Boiko A.I., Molchanova D.A., Ustinin M.N., “Lokalizatsiya spektralnykh osobennostei entsefalogramm pri psikhicheskikh rasstroistvakh”, Matematicheskaya biologiya i bioinformatika, 13:2 (2018), 322–336 | DOI

[16] Ustinin M.N., Rykunov S.D., Boiko A.I., Maslova O.A., Pankratova N.M., “Izuchenie sindroma defitsita vnimaniya i giperaktivnosti metodom funktsionalnoi tomografii po dannym magnitnoi entsefalografii”, Matematicheskaya biologiya i bioinformatika, 14:2 (2019), 517–533 | DOI

[17] R. Llinás, U. Ribary, D. Jeanmonod, E. Kronberg, P. Mitra, “Thalamocortical dysrhythmia: A neurological and neuropsychiatric syndrome characterized by magnetoencephalography”, Proceedings of the National Academy of Sciences of the United States of America, 96 (1999), 15222–15227 | DOI

[18] W. Andra, H. Nowak (eds.), Magnetism in medicine: a handbook, Wiley-VCH, 2007, 630 pp. | DOI

[19] D. Cohen, U. Schlapfer, S. Ahlfors, M. Hamalainen, E. Halgren, “New Six-Layer Magnetically Shielded Room for MEG”, Proceedings of 13th International Conference on Biomagnetism, Biomag 2002 (Berlin, 2002), 919–921

[20] J. Bork, H. D. Hahlbohm, R. Klein, A. Schnabel, “The 8-layered magnetically shielded room of the PTB: Design and construction”, Proceedings of the 12th International Conference on Biomagnetism, Biomag 2000, Springer, 2000

[21] R. R. Llinás, M. Ustinin, S. Rykunov, K. D. Walton, G. M. Rabello, J. Garcia, A. Boyko, V. Sychev, “Noninvasive muscle activity imaging using magnetography”, Proceedings of the National Academy of Sciences of the United States of America, 117:9 (2020), 4942–4947 | DOI

[22] R. R. Llinás, M. N. Ustinin, “Frequency-pattern functional tomography of magnetoencephalography data allows new approach to the study of human brain organization”, Front. Neural Circuits, 8 (2014), 43 | DOI

[23] R. R. Llinás, M. N. Ustinin, S. D. Rykunov, A. I. Boyko, V. V. Sychev, K. D. Walton, G. M. Rabello, J. Garcia, “Reconstruction of human brain spontaneous activity based on frequency-pattern analysis of magnetoencephalography data”, Front. Neurosci, 9 (2015), 373 | DOI

[24] J. Sarvas, “Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem”, Phys. Med. Biol., 32:1 (1987), 11–22 | DOI

[25] G. Niso, C. Rogers, J. T. Moreau, L. Y. Chen, C. Madjar, S. Das, E. Bock, F. Tadel, A. Evans, P. Jolicoeur, S. Baillet, “OMEGA: The Open MEG Archive”, Neuroimage, 124 (2015), 1182–1187 | DOI

[26] E. B.J. Coffey, S. C. Herholz, S. Scala, R. J. Zatorre, “Montreal Music History Questionnaire: a tool for the assessment of music-related experience”, The Neurosciences and Music IV: Learning and Memory, Annals of the New York Academy of Sciences, 1252, eds. K. Overy, I. Peretz, R. J. Zatorre, L. Lopez, M. Manjo, Wiley-Blackwell, 2012

[27] M. N. Ustinin, V. V. Sychev, K. D. Walton, R. R. Llinás, “New Methodology for the Analysis and Representation of Human Brain Function: MEGMRIAn”, Mathematical Biology and Bioinformatics, 9:2 (2014), 464–481 | DOI

[28] J. McCubbin, J. Vrba, P. Spear, D. McKenzie, R. Willis, R. Loewen, S. E. Robinson, A. A. Fife, “Advanced electronics for the CTF MEG system”, Neurol. Clin. Neurophysiol, 2004 (2004), 69

[29] M. Frigo, S. G. Johnson, “The Design and Implementation of FFTW3”, Proceedings of the IEEE, 93:2 (2005), 216–231 | DOI

[30] A. Belouchrani, K. Abed-Meraim, J. F. Cardoso, E. Moulines, “A blind source separation technique using second-order statistics”, IEEE Trans. Signal Processing, 45 (1997), 434–444 | DOI

[31] Rykunov S.D., Rykunova E.D., Boiko A.I., Ustinin M.N., “Programmnyi kompleks «VirtEl» dlya analiza dannykh magnitnoi entsefalografii metodom virtualnykh elektrodov”, Matematicheskaya biologiya i bioinformatika, 14:1 (2019), 340–354 | DOI

[32] B. Fischl, “Automatically Parcellating the Human Cerebral Cortex”, Cereb. Cortex., 14:1 (2004), 11–22 | DOI

[33] R. S. Desikan, F. S-gonne, B. Fischl, B. T. Quinn, B. C. Dickerson, D. Blacker, R. L. Buckner, A. M. Dale, R. P. Maguire, B. T. Hyman, M. S. Albert, R. J. Killiany, “An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest”, Neuroimage, 31:3 (2006), 968–980 | DOI

[34] B. Fischl, D. H. Salat, A. J.W. Van Der Kouwe, N. Makris, F. Ségonne, B. T. Quinn, A. M. Dale, “Sequence-independent segmentation of magnetic resonance images”, Neuroimage, 23 (2004), 69–84 | DOI