Localization of encephalogram spectral features in psychic disorders
Matematičeskaâ biologiâ i bioinformatika, Tome 13 (2018) no. 2, pp. 322-336.

Voir la notice de l'article provenant de la source Math-Net.Ru

Changes in spectral and spatial features of encephalograms are considered, which are observed in various mental disorders. A systematic bibliographic review is presented, including papers on the estimation of pathology spectrum and its position in brain. First of all, the spontaneous activity of the brain in various states is considered, differing in spatial localization and in coherence between brain areas. Articles presented in review show, that the rhythmic activity of the brain in mental disorders differs from normal in several frequency bands. The method is proposed for precise quantitative analysis of this activity based on the encephalography data. Spatial position of the sources of pathological activity is a key issue of brain studies and it is solved by various localization methods. Results of the localization are presented at the anatomical scheme of the brain or at the subject’s magnetic resonance image and as a result, hypotheses of neurophysiological mechanism of pathology under study are proposed. The comparative analysis of encephalography spectra, registered in various channels, distributed over the scalp, can be considered as the simplest of pathology localization methods. Such localization is qualitative and makes it possible to make very rough conclusions. The method, proposed in this paper, is based on Fourier transform of multichannel encephalography data and on the localization of spectral components. Such approach permits to study in detail some or other frequency features of the brain pathological activity and to reveal their connections with the brain anatomy.
@article{MBB_2018_13_2_a0,
     author = {N. M. Pankratova and S. D. Rykunov and A. I. Boyko and D. A. Molchanova and M. N. Ustinin},
     title = {Localization of encephalogram spectral features in psychic disorders},
     journal = {Matemati\v{c}eska\^a biologi\^a i bioinformatika},
     pages = {322--336},
     publisher = {mathdoc},
     volume = {13},
     number = {2},
     year = {2018},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MBB_2018_13_2_a0/}
}
TY  - JOUR
AU  - N. M. Pankratova
AU  - S. D. Rykunov
AU  - A. I. Boyko
AU  - D. A. Molchanova
AU  - M. N. Ustinin
TI  - Localization of encephalogram spectral features in psychic disorders
JO  - Matematičeskaâ biologiâ i bioinformatika
PY  - 2018
SP  - 322
EP  - 336
VL  - 13
IS  - 2
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/MBB_2018_13_2_a0/
LA  - ru
ID  - MBB_2018_13_2_a0
ER  - 
%0 Journal Article
%A N. M. Pankratova
%A S. D. Rykunov
%A A. I. Boyko
%A D. A. Molchanova
%A M. N. Ustinin
%T Localization of encephalogram spectral features in psychic disorders
%J Matematičeskaâ biologiâ i bioinformatika
%D 2018
%P 322-336
%V 13
%N 2
%I mathdoc
%U http://geodesic.mathdoc.fr/item/MBB_2018_13_2_a0/
%G ru
%F MBB_2018_13_2_a0
N. M. Pankratova; S. D. Rykunov; A. I. Boyko; D. A. Molchanova; M. N. Ustinin. Localization of encephalogram spectral features in psychic disorders. Matematičeskaâ biologiâ i bioinformatika, Tome 13 (2018) no. 2, pp. 322-336. http://geodesic.mathdoc.fr/item/MBB_2018_13_2_a0/

[1] R. Llinás, “The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function”, Science, 242:4886 (1988), 1654–1664 | DOI

[2] R. R. Llinás, D. Pare, “Of dreaming, wakefulness”, Neuroscience, 1991, no. 3, 521–535 | DOI

[3] J. J. Schulman, R. Cancro, S. Lowe, F. Lu, K. D. Walton, R. R. Llinás, “Imaging of thalamocortical dysrhythmia in neuropsychiatry”, Frontiers in Human Neuroscience, 5 (2011), 69 | DOI

[4] R. R. Llinás, M. N. Ustinin, Precise Frequency-Pattern Analysis to Decompose Complex Systems into Functionally Invariant Entities, U.S. Patent. US Patent App. Publ. 20160012011 A1.01/14/2016

[5] 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

[6] 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

[7] S. D. Rykunov, M. N. Ustinin, A. G. Polyanin, V. V. Sychev, R. R. Linas, “Kompleks programm dlya rascheta partsialnykh spektrov golovnogo mozga cheloveka”, Matematicheskaya biologiya i bioinformatika, 11:1 (2016), 127–140 | DOI

[8] E. Honaga, R. Ishii, R. Kurimoto, L. Canuet, K. Ikezawa, H. Takahashi, T. Nakahachi, M. Iwase, I. Mizuta, N. Yoshimine, M. Takeda, “Post-movement beta rebound abnormality as indicator of mirror neuron system dysfunction in autistic spectrum disorder: An MEG study”, Neuroscience Letters, 478 (2010), 141–145 | DOI

[9] A. V. Kirenskaya, “EEG-issledovaniya v biologicheskoi psikhiatrii: osnovnye napravleniya i perspektivy”, Rossiiskii psikhiatricheskii zhurnal, 2006, no. 6, 19–27

[10] V. G. Ragozinskaya, “Osobennosti spektralnoi moschnosti EEG pri autoagressii”, Izvestiya vysshikh uchebnykh zavedenii. Uralskii region, 2015, no. 2, 97–104

[11] A. V. Kirenskaya-Berus, A. Ya. Gavrilenko, A. B. Zhuravlev, T. N. Lavrova, N. V. Maksimova, V. V. Myamlin, B. Yu. Novototskii-Vlasov, V. V. Vandysh-Bubko, A. A. Tkachenko, “EEG-issledovanie neirofiziologicheskikh mekhanizmov predispozitsii k gomitsidnomu povedeniyu u lits s organicheskimi psikhicheskimi rasstroistvami”, Agressiya i psikhicheskoe zdorove, eds. Dmitrieva T. B., Shostakovich B. V., M., 2002, 323–349

[12] A. Harrewijn, M. J.W. Van der Molen, P. M. Westenberg, “Putative EEG measures of social anxiety: Comparing frontal alpha asymmetry and delta-beta cross-frequency correlation”, Cognitive, Affective and Behavioral Neuroscience, 16:6 (2016), 1086–1098 | DOI

[13] A. V. Kirenskaya-Berus, A. A. Tkachenko, “Osobennosti spektralnykh kharakteristik EEG lits s deviantnym seksualnym povedeniem”, Fiziologiya cheloveka, 29:3 (2003), 22–32

[14] A. A. Fingelkurts, “Altered structure of dynamic electroencephalogram oscillatory pattern in major depression”, Biological Psychiatry, 77:12 (2015), 1050–1060 | DOI

[15] J. F. Cavanagh, A. J. Shackman, “Frontal Midline Theta Reflects Anxiety and Cognitive Control: Meta-Analytic Evidence”, Journal of Physiology, Paris, 109 (2015), 3–15 | DOI

[16] J. F. L. Pinner, J. F. Cavanagh, “Frontal theta accounts for individual differences in the cost of conflict on decision making”, Brain Research, 1672 (2017), 73–80 | DOI

[17] S. S. Jeste, J. Frohlich, S. K. Loo, “Electrophysiological biomarkers of diagnosis and outcome in neurodevelopmental disorders”, Current Opinion in Neurology, 28:2 (2015), 110–116 | DOI

[18] A. A. Pashkov, I. S. Dakhtin, N. S. Kharisova, “Elektroentsefalograficheskie biomarkery eksperimentalno indutsirovannogo stressa”, Vestnik YuUrGU. Seriya «Psikhologiya», 10:4 (2017), 68–82

[19] M. Palmiero, L. Piccardi, “Frontal EEG Asymmetry of Mood: A Mini-Review”, Frontiers in Behavioral Neuroscience, 11 (2017), 224 | DOI

[20] J. M. Koolhaas, A. Bartolomucci, B. Buwalda, S. F. de Boer, G. Flugge, S. M. Korte, P. Meerlo, R. Murison, B. Olivier, P. Palanza et al., “Stress revisited: a critical evaluation of stress concept”, Neuroscience and Biobehavioral Reviews, 35:5 (2011), 1291–1301 | DOI

[21] M. Fumoto, I. Sato-Suzuki, Y. Seki, Y. Mohri, H. Arita, “Appearance of high-frequency alpha band with disappearance of low-frequency alpha band in EEG is produced during voluntary abdominal breathing in an eyes-closed condition”, Neuroscience Research, 50:3 (2004), 307–317 | DOI

[22] B. T. Dunkley, P. A. Sedge, S. M. Doesburg, R. J. Grodecki, R. Jetly, P. N. Shek, M. J. Taylor, Pang E. W., “Theta, mental flexibility and post-traumatic stress disorder: connecting in the parietal cortex”, PLOS One, 10:4 (2015), e0123541 | DOI

[23] S. J. Werff, S. M. van der Berg, J. N. Pannekoek, B. M. Elzinga, N. J. van der Wee, “Neuroimaging resilience to stress: a review”, Frontiers in Behavioral Neuroscience, 7 (2013), 39 | DOI

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

[25] J. J. Schulman, R. R. Ramirez, M. Zonenshayn, U. Ribary, R. R. Llinás, “Thalamocortical dysrhythmia syndrome: MEG imaging of neuropathic pain”, Thalamus Related Systems, 3:1 (2005), 33–39 | DOI

[26] N. M. Pankratova, M. N. Ustinin, R. Linas, “Obnaruzhenie patologicheskoi aktivnosti golovnogo mozga po dannym magnitnoi entsefalografii”, Matematicheskaya biologiya i bioinformatika, 8:2 (2013), 679–690 | DOI

[27] T. S. Melnikova, I. A. Lapin, V. V. Sarkisyan, “Obzor ispolzovaniya kogerentnogo analiza EEG v psikhiatrii”, Sotsialnaya i klinicheskaya psikhiatriya, 19:1 (2009), 90–94 | MR

[28] E. A. Luschekina, O. Yu. Khaerdinova, V. S. Luschekin, V. B. Strelets, “Mezhpolusharnye razlichiya spektralnoi moschnosti i kogerentnosti ritmov EEG u detei s rastroistvami autisticheskogo spektra”, Fiziologiya cheloveka, 43:3 (2017), 32–42 | DOI

[29] V. B. Strelets, Magomedov R.A, Zh. V. Garakh, V. Yu. Novototskii-Vlasov, “Spektralnaya moschnost i vnutrikorkovye vzaimodeistviya po beta-ritmu v norme i pri shizofrenii”, Zhurn. vyssh. nervn. deyat., 54:2 (2004), 259–266 | MR

[30] C. Basar-Eroglu, C. Schmiedt-Fehr, S. Marbach, A. Brand, B. Mathes, “Altered oscillatory alpha and theta networks in schizophrenia”, Brain Res., 1235 (2008), 143–152 | DOI

[31] M. G. Knyazeva, M. Jalili, R. Meuli, M. Hasler, O. De Feo, K. Q. Do, “Alpha rhythm and hypofrontality in schizophrenia”, Acta Psychiatr. Scand., 118:3 (2008), 188–199 | DOI

[32] M. Gregory, D. Mandelbaum, “Evidence of a faster posterior dominant EEG rhythm in children with autism”, Research in Autism Spectrum Disorders, 2012, no. 6, 1000 | DOI | MR

[33] G. Rizzolatti, L. Craighero, “The mirror-neuron system”, Annual Review of Neuroscience, 27 (2004), 169–192 | DOI

[34] E. A. Luschekina, E. D. Podreznaya, V. S. Novototskii-Vlasov V. Yu. Luschekin, V. B. Strelets, “Sravnitelnoe issledovanie teta- i gamma-ritmov EEG v norme i pri rannem detskom autizme”, Zhurn. vyssh. nerv. deyat., 63:4 (2013), 451–459 | DOI

[35] D. A. Menassa, S. Braeutigama, A. Bailey, C. M. Falter-Wagner, “Frontal evoked $\gamma$ activity modulates behavioural performance in Autism Spectrum Disorders in a perceptual simultaneity task”, Neuroscience Letters, 665:5 (2018), 86–91 | DOI

[36] S. Makeig, T. P. Jung, A. J. Bell, D. Ghahremani, T. J. Sejnowski, “Blind separation of auditory event-related brain responses into independent components”, Proc. Natl.Acad. Sci. U.S.A., 94 (1997), 10979–10984 | DOI

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

[38] 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

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

[40] N. M. Pankratova, S. D. Rykunov, M. N. Ustinin, “Lokalizatsiya spektralnykh osobennostei entsefalogramm pri psikhicheskikh rasstroistvakh”, Preprinty IPM im. M.V. Keldysha, 2018, 138, 20 pp. | DOI