Neuroimaging of the effects of psychoactive substances by means of normalization of brain electrograms
https://doi.org/10.33647/2074-5982-15-1-12-34
Abstract
The lack of adequate methods for identifying the psychotropic properties of various chemical compounds has dramatically hindered the search for new psychoactive substances. The role of β- and γ-rhythms of brain electrograms in the elucidation of mental processes is poorly investigated. On the other hand, even the isolation of the main acting components of psychotropic substances in animal studies for extrapolating to humans presents a challenge. This article is devoted to the aforementioned issues. This research was performed on cats with electrodes that had been stereotactically implanted in different parts of the brain. Since commercial electroencephalographs were not suitable for our purposes, specialized microchip devices were designed at the Scientific Centre for Biomedical Technologies (SCBMT), Russia. As a mathematical instrument, the fast Fourier transform (FFT) algorithm with a window discrete Fourier transform based on Hamming functions was implemented.
Normalization of the FFT-transformed brain electrograms recorded under the influence of doxylamine, xylazine, caffeine, sertraline, phenotropyl was carried out. The suitability of the proposed approach for assessing the pharmacodynamics of the studied substances, which are characterized by a phase character coinciding with the main pharmacokinetic points, is demonstrated. The β- and γ- rhythms are shown to be the most important indicators of the effects of psychotropic substances.
About the Authors
N. N. Karkischenko
Scientific Center of Biomedical Technologies of the Federal Medical Biological Agency of Russia
Russian Federation
Russian Federation
Dr. Sci. (Med.), Prof., Corresponding Member of the Russian Academy of Sciences, supervisor,
143442, Moscow region, Krasnogorsk, Setllement Svetlye Gory, building 1
V. N. Karkischenko
Scientific Center of Biomedical Technologies of the Federal Medical Biological Agency of Russia
Russian Federation
Russian Federation
Dr. Sci. (Med.), Prof., Director,
143442, Moscow region, Krasnogorsk, Setllement Svetlye Gory, building 1
Yu. V. Fokin
Scientific Center of Biomedical Technologies of the Federal Medical Biological Agency of Russia
Russian Federation
Russian Federation
PhD Sci. (Biol.),
143442, Moscow region, Krasnogorsk, Setllement Svetlye Gory, building 1
S. Yu. Kharitonov
Scientific Center of Biomedical Technologies of the Federal Medical Biological Agency of Russia
Russian Federation
143442, Moscow region, Krasnogorsk, Setllement Svetlye Gory, building 1
Russian Federation
143442, Moscow region, Krasnogorsk, Setllement Svetlye Gory, building 1
References
1. Gnezdickij V.V. Obratnaya zadacha EhEhG i klinicheskaya ehlektroehncefalografiya [Reverse EEG problem and clinical electroencephalography]. Taganrog: Izd-vo TRTU, 2000. 268 p. (In Russian).
2. Efremova T.N., Kulikov M.A. Haoticheskaya sostavlyayushchaya vysokochastotnoj EhEhG cheloveka v sostoyanii spokojnogo bodrstvovaniya [Chaotic component of the high-frequency EEG of a person in a state of calm wakefulness]. Zhurn. vyssh. nervn. deyatel’nosti [J. of Higher Nervous Activity]. 2002. Vol. 52, No. 3. Pp. 283–291. (In Russian).
3. Karkischenko N.N. Al’ternativy biomediciny. T. 2. Klassika i al’ternativy farmakotoksikologii [Alternatives to biomedicine. Vol. 2. Classics and alternatives to pharmacotoxicology]. Moscow: Izd-vo VPK, 2007. 448 p. (In Russian).
4. Karkischenko N.N. Psihounitropizm lekarstvennyh sredstv [Psychunitropism of medicines]. Moscow: Medicina, 1993. 208 p. (In Russian).
5. Karkischenko N.N. Farmakologiya sistemnoj deyatel’nosti mozga [Pharmacology of systemic activity of the brain]. Rostov: Rostizdat, 1975. 152 p. (In Russian).
6. Karkischenko N.N., Fokin Yu.V., Karkischenko V.N., Taboyakova L.A., Mokrousov M.I., Alimkina O.V. Konvergentnaya validaciya intracentral’nyh otnoshenij golovnogo mozga zhivotnyh [Convergent validation of intracentral relationships of the brain of animals]. Biomedicine. 2017. No. 3. Pp. 16–39. (In Russian).
7. Karkischenko N.N., Fokin Yu.V., Karkischenko V.N., Taboyakova L.A., Kharitonov S.Yu., Alimkina O.V. Novye podhody k ocenke intracentral’nyh otnoshenij po pokazatelyam operantnogo povedeniya i ehlektrogramm mozga koshek [New approaches to the assessment of intracentral relations in terms of operant behavior and electrograms of the cats brain]. Biomedicine. 2018. No. 4. Pp. 4–17. (In Russian).
8. Mekler A.A. Primenenie apparata nelinejnogo analiza dinamicheskih sistem dlya obrabotki signalov EhEhG [The use of the apparatus for nonlinear analysis of dynamic systems for EEG signal processing]. V sb.: Aktual’nye problemy sovremennoj matematiki: uchenye zapiski. T. 13 (vyp. 2) [In the collection: Actual problems of modern mathematics: scientific notes. Vol. 13 (Issue 2)]. Ed. by prof. Kalashnikov E.V. Saint Petersburg: Izd-vo LGU, 2004. Pp. 112–140. (In Russian).
9. Rukovodstvo po laboratornym zhivotnym i al’ternativnym modelyam v biomedicinskih issledovaniyah [Manual on laboratory animals and alternative models in biomedical research]. Ed. by N.N. Karkischenko, et al. Moscow: Profil’-2S, 2010. 358 p. (In Russian).
10. Tsygan V.N., Bogolovskij M.M., Mirolyubov A.V. Ehlektroehncefalografiya [Electroencephalography]. Ed. by M.M. D’yakonov. Saint Petersburg: Nauka, 2008. Pp. 19–23. (In Russian).
11. Berger H. Über das Elektrenkephalogramm des Menschen. Arch. Psychiat., 1929. No. 87. Pp. 527– 570.
12. Caton R. The electric currents of the brain. Brit. Med. J., 1875. No. 2. P. 278.
13. Condray R., Morrow L.A., Steinhauer S.R., et al. Mood and behavioral symptoms in individuals with chronic solvent exposure. Psychiatry Res. 2000. No. 97(2–3). Pp. 191–206.
14. Conway C.R., Sheline Y.I., Chibnall J.T., et al. Brain blood-flow change with acute vagus nerve stimulation in treatment-refractory major depressive disorder. Brain Stimul. 2012. No. 5(2). Pp. 163–171.
15. Date C.J. Date on Database: Writing 2000–2006, Apress, 2006. 566 p. 1-59059-746-х.
16. Dougherty D.D., Weiss A.P., Cosgrove G.R., et al. Cerebral metabolic correlates as potential predictors of response to anterior cingulotomy for treatment of major depression. J. Neurosurg. 2003. No. 99(6). Pp. 1010–1017.
17. Drevets W.C., Savitz J., Trimble M. The subgenual anterior cingulate cortex in mood disorders. CNS Spectr. 2008. No. 13(8). Pp. 663–681.
18. Filler A.G. The history, development, and impact of computed imaging in neurological diagnosis and neurosurgery: CT, MRI, DTI. Available from Nature Precedings. Neurosurgical Focus. 2009.
19. Griebel G., Holmes A. 50 years of hurdles and hope in anxiolytic drug discovery. Nature reviers drug discovery. 2013. No. 12 (9). Pp. 667–687.
20. Hyman S. Psychiatric Drug Development: Diagnosing a Crisis. Cerebrum. 02.04.2019.
21. Itil T.M. Short pharmaco EEG analisis by measure. — Oxford Press, 2005. 314 p.
22. Jeeves M. Mind Fields: Reflections on the Science of Mind and Brain. Grand Rapids, MI: Baker Books. 1994. No. 134. P. 21.
23. Kito S., Hasegawa T., Koga Y. Cerebral blood flow ratio of the dorsolateral prefrontal cortex to the ventromedial prefrontal cortex as a potential predictor of treatment response to transcranial magnetic stimulation in depression. Brain Stimul. 2012. No. 5(4). Pp. 547–553.
24. Kreisl W.C., Fujita M., Fujimura Y., et al. Comparison of [(11)C]-(R)-PK 11195 and [(11)C]PBR28, two radioligands for translocator protein (18 kDa) in human and monkey: Implications for positron emission tomographic imaging of this inflammation biomarker. Neuroimage. 2010. No. 49(4). Pp. 2924–2932.
25. Kreisl W.C., Lyoo C.H., McGwier M., et al. Biomarkers Consortium PET Radioligand Project Team. In vivo radioligand binding to translocator protein correlates with severity of Alzheimer’s disease. Brain: J. Neurol. No. 136. Pp. 2228–2238.
26. Liu G.J., Middleton R.J., Hatty C.R., et al. The 18 kDa translocator protein, microglia and neuroinflammation. Brain Pathol. 2014. No. 24(6). Pp. 631–653.
27. Masdeu J.C. Neuroimaging in psychiatric disorders. Neurotherapeutics. 2011. No. 8(1). Pp. 93–102.
28. Nagafusa Y., Okamoto N., Sakamoto K., et al. Assessment of cerebral blood flow findings using 99mTcECD single-photon emission computed tomography in patients diagnosed with major depressive disorder. J. Affect Disord. 2012. No. 140(3). Pp. 296–299.
29. Nilsson L.-G., Markowitsch H.J. Cognitive Neuroscience of Memory. — Seattle: Hogrefe & Huber Publishers, 1999. P. 57.
30. Polunina A.G., Davydov D.M. EEG correlates of Wechsler Adult Intelligence Scale. Int. J. Neurosc. 2006. No. 116(10). Pp. 1231–1248.
31. Postuma R.B., Aarsland D., Barone P., et al. Identifying prodromal Parkinson’s disease: pre-motor disorders in Parkinson’s disease. Mov. Disord. 2012. No. 27(5). Pp. 617–626.
32. Price J.L., Drevets W.C. Neural circuits underlying the pathophysiology of mood disorders. Trends Cogn. Sci. 2012. No. 16(1). Pp. 61–71.
33. Sehm B., Schnitzler T., Obleser J., Groba A., Ragert P., Villringer A., Obrig H. Facilitation of inferior frontal cortex by transcranial direct current stimulation induces perceptual learning of severely degraded speech. J. Neurosci. 2013. No. 33(40). Pp. 15868–15878. DOI: 10.1523/JNEUROSCI.5466-12.2013
34. Smith K. Mind-reading with a brain scan. Nature News. 2008.
35. Stella F., Radanovic M., Balthazar M.L., et al. Neuropsychiatric symptoms in the prodromal stages of dementia. Curr. Opin Psychiatry. 2014. No. 27(3). Pp. 230–235.
36. Thornton J.F., Schneider H., McLean M.K., et al. Improved outcomes using brain SPECT-guided treatment versus treatment-as-usual in community psychiatric outpatients: a retrospective case-control study. J. Neuropsychiatry Clin. Neurosci. 2014. No. 26(1). Pp. 51–56.
37. Willeumier K., Taylor D.V., Amen D.G. Decreased cerebral blood flow in the limbic and prefrontal cortex using SPECT imaging in a cohort of completed suicides. Transl. Psychiatry. 2011. No. 1. P. e28.
Review
For citations:
Karkischenko N.N., Karkischenko V.N., Fokin Yu.V., Kharitonov S.Yu. Neuroimaging of the effects of psychoactive substances by means of normalization of brain electrograms. Journal Biomed. 2019;(1):12-34. (In Russ.) https://doi.org/10.33647/2074-5982-15-1-12-34
Views:
346
Email this article 
