Functional study of the brain based on magnetic resonance imaging in patients with insomnia disorders

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Abstract

BACKGROUND: Insomnia has a significant impact on the quality of life of patients. Despite the progress in understanding the pathophysiological mechanisms of insomnia, the possibilities of its objective diagnosis remain insufficiently studied. This study can contribute to understanding the neural mechanisms of insomnia, contribute to the development of new diagnostic and treatment methods, and personalize therapeutic approaches to improve the quality of life of patients with insomnia disorders.

AIM: to evaluate changes in the brain connectome in patients with psychophysiological and paradoxical insomnia by performing functional magnetic resonance imaging.

MATERIALS AND METHODS: A total of 31 patients were examined who applied for a somnologist appointment at the Federal State Budgetary Institution Almazov National Medical Research Centre of the Ministry of Health of the Russian Federation with diagnosed chronic insomnia. All patients underwent polysomnographic examination using Embla N 7000 (Natus, USA) and SOMNO HD (SOMNOmedics, Germany) for one night with an assessment of the main characteristics of sleep according to the rules of AASM 2.5. Also, all study participants underwent magnetic resonance imaging of the brain on tomographs with a magnetic field induction force of 3.0 Tl at two time points. Statistical analysis of MRI data was performed using MathLab 2023a, CONN v22.a packages. Descriptive statistics, the Kolmogorov–Smirnov criterion were used for processing materials, depending on the characteristics of the data, the Mann–Whitney U-criterion and Pearson Chi-squared were used to analyze demographic data.

DISCUSSION: The study demonstrates the possibilities of functional magnetic resonance imaging in obtaining data on the functional connections of the brain in insomnia. The detected changes in the activity of various brain regions indirectly or directly involved in the regulation of sleep and wakefulness are consistent with the most common pathogenetic models of insomnia, in particular with the theory of hyperactivation and the model of sleep reactivity to stress.

CONCLUSION: The results of the study emphasize the relevance of studying functional changes in the brain in insomnia, opening up new opportunities for more accurate diagnosis and the development of personalized treatment methods.

About the authors

Anastasia A. Borshevetskaya

Almazov National Medical Research Centre

Author for correspondence.
Email: borshevetskaya@yandex.ru
ORCID iD: 0000-0003-0613-7385
Russian Federation, St. Petersburg

Alexander Yu. Efimtsev

Almazov National Medical Research Centre

Email: atralf@mail.ru
ORCID iD: 0000-0003-2249-1405
SPIN-code: 3459-2168

MD, Dr. Sci. (Medicine), Associate Professor at the Department

Russian Federation, St. Petersburg

Gennady E. Trufanov

Almazov National Medical Research Centre

Email: trufanovge@mail.ru
ORCID iD: 0000-0002-1611-5000
SPIN-code: 3139-3581

MD, Dr. Sci. (Medicine), Professor

Russian Federation, St. Petersburg

Yurii V. Sviryaev

Almazov National Medical Research Centre

Email: yusvyr@yandex.ru
ORCID iD: 0000-0002-3170-0451

MD, Dr. Sci. (Medicine)

Russian Federation, St. Petersburg

Valeria V. Amelina

Almazov National Medical Research Centre

Email: v.kemstach@icloud.com
ORCID iD: 0000-0002-0047-3428

MD, Cand. Sci. (Medicine)

Russian Federation, St. Petersburg

Konstantin I. Sebelev

Almazov National Medical Research Centre

Email: ki_sebelev@list.ru
ORCID iD: 0000-0003-0075-7807

MD, Dr. Sci. (Medicine), Professor

Russian Federation, St. Petersburg

Yana A. Filin

Almazov National Medical Research Centre

Email: filin_yana@mail.ru
ORCID iD: 0009-0009-0778-6396
Russian Federation, St. Petersburg

Daniil A. Beregovskii

Almazov National Medical Research Centre

Email: bereg.daniil96@mail.ru
ORCID iD: 0009-0008-7964-7857
Russian Federation, St. Petersburg

References

  1. Burchakov DI, Tardov MV. Insomnia: origins, treatment and clinical vignettes. Consilium Medicum. 2020;22(2):75–82. (In Russ.) EDN: CCDHJH doi: 10.26442/20751753.2020.2.200101
  2. Strygin KN, Poluektov MG. Insomnia. Medical Council. 2017;(S):52–58. (In Russ.) EDN: XUYAWZ doi: 10.21518/2079-701X-2017-0-52-58
  3. Baymukanov AM, Bulavina IA, Petrova GA, et al. Sleep apnea in patients with atrial fibrillation. Lechebnoye delo. 2022;(2):132–136. (In Russ.) EDN: VTGDHZ doi: 10.24412/2071-5315-2022-12817
  4. Laugsand LE, Strand LB, Vatten LJ, et al. Insomnia symptoms and risk for unintentional fatal injuries-the HUNT Study. Sleep. 2014;37(11):1777–1786. doi: 10.5665/sleep.4170
  5. Poluektov MG, Akarachkova ES, Dovgan EV, et al. Management of patients with insomnia and polymorbidity: expert consensus. Zh Nevrol Psikhiatr Im S S Korsakova. 2023;123(5–2): 49–57. (In Russ.) EDN: EVPUSF doi: 10.17116/jnevro202312305249
  6. Rundo JV, Downey R3rd. Polysomnography. Handbook of Clinical Neurology Volume. 2019;160:381–392. doi: 10.1016/B978-0-444-64032-1.00025-4
  7. Surikova NA, Glukhova AS. Obstructive sleep apnea syndrome: literature review. CardioSomatics. 2023;14(1):67–76. (In Russ.) EDN: MTMATB doi: 10.17816/CS321374
  8. Kim SG, Bandettini PA. Principles of BOLD Functional MRI. In: Faro SH, Mohamed FB, Law M, Ulmer JL. Functional Neuroradiology: Principles and Clinical Applications. 2012. P. 293–303. doi: 10.1007/978-1-4419-0345-7_16
  9. Kim T, Masamoto K, Hendrich K, Kim S-G. Arterial versus total blood volume changes during neural activity-induced cerebral blood flow change: implication for BOLD fMRI. J Cereb Blood Flow Metab. 2007;27(6):1235–1247. doi: 10.1038/sj.jcbfm.9600429
  10. Eklund A, Nichols T, Andersson M, Knutsson H. Empirically investigating the statistical validity of SPM, FSL and AFNI for single subject fMRI analysis. In: 2015 IEEE12th International Symposium on Biomedical Imaging (ISBI). Brooklyn, NY, USA; 2015. P. 1376–1380. doi: 10.1109/ISBI.2015.7164132
  11. Zang ZX, Yan CG, Dong ZY, et al. Granger causality analysis implementation on MATLAB: A graphic user interface toolkit for fMRI data processing. Journal of Neuroscience Methods. 2012;203(2): 418–426. doi: 10.1016/j.jneumeth.2011.10.006
  12. Zigmantovich AS, Sharova EV, Kopachka MМ, et al. Changes in resting fMRI networks in patients with severe traumatic brain injury during therapeutic rhythmic transcranial magnetic stimulation (case report). Obshchaya reanimatologiya. 2022;18(2):53–64. (In Russ.) EDN: INDIYM doi: 10.15360/1813-9779-2022-2-53-64
  13. Etindele Sosso FA. Measuring sleep health disparities with polysomnography: a systematic review of preliminary findings. Clocks & Sleep. 2022;4(1):80–87. doi: 10.3390/clockssleep4010009

Supplementary files

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1. JATS XML
2. Fig. 1. Functional connections in chronic insomnia group, compared to control group data. Evening control point. Blue lines demonstrate weaker functional connectivity, the red ones show stronger connectivity

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3. Fig. 2. Functional connections in chronic insomnia group, compared to control group data. Evening control point. Blue lines demonstrate weaker functional connectivity, the orange ones show stronger connectivity

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4. Fig. 3. Functional connections in chronic insomnia group, compared to control group data. Morning control point. Blue lines demonstrate weaker functional connectivity, the red ones show stronger connectivity

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5. Fig. 4. Functional connections in chronic insomnia group, compared to control group data. Morning control point. Blue lines demonstrate weaker functional connectivity

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6. Fig. 5. Functional connections in paradoxical insomnia group, compared to control group data. Morning control point. Blue lines demonstrate weaker functional connectivity, the red one shows stronger connectivity

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7. Fig. 6. Functional connections in paradoxical insomnia group, compared to control group data. Morning control point. Blue lines demonstrate weaker functional connectivity, the red one shows stronger connectivity

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8. Fig. 7. Functional connections in paradoxical insomnia group, compared to control group data. Evening control point. Blue lines demonstrate weaker functional connectivity, the red ones show stronger connectivity

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9. Fig. 8. Functional connections in paradoxical insomnia group, compared to control group data. Evening control point. Blue lines demonstrate weaker functional connectivity, the red ones show stronger connectivity

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