Sex Differences in the Effect of Brain-derived Neurotrophic Factor (BDNF) Val66Met Polymorphism on Baseline EEG Connectivity

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Дәйексөз келтіру

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Аннотация

Dependent on Val66Met polymorphism in BDNF gene secretion of neurotrophin affects morphological and functional changes in the developing and mature nervous system, in particular, may contribute to associated with white matter degradation changes in connectivity observed with aging. It was also shown that the associated with Val66Met polymorphism differences in connectivity between cortical structures are moderated by the sex of the subjects. However, there are no studies examining the effect of polymorphism on connectivity, taking into account age and gender differences. In this regard, the present study examined the associations of the Val66Met polymorphism of the BDNF gene with the characteristics of delayed phase synchronization based on EEG data in 223 younger (from 18 to 35 years old) and 134 older (over 55 years old) men and women. The analysis included connections between 84 cortical areas, identified on the basis of 42 Brodmann areas located in the left and right hemispheres. A statistically significant effect, including the factor of polymorphism, was the SEX × GENOTYPE interaction when considering associations at the frequency of the α1-rhythm: in Val/Met men, the strength of thirty-three connections was higher compared to Val/Val. Strengthening of connections was observed mainly between the parahippocampal regions of different hemispheres. At the frequency of the gamma rhythm, associated with the genotype differences in connectivity depended on gender and age. In young subjects, the scores of connectivity in Val/Val women were lower in comparison with men, however, no differences between Val/Val and Met carriers were found in any age group. The combined effect of sex and BDNF genotype on the baseline EEG parameters of brain connectivity may be a background for further study of the role of these factors in the formation of basic characteristics of brain activity.

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Авторлар туралы

E. Privodnova

Scientific Research Institute of Neurosciences and Medicine; Novosibirsk State University

Хат алмасуға жауапты Автор.
Email: privodnovaeu@neuronm.ru
Ресей, Novosibirsk; Novosibirsk

N. Volf

Scientific Research Institute of Neurosciences and Medicine; Novosibirsk State University

Email: privodnovaeu@neuronm.ru
Ресей, Novosibirsk; Novosibirsk

Әдебиет тізімі

  1. Anastasia A., Deinhardt K., Chao M.V. et al. Val66Met polymorphism of BDNF alters prodomain structure to induce neuronal growth cone retraction // Nat. Commun. 2013. V. 4. P. e2490.
  2. Szarowicz C.A., Steece-Collier K., Caulfield M.E. New Frontiers in Neurodegeneration and Regeneration Associated with Brain-Derived Neurotrophic Factor and the rs6265 Single Nucleotide Polymorphism // Int. J. Mol. Sci. 2022. V. 23. № 14. P. 8011.
  3. Kowiański P., Lietzau G., Czuba E. et al. BDNF: A Key Factor with Multipotent Impact on Brain Signaling and Synaptic Plasticity // Cell. Mol. Neurobiol. 2018. V. 38. № 3. P. 579.
  4. Knyazev G.G., Volf N.V., Belousova L.V. Age-related differences in electroencephalogram connectivity and network topology // Neurobiol. Aging. 2015. V. 36. № 5. P. 1849.
  5. Varangis E., Habeck C.G., Razlighi Q.R., Stern Y. The Effect of Aging on Resting State Connectivity of Predefined Networks in the Brain // Front. Aging Neurosci. 2019. V. 11. P. 234.
  6. Kailainathan S., Piers T.M., Yi J.H. et al. Activation of a synapse weakening pathway by human Val66 but not Met66 pro-brain-derived neurotrophic factor (proBDNF) // Pharmacol. Res. 2016. V. 104. P. 97.
  7. Thomason M.E., Yoo D.J., Glover G.H., Gotlib I.H. BDNF genotype modulates resting functional connectivity in children // Front. Hum. Neurosci. 2009. V. 3. P. 55.
  8. Wei S.M., Eisenberg D.P., Kohn P.D. et al. Brain-derived neurotrophic factor Val⁶⁶Met polymorphism affects resting regional cerebral blood flow and functional connectivity differentially in women versus men // J. Neurosci. 2012. V. 32. № 20. P. 7074.
  9. Yin Y., Hou Z., Wang X. et al. The BDNF Val66Met polymorphism, resting-state hippocampal functional connectivity and cognitive deficits in acute late-onset depression // J. Affect. Disord. 2015. V. 183. P. 22.
  10. Toh Y.L., Ng T., Tan M. et al. Impact of brain-derived neurotrophic factor genetic polymorphism on cognition: A systematic review // Brain Behave. 2018. V. 8 № 7. P. e01009.
  11. Rodríguez-Rojo I.C., Cuesta P., López M.E. et al. BDNF Val66Met Polymorphism and Gamma Band Disruption in Resting State Brain Functional Connectivity: A Magnetoencephalography Study in Cognitively Intact Older Females // Front. Neurosci. 2018. V. 12. P. 684.
  12. Wang C., Zhang Y., Liu B. et al. Dosage effects of BDNF Val66Met polymorphism on cortical surface area and functional connectivity // J. Neurosci. 2014. V. 34. № 7. P. 2645.
  13. Jang J.H., Yun J.-Y., Jung W.H. et al. The impact of genetic variation in comt and bdnf on resting-state functional connectivity // Int. J. Imaging Syst. Technol. 2012. V. 22. № 1. P. 97.
  14. Colclough G.L., Smith S.M., Nichols T.E. et al. The heritability of multi-modal connectivity in human brain activity // eLife. 2017. V. 6. P. e20178.
  15. Barber A.D., Hegarty C.E., Lindquist M., Karlsgodt K.H. Heritability of Functional Connectivity in Resting State: Assessment of the Dynamic Mean, Dynamic Variance, and Static Connectivity across Networks // Cereb. Cortex. 2021. V. 31. № 6. P. 2834.
  16. Popov T., Tröndle M., Baranczuk-Turska Z. et al. Test-retest reliability of resting-state EEG in young and older adults // Psychophysiology. 2023. V. 60. № 7. P. e14268.
  17. Volf N.V., Privodnova E.Y., Bazovkina D.V. [Associations between the efficiency of hemispheric verbal memory processes and the BDNF Val66Met polymorphism in men and women] // Zh. Vyssh. Nerv. Deyat. Im. I.P. Pavlova. 2022. V. 72. № 6. P. 826.
  18. Volf N.V., Privodnova E.Y. [Background EEG activity mediates associations between BDNF-VAL66MET polymorphism and memory during aging] // Zh. Vyssh. Nerv. Deyat. Im. I.P. Pavlova. 2023. V. 73. № 3. P. 398.
  19. Annett M. A classification of hand preference by association analysis // Br. J. Psychol. 1970. V. 61. № 3. P. 303.
  20. van Diessen E., Numan T., van Dellen E. et al. Opportunities and methodological challenges in EEG and MEG resting state functional brain network research // Clin. Neurophysiol. 2015. V. 126. № 8. P. 1468.
  21. Rossini P.M., Di Iorio R., Bentivoglio M. et al. Methods for analysis of brain connectivity: An IFCN-sponsored review // Clin. Neurophysiol. 2019. V. 130. № 10. P. 1833.
  22. Pascual-Marqui R.D. Instantaneous and lagged measures of linear and nonlinear dependence between groups of multivariate time series: Frequency decomposition // Int. J. Psychophysiol. 2007. V. 79. P. 55.
  23. Chella F., Pizzella V., Zappasodi F., Marzetti L. Impact of the reference choice on scalp EEG-connectivity estimation // J. Neural Eng. 2016. V. 13. № 3. P. e036016.
  24. Miljevic A., Bailey N.W., Vila-Rodriguez F. et al. Electroencephalographic Connectivity: A Fundamental Guide and Checklist for Optimal Study Design and Evaluation // Biol. Psychiatry Cogn. Neurosci. Neuroimaging. 2022. V. 7. № 6. P. 546.
  25. Scally B., Burke M.R., Bunce D., Delvenne J.F. Resting-state EEG power and connectivity are associated with alpha peak frequency slowing in healthy aging // Neurobiol. Aging. 2018. V. 71. P. 149.
  26. Angelakis E., Lubar J.F., Stathopoulou S., Kounios J. Peak alpha frequency: an electroencephalographic measure of cognitive preparedness // Clin. Neurophysiol. 2004. V. 115. № 4. P. 887.
  27. Doppelmayr M., Klimesch W., Pachinger T., Ripper B. Individual differences in brain dynamics: important implications for the calculation of event-related band power // Biol. Cybern. 1998. V. 79. № 1. Р. 49.
  28. Sheikh H.I., Hayden E.P., Kryski K.R. et al. Genotyping the BDNF rs6265 (val66met) polymorphism by one-step amplified refractory mutation system PCR // Psychiatr. Genet. 2010. V. 20. № 3. P. 109.
  29. Utoomprurkporn N., Hardy C.J.D., Stott J. et al. “The Dichotic Digit Test” as an Index Indicator for Hearing Problem in Dementia: Systematic Review and Meta-Analysis // J. Am. Acad. Audiol. 2020. V. 31. № 9. P. 646.
  30. Zalesky A., Fornito A., Bullmore E.T. Network-based statistic: Identifying differences in brain networks // NeuroImage. 2010. V. 53. № 4. P. 1197.
  31. Bullmore E.T., Suckling J., Overmeyer S. et al. Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain // IEEE Trans. Med. Imaging. 1999. V. 18. № 1. P. 32.
  32. West S.G., Finch J.F., Curran P.J. Structural equation models with non-normal variables / Structural equation modeling: Concepts, issues and applications // Ed. Hoyle R.H. Thousand Oaks, CA: Sage, 1995. P. 56.
  33. Bagit A., Hayward, G.C., MacPherson R.E.K. Exercise and estrogen: common pathways in Alzheimer’s disease pathology // Am. J. Physiol. Endocrinol. Metab. 2021. V. 321. № 1. P. E164.
  34. Allen A., McCarson K. Estrogen increases nociception-evoked brain-derived neurotrophic factor gene expression in the female rat // Neuroendocrinology. 2005. V. 81. № 3. P. 193.
  35. Barha C.K., Liu-Ambrose T., Best J.R. et al. Sex-dependent effect of the BDNF Val66Met polymorphism on executive functioning and processing speed in older adults: Evidence from the Health ABC study // Neurobiol. Aging. 2018. V. 74. P. 161.
  36. Filová B., Ostatníková D., Celec P., Hodosy J. The effect of testosterone on the formation of brain structures // Cells Tissues Organs. 2013. V. 197. № 3. P. 169.
  37. Spets D.S., Slotnick S.D. Are there sex differences in brain activity during long-term memory? A systematic review and fMRI activation likelihood estimation meta-analysis // Cogn. Neurosci. 2021. V. 12. № 3-4. P. 163.
  38. Luft C.D.B., Zioga I., Thompson N.M. et al. Right temporal alpha oscillations as a neural mechanism for inhibiting obvious associations // Proc. Natl. Acad. Sci. U.S.A. 2018. V. 115. № 52. P. e12144.
  39. De Vincenti A.P., Ríos A.S., Paratcha G., Ledda F. Mechanisms That Modulate and Diversify BDNF Functions: Implications for Hippocampal Synaptic Plasticity // Front. Cell. Neurosci. 2019. V. 13. P. e135.
  40. Stacho M., Manahan-Vaughan D. The Intriguing Contribution of Hippocampal Long-Term Depression to Spatial Learning and Long-Term Memory // Front. Behave. Neurosci. 2022. V. 16. P. 806356.
  41. Matyi M.A., Spielberg J.M. The structural brain network topology of episodic memory // PloS One. 2022. V. 17. № 6. P. e0270592.

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2. Fig. 1. Indicators of neural network connectivity at the frequency of the a1 rhythm in men: the effect of the genotype. Each circle represents a node, the size of the circle is proportional to the number of connections with other nodes. The figure shows all nodes and connections (n = 33), which are more pronounced in Val/Met carriers compared to Val/Val (FWEcorrected p = 0.013).

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3. Fig. 2. Neural network connectivity indicator (the average value of the strength of all incoming connections in the neural network) at the frequency α1-rhythm depending on Val66Met polymorphism and gender. The same icons indicate significant differences between the corresponding values, p < 0.001. Here and below, the error bars indicate the confidence interval.

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