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Peculiarities in Interaction of Independent Components of Resting-State fMRI Signal in Patients with Mild Depressions

Some aspects of resting-state fMRI signal can be the key markers of depression. fMRI was recoded over 4 min in evidently healthy persons (N=21) and in patients with mild depression (N=21). The data were separated into the independent spatial components, and the strength of their association with established brain networks was analyzed. The patients with mild depression were characterized with greater correlations between the components representing the ventral and dorsal subdivisions of default mode network (DMN), whereas correlations between the components relating to cerebellum and to the left hemisphere language system were less pronounced. The data revealed a significant role of DMN in the development of affective abnormalities and importance of its functional state as a probable marker of mild depression.

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References

  1. Knyazev GG, Savost’yanov AN, Bocharov AV, Saprygin AE, Tamozhnikov SS. Depressive symptomatology and the activity of oscillatory resting state networks. Neurosc. Behav. Physiol. 2016;46(8):942-947.

    Article  Google Scholar 

  2. Tumyalis AV, Aftanas LI. Contribution of neurophysiological endophenotype, individual frequency of EEG alpha oscillations, to mechanisms of emotional reactivity. Bull. Exp. Biol. Med. 2014;156(6):711-716.

    CAS  Article  PubMed  Google Scholar 

  3. Alalade E, Denny K, Potter G, Steffens D, Wang L. Altered cerebellar-cerebral functional connectivity in geriatric depression. PLoS One. 2011;6(5):e20035.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. Bell AJ, Sejnowski TJ. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 1995;7(6):1129-1159.

    CAS  Article  PubMed  Google Scholar 

  5. Belleau EL, Taubitz LE, Larson CL. Imbalance of default mode and regulatory networks during externally focused processing in depression. Soc. Cogn. Affect. Neurosci. 2015;10(5):744-751.

    Article  PubMed  Google Scholar 

  6. Briceño EM, Rapport LJ, Kassel MT, Bieliauskas LA, Zubieta JK, Weisenbach SL, Langenecker SA. Age and gender modulate the neural circuitry supporting facial emotion processing in adults with major depressive disorder. Am. J. Geriatr. Psychiatry. 2015;23(3):304-313.

    Article  PubMed  Google Scholar 

  7. Chen Y, Wang C, Zhu X, Tan Y, Zhong Y. Aberrant connectivity within the default mode network in first-episode, treatment-naïve major depressive disorder. J. Affect. Disord. 2015;183:49-56.

    Article  PubMed  Google Scholar 

  8. Goya-Maldonado R, Brodmann K, Keil M, Trost S, Dechent P, Gruber O. Differentiating unipolar and bipolar depression by alterations in large-scale brain networks. Hum. Brain Mapp. 2016;37(2):808-818.

    Article  PubMed  Google Scholar 

  9. Jafri MJ, Pearlson GD, Stevens M, Calhoun VD. A method for functional network connectivity among spatially Independent resting-state components in schizophrenia. Neuroimage. 2008;39(4):1666-1681.

    Article  PubMed  Google Scholar 

  10. Liu L, Zeng LL, Li Y, Ma Q, Li B, Shen H, Hu D. Altered cerebellar functional connectivity with intrinsic connectivity networks in adults with major depressive disorder. PLoS One. 2012;7(6):e39516.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. McKeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, Bell AJ, Sejnowski TJ. Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp. 1998;6(3):160-188.

    CAS  Article  PubMed  Google Scholar 

  12. Nixon NL, Liddle PF, Nixon E, Worwood G, Liotti M, Palaniyappan L. Biological vulnerability to depression: linked structural and functional brain network findings. Br. J. Psychiatry. 2014;204:283-289.

    CAS  Article  PubMed  Google Scholar 

  13. Pannekoek JN, van der Werff SJ, Meens PH, van den Bulk BG, Jolles DD, Veer IM, van Lang ND, Rombouts SA, van der Wee NJ, Vermeiren RR. Aberrant resting-state functional connectivity in limbic and salience networks in treatment-naïve clinically depressed adolescents. J. Child Psychol. Psychiatry. 2014;55(12):1317-1327.

    Article  PubMed  Google Scholar 

  14. Phillips JR, Hewedi DH, Eissa AM, Moustafa AA. The cerebellum and psychiatric disorders. Front. Public Health. 2015;3:66. doi: https://doi.org/10.3389/fpubh.2015.00066.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Yuan H, Young KD, Phillips R, Zotev V, Misaki M, Bodurka J. Resting-state functional connectivity modulation and sustained changes after real-time functional magnetic resonance imaging neurofeedback training in depression. Brain Connect. 2014;4(9):690-701.

    Article  PubMed  PubMed Central  Google Scholar 

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Correspondence to M. E. Mel’nikov.

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Translated from Byulleten’ Eksperimental’noi Biologii i Meditsiny, Vol. 163, No. 4, pp. 499-502, April, 2017

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Mel’nikov, M.E., Bezmaternykh, D.D., Petrovskii, E.D. et al. Peculiarities in Interaction of Independent Components of Resting-State fMRI Signal in Patients with Mild Depressions. Bull Exp Biol Med 163, 497–499 (2017). https://doi.org/10.1007/s10517-017-3837-4

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  • DOI: https://doi.org/10.1007/s10517-017-3837-4

Key Words

  • depression
  • default mode network
  • analysis of independent components
  • resting-state fMRI