Skip to main content

Advertisement

SpringerLink
Log in

The Resting State Functional MRI in Neurology and Psychiatry

  • Published:
Neuroscience and Behavioral Physiology Aims and scope Submit manuscript

Resting state functional MRI (fMRI) is a neuroimaging method based on analysis of spontaneous fluctuations in the activity of brain areas using measurements of their magnetic resonance signals to investigate the functional architecture of the brain at rest. Use of resting state fMRI opens up the possibility of assessing functional interactions both in normal conditions and in various types of CNS pathology with the aims of clarifying impaired mechanisms of brain functions, developing approaches to noninvasive therapeutic neuromodulation, and for preoperative mapping. An understanding of the features of data acquisition, preprocessing, and analysis is very important for clinical specialists using resting state fMRI investigations, as it is neurologists, psychiatrists, and neurosurgeons who set the tasks for clinical application of this methodology and who are the final consumers of the results. This article provides a detailed review of the methodological characteristics of resting state data acquisition and analysis and its advantages and drawbacks. The article is intended for a wide range of specialists using resting state fMRI in their work or who are planning to use it.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. S. Ogawa, R. S. Menon, S. G. Kim, and K. Ugurbil, “On the characteristics of functional magnetic resonance imaging of the brain,” Annu. Rev. Biophys. Biomol. Struct., 27, 447–474 (1998), https://doi.org/10.1146/annurev.biophys.27.1.447.

    Article  CAS  PubMed  Google Scholar 

  2. B. Biswal, F. Z. Yetkin, V. M. Haughton, and J. S. Hyde, “Functional connectivity in the motor cortex of resting human brain using echo-planar MRI,” Magn. Reson. Med., 34, No. 4, 537–541 (1995), https://doi.org/10.1002/mrm.1910340409.

    Article  CAS  PubMed  Google Scholar 

  3. F. X. Castellanos, A. Di Martino, R. C. Craddock, et al., “Clinical applications of the functional connectome,” NeuroImage, 80, 527–540 (2013), https://doi.org/10.1016/j.neuroimage.2013.04.083.

    Article  CAS  PubMed  Google Scholar 

  4. K. R. Van Dijk, T. Hedden, A. Venkataraman, et al., “Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization,” J. Neurophysiol., 103, No. 1, 297–321 (2010), https://doi.org/10.1152/jn.00783.2009.

    Article  PubMed  Google Scholar 

  5. S. M. Smith, C. F. Beckmann, J. Andersson, et al., “Resting-state fMRI in the Human Connectome Project,” NeuroImage, 80, 144–168 (2013), https://doi.org/10.1016/j.neuroimage.2013.05.039.

    Article  PubMed  Google Scholar 

  6. K. A. Smitha, K. Akhil Raja, K. M. Arun, et al., “Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks,” Neuroradiol. J., 30, No. 4, 305–317 (2017), https://doi.org/10.1177/1971400917697342.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. S. A. Huettel, A. W., Song, and G. McCarthy, Functional Magnetic Resonance Imaging, Sinauer Associates, Sunderland, MA (2004), Vol. 1.

  8. B. Janine, Smith St M., and C. F. Beckmann, An Introduction to Resting State fMRI Functional Connectivity, Oxford University Press (2017).

    Google Scholar 

  9. M. Jenkinson and Michael Chappell, Introduction to Neuroimaging Analysis, Oxford University Press (2018).

    Google Scholar 

  10. D. Bulte and K. Wartolowska, “Monitoring cardiac and respiratory physiology during fMRI,” NeuroImage, 154, 81–91 (2017), https://doi.org/10.1016/j.neuroimage.2016.12.001.

    Article  PubMed  Google Scholar 

  11. D. M. Cole, S. M. Smith, and C. F. Beckmann, “Advances and pitfalls in the analysis and interpretation of resting-state fMRI data,” Front. Syst. Neurosci., 4, 8 (2010), https://doi.org/10.3389/fnsys.2010.00008.

    Article  PubMed  PubMed Central  Google Scholar 

  12. K. J. Friston, “Functional and effective connectivity: a review,” Brain Connect., 1, No. 1, 13–36 (2011), https://doi.org/10.1089/brain.2011.0008.

    Article  PubMed  Google Scholar 

  13. M. A. Piradov, N. A. Suponeva, Yu. A. Seliverstov, et al., “Potentials in Current Neuroimaging Methods for Studies of Spontaneous Brain Activity in the Resting State,” Nevrol. Zh., 21, No. 1, 4–12 (2016), https://doi.org/10.18821/1560-9545-2016-21-1-4-12.

  14. T. A. Bukkieva, D. S. Chegina, A. Yu. Efimtsev, T. A., et al., “Resting state functional MRI. General questions and clinical application,” Russ. Electron. J. Radiol., 9, No. 2, 150–170 (2019), https://doi.org/10.21569/2222-7415-2019-9-2-150-170.

  15. M. D. Fox and M. Greicius, “Clinical applications of resting state functional connectivity,” Front. Syst. Neurosci., 4, 19 (2010), https://doi.org/10.3389/fnsys.2010.00019.

    Article  PubMed  PubMed Central  Google Scholar 

  16. M. P. van den Heuvel and H. E. Hulshoff Pol, “Exploring the brain network: a review on resting-state fMRI functional connectivity,” Eur Neuropsychopharmacol., 20, No. 8, 519–534 (2010), https://doi.org/10.1016/j.euroneuro.2010.03.008.

    Article  CAS  PubMed  Google Scholar 

  17. A. Nieto-Castanon, A Handbook of Functional Connectivity Magnetic Resonance Imaging Methods in CONN, Hilbert-Press, Boston, MA (2020).

    Book  Google Scholar 

  18. R. Martuzzi, R. Ramani, M. Qiu, et al., “A whole-brain voxel based measure of intrinsic connectivity contrast reveals local changes in tissue connectivity with anesthetic without a priori assumptions on thresholds or regions of interest,” NeuroImage, 58, No. 4, 1044–1050 (2011), https://doi.org/10.1016/j.neuroimage.2011.06.075.

    Article  PubMed  Google Scholar 

  19. Y. Zang, T. Jiang, Y. Lu, et al., “Regional homogeneity approach to fMRI data analysis,” NeuroImage, 22, No. 1, 394–400 (2004), https://doi.org/10.1016/j.neuroimage.2003.12.030.

    Article  PubMed  Google Scholar 

  20. H. Yang, X. Y. Long, Y. Yang, et al., “Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI,” NeuroImage, 36, No. 1, 144–152 (2007), https://doi.org/10.1016/j.neuroimage.2007.01.054.

    Article  PubMed  Google Scholar 

  21. Q. H. Zou, C. Z. Zhu, Y. Yang, et al., “An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF,” J. Neurosci. Meth., 172, No. 1, 137–141 (2008), https://doi.org/10.1016/j.jneumeth.2008.04.012.

  22. V. Kiviniemi, J. H. Kantola, J. Jauhiainen, et al., “Independent component analysis of nondeterministic fMRI signal sources,” Neuro-Image, 19, No. 2, Pt. 1, 253–260 (2003), https://doi.org/10.016/s1053-8119(03)00097-1.

  23. Yu. A. Seliverstov, E. V. Seliverstova, R. N. Konovalov, et al., “Resting state functional magnetic resonance tomography: potential and future of the method,” Byull. Nats. Obshch. Izuch. Bol. Park. Rass. Dvizh., 1, 16–19 (2014).

    Google Scholar 

  24. E. V. Seliverstova, Yu. A. Seliverstov, R. N. Konovalov, and S. N. Illarioshkin, “Resting state functional magnetic resonance tomography: new opportunities in the study of brain physiology and pathology,” Ann. Klin. Esperim. Nevrol., 7, No. 46, 39–43 (2013).

    Google Scholar 

  25. S. Chenji, S. Jha, D. Lee, et al., “Investigating default mode and sensorimotor network connectivity in amyotrophic lateral sclerosis,” PLoS One, 11, No. 6, e0157443 (2016), https://doi.org/10.1371/journal.pone.0157443.

  26. W. Shen, Y. Tu, R. L. Gollub, et al., “Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study,” NeuroImage Clin, 22, 101775 (2019), https://doi.org/10.1016/j.nicl.2019.01775.

    Article  PubMed  PubMed Central  Google Scholar 

  27. C. Rosazza and L. Minati, “Resting-state brain networks: literature review and clinical applications,” Neurol. Sci., 32, No. 5, 773–785 (2011), https://doi.org/10.1007/s10072-011-0636-y.

    Article  PubMed  Google Scholar 

  28. A. R. Luriya, Higher Cortical Functions in Humans, Moscow State University Press, Moscow (1980), 2nd ed.

    Book  Google Scholar 

  29. E. Diachek, I. Blank, M. Siegelman, et al., “The domain-general multiple demand (md) network does not support core aspects of language comprehension: A large-scale fMRI investigation,” J. Neurosci., 40, No. 23, 4536–4550 (2020), https://doi.org/10.1523/JNEUROSCI.2036-19.2020.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. J. Duncan, “The structure of cognition: attentional episodes in mind and brain,” Neuron, 80, No. 1, 35–50 (2013), https://doi.org/10.1016/j.neuron.2013.09.015.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. A. W. Toga, Brain Mapping: An Encyclopedic Reference, Academic Press (2015), pp. 597–611.

    Google Scholar 

  32. S. N. Morozova, E. I. Kremneva, Z. Sh. Gadzhieva, et al., “Determination of the effectiveness of using counting as an fMRI paradigm in studies of functional connections in health for assessment of the executive functions of the brain,” Med. Vizualiz., 24, No. 2, 119–130 (2020), https://doi.org/10.24835/1607-0763-2020-2-119-130.

  33. M. E. Raichle, A. M. MacLeod, A. Z. Snyder, et al., “A default mode of brain function,” Proc. Natl. Acad. Sci. USA, 98, No. 2, 676–682 (2001), https://doi.org/10.1073/pnas.98.2.676.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. S. Vossel, J. J. Geng, and G. R. Fink, “Dorsal and ventral attention systems: distinct neural circuits but collaborative roles,” Neuroscientist, 20, No. 2, 150–159 (2014), https://doi.org/10.1177/1073858413494269.

    Article  PubMed  PubMed Central  Google Scholar 

  35. F. V. Farahani, W. Karwowski, and N. R. Lighthall, “Application of graph theory for identifying connectivity patterns in human brain networks: A systematic review,” Front. Neurosci., 13, 585 (2019), https://doi.org/10.3389/fnins.2019.00585.

    Article  PubMed  PubMed Central  Google Scholar 

  36. X. Shen, F. Tokoglu, X. Papademetris, and R. T. Constable, “Groupwise whole-brain parcellation from resting-state fMRI data for network node identification,” NeuroImage, 82, 403–415 (2013), https://doi.org/10.1016/j.neuroimage.2013.05.081.

    Article  CAS  PubMed  Google Scholar 

  37. K. Supekar, V. Menon, D. Rubin, et al., “Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease,” PLoS Comput. Biol., 4, No. 6, e1000100 (2008), https://doi.org/10.1371/journal.pcbi.1000100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. A. Hafkemeijer, C. Möller, E. G. Dopper, et al., “Resting state functional connectivity differences between behavioral variant frontotemporal dementia and Alzheimer’s disease,” Front. Hum. Neurosci., 9, 474 (2015), https://doi.org/10.3389/fnhum.2015.00474.

    Article  PubMed  PubMed Central  Google Scholar 

  39. M. Argyelan, T. Ikuta, P. DeRosse, et al., “Resting-state fMRI connectivity impairment in schizophrenia and bipolar disorder,” Schizophr. Bull., 40, No. 1, 100–110 (2014), https://doi.org/10.1093/schbul/sbt092.

    Article  PubMed  Google Scholar 

  40. M. Greicius, “Resting-state functional connectivity in neuropsychiatric disorders,” Curr. Opin. Neurol., 21, No. 4, 424–430 (2008), https://doi.org/10.1097/WCO.0b013e328306f2c5.

    Article  PubMed  Google Scholar 

  41. A. Badhwar, A. Tam, C. Dansereau, et al., “Resting-state network dysfunction in Alzheimer’s disease: A systematic review and meta-analysis,” Alzheimers Dement. (Amst.), 8, 73–85 (2017), https://doi.org/10.1016/j.dadm.2017.03.00.

    Article  Google Scholar 

  42. D. Zhang, X. Liu, J. Chen, et al., “Widespread increase of functional connectivity in Parkinson’s disease with tremor: a resting-state fMRI study,” Front. Aging Neurosci., 7, 6 (2015), https://doi.org/10.3389/fnagi.2015.00006.

    Article  PubMed  PubMed Central  Google Scholar 

  43. A. S. Smirnov, M. G. Sharaev, T. V. Mel’nikova-Pitskhelauri, et al., “Resting state functional MRI of the brain in preoperative planning. Literature review,” Med. Vizualiz., No. 5, 6–13 (2018), https://doi.org/10.24835/1607-0763-2018-5-6-13.

  44. V. A. Kumar, I. M. Heiba, S. S. Prabhu, et al., “The role of resting-state functional MRI for clinical preoperative language mapping,” Cancer Imaging, 20, No. 1, 47 (2020), https://doi.org/10.1186/s40644-020-00327-w.

  45. D. Picchioni, J. H. Duyn, and S. G. Horovitz, “Sleep and the functional connectome,” NeuroImage, 80, 387–396 (2013), https://doi.org/10.1016/j.neuroimage.2013.05.067.

    Article  PubMed  Google Scholar 

  46. R. M. Hutchison, T. Womelsdorf, E. A. Allen, et al., “Dynamic functional connectivity: promise, issues, and interpretations,” NeuroImage, 80, 360–378 (2013), https://doi.org/10.1016/j.neuroimage.2013.05.079.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Center of Neurology, Moscow, Russia

    E. I. Kremneva, D. O. Sinitsyn, L. A. Dobrynina, A. D. Suslina & M. V. Krotenkova

Authors
  1. E. I. Kremneva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. O. Sinitsyn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. A. Dobrynina
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. D. Suslina
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. V. Krotenkova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. I. Kremneva.

Additional information

Translated from Zhurnal Nevrologii i Psikhiatrii imeni S. S. Korsakova, Vol. 122, No. 2, pp. 5–14, February, 2022.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kremneva, E.I., Sinitsyn, D.O., Dobrynina, L.A. et al. The Resting State Functional MRI in Neurology and Psychiatry. Neurosci Behav Physi 52, 855–864 (2022). https://doi.org/10.1007/s11055-022-01309-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11055-022-01309-0

Keywords

Search

Navigation