Skip to main content
SpringerLink
Log in
Book cover

International Conference in Software Engineering for Defence Applications

SEDA 2018: Proceedings of 6th International Conference in Software Engineering for Defence Applications pp 325–332Cite as

On the Parcellation of Functional Magnetic Resonance Images

  • Conference paper
  • First Online:

  • 454 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 925))

Abstract

Functional Magnetic Resonance Imaging (fMRI) is one of the techniques for measuring activities in the brain and it has been demonstrated to have a high potential in clinical application. However, fMRI is limited by some of the contradictory results reported by different studies. One of the possible reasons for this contradiction is the lack of standard and acceptable methods of analyzing fMRI data. Analysis of fMRI data in studies focusing on brain connectivity normally requires the definition of region of interest. This is normally done using regions of interest drawn on high resolution anatomical images. The use of anatomical images implies using structural information, thereby losing any functional information that could improve the analysis of fMRI data. In this article, we present the framework for a region of interest definition for fMRI using structural and functional information. Contrary to existing approaches, the proposed method will also consider the use of network properties. The method uses a bottom-up approach as it starts with structural information, then include functional information before it finally includes network properties. We hypothesize that the use of multiple information in defining the regions of interests in fMRI data will produce a more accurate, more reproducible and more trusted results than the use of structural information only. It is hoped that the use of the proposed model will lead to improved analysis of fMRI brain data, hence increasing its diagnostic potential.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Chen JE, Glover GH (2015) Functional magnetic resonance imaging methods. Neuropsychol Rev 25(3):289–313

    Article  Google Scholar 

  2. Ogawa S et al (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci 87(24):9868–9872

    Article  Google Scholar 

  3. Elliott ML et al (2018) General functional connectivity: shared features of resting-state and task fMRI drive reliable individual differences in functional brain networks, p 330530

    Google Scholar 

  4. Martuzzi R et al (2010) Functional connectivity and alterations in baseline brain state in humans. Neuroimage 49(1):823–834

    Article  Google Scholar 

  5. Hampson M et al (2006) Brain connectivity related to working memory performance. J Neurosci 26(51):13338–13343

    Article  Google Scholar 

  6. Dezhina Z et al (2018) A systematic review of associations between functional MRI activity and polygenic risk for schizophrenia and bipolar disorder 1–16

    Google Scholar 

  7. Kenny ER et al (2010) Functional connectivity in late-life depression using resting-state functional magnetic resonance imaging. Am J Geriatr Psychiatry 18(7):643–651

    Article  Google Scholar 

  8. Friston K et al (1993) Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab 13(1):5–14

    Article  Google Scholar 

  9. McKeown MJ et al (1998) Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp 6(3):160–188

    Article  Google Scholar 

  10. Salvador R et al (2005) Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex 15(9):1332–1342

    Article  Google Scholar 

  11. Griffanti L et al (2016) Challenges in the reproducibility of clinical studies with resting state fMRI: an example in early Parkinson’s disease. Neuroimage 124:704–713

    Article  Google Scholar 

  12. Bennett CM, Miller MB (2010) How reliable are the results from functional magnetic resonance imaging? Ann N Y Acad Sci 1191(1):133–155

    Article  Google Scholar 

  13. Cole DM, Smith SM, Beckmann CF (2010) Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front Syst Neurosci 4:8

    Google Scholar 

  14. Islam M et al (2018) A survey of graph based complex brain network analysis using functional and diffusional MRI. Am J Appl Sci 14(12):1186–1208

    Article  Google Scholar 

  15. Strogatz SH (2001) Exploring complex networks. Nature 410(6825):268

    Article  Google Scholar 

  16. Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47

    Article  MathSciNet  Google Scholar 

  17. Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256

    Article  MathSciNet  Google Scholar 

  18. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186

    Article  Google Scholar 

  19. Rubinov M et al (2009) Small-world properties of nonlinear brain activity in schizophrenia. Hum Brain Mapp 30(2):403–416

    Article  Google Scholar 

  20. Finotellia P, Dulioa P (2015) Graph theoretical analysis of the brain. An overview. Scienze e Ricerche 9:89–96

    Google Scholar 

  21. Bassett DS, Bullmore ET (2017) Small-world brain networks revisited. Neuroscientist 23(5):499–516

    Article  Google Scholar 

  22. Meunier D et al (2009) Hierarchical modularity in human brain functional networks. Front Neuroinform 3:37

    Article  Google Scholar 

  23. Di X et al (2013) Task vs. rest—different network configurations between the coactivation and the resting-state brain networks. Front Hum Neurosci 7:493

    Google Scholar 

  24. Supekar K, Musen M, Menon V (2009) Development of large-scale functional brain networks in children. PLoS Biol 7(7):e1000157

    Article  Google Scholar 

  25. Wang L et al (2010) Age-related changes in topological patterns of large-scale brain functional networks during memory encoding and recognition. Neuroimage 50(3):862–872

    Article  Google Scholar 

  26. Achard S, Bullmore E (2007) Efficiency and cost of economical brain functional networks. PLoS Comput Biol 3(2):e17

    Article  Google Scholar 

  27. Buckner RL et al (2009) Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci 29(6):1860–1873

    Article  Google Scholar 

  28. Cieri F et al (2017) Late-life depression: modifications of brain resting state activity. J Geriatr Psychiatry Neurol 30(3):140–150

    Article  Google Scholar 

  29. Korhonen O et al (2017) Consistency of regions of interest as nodes of fMRI functional brain networks. Netw Neurosci 1(3):254–274

    Article  MathSciNet  Google Scholar 

  30. Fornito A, Zalesky A, Bullmore ET (2010) Network scaling effects in graph analytic studies of human resting-state fMRI data. Front Syst Neurosci 4:22

    Google Scholar 

  31. Stanley ML et al (2013) Defining nodes in complex brain networks. Front Comput Neurosci 7:169

    Article  Google Scholar 

  32. Wang J, Zuo X, He Y (2010) Graph-based network analysis of resting-state functional MRI. Front Syst Neurosci 4:16

    Google Scholar 

  33. Zalesky A et al (2010) Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50(3):970–983

    Article  Google Scholar 

  34. Achard S et al (2006) A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 26(1):63–72

    Article  Google Scholar 

  35. Aubert-Broche B et al (2009) Clustering of atlas-defined cortical regions based on relaxation times and proton density. Neuroimage 47(2):523–532

    Article  Google Scholar 

  36. Chen ZJ et al (2008) Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cereb Cortex 18(10):2374–2381

    Article  Google Scholar 

  37. Deleus F, Van Hulle MM (2009) A connectivity-based method for defining regions-of-interest in fMRI data. IEEE Trans Image Process 18(8):1760–1771

    Article  MathSciNet  Google Scholar 

  38. Estrada E, Hatano N (2008) Communicability in complex networks. Phys Rev E 77(3):036111

    Article  MathSciNet  Google Scholar 

  39. Honey C et al (2009) Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci 106(6):2035–2040

    Article  Google Scholar 

  40. Zhang J et al (2013) A manual, semi-automated and automated ROI study of fMRI hemodynamic response in the caudate. Nucleus 2(150):2

    Google Scholar 

  41. Wang J et al (2009) Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Hum Brain Mapp 30(5):1511–1523

    Article  Google Scholar 

  42. Collins DL et al (1995) Automatic 3-D model-based neuroanatomical segmentation. Hum Brain Mapp 3(3):190–208

    Article  Google Scholar 

  43. Tzourio-Mazoyer N et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1):273–289

    Article  Google Scholar 

  44. Fischl B et al (2004) Automatically parcellating the human cerebral cortex. Cereb Cortex 14(1):11–22

    Article  Google Scholar 

  45. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Article  Google Scholar 

  46. Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582

    Article  Google Scholar 

  47. DonGiovanni D, Vaina LM (2016) Select and cluster: a method for finding functional networks of clustered voxels in fMRI. Comput Intell Neurosci 2016

    Google Scholar 

  48. Hagmann P et al (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6(7):e159

    Article  Google Scholar 

  49. Hagmann P et al (2007) Mapping human whole-brain structural networks with diffusion MRI. PloS One 2(7):e597

    Article  Google Scholar 

  50. Shen X, Papademetris X, Constable RT (2010) Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data. Neuroimage 50(3):1027–1035

    Article  Google Scholar 

  51. McLachlan G, Peel D (2000) Finite mixture models, Willey series in probability and statistics. Wiley, New York

    Book  Google Scholar 

  52. Heckemann RA et al (2006) Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33(1):115–126

    Article  Google Scholar 

  53. Golland Y, Golland P, Bentin S, Malach RJN (2008) Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems, 46(2):540–553

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Lagos State University, Ojo, PMB 0001, Nigeria

    Adam Folohunsho Zubair & Segun Benjamin Aribisala

  2. Institute of Technologies and Software Development, Innopolis University, Innopolis, 420500, Russia

    Adam Folohunsho Zubair & Manuel Mazzara

  3. Conseil Européen pour la Recherche Nucléaire Openlab, 1211, Geneva, Switzerland

    Marco Manca

Authors
  1. Adam Folohunsho Zubair
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Segun Benjamin Aribisala
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marco Manca
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manuel Mazzara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adam Folohunsho Zubair .

Editor information

Editors and Affiliations

  1. University of Bologna, Bologna, Italy

    Paolo Ciancarini

  2. Innopolis University, Innopolis, Russia

    Manuel Mazzara

  3. Innopolis University, Innopolis, Russia

    Angelo Messina

  4. Innopolis University, Innopolis, Russia

    Alberto Sillitti

  5. Innopolis University, Innopolis, Russia

    Giancarlo Succi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zubair, A.F., Aribisala, S.B., Manca, M., Mazzara, M. (2020). On the Parcellation of Functional Magnetic Resonance Images. In: Ciancarini, P., Mazzara, M., Messina, A., Sillitti, A., Succi, G. (eds) Proceedings of 6th International Conference in Software Engineering for Defence Applications. SEDA 2018. Advances in Intelligent Systems and Computing, vol 925. Springer, Cham. https://doi.org/10.1007/978-3-030-14687-0_29

Download citation

Publish with us

Policies and ethics

Search

Navigation