Advertisement

A Framework for the Automation of Multimodalbrain Connectivity Analyses

  • Paulo MarquesEmail author
  • Jose Miguel Soares
  • Ricardo Magalhaes
  • Nuno Sousa
  • Victor Alves
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 616)

Abstract

In neuroscience research, there has been an increasing interest in multimodal analysis, combining the strengths of unimodal analysis while reducing some of its drawbacks. However, this increases complexity in data processing and analysis, requiring a big amount of technical knowledge in image manipulation and a lot of iterative processes requiring user intervention. In this work we present a framework that incorporates some of this technical knowledge and enables the automation of most of the processing in the context of combined resting-state functional Magnetic Resonance Imaging (rs-fMRI) and Diffusion Tensor Imaging (DTI) data processing and analysis. The proposed framework presents an object-oriented architecture and its structure reflects the nature of three levels of data processing (i.e. acquisition level, subject level and study level). This framework opens the door to more intelligent and scalable systems for neuroimaging data processing and analysis that ultimately will lead to the dissemination of such advanced techniques.

Notes

Acknowledgments

This work has been supported by FCT—Fundao para a Cincia e Tecnologia within the Project Scope UID/CEC/00319/2013. PM was supported by the SWITCHBOX project through the grant SwitchBox-FP7-HEALTH-2010-grant 259772-2 and RM is supported by the Portuguese North Regional Operational Program (ON.2 O Novo Norte) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER) by a fellowship from the project FCT-ANR/NEU-OSD/0258/2012 funded by FCT/MEC (www.fct.pt) and by FEDER.

References

  1. 1.
    Gong, G., He, Y., Concha, L., et al.: Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex 19(3), 524–536 (2009). doi: 10.1093/cercor/bhn102 CrossRefGoogle Scholar
  2. 2.
    Damoiseaux, J.S., Rombouts, S.A., Barkhof, F., et al.: Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. U.S.A. 103, 13848–13853 (2006). doi: 10.1073/pnas.0601417103 CrossRefGoogle Scholar
  3. 3.
    Basser, P.J., Pajevic, S., Pierpaoli, C., et al.: In vivo fiber tractography using DT? MRI data. Magn. Reson. Med. 44(4), 625–632 (2000)CrossRefGoogle Scholar
  4. 4.
    van den Heuvel, M.P., Mandl, R., Luigjes, J., et al.: Microstructural organization of the cingulum tract and the level of default mode functional connectivity. J. Neurosci. 28, 1084410851 (2008). doi: 10.1523/JNEUROSCI.2964-08.2008 Google Scholar
  5. 5.
    Hasan, K.M., Walimuni, I.S., Abid, H., et al.: A review of diffusion tensor magnetic resonance imaging computational methods and software tools. Comput. Biol. Med. 41, 10621072 (2011). doi: 10.1016/j.compbiomed.2010.10.008 CrossRefGoogle Scholar
  6. 6.
    Haller, S., Bartsch, A.J.: Pitfalls in FMRI. Eur. Radiol. 19, 2689–2706 (2009). doi: 10.1007/s00330-009-1456-9 CrossRefGoogle Scholar
  7. 7.
    Vasilakos, A., Witold, P.: Ambient Intelligence, Wireless Networking, and Ubiquitous Computing. Artech House, Inc (2006)Google Scholar
  8. 8.
    Rech, J., Klaus-Dieter, A.: Artificial intelligence and software engineering: Status and future trends. KI 18(3), 5–11 (2004)Google Scholar
  9. 9.
    Digital imaging and communications in medicine (DICOM): National Electrical Manufacturers Association (1998)Google Scholar
  10. 10.
    Cox, R.W., Ashburner, J., Breman, H., et al.: A (sort of) new image data format standard: nifti-1. Human Brain Mapp. 25, 33 (2004)Google Scholar
  11. 11.
    Penny, W.D., Friston, K.J., Ashburner, J.T. et al (2011) Statistical Parametric Mapping: The Analysis of Functional Brain Images: The Analysis of Functional Brain Images. Academic pressGoogle Scholar
  12. 12.
    Smith, S.M., Jenkinson, M., Woolrich, M.W., et al.: Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23, S208–S219 (2004)CrossRefGoogle Scholar
  13. 13.
    Cox, R.W.: AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res., Int. J. 29(3), 162–173 (1996)CrossRefGoogle Scholar
  14. 14.
    Goebel, R.: Brainvoyager: a program for analyzing and visualizing functional and structural magnetic resonance data sets. Neuroimage 3(3), S604 (1996)CrossRefGoogle Scholar
  15. 15.
    Jiang, H., van Zijl, P.C., Kim, J., et al.: DtiStudio:resource program for diffusion tensor computation and fiber bundle tracking. Comput. Methods Programs Biomed. 81(2), 106–116 (2006)CrossRefGoogle Scholar
  16. 16.
    Wang R, Benner T, Sorensen AG et al (2007) Diffusion toolkit: a software package for diffusion imaging data processing and tractography. Proc. Intl. Soc. Mag. Reson. Med. 15(3720)Google Scholar
  17. 17.
    Pieper S, Halle M, Kikinis R (2004) 3D Slicer. In: IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2004, pp. 632–635. IEEEGoogle Scholar
  18. 18.
    Marques, P., Soares, J.M., Alves, V. et al. (2013) BrainCAT-a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis. Frontiers Human Neurosci. 7 Google Scholar
  19. 19.
    Rorden, C., Brett, M.: Stereotaxic display of brain lesions. Behav. Neurol. 12, 191200 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Paulo Marques
    • 1
    • 2
    • 3
    Email author
  • Jose Miguel Soares
    • 1
    • 2
  • Ricardo Magalhaes
    • 1
    • 2
  • Nuno Sousa
    • 1
    • 2
  • Victor Alves
    • 4
  1. 1.Life and Health Sciences Research Institute (ICVS), School of Health SciencesUniversity of MinhoBragaPortugal
  2. 2.ICVS/3Bs - PT Government Associate LaboratoryBraga/guimaresPortugal
  3. 3.Clinical Academic Center BragaBragaPortugal
  4. 4.Department of InformaticsUniversity of MinhoBragaPortugal

Personalised recommendations