Advertisement

Personode: A Toolbox for ICA Map Classification and Individualized ROI Definition

  • Gustavo S. P. PamplonaEmail author
  • Bruno H. Vieira
  • Frank Scharnowski
  • Carlos E. G. Salmon
Original Article

Abstract

Canonical resting state networks (RSNs) can be obtained through independent component analysis (ICA). RSNs are reproducible across subjects but also present inter-individual differences, which can be used to individualize regions-of-interest (ROI) definition, thus making fMRI analyses more accurate. Unfortunately, no automatic tool for defining subject-specific ROIs exists, making the classification of ICAs as representatives of RSN time-consuming and largely dependent on visual inspection. Here, we present Personode, a user-friendly and open source MATLAB-based toolbox that semi-automatically performs the classification of RSN and allows for defining subject- and group-specific ROIs. To validate the applicability of our new approach and to assess potential improvements compared to previous approaches, we applied Personode to both task-related activation and resting-state data. Our analyses show that for task-related activation analyses, subject-specific spherical ROIs defined with Personode produced higher activity contrasts compared to ROIs derived from single-study and meta-analytic coordinates. We also show that subject-specific irregular ROIs defined with Personode improved ROI-to-ROI functional connectivity analyses.

Hence, Personode might be a useful toolbox for ICA map classification into RSNs and group- as well as subject-specific ROI definitions, leading to improved analyses of task-related activation and functional connectivity.

Keywords

ROI individualization ICA map classification Resting-state networks Toolbox Independent component analysis Functional connectivity 

Notes

Acknowledgements

This work was supported by the Brazilian National Council for Scientific and Technological Development (CNPq), the Brazilian National Council for the Improvement of Higher Education (CAPES), the Swiss National Science Foundation (BSSG10_155915, 100014_178841, 32003B_166566), the Foundation for Research in Science and the Humanities at the University of Zurich (STWF-17-012), the Baugarten Stiftung, and the Swiss Government. We also thank Dr. Ludovica Griffanti for insightful discussions and comments on the manuscript.

Compliance with Ethical Standards

Information Sharing Statement

NKI/Rockland Sample (RRID:SCR_009435) data used in this work is available at https://fcon_1000.projects.nitrc.org/indi/enhanced/neurodata.html. UK Biobank (RRID:SCR_012815) group-averaged RSNs templates where obtained at http://biobank.ctsu.ox.ac.uk/showcase/refer.cgi?id=9028.

SPM12 (RRID:SCR_007037,https://www.fil.ion.ucl.ac.uk/spm/software/spm12/), MarsBaR (RRID:SCR_009605,http://marsbar.sourceforge.net), and GIFT (RRID:SCR_001953,http://trendscenter.org/trends/software/gift/) are available to the general public.

The source code of Personode is available free of charge for non-commercial use and adaptation, under the condition of proper attribution, at https://github.com/gustavopamplona/Personode.

Supplementary material

12021_2019_9449_MOESM1_ESM.pdf (637 kb)
ESM 1 (PDF 636 kb)

References

  1. Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 360(1457), 1001–1013.  https://doi.org/10.1098/rstb.2005.1634.CrossRefPubMedPubMedCentralGoogle Scholar
  2. Bullmore, E. T., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews. Neuroscience, 10(3), 186–198.  https://doi.org/10.1038/nrn2575.CrossRefGoogle Scholar
  3. Cameron Craddock, R., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S. (2009). Disease state prediction from resting state functional connectivity. Magnetic Resonance in Medicine, 62(6), 1619–1628.  https://doi.org/10.1002/mrm.22159.CrossRefPubMedGoogle Scholar
  4. Dai, W., Varma, G., Scheidegger, R., & Alsop, D. C. (2016). Quantifying fluctuations of resting state networks using arterial spin labeling perfusion MRI. Journal of Cerebral Blood Flow & Metabolism, 36(3), 463–473.  https://doi.org/10.1177/0271678X15615339.CrossRefGoogle Scholar
  5. Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences, 103(37), 13848–13853.  https://doi.org/10.1073/pnas.0601417103.CrossRefGoogle Scholar
  6. Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., et al. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18, 1664–1671.  https://doi.org/10.1038/nn.4135.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences, 102(27), 9673–9678.  https://doi.org/10.1073/pnas.0504136102.CrossRefGoogle Scholar
  8. Golestani, A. M., & Goodyear, B. G. (2011). Regions of interest for resting-state fMRI analysis determined by inter-voxel cross-correlation. NeuroImage, 56(1), 246–251.  https://doi.org/10.1016/j.neuroimage.2011.02.038.CrossRefPubMedGoogle Scholar
  9. Harrison, B. J., Pujol, J., López-Solà, M., Hernández-Ribas, R., Deus, J., Ortiz, H., Soriano-Mas, C., Yücel, M., Pantelis, C., & Cardoner, N. (2008). Consistency and functional specialization in the default mode brain network. Proceedings of the National Academy of Sciences, 105(28), 9781–9786.  https://doi.org/10.1073/pnas.0711791105.CrossRefGoogle Scholar
  10. Liu, T. (2011). A few thoughts on brain ROIs. Brain Imaging and Behavior, 5(3), 189–202.  https://doi.org/10.1007/s11682-011-9123-6.CrossRefPubMedPubMedCentralGoogle Scholar
  11. De Luca, M., Beckmann, C. F., De Stefano, N., Matthews, P. M., & Smith, S. M. (2006). fMRI resting state networks define distinct modes of long-distance interactions in the human brain. NeuroImage, 29(4), 1359–1367.  https://doi.org/10.1016/j.neuroimage.2005.08.035.CrossRefPubMedGoogle Scholar
  12. Mcdonald, A. R., Muraskin, J., Van Dam, N. T., Froehlich, C., Puccio, B., Pellman, J., et al. (2017). The real-time fMRI neurofeedback based stratification of default network regulation neuroimaging data repository. NeuroImage, 146, 157–170.  https://doi.org/10.1016/j.neuroimage.2016.10.048.CrossRefPubMedGoogle Scholar
  13. Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L., Griffanti, L., Douaud, G., Okell, T. W., Weale, P., Dragonu, I., Garratt, S., Hudson, S., Collins, R., Jenkinson, M., Matthews, P. M., & Smith, S. M. (2016). Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nature Neuroscience, 19(11), 1523–1536.  https://doi.org/10.1038/nn.4393.CrossRefPubMedPubMedCentralGoogle Scholar
  14. Nucifora, P. G. P., Verma, R., Lee, S.-K., & Melhem, E. R. (2007). Diffusion-tensor MR imaging and tractography: Exploring brain microstructure and connectivity. Radiology, 245(2), 367–384.  https://doi.org/10.1148/radiol.2452060445.CrossRefPubMedGoogle Scholar
  15. Poldrack, R. A. (2007). Region of interest analysis for fMRI. Social Cognitive and Affective Neuroscience, 2(1), 67–70.  https://doi.org/10.1093/scan/nsm006.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069.  https://doi.org/10.1016/J.NEUROIMAGE.2009.10.003.CrossRefPubMedGoogle Scholar
  17. Schilbach, L., Bzdok, D., Timmermans, B., Fox, P. T., Laird, A. R., Vogeley, K., & Eickhoff, S. B. (2012). Introspective minds: Using ALE meta-analyses to study commonalities in the neural correlates of emotional processing, social & unconstrained cognition. PLoS One, 7(2), 1–10.  https://doi.org/10.1371/journal.pone.0030920.CrossRefGoogle Scholar
  18. Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience, 27(9), 2349–2356.  https://doi.org/10.1523/JNEUROSCI.5587-06.2007.CrossRefPubMedGoogle Scholar
  19. Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., et al. (2016). Closed-loop brain training: The science of neurofeedback. Nature Reviews Neuroscience, 18, 86.CrossRefGoogle Scholar
  20. Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., & Beckmann, C. F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences, 106(31), 13040–13045.  https://doi.org/10.1073/pnas.0905267106.CrossRefGoogle Scholar
  21. Sohn, W., Yoo, K., Lee, Y.-B., Seo, S., Na, D., & Jeong, Y. (2015). Influence of ROI selection on resting state functional connectivity: An individualized approach for resting state fMRI analysis. Frontiers in Neuroscience, 9, 280.  https://doi.org/10.3389/fnins.2015.00280.CrossRefPubMedPubMedCentralGoogle Scholar
  22. Sulzer, J., Haller, S., Scharnowski, F., Weiskopf, N., Birbaumer, N., Blefari, M. L., Bruehl, A. B., Cohen, L. G., DeCharms, R., Gassert, R., Goebel, R., Herwig, U., LaConte, S., Linden, D., Luft, A., Seifritz, E., & Sitaram, R. (2013). Real-time fMRI neurofeedback: Progress and challenges. NeuroImage, 76, 386–399.  https://doi.org/10.1016/j.neuroimage.2013.03.033.CrossRefPubMedPubMedCentralGoogle Scholar
  23. van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010). Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20(8), 519–534.  https://doi.org/10.1016/j.euroneuro.2010.03.008.CrossRefGoogle Scholar
  24. Veer, I. M., Beckmann, C. F., van Tol, M.-J., Ferrarini, L., Milles, J., Veltman, D. J., et al. (2010). Whole brain resting-state analysis reveals decreased functional connectivity in major depression. Frontiers in Systems Neuroscience.  https://doi.org/10.3389/fnsys.2010.00041.
  25. Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity, 2(3), 125–141.  https://doi.org/10.1089/brain.2012.0073.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Sensory-Motor Laboratory (SeMoLa), Jules-Gonin Eye Hospital/Fondation Asile des AveuglesDepartment of Ophthalmology/University of LausanneLausanneSwitzerland
  2. 2.Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric HospitalUniversity of ZürichZürichSwitzerland
  3. 3.Inbrain Lab, Department of PhysicsUniversity of São PauloRibeirão PretoBrazil
  4. 4.Neuroscience Center ZürichUniversity of Zürich and Swiss Federal Institute of TechnologyZürichSwitzerland
  5. 5.Zürich Center for Integrative Human Physiology (ZIHP)University of ZürichZürichSwitzerland
  6. 6.Department of Basic Psychological Research and Research Methods, Faculty of PsychologyUniversity of ViennaViennaAustria

Personalised recommendations