Mutual Connectivity Analysis (MCA) for Nonlinear Functional Connectivity Network Recovery in the Human Brain Using Convergent Cross-Mapping and Non-metric Clustering | SpringerLink
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Mutual Connectivity Analysis (MCA) for Nonlinear Functional Connectivity Network Recovery in the Human Brain Using Convergent Cross-Mapping and Non-metric Clustering

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Advances in Self-Organizing Maps and Learning Vector Quantization

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 428))

Abstract

We explore a computational framework for functional connectivity analysis in resting-state functional MRI (fMRI) data acquired from the human brain for recovering the underlying network structure and understanding causality between network components. Termed mutual connectivity analysis (MCA), this framework involves two steps, the first of which is to evaluate the pair-wise cross-prediction performance between fMRI pixel time series within the brain. Here, we use a Generalized Radial Basis Functions (GRBF) neural network as a nonlinear time series predictor. In a second step, the underlying network structure is subsequently recovered from the affinity matrix using non-metric network clustering approaches, such as the so-called Louvain method. Finally, we use convergent cross-mapping (CCM) to study causality between different network components. We demonstrate our MCA framework in the problem of recovering the motor cortex network associated with hand movement from resting state fMRI data. Results are compared with a ground truth of active motor cortex regions as identified by a task-based fMRI sequence involving a finger-tapping stimulation experiment. Our results on whole-slice fMRI analysis demonstrate that MCA-based model-free recovery of regions associated with the primary motor cortex and supplementary motor area are in close agreement with localization of similar regions achieved with a task-based fMRI acquisition.

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References

  1. Margulies, D.S., Böttger, J., Long, X., Lv, Y., Kelly, C., Schäfer, A., Goldhahn, D., Abbushi, A., Milham, M.P., Lohmann, G., Villringer, A.: Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity. Magn. Reson. Mater. Phys. Biol. Med. 23(5–6), 289–307 (2010)

    Article  Google Scholar 

  2. Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995)

    Article  Google Scholar 

  3. Beckmann, C.F., DeLuca, M., Devlin, J.T., Smith, S.M.: Investigations into resting-state connectivity using independent component analysis. Philos. Translations R. S. B Biol. Sci. 360, 1001–1013 (2005)

    Article  Google Scholar 

  4. Zhou, Z., Ding, M., Chen, Y., Wright, P., Lu, Z., Liu, Y.: Detecting directional influence in fMRI connectivity analysis using PCA based Granger causality. Brain Res. 1289, 22–29 (2009)

    Article  Google Scholar 

  5. Wismüller, A., Lange, O., Auer, D.P., Leinsinger, G.: Model-free functional MRI analysis for detecting low-frequency functional connectivity in the human brain. In:Proceedings of SPIE Medical Imaging 7624: 1M1-8 (2010)

    Google Scholar 

  6. Blondel, V.D., Guillame, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. P10008 (2008)

    Google Scholar 

  7. Sugihara, G., May, R., Ye, H., Hsieh, C.H., Deyle, E.R., Fogarty, M., Munch, S.: Detecting causality in complex ecosystems. Science 338, 496–500 (2012)

    Article  Google Scholar 

  8. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer, US (1981)

    Book  MATH  Google Scholar 

  9. Wismüller, A., Lange, O., Dersch, D.R., Leinsinger, G.L., Hahn, K., Pütz, B., Auer, D.: Cluster analysis of biomedical image time-series. Int. J. Comput. Vision 46, 103–128 (2002)

    Article  MATH  Google Scholar 

  10. Moody, J., Darken, C.J.: Fast learning in networks of locally-tuned processing units. Neural Comput. 1, 281–294 (1989)

    Article  Google Scholar 

  11. Newman, M.E.J.: Analysis of weighted networks. Phys. Rev. E 70, 056131 (2004)

    Article  Google Scholar 

  12. von Luxburg, U.: A tutorial on spectral clustering. Technical Report TR-149, Max Planck Institute for Biological Cybernetics (2006)

    Google Scholar 

  13. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)

    Article  Google Scholar 

  14. Scherrer, A.: Community detection algorithm based on louvain method [software]. http://perso.uclouvain.be/vincent.blondel/research/Community_BGLL_Matlab.zip

  15. Wismüller, A., Vietze, F., Dersch, D. R.: Segmentation with neural networks. In: Handbook of Medical Imaging, pp. 107–126 (2000)

    Google Scholar 

  16. Duda, R.O., Hart, P.E., Storck, D.G.: Pattern Classification, 2nd edn. Wiley (2001)

    Google Scholar 

  17. Hofmann, T., Buhmann, J.: Pairwise data clustering by deterministic annealing. IEEE Trans. Pattern Anal. Mach. Intell. 19, 1–14 (1997)

    Article  Google Scholar 

  18. Graepel, T., OberMayer, K.: A stochastic self-organizing map for proximity data. Neurocomputing 11, 139–155 (1999)

    Google Scholar 

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

    Article  Google Scholar 

  20. Taşdemir, K., Merényi, E.: Exploiting the data topology in visualizing and clustering of self-organizing maps. IEEE Trans. Neural Netw. 20(4), 549–562 (2009)

    Article  Google Scholar 

  21. Moody, John, Darken, Christian J.: Fast learning in networks of locally-tuned processing units. Neural Comput. 1(2), 281–294 (1989)

    Article  Google Scholar 

  22. Lange, O.: MRT-bildverarbeitung durch intelligente mustererkennungsalgorithmen: Zeitreihenanalyse durch selbstorganisierende Clustersegmentierung. Dissertation, LMU München (2004)

    Google Scholar 

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Acknowledgments

This research was funded by the National Institutes of Health (NIH) Award R01-DA-034977. This work was conducted as a Practice Quality Improvement (PQI) project related to American Board of Radiology (ABR) Maintenance of Certificate (MOC) for Prof. Dr. Dr. Axel Wismüller. The authors would like to thank Prof. Dr. Dorothee Auer at the Institute of Neuroscience, University of Nottingham, UK, for her assistance with the fMRI data acquisition process. The authors would also like to thank Prof. Dr. Herbert Witte and Dr. Lutz Leistritz, Institute of Medical Statistics, Computer Sciences, and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Germany, Dr. Oliver Lange and Prof. Dr. Dr. h.c. Maximilian F. Reiser, FACR, FRCR, of the Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany for their support.

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Wismüller, A., Abidin, A.Z., DSouza, A.M., Nagarajan, M.B. (2016). Mutual Connectivity Analysis (MCA) for Nonlinear Functional Connectivity Network Recovery in the Human Brain Using Convergent Cross-Mapping and Non-metric Clustering. In: Merényi, E., Mendenhall, M., O'Driscoll, P. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-319-28518-4_19

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  • DOI: https://doi.org/10.1007/978-3-319-28518-4_19

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