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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
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)
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)
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)
Blondel, V.D., Guillame, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. P10008 (2008)
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)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer, US (1981)
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)
Moody, J., Darken, C.J.: Fast learning in networks of locally-tuned processing units. Neural Comput. 1, 281–294 (1989)
Newman, M.E.J.: Analysis of weighted networks. Phys. Rev. E 70, 056131 (2004)
von Luxburg, U.: A tutorial on spectral clustering. Technical Report TR-149, Max Planck Institute for Biological Cybernetics (2006)
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Scherrer, A.: Community detection algorithm based on louvain method [software]. http://perso.uclouvain.be/vincent.blondel/research/Community_BGLL_Matlab.zip
Wismüller, A., Vietze, F., Dersch, D. R.: Segmentation with neural networks. In: Handbook of Medical Imaging, pp. 107–126 (2000)
Duda, R.O., Hart, P.E., Storck, D.G.: Pattern Classification, 2nd edn. Wiley (2001)
Hofmann, T., Buhmann, J.: Pairwise data clustering by deterministic annealing. IEEE Trans. Pattern Anal. Mach. Intell. 19, 1–14 (1997)
Graepel, T., OberMayer, K.: A stochastic self-organizing map for proximity data. Neurocomputing 11, 139–155 (1999)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000)
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)
Moody, John, Darken, Christian J.: Fast learning in networks of locally-tuned processing units. Neural Comput. 1(2), 281–294 (1989)
Lange, O.: MRT-bildverarbeitung durch intelligente mustererkennungsalgorithmen: Zeitreihenanalyse durch selbstorganisierende Clustersegmentierung. Dissertation, LMU München (2004)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-28518-4_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28517-7
Online ISBN: 978-3-319-28518-4
eBook Packages: EngineeringEngineering (R0)