Abstract
Functional Magnetic Resonance Imaging (fMRI) has become one of the leading methods for brain mapping in neuroscience and it is an important tool in modern neuroscience investigation. Moreover, the recent advances in fMRI analysis are widely used to define the default state of brain activity, functional connectivity and basal activity. Signal processing schemes have been suggested to analyze the resting state Blood-Oxygenation-Level-Dependent (BOLD) signal from simple correlations to spectral decomposition. Our goal is to determine which brain areas behave similarly in the time domain. To address this question, we apply functional curve clustering methods. We carry out an exploratory study using classical functional clustering of fMRI time series. The analysis confirms the hypothesis of a possible spatial influence on the results and therefore suggests the development of spatial curve clustering methods for brain data.
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References
Abraham, C., Cornillon, P.A., Matzner-Løber, E., Molinari, N.: Unsupervised curve clustering using b-splines. Scand. J. Statist. 30(3), 581–595 (2003)
Craddock, R.C., Jbabdi, S., Yan, C.G., Vogelstein, J.T., Castellanos, F.X., Di Martino, A., Kelly, C., Heberlein, K., Colcombe, S., Milham, M.P.: Imaging human connectomes at the macroscale. Nature Methods 10(6), 524 (2013)
Craven, P., Wahba, G.: Smoothing noisy data with spline functions. Numerische Mathematik 31(4), 377–403 (1978)
De Boor, C.: A Practical Guide to Splines. Springer, New York (1978)
Desikan, R.S., Ségonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968–980 (2006)
Dudoit, S., Fridlyand, J.: A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol. 3(7), 1–21 (2002)
Fraley, C., Raftery, A.E.: Model-based clustering, discriminant analysis and density estimation. J. Am. Statist. Assoc. 97, 611–631 (2002)
Fraley, C., Raftery, A.E., Murphy, T.B., Scrucca, L.: mclust Version 4 for R: Normal Mixture Modeling for model-based clustering, classification, and density estimation (2012)
Goense, J., Bohraus, Y., Logothetis, N.K.: fMRI at high spatial resolution: implications for BOLD-models. Front. Computat. Neurosci. 10, 66 (2016)
Goutte, C., Toft, P., Rostrup, E., Nielsen, F.Å., Hansen, L.K.: On clustering fMRI time series. NeuroImage 9(3), 298–310 (1999)
Greve, D.N., Brown, G.G., Mueller, B.A., Glover, G., Liu, T.T., et al.: A survey of the sources of noise in fMRI. Psychometrika 78(3), 396–416 (2013)
Heller, R., Stanley, D., Yekutieli, D., Rubin, N., Benjamini, Y.: Cluster-based analysis of fMRI data. NeuroImage 33(2), 599–608 (2006)
Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)
Jacques, J., Preda, C.: Functional data clustering: a survey. Adv. Data Anal. Classif. 8(3), 231–255 (2014)
López-Pintado, S., Romo, J.: On the concept of depth for functional data. J. Am. Statist. Assoc. 104(486), 718–734 (2009)
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA vol. 1, pp. 281–297 (1967)
Monti, M.M.: Statistical analysis of fMRI time-series: a critical review of the GLM approach. Front. Hum. Neurosci. 5, 28 (2011)
Ramsay, J.O., Wickham, H., Graves, S., Hooker, G.: fda: Functional data analysis (2017). https://CRAN.R-project.org/package=fda/
Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Statist. Assoc. 66(336), 846–850 (1971)
Sporns, O.: Structure and function of complex brain networks. Dialogues Clin. Neurosci. 15(3), 247–262 (2013)
Stam, C.J.: Modern network science of neurological disorders. Nature Rev. Neurosci. 15(10), 683–695 (2014)
Sun, Y., Genton, M.G.: Functional Boxplots. J. Comput. Graph. Statist. 20(2), 316–334 (2011)
Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. J. Royal Statist. Soc. Ser. B (Statist. Methodol.) 63(2), 411–423 (2001)
Tukey, J.W.: Exploratory Data Analysis, vol. 2. Reading, Mass (1977)
Yeo, B., Ou, W.: Clustering fMRI time series (2004). http://people.csail.mit.edu/ythomas/unpublished/6867fMRI.pdf
Acknowledgements
We are grateful to Greg Kiar and Eric Bridgeford from NeuroData at Johns Hopkins University, who graciously pre-processed the raw DTI and R-fMRI imaging data available at http://fcon_1000.projects.nitrc.org/indi/CoRR/html/nki_1.html, using the pipelines ndmg and C-PAC and to the reviewers and The Scientific Committee of StartUp Research for all the suggestions aimed at improving this paper.
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Bertarelli, G., Corbella, A., Di Iorio, J., Gorshechnikova, A., Scott, M. (2018). Curve Clustering for Brain Functional Activity and Synchronization. In: Canale, A., Durante, D., Paci, L., Scarpa, B. (eds) Studies in Neural Data Science. START UP RESEARCH 2017. Springer Proceedings in Mathematics & Statistics, vol 257. Springer, Cham. https://doi.org/10.1007/978-3-030-00039-4_5
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