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Tractome: a visual data mining tool for brain connectivity analysis

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Abstract

Diffusion magnetic resonance imaging data allows reconstructing the neural pathways of the white matter of the brain as a set of 3D polylines. This kind of data sets provides a means of study of the anatomical structures within the white matter, in order to detect neurologic diseases and understand the anatomical connectivity of the brain. To the best of our knowledge, there is still not an effective or satisfactory method for automatic processing of these data. Therefore, a manually guided visual exploration of experts is crucial for the purpose. However, because of the large size of these data sets, visual exploration and analysis has also become intractable. In order to make use of the advantages of both manual and automatic analysis, we have developed a new visual data mining tool for the analysis of human brain anatomical connectivity. With such tool, humans and automatic algorithms capabilities are integrated in an interactive data exploration and analysis process. A very important aspect to take into account when designing this tool, was to provide the user with comfortable interaction. For this purpose, we tackle the scalability issue in the different stages of the system, including the automatic algorithm and the visualization and interaction techniques that are used.

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Notes

  1. http://trackvis.org.

  2. Zoom is referred here to the definition in Keim (2002) which means that the data representation changes to present more details at higher zoom levels.

  3. http://nipy.org/dipy.

  4. https://github.com/fos/fos.

  5. http://opengl.org.

  6. http://www.tractome.org.

  7. http://scikit-learn.org.

  8. Note that negative correlation is not considered as accurate approximation. Moreover it never occurred during experiments.

  9. The figure is restricted to 6 of the 10 subjects for lack of space. The graphs of all subjects showed an equivalent behaviour.

  10. The clustering of the whole tractography can be computed once and stored, so its time does not affect the interactive use.

References

  • Arthur D, Manthey B, Röglin H (2009) k-Means has polynomial smoothed complexity. In: Proceedings of the 2009 50th annual IEEE symposium on foundations of computer science, FOCS ’09, pp. 405–414. IEEE Computer Society, Washington, DC, USA. doi:10.1109/focs.2009.14

  • Basser PJ, Mattiello J, LeBihan D (1994) MR diffusion tensor spectroscopy and imaging. Biophys J 66(1):259–267. doi:10.1016/s0006-3495(94)80775-1

    Article  Google Scholar 

  • Clayden JD (2013) Imaging connectivity: MRI and the structural networks of the brain. Funct Neurol 28(3): 197–203. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3812744/

  • Corouge I, Gouttard S, Gerig G (2004) Towards a shape model of white matter fiber bundles using diffusion tensor MRI. In: IEEE international symposium on biomedical imaging: nano to macro, 2004. pp. 344–347 Vol. 1. IEEE. doi:10.1109/isbi.2004.1398545

  • Dubuisson MPP, Jain AK (1994) A modified Hausdorff distance for object matching. In: pattern recognition, 1994. Vol. 1 - Conference A: Computer Vision Image Processing., Proceedings of the 12th IAPR International Conference on,1: 566–568 vol. 1. IEEE. doi:10.1109/icpr.1994.576361

  • Duin RPW, Pkalska E (2012) The dissimilarity space: bridging structural and statistical pattern recognition. Pattern Recognit Lett 33(7):826–832. doi:10.1016/j.patrec.2011.04.019

    Article  Google Scholar 

  • Eick SG, Karr AF (2002) Visual scalability. J Comput GraphStat 11(1):22–43. doi:10.1198/106186002317375604

    Article  MathSciNet  Google Scholar 

  • Fields RD (2008) White Matter Matters. Sci Am 298(3):54–61

    Article  Google Scholar 

  • Fitzsimmons J, Kubicki M, Shenton ME (2013) Review of functional and anatomical brain connectivity findings in schizophrenia. Current opinion in psychiatry 26(2): 172–187. http://view.ncbi.nlm.nih.gov/pubmed/23324948

  • Garyfallidis E (2012) Towards an accurate brain tractography. Ph.D. thesis, University of Cambridge

  • Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, Nimmo-Smith I, Contributors D (2014) Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics 8(8): 1+. http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00008/full

  • Garyfallidis E, Brett M, Correia MM, Williams GB, Nimmo-Smith I (2012) QuickBundles, a method for tractography simplification. Front Neurosci 6:175. doi:10.3389/fnins.2012.00175

  • Guevara P, Poupon C, Rivière D, Cointepas Y, Descoteaux M, Thirion B, Mangin JFF (2011) Robust clustering of massive tractography datasets. NeuroImage 54(3):1975–1993. doi:10.1016/j.neuroimage.2010.10.028

    Article  Google Scholar 

  • Keim D, Mansmann F, Schneidewind J, Thomas J, Ziegler H (2008) Visual analytics: scope and challenges. In: S. Simoff, M. Böhlen, A. Mazeika (eds.) Visual data mining, lecture notes in computer science, vol. 4404, chap. 6, pp. 76–90. Springer, Berlin Heidelberg, Berlin, Heidelberg. doi:10.1007/978-3-540-71080-6_6

  • Keim DA (2002) Information visualization and visual data mining. IEEE Trans Vis Comput Graph, 8(1):1–8. doi:10.1109/2945.981847

  • Lang EW, Tomé AM, Keck IR, Sáez JMG, Puntonet CG (2012) Brain connectivity analysis: a short survey. Intell Neurosci 2012:412512. doi:10.1155/2012/412512

  • Lazar M (2010) Mapping brain anatomical connectivity using white matter tractography. NMR Biomed 23(7):821–835. doi:10.1002/nbm.1579

    Article  MathSciNet  Google Scholar 

  • Mori S, van Zijl PCM (2002) Fiber tracking: principles and strategies, a technical review. NMR Biomed 15(7–8):468–480. doi:10.1002/nbm.781

    Article  Google Scholar 

  • O’Donnell LJ, Westin CFF (2007) Automatic tractography segmentation using a highdimensional white matter atlas. IEEE Trans Med Imag, pp. 1562–1575. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.163.7804

  • Olivetti E, Nguyen TB, Garyfallidis E (2012) The approximation of the dissimilarity projection. In: IEEE Intl Workshop on Pattern Recognition in NeuroImaging, pp 85–88. doi:10.1109/prni.2012.13

  • Olivetti E, Nguyen TB, Garyfallidis E, Agarwal N, Avesani P (2013) Fast clustering for interactive tractography segmentation. In: International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2013, pp. 42–45. IEEE. doi:10.1109/prni.2013.20

  • Orozco-Alzate M., Castellanos-Domínguez CG (2007) Clustering on dissimilarity representations for detecting mislabelled seismic signals at nevado del ruiz volcano. Earth Sci Res J 11(2), 135–140

  • Pajevic S, Pierpaoli C (1999) Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain. Magnetic resonance in medicine 42(3):526–540. http://view.ncbi.nlm.nih.gov/pubmed/10467297

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res. http://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a

  • Pekalska E, Duin R, Paclik P (2006) Prototype selection for dissimilarity-based classifiers. Pattern Recognit 39(2):189–208. doi:10.1016/j.patcog.2005.06.012

    Article  MATH  Google Scholar 

  • Pekalska E, Duin RPW (2005) The Dissimilarity Representation for Pattern Recognition: Foundations and Applications (Machine Perception and Artificial Intelligence). World Scientific Publishing Company. http://www.worldcat.org/isbn/9812565302

  • Piringer H (2011) Large data scalability in interactive visual analysis. Ph.D. thesis, Institute of Computer Graphics and Algorithms, University of Technology, Vienna, Favoritenstrasse 9–11/186, A-1040 Vienna, Austria. http://www.cg.tuwien.ac.at/research/publications/2011/PH-2011-LDS/

  • Ros C, Güllmar D, Stenzel M, Mentzel HJ, Reichenbach JR (2013) Atlas-guided cluster analysis of large tractography datasets. PLoS One 8(12):e83,847+

    Article  Google Scholar 

  • Sculley D (2010) Web-scale K-means clustering. In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pp. 1177–1178. ACM, New York, NY, USA. doi:10.1145/1772690.1772862

  • Simoff S, Böhlen M, Mazeika A (2008) Visual data Mining: An Introduction and Overview. In: S. Simoff, M. Böhlen, A. Mazeika (eds.) Visual Data mining, lecture notes in computer science. vol. 4404, pp. 1–12. Springer, Berlin Heidelberg. doi:10.1007/978-3-540-71080-6_1

  • Sotiropoulos SN, Jbabdi S, Xu J, Andersson JL, Moeller S, Auerbach EJ, Glasser MF, Hernandez M, Sapiro G, Jenkinson M, Feinberg DA, Yacoub E, Lenglet C, Van Essen DC, Ugurbil K, Behrens TE (2013) WU-Minn HCP consortium: advances in diffusion MRI acquisition and processing in the Human Connectome Project. NeuroImage 80, 125–143. http://view.ncbi.nlm.nih.gov/pubmed/23702418

  • Stahl F, Gabrys B, Gaber MM, Berendsen M (2013) An overview of interactive visual data mining techniques for knowledge discovery. WIREs Data Mining Knowl Discov 3(4):239–256. doi:10.1002/widm.1093

    Article  Google Scholar 

  • Turnbull D, Elkan C (2005) Fast recognition of musical genres using RBF networks. IEEE Trans Knowl Data Eng 17(4):580–584. doi:10.1109/tkde.2005.62

    Article  Google Scholar 

  • Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TEJ, Bucholz R, Chang A, Chen L, Corbetta M, Curtiss SW, Della Penna S, Feinberg D, Glasser MF, Harel N, Heath AC, Larson-Prior L, Marcus D, Michalareas G, Moeller S, Oostenveld R, Petersen SE, Prior F, Schlaggar BL, Smith SM, Snyder AZ, Xu J, Yacoub E (2012) The human connectome project: a data acquisition perspective. NeuroImage 62(4):2222–2231. doi:10.1016/j.neuroimage.2012.02.018

    Article  Google Scholar 

  • Wang Q, Yap PT, Wu G, Shen D (2013) Application of neuroanatomical features to tractography clustering. Hum Brain Mapp 34(9):2089–2102. doi:10.1002/hbm.22051

    Article  Google Scholar 

  • Wang R, Benner T, Sorensen A, Wedeen V (2007) Diffusion toolkit: a software package for diffusion imaging data processing and tractography - 03720. http://cds.ismrm.org/ismrm-2007/files/03720

  • Wang X, Grimson WE, Westin CFF (2011) Tractography segmentation using a hierarchical Dirichlet processes mixture model. NeuroImage 54(1):290–302. doi:10.1016/j.neuroimage.2010.07.050

    Article  Google Scholar 

  • Zhang S, Correia S, Laidlaw DH (2008) Identifying white-matter fiber bundles in DTI data using an automated proximity-based fiber-clustering method. IEEE Trans Vis Comput Graph 14(5):1044–1053. doi:10.1109/tvcg.2008.52

    Article  Google Scholar 

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Authors and Affiliations

  1. NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation, Trento, Italy

    Diana Porro-Muñoz, Emanuele Olivetti, Nusrat Sharmin, Thien Bao Nguyen & Paolo Avesani

  2. Centro Interdipartimentale Mente e Cervello (CIMeC), University of Trento, Via Delle Regole 101, 38123, Mattarello, TN, Italy

    Diana Porro-Muñoz, Emanuele Olivetti, Nusrat Sharmin, Thien Bao Nguyen & Paolo Avesani

  3. Computer Science Department, University of Sherbrooke, Sherbrooke, QC, Canada

    Eleftherios Garyfallidis

Authors
  1. Diana Porro-Muñoz
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  2. Emanuele Olivetti
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  3. Nusrat Sharmin
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  4. Thien Bao Nguyen
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  5. Eleftherios Garyfallidis
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  6. Paolo Avesani
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Corresponding author

Correspondence to Paolo Avesani.

Additional information

Communicated by João Gama, Indrė Z̆ liobaitė, Alípio M. Jorge, Concha Bielza.

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Porro-Muñoz, D., Olivetti, E., Sharmin, N. et al. Tractome: a visual data mining tool for brain connectivity analysis. Data Min Knowl Disc 29, 1258–1279 (2015). https://doi.org/10.1007/s10618-015-0408-z

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  • DOI: https://doi.org/10.1007/s10618-015-0408-z

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