Skip to main content
SpringerLink
Log in

Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data

  • Conference paper
Visualization and Processing of Higher Order Descriptors for Multi-Valued Data

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

In neuroimaging research, a wide variety of quantitative computational methods enable inference of results regarding the brain’s structure and function. In this chapter, we survey two broad families of approaches to quantitative analysis of neuroimaging data: statistical testing and machine learning. We discuss how methods developed for traditional scalar structural neuroimaging data have been extended to diffusion magnetic resonance imaging data. Diffusion MRI data have higher dimensionality and allow the study of the brain’s connection structure. The intended audience of this chapter includes students or researchers in neuroimage analysis who are interested in a high-level overview of methods for analyzing their data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Anderson, A., Dinov, I.D., Sherin, J.E., Quintana, J., Yuille, A.L., Cohen, M.S.: Classification of spatially unaligned fMRI scans. NeuroImage 49(3), 2509–2519 (2010)

    Article  Google Scholar 

  2. Arribas, J.I., Calhoun, V.D., Adalı, T.: Automatic bayesian classification of healthy controls, bipolar disorder and schizophrenia using intrinsic connectivity maps from fMRI data. IEEE Trans. Biomed. Eng. 57(12), 2850–2860 (2010)

    Article  Google Scholar 

  3. Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Log-euclidean metrics for fast and simple calculus on diffusion tensors. Magn. Reson. Med. 56(2), 411–421 (2006)

    Article  Google Scholar 

  4. Ashburner, J., Friston, K.J.: Voxel-based morphometry—the methods. NeuroImage 11(6), 805–821 (2000)

    Article  Google Scholar 

  5. Ashburner, J., Hutton, C., Frackowiak, R., Johnsrude, I., Price, C., Friston, K., et al.: Identifying global anatomical differences: deformation-based morphometry. Hum. Brain Mapp. 6(5–6), 348–357 (1998)

    Article  Google Scholar 

  6. Batchelor, P., Moakher, M., Atkinson, D., Calamante, F., Connelly, A.: A rigorous framework for diffusion tensor calculus. Magn. Reson. Med. 53(1), 221–225 (2005)

    Article  Google Scholar 

  7. Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodological) 57, 289–300 (1995)

    MathSciNet  Google Scholar 

  8. Bennett, C.M., Baird, A.A., Miller, M.B., Wolford, G.L.: Neural correlates of interspecies perspective taking in the post-mortem atlantic salmon: an argument for proper multiple comparisons correction. J. Serendipitous Unexpected Results 1, 1–5 (2010)

    Google Scholar 

  9. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)

    Google Scholar 

  10. Bloy, L., Ingalhalikar, M., Eavani, H., Roberts, T.P.L., Schultz, R.T., Verma, R.: HARDI based pattern classifiers for the identification of white matter pathologies. In: Fichtinger, G., Martel, A., Peters, T. (eds.) Medical Image Computing and Computer Assisted Intervention (MICCAI). Lecture Notes in Computer Science, vol. 6892, pp. 234–241. Springer, Berlin (2011)

    Google Scholar 

  11. Chumbley, J.R., Friston, K.J.: False discovery rate revisited: FDR and topological inference using gaussian random fields. NeuroImage 44(1), 62–70 (2009)

    Article  Google Scholar 

  12. Colby, J.B., Soderberg, L., Lebel, C., Dinov, I.D., Thompson, P.M., Sowell, E.R.: Along-tract statistics allow for enhanced tractography analysis. Neuroimage 59(4), 3227–3242 (2012)

    Article  Google Scholar 

  13. Corouge, I., Fletcher, P.T., Joshi, S., Gouttard, S., Gerig, G.: Fiber tract-oriented statistics for quantitative diffusion tensor mri analysis. Med. Image Anal. 10(5), 786–798 (2006)

    Article  Google Scholar 

  14. Cox, D.D., Savoy, R.L.: Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage 19(2 Pt 1), 261–270 (2003)

    Article  Google Scholar 

  15. Cui, Y., Wen, W., Lipnicki, D.M., Beg, M.F., Jin, J.S., Luo, S., Zhu, W., Kochan, N.A., Reppermund, S., Zhuang, L., Raamana, P.R., Liu, T., Trollor, J.N., Wang, L., Brodaty, H., Sachdev, P.S.: Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: a combined spatial atrophy and white matter alteration approach. NeuroImage 59, 1209–1217 (2012)

    Article  Google Scholar 

  16. Cuingnet, R., Rosso, C., Chupin, M., Lehéricy, S., Dormont, D., Benali, H., Colliot, O.: Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome. Med. Image Anal. 15(5), 729–737 (2011)

    Article  Google Scholar 

  17. Cuingnet, R., Glaunès, J.A., Chupin, M., Benali, H., Colliot, O.: Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 682–696 (2013)

    Article  Google Scholar 

  18. Dai, Z., Yan, C., Wang, Z., Wang, J., Xia, M., Li, K., He, Y.: Discriminative analysis of early alzheimer’s disease using multi-modal imaging and multi-level characterization with multi-classifier (m3). NeuroImage 59, 2187–2195 (2012)

    Article  Google Scholar 

  19. Davatzikos, C., Ruparel, K., Fan, Y., Shen, D., Acharyya, M.: Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. NeuroImage 28(3), 663–668 (2005)

    Article  Google Scholar 

  20. Deshpande, G., Li, Z., Santhanam, P., Coles, C.D., Lynch, M.E., Hamann, S., Hu, X.: Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity. PLOS One 5(12), e14277 (2010)

    Article  Google Scholar 

  21. Dosenbach, N.U.F., Nardos, B., Cohen, A.L., Fair, D.A., Power, J.D., Church, J.A., Nelson, S.M., Wig, G.S., Vogel, A.C., Lessov-Schlaggar, C.N., Barnes, K.A., Dubis, J.W., Feczko, E., Coalson, R.S., Pruett J.R., Jr., Barch, D.M., Petersen, S.E., Schlaggar, B.L.: Prediction of individual brain maturity using fMRI. Science 329, 1358–1361 (2010)

    Article  Google Scholar 

  22. Dryden, I.L., Koloydenko, A., Zhou, D.: Non-euclidean statistics for covariance matrices, with applications to diffusion tensor imaging. Ann. Appl. Stat. 3(3), 1102–1123 (2009)

    Article  MathSciNet  Google Scholar 

  23. Fillard, P., Pennec, X., Arsigny, V., Ayache, N.: Clinical dt-mri estimation, smoothing, and fiber tracking with log-euclidean metrics. IEEE Trans. Med. Imaging 26(11), 1472–1482 (2007)

    Article  Google Scholar 

  24. Fletcher, P.T., Joshi, S.: Principal geodesic analysis on symmetric spaces: statistics of diffusion tensors. In: Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis, pp. 87–98. Springer, Berlin (2004)

    Google Scholar 

  25. Ford, J., Farid, H., Makedon, F., Flashman, L.A., McAllister, T.W., Megalooikonomou, V., Saykin, A.J.: Patient classification of fMRI activation maps. In: Medical Image Computing and Computer Assisted Intervention (MICCAI). Lecture Notes in Computer Science, vol. 2879, pp. 58–65. Springer, Berlin (2003)

    Google Scholar 

  26. Frackowiak, R.S., Friston, K.J., Frith, C.D., Dolan, R.J., Price, C.J., Zeki, S., Ashburner, J.T., Penny, W.D.: Human brain function. Academic, New York (2004)

    Google Scholar 

  27. Franke, K., Luders, E., May, A., Wilke, M., Gaser, C.: Brain maturation: predicting individual BrainAGE in children and adolescents using structural MRI. NeuroImage 63, 1305–1312 (2012)

    Article  Google Scholar 

  28. Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.P., Frith, C.D., Frackowiak, R.S.: Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2(4), 189–210 (1994)

    Article  Google Scholar 

  29. Friston, K.J., Holmes, A.P., Poline, J., Grasby, P., Williams, S., Frackowiak, R.S., Turner, R.: Analysis of fmri time-series revisited. NeuroImage 2(1), 45–53 (1995)

    Article  Google Scholar 

  30. Genovese, C.R., Lazar, N.A., Nichols, T.: Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage 15(4), 870–878 (2002)

    Article  Google Scholar 

  31. Goodlett, C.B., Fletcher, P.T., Gilmore, J.H., Gerig, G.: Group analysis of dti fiber tract statistics with application to neurodevelopment. NeuroImage 45(1), S133–S142 (2009)

    Article  Google Scholar 

  32. Grosenick, L., Greer, S., Knutson, B.: Interpretable classifiers for fMRI improve prediction of purchases. IEEE Trans. Neural Syst. Rehabil. Eng. 16(6), 539–548 (2008)

    Article  Google Scholar 

  33. Haller, S., Nguyen, D., Rodriguez, C., Emch, J., Gold, G., Bartsch, A., Lovblad, K.O., Giannakopoulos, P.: Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data. J Alzheimers Dis. 22(1), 315–327 (2010)

    Google Scholar 

  34. Haufe, S., Meinecke, F., Görgen, K., Dähne, S., Haynes, J.D., Blankertz, B., Bießmann, F.: On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage 87, 96–110 (2014)

    Article  Google Scholar 

  35. Honorio, J., Tomasi, D., Goldstein, R.Z., Leung, H.C., Samaras, D.: Can a single brain region predict a disorder? IEEE Trans. Med. Imaging 31(11), 2062–2072 (2012)

    Article  Google Scholar 

  36. Jones, D.K., Symms, M.R., Cercignani, M., Howard, R.J.: The effect of filter size on VBM analyses of DT-MRI data. Neuroimage 26(2), 546–554 (2005)

    Article  Google Scholar 

  37. Keihaninejad, S., Zhang, H., Ryan, N.S., Malone, I.B., Modat, M., Cardoso, M.J., Cash, D.M., Fox, N.C., Ourselin, S.: An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to alzheimer’s disease. NeuroImage 72, 153–163 (2013)

    Article  Google Scholar 

  38. Kindlmann, G., Estepar, R.S.J., Niethammer, M., Haker, S., Westin, C.F.: Geodesic-loxodromes for diffusion tensor interpolation and difference measurement. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2007, pp. 1–9. Springer, Heidelberg (2007)

    Google Scholar 

  39. Klöppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack, C.R., Ashburner, J., Frackowiak, R.S.J.: Automatic classification of MR scans in alzheimer’s disease. Brain 131(3), 681–689 (2008)

    Article  Google Scholar 

  40. LaConte, S., Strother, S., Cherkassky, V., Anderson, J., Hu, X.: Support vector machines for temporal classification of block design fMRI data. NeuroImage 26(2), 317–329 (2005)

    Article  Google Scholar 

  41. Lenglet, C., Rousson, M., Deriche, R., Faugeras, O.: Statistics on the manifold of multivariate normal distributions: theory and application to diffusion tensor mri processing. J. Math. Imaging Vision 25(3), 423–444 (2006)

    Article  MathSciNet  Google Scholar 

  42. Lipton, M.L., Gellella, E., Lo, C., Gold, T., Ardekani, B.A., Shifteh, K., Bello, J.A., Branch, C.A.: Multifocal white matter ultrastructural abnormalities in mild traumatic brain injury with cognitive disability: a voxel-wise analysis of diffusion tensor imaging. J Neurotrauma 25(11), 1335–1342 (2008)

    Article  Google Scholar 

  43. Lipton, M.L., Kim, N., Park, Y.K., Hulkower, M.B., Gardin, T.M., Shifteh, K., Kim, M., Zimmerman, M.E., Lipton, R.B., Branch, C.A.: Robust detection of traumatic axonal injury in individual mild traumatic brain injury patients: intersubject variation, change over time and bidirectional changes in anisotropy. Brain Imaging Behav. 6(2), 329–342 (2012)

    Article  Google Scholar 

  44. Martínez-Ramón, M., Kltchinskii, V., Heileman, G.L., Posse, S.: fMRI pattern classification using neuroanatomically constrained boosting. NeuroImage 31(3), 1129–1141 (2006)

    Article  Google Scholar 

  45. Mitchell, T.M., Hutchinson, R., Niculescu, R.S., Pereira, F., Wang, X.: Learning to decode cognitive states from brain images. Mach. Learn. 57, 145–175 (2004)

    Article  Google Scholar 

  46. Nagy, Z., Alexander, D.C., Thomas, D.L., Weiskopf, N., Sereno, M.I.: Using high angular resolution diffusion imaging data to discriminate cortical regions. PLOS One 8(5), e63842 (2013)

    Article  Google Scholar 

  47. Nichols, T.E., Holmes, A.P.: Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15(1), 1–25 (2002)

    Article  Google Scholar 

  48. O’Donnell, L., Westin, C., Golby, A.: Tract-based morphometry for white matter group analysis. NeuroImage 45(3), 832–844 (2009)

    Article  Google Scholar 

  49. O’Donnell, L.J., Golby, A.J., Westin, C.F.: Fiber clustering versus the parcellation-based connectome. NeuroImage 80, 283–289 (2013)

    Article  Google Scholar 

  50. O’Dwyer, L., Lamberton, F., Matura, S., Scheibe, M., Miller, J., Rujescu, D., Prvulovis, D., Hampel, H.: White matter differences between healthy young ApoE4 carriers and non-carriers identified with tractography and support vector machines. PLOS One 7(4), e36024 (2012)

    Article  Google Scholar 

  51. Pasternak, O., Sochen, N., Basser, P.J.: The effect of metric selection on the analysis of diffusion tensor mri data. NeuroImage 49(3), 2190–2204 (2010)

    Article  Google Scholar 

  52. Pereira, F., Mitchell, T., Botvinick, M.: Machine learning classifiers and fmri: a tutorial overview. NeuroImage 45(1 Suppl.), S199–S209 (2009)

    Article  Google Scholar 

  53. Rasmussen, P.M., Madsen, K.H., Lund, T.E., Hansen, L.K.: Visualization of nonlinear kernel models in neuroimaging by sensitivity maps. NeuroImage 55, 1120–1131 (2011)

    Article  Google Scholar 

  54. Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of relieff and rrelieff. Mach. Learn. 53(1–2), 23–69 (2003)

    Article  Google Scholar 

  55. Schlaffke, L., Lissek, S., Lenz, M., Juckel, G., Schultz, T., Tegenthoff, M., Schmidt-Wilcke, T., Brüne, M.: Shared and non-shared neural networks of cognitive and affective theory-of-mind: a neuroimaging study using cartoon picture stories. Hum. Brain Mapp. (2014). Early View. doi: 10.1002/hbm.22610

    Google Scholar 

  56. Schmidt-Wilcke, T., Cagnoli, P., Wang, P., Schultz, T., Lotz, A., Mccune, W.J., Sundgren, P.C.: Diminished white matter integrity in patients with systemic lupus erythematosus. NeuroImage Clin. (2014). DOI 10.1016/j.nicl.2014.07.001

    Google Scholar 

  57. Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Massachusetts (2002)

    Google Scholar 

  58. Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L.H.: Higher-order tensors in diffusion imaging. In: Westin, C.F., Vilanova, A., Burgeth, B. (eds.) Visualization and Processing of Tensors and Higher Order Descriptors for Multi-valued Data, pp. 129–161. Springer, Berlin (2014)

    Google Scholar 

  59. Schwartzman, A.: Random ellipsoids and false discovery rates: statistics for diffusion tensor imaging data. Ph.D. thesis, Stanford University (2006)

    Google Scholar 

  60. Schwartzman, A., Dougherty, R.F., Taylor, J.E.: Cross-subject comparison of principal diffusion direction maps. Magn. Reson. Med. 53(6), 1423–1431 (2005)

    Article  Google Scholar 

  61. Schwartzman, A., Dougherty, R.F., Taylor, J.E.: False discovery rate analysis of brain diffusion direction maps. Ann. Appl. Stat. 2(1), 153–175 (2008)

    Article  MathSciNet  Google Scholar 

  62. Shenton, M.E., Kikinis, R., Jolesz, F.A., Pollak, S.D., LeMay, M., Wible, C.G., Hokama, H., Martin, J., Metcalf, D., Coleman, M., et al.: Abnormalities of the left temporal lobe and thought disorder in schizophrenia: a quantitative magnetic resonance imaging study. N. Engl. J. Med. 327(9), 604–612 (1992)

    Article  Google Scholar 

  63. Smith, S., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T., Mackay, C., Watkins, K., Ciccarelli, O., Cader, M., Matthews, P., et al.: Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage 31(4), 1487–1505 (2006)

    Article  Google Scholar 

  64. Student: The probable error of a mean. Biometrika 6(1), 1–25 (1908)

    Google Scholar 

  65. Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E.J., Yacoub, E., Ugurbil, K.: The WU-Minn human connectome project: an overview. NeuroImage 80, 62–79 (2013)

    Article  Google Scholar 

  66. Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Information Science and Statistics. Springer, New York (1999)

    Google Scholar 

  67. Viswanath, V., Fletcher, E., Singh, B., Smith, N., Paul, D., Peng, J., Chen, J., Carmichael, O.: Impact of dti smoothing on the study of brain aging. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 94–97. IEEE, New York (2012). doi: 10.1109/EMBC.2012.6345879

    Google Scholar 

  68. Vul, E., Harris, C., Winkielman, P., Pashler, H.: Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspect. Psychol. Sci. 4(3), 274–290 (2009)

    Article  Google Scholar 

  69. Wakana, S., Jiang, H., Nagae-Poetscher, L.M., Van Zijl, P.C., Mori, S.: Fiber tract–based atlas of human white matter anatomy 1. Radiology 230(1), 77–87 (2004)

    Article  Google Scholar 

  70. Wang, X., Hutchinson, R., Mitchell, T.M.: Training fMRI classifiers to detect cognitive states across multiple human subjects. In: Thrun, S., Saul, L.K., Schölkopf, B. (eds.) Proceedings of Neural Information Processing Systems, pp. 709–716 (2003)

    Google Scholar 

  71. Wee, C.Y., Yap, P.T., Li, W., Denny, K., Browndyke, J.N., Potter, G.G., Welsh-Bohmer, K.A., Wang, L., Shen, D.: Enriched white matter connectivity networks for accurate identification of MCI patients. NeuroImage 54, 1812–1822 (2011)

    Article  Google Scholar 

  72. Wee, C.Y., Yap, P.T., Zhang, D., Denny, K., Browndyke, J.N., Potter, G.G., Welsh-Bohmer, K.A., Wang, L., Shen, D.: Identification of MCI individuals using structural and functional connectivity networks. NeuroImage 59, 2045–2056 (2012)

    Article  Google Scholar 

  73. Whitcher, B., Wisco, J.J., Hadjikhani, N., Tuch, D.S.: Statistical group comparison of diffusion tensors via multivariate hypothesis testing. Magn. Reson. Med. 57(6), 1065–1074 (2007)

    Article  Google Scholar 

  74. Yushkevich, P.A., Zhang, H., Simon, T.J., Gee, J.C.: Structure-specific statistical mapping of white matter tracts. NeuroImage 41(2), 448–461 (2008)

    Article  Google Scholar 

  75. Zhu, H., Styner, M., Tang, N., Liu, Z., Lin, W., Gilmore, J.H.: Frats: functional regression analysis of dti tract statistics. IEEE Trans. Med. Imaging 29(4), 1039–1049 (2010)

    Article  Google Scholar 

  76. Ziliak, S.T., McCloskey, D.N.: The Cult of Statistical Significance: How the Standard Error Costs us Jobs, Justice, and Lives. University of Michigan Press, Ann Arbor (2008)

    Google Scholar 

Download references

Acknowledgements

This work has resulted from a series of breakout sessions at Dagstuhl seminar 14082. We thank Anna Vilanova (TU Delft, The Netherlands) for her collaboration in those sessions, and for her help in organizing the LaTeX structure of this chapter. Author LJO thanks NIH grant support R01MH074794, P41EB015902, R21CA156943, P41EB015898, and U01NS083223.

Author information

Authors and Affiliations

  1. Harvard Medical School, Boston, MA, USA

    Lauren J. O’Donnell

  2. University of Bonn, Bonn, Germany

    Thomas Schultz

Authors
  1. Lauren J. O’Donnell
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas Schultz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lauren J. O’Donnell .

Editor information

Editors and Affiliations

  1. Linköping University, Norrköping, Sweden

    Ingrid Hotz

  2. Visualization and Medical Image Analysis, University of Bonn, Bonn, Germany

    Thomas Schultz

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

O’Donnell, L.J., Schultz, T. (2015). Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data. In: Hotz, I., Schultz, T. (eds) Visualization and Processing of Higher Order Descriptors for Multi-Valued Data. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-15090-1_15

Download citation

Publish with us

Policies and ethics

Search

Navigation