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Real-Time Functional MRI Classification of Brain States Using Markov-SVM Hybrid Models: Peering Inside the rt-fMRI Black Box

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Machine Learning and Interpretation in Neuroimaging

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7263))

Abstract

Real-time functional MRI (rt-fMRI) methods provide the ability to predict and detect online changes in cognitive states. Applications require appropriate selection of features, preprocessing routines, and efficient computational models in order to be both practical to implement and deliver interpretable results. We predict video activity in nicotine-addicted subjects using both regional spatial averages and pre-constructed independent component spatial maps we refer to as an ”IC dictionary.” We found that this dictionary predicted better than the anatomical summaries and was less sensitive to preprocessing steps. When prior state information was incorporated using hybrid SVM-Markov models, the online models were able to predict even more accurately in real-time whether an individual was viewing a video while either resisting or indulging in nicotine cravings. Collectively, this work proposes and evaluates models that could be used for biofeedback. The IC dictionary offered an interpretable feature set proposing functional networks responsible for cognitive activity. We explore what is inside the black box of real-time fMRI, and examine both the advantages and shortcomings when machine learning methods are applied to predict and interpret cognitive states in the real-time context.

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References

  1. Cohen, M.S., Weisskoff, R.M.: Ultra-fast imaging. Magn. Reson. Imaging 9, 1–37 (1991)

    Article  Google Scholar 

  2. Cox, R.W., Jesmanowicz, A., Hyde, J.S.: Real-time functional magnetic resonance imaging. Magn. Reson. Med. 33, 230–236 (1995)

    Article  Google Scholar 

  3. Gembris, D., Taylor, J.G., Schor, S., Frings, W., Suter, D., Posse, S.: Functional magnetic resonance imaging in real time (FIRE): sliding-window correlation analysis and reference-vector optimization. Magn. Reson. Med. 43, 259–268 (2000)

    Article  Google Scholar 

  4. Weiskopf, N., Sitaram, R., Josephs, O., Veit, R., Scharnowski, F., Goebel, R., Birbaumer, N., Deichmann, R., Mathiak, K.: Real-time functional magnetic resonance imaging: methods and applications. Magn. Reson. Imaging 25, 989–1003 (2007)

    Article  Google Scholar 

  5. Bleier, A.R., Jolesz, F.A., Cohen, M.S., Weisskoff, R.M., Dalcanton, J.J., Higuchi, N., Feinberg, D.A., Rosen, B.R., McKinstry, R.C., Hushek, S.G.: Real-time magnetic resonance imaging of laser heat deposition in tissue. Magn. Reson. Med. 21, 132–137 (1991)

    Article  Google Scholar 

  6. Voyvodic, J.T.: Real-time fMRI paradigm control, physiology, and behavior combined with near real-time statistical analysis. Neuroimage 10, 91–106 (1999)

    Article  Google Scholar 

  7. Cohen, M.S.: Real-time functional magnetic resonance imaging. Methods 25(2), 201–220 (2001)

    Article  MathSciNet  Google Scholar 

  8. Goddard, N.H., Cohen, J.D., Eddy, W.F., Genovese, C.R., Noll, D.C., Nystrom, L.E.: Online analysis of functional MRI datasets on parallel platforms. The Journal of Supercomputing 11, 295–318 (1997)

    Article  Google Scholar 

  9. Grill-Spector, K., Sayres, R., Ress, D.: High-resolution imaging reveals highly selective nonface clusters in the fusiform face area. Nat. Neurosci. 9, 1177–1185 (2006)

    Article  Google Scholar 

  10. Schneider, W., Noll, D.C., Cohen, J.D.: Functional topographic mapping of the cortical ribbon in human vision with conventional MRI scanners. Nature 365, 150–153 (1993)

    Article  Google Scholar 

  11. Gasser, T., Ganslandt, O., Sandalcioglu, E., Stolke, D., Fahlbusch, R., Nimsky, C.: Intraoperative functional MRI: implementation and preliminary experience. Neuroimage 26, 685–693 (2005)

    Article  Google Scholar 

  12. Gasser, T., Sandalcioglu, E., Schoch, B., Gizewski, E., Forsting, M., Stolke, D., Wiedemayer, H.: Functional magnetic resonance imaging in anesthetized patients: a relevant step toward real-time intraoperative functional neuroimaging. Neurosurgery 57, 94–99 (2005)

    Google Scholar 

  13. Gasser, T., Szelenyi, A., Senft, C., Muragaki, Y., Sandalcioglu, I.E., Sure, U., Nimsky, C., Seifert, V.: Intraoperative MRI and functional mapping. Acta Neurochir. Suppl. 109, 61–65 (2011)

    Article  Google Scholar 

  14. Schwindack, C., Siminotto, E., Meyer, M., McNamara, A., Marshall, I., Wardlaw, J.M., Whittle, I.R.: Real-time functional magnetic resonance imaging (rt-fMRI) in patients with brain tumours: preliminary findings using motor and language paradigms. Br. J. Neurosurg. 19, 25–32 (2005)

    Article  Google Scholar 

  15. Gering, D.T., Weber, D.M.: Intraoperative, real-time, functional MRI. J. Magn. Reson. Imaging 8, 254–257 (1998)

    Article  Google Scholar 

  16. Moller, M., Freund, M., Greiner, C., Schwindt, W., Gaus, C., Heindel, W.: Real time fMRI: a tool for the routine presurgical localisation of the motor cortex. Eur. Radiol. 15, 292–295 (2005)

    Article  Google Scholar 

  17. Hong, X., Rohan, M., Cohen, M.S., Terwilliger, R., Roemer, P.: Real-time observation of mental activity: the autocerebroscope (1997)

    Google Scholar 

  18. DeCharms, C.R.: Applications of real-time fMRI. Nat. Rev. Neurosci. 9(9), 720–729 (2008)

    Article  Google Scholar 

  19. LaConte, S.M.: Decoding fMRI brain states in real-time. Neuroimage 56, 440–454 (2011)

    Article  Google Scholar 

  20. Caria, A., Veit, R., Sitaram, R., Lotze, M., Weiskopf, N., Grodd, W., Birbaumer, N.: Regulation of anterior insular cortex activity using real-time fMRI. Neuroimage 35, 1238–1246 (2007)

    Article  Google Scholar 

  21. Yoo, S.-S., Fairneny, T., Chen, N.-K., Choo, S.-E., Panych, L.P., Park, H., Lee, S.-Y., Jolesz, F.A.: Brain-computer interface using fMRI: Spatial navigation by thoughts 15(10), 1591–1595 (2004)

    Google Scholar 

  22. LaConte, S.M., Peltier, S.J., Hu, X.P.: Real-time fMRI using brain-state classification. Hum. Brain Mapp. 28, 1033–1044 (2007)

    Article  Google Scholar 

  23. Ohlsson, H., Rydell, J., Brun, A., Roll, J., Andersson, M., Ynnerman, A., Knutsson, H.: Enabling bio-feedback using real-time fMRI

    Google Scholar 

  24. DeCharms, R.C.: Reading and controlling human brain activation using real-time functional magnetic resonance imaging. Trends Cogn. Sci. (2007)

    Google Scholar 

  25. DeCharms, R.C., Maeda, F., Glover, G.H., Ludlow, D., Pauly, J.M., Soneji, D., Gabrieli, J.D., Mackey, S.C.: Control over brain activation and pain learned by using real-time functional MRI. Proc. Natl. Acad. Sci. U.S.A. 102, 18626–18631 (2005)

    Article  Google Scholar 

  26. Weiskopf, N., Sitaram, R., Josephs, O., Veit, R., Scharnowski, F., Goebel, R., Birbaumer, N., Deichmann, R., Mathiak, K.: Real-time functional magnetic resonance imaging: methods and applications. Magn. Reson. Imaging (2007)

    Google Scholar 

  27. Zarahn, E., Aguirre, G.K., D’Esposito, M.: Empirical analyses of BOLD fMRI statistics. NeuroImage 5, 179–197 (1997)

    Article  Google Scholar 

  28. Cohen, M.S., DuBois, R.M.: Stability, repeatability, and the expression of signal magnitude in functional magnetic resonance imaging. J. Magn. Reson. Imaging 10, 33–40 (1999)

    Article  Google Scholar 

  29. Monti, M.M.: Statistical analysis of fMRI time-series: A critical evaluation of the GLM approach. Preprint Submitted to Frontiers Special Topics (2006)

    Google Scholar 

  30. Pereira, F., Mitchell, T., Botvinick, M.: Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 45(1 suppl.) (March 2009)

    Google Scholar 

  31. Douglas, P.K., Harris, S., Yuille, A., Cohen, M.S.: Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief. Neuroimage 56, 544–553 (2011)

    Article  Google Scholar 

  32. Liu, W., Zhu, P., Anderson, J.S., Yurgelun-Todd, D., Fletcher, P.T.: Spatial Regularization of Functional Connectivity Using High-Dimensional Markov Random Fields. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 363–370. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  33. Svensén, M., Kruggel, F., von Cramon, D.Y.: Markov Random Field Modelling of fMRI Data Using a Mean Field EM-algorithm. In: Hancock, E.R., Pelillo, M. (eds.) EMMCVPR 1999. LNCS, vol. 1654, pp. 317–330. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  34. Nagori, M.B., Mane, T.N., Agrawal, S.A., Joshi, M.S.: Evaluation of markov blanket algorithms for fMRI data analysis. In: International Conference on Information and Network Technology (2011)

    Google Scholar 

  35. Nan, F., Wang, Y., Ma, X.: fMRI Activation Detection by MultiScale Hidden Markov Model. In: Rajasekaran, S. (ed.) BICoB 2009. LNCS, vol. 5462, pp. 295–306. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  36. Woolrich, M.W., Behrens, T.E., Beckmann, C.F., Jenkinson, M., Smith, S.M.: Multilevel linear modelling for FMRI group analysis using Bayesian inference. Neuroimage 21, 1732–1747 (2004)

    Article  Google Scholar 

  37. Hyvärinen, A., Oja, E.: Independent component analysis: Algorithms and applications. Neural Networks 13(4-5), 411–430 (2000)

    Article  Google Scholar 

  38. Mckeown, M., Makeig, S., Brown, G., Jung, T., Kindermann, S., Bell, A., Sejnowski, T.: Analysis of fMRI data by blind separation into independent spatial components (1998)

    Google Scholar 

  39. Esposito, F., Seifritz, E., Formisano, E., Morrone, R., Scarabino, T., Tedeschi, G., Cirillo, S., Goebel, R., Di Salle, F.: Real-time independent component analysis of fMRI time-series. Neuroimage 20, 2209–2224 (2003)

    Article  Google Scholar 

  40. De Martino, F., Valente, G., Staeren, N., Ashburner, J., Goebel, R., Formisano, E.: Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. Neuroimage 43, 44–58 (2008)

    Article  Google Scholar 

  41. Anderson, A., Bramen, J., Douglas, P.K., Lenartowicz, A., Cho, A., Culbertson, C., Brody, A.L., Yuille, A.L., Cohen, M.S.: Large sample group independent component analysis of functional magnetic resonance imaging using anatomical atlas-based reduction and bootstrapped clustering. International Journal of Imaging Systems and Technology 21(2), 223–231 (2011)

    Article  Google Scholar 

  42. Brody, A.L., Mandelkern, M.A., Olmstead, R.E., Jou, J., Tiongson, E., Allen, V., Scheibal, D., London, E.D., Monterosso, J.R., Tiffany, S.T., Korb, A., Gan, J.J., Cohen, M.S.: Neural substrates of resisting craving during cigarette cue exposure. Biol. Psychiatry 62, 642–651 (2007)

    Article  Google Scholar 

  43. Smith, S.M., et al.: Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23, 208–219 (2004)

    Article  Google Scholar 

  44. Desikan, R.S., Segonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., Albert, M.S., Killiany, R.J.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006)

    Article  Google Scholar 

  45. Smith, S.M., Fox, P.T., Miller, K.L., Glahn, D.C., Mickle Fox, P., Mackay, C.E., Filippini, N., Watkins, K.E., Toro, R., Laird, A.R., Beckmann, C.F.: Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America 106(31), 13040–13045 (2009)

    Article  Google Scholar 

  46. Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning, 273–297 (1995)

    Google Scholar 

  47. Garczarek, U.M.: Classification rules in standardized partition spaces. Doctoral Dissertation: University of Dortmund (2002)

    Google Scholar 

  48. Chang, Y.-W., Lin, C.-J.: Feature ranking using linear svm. Journal of Machine Learning Research - Proceedings Track 3, 53–64 (2008)

    Google Scholar 

  49. Maldjian, J.A., Laurienti, P.J., Kraft, R.A., Burdette, J.H.: An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 19, 1233–1239 (2003)

    Article  Google Scholar 

  50. Marrelec, G., Fransson, P.: Assessing the influence of different ROI selection strategies on functional connectivity analyses of fMRI data acquired during steady-state conditions. PLoS ONE 6, e14788 (2011)

    Article  Google Scholar 

  51. Margulies, D.S., Vincent, J.L., Kelly, C., Lohmann, G., Uddin, L.Q., Biswal, B.B., Villringer, A., Castellanos, F.X., Milham, M.P., Petrides, M.: Precuneus shares intrinsic functional architecture in humans and monkeys. Proc. Natl. Acad. Sci. U.S.A. 106, 20069–20074 (2009)

    Article  Google Scholar 

  52. Barkhof, F., Scheltens, P., Stam, C.J., Smith, S.M., Beckmann, C.F., Damoiseaux, J.S., Rombouts, S.A.: Consistent resting-state networks across healthy subjects. Proc. National Academy of Science (2006)

    Google Scholar 

  53. Scoville, W.B., Milner, B., Scoville, W.B., Miller, B.: Loss of recent memory after bilateral hippocampal lesions. J. Neuropsychiatry Clin. Neurosci. 12, 103–113 (1957, 2000)

    Article  Google Scholar 

  54. Ekstrom, A.D., Kahana, M.J., Caplan, J.B., Fields, T.A., Isham, E.A., Newman, E.L., Fried, I.: Cellular networks underlying human spatial navigation. Nature 425, 184–188 (2003)

    Article  Google Scholar 

  55. Squire, L.R., Knowlton, B.J.: The medial temporal lobe, the hippocampus, and the memory systems of the brain. Memory, 765–779

    Google Scholar 

  56. Shams, L., Kamitani, Y., Shimojo, S.: Illusions. What you see is what you hear. Nature 408, 788 (2000)

    Article  Google Scholar 

  57. Gall, F.J.: Anatomie et physiologie du système nerveux en général et du cerveau en particulier: avec des observations sur la possibilité de reconnoitre plusieurs dispositions intellectuelles et morales de l’homme et des animaux. Chez N. Maze, libraire (1819)

    Google Scholar 

  58. Pribram, K.H.: Languages of the brain: experimental paradoxes and principles in neuropsychology. Brandon House (1981)

    Google Scholar 

  59. Kriegeskorte, N., Goebel, R., Bandettini, P.: Information-based functional brain mapping. Proc. Natl. Acad. Sci. U.S.A. 103, 3863–3868 (2006)

    Article  Google Scholar 

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Anderson, A., Han, D., Douglas, P.K., Bramen, J., Cohen, M.S. (2012). Real-Time Functional MRI Classification of Brain States Using Markov-SVM Hybrid Models: Peering Inside the rt-fMRI Black Box. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_31

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  • DOI: https://doi.org/10.1007/978-3-642-34713-9_31

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