Advertisement

START UP RESEARCH 2017: Studies in Neural Data Science pp 131-156 | Cite as

Challenges in the Analysis of Neuroscience Data

  • Michele GuindaniEmail author
  • Marina Vannucci
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 257)

Abstract

In the last two decades, our understanding of the mechanisms underlying the functioning and disruption of the human brain has advanced considerably. The previous chapters of the book have provided a compelling argument for demonstrating the advantages of thoughtful, non-naive, statistical approaches for analyzing brain imaging data. Here, we provide a review of the main themes highlighted in those chapters, and we further discuss some of the challenges that statistical imaging is currently confronted with. In particular, we emphasize the importance of developing analytical frameworks that allow to characterize the heterogeneity typically observed in brain imaging both within- and between- subjects, by capturing the main sources of variability in the data. More specifically, we focus on clustering methods that identify groups of subjects characterized by similar patterns of brain responses to a task; on dynamic temporal models that characterize the heterogeneity in individual functional connectivity networks; and on multimodal imaging analysis and imaging genetics that combine information from multiple data sources in order to achieve a better understanding of brain processes.

Keywords

Brain imaging data fMRI data Clustering Dynamic functional connectivity Multimodal analysis Imaging genetics 

References

  1. 1.
    Friston, K.J., Ashburnet, J.T., Kiebe, Nichols, T.E., Penny, W.D.: Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic Press (2007)Google Scholar
  2. 2.
    Prados, F., Boada, I., Prats-Galino, A., Martin-Fernandez, J.A., Feixas, M., Blasco, G., Puig, J., Pedraza, S.: Analysis of new diffusion tensor imaging anisotropy measures in the three-phase plot. J. Magn. Reson. Imaging 31(6), 1435–1444 (2010)Google Scholar
  3. 3.
    Weber, B., Fliessbach, K., Elger, C.: Magnetic resonance imaging in epilepsy research: recent and upcoming developments. In: Schwartzkroin, P.A. (ed.) Encyclopedia of Basic Epilepsy Research, pp. 1549–1554. Academic Press, Oxford (2009)Google Scholar
  4. 4.
    Oguz, I., Farzinfar, M., Matsui, J., Budin, F., Liu, Z., Gerig, G., Johnson, H., Styner, M.: Dtiprep: quality control of diffusion-weighted images. Front. Neuroinformatics 8, 4 (2014)Google Scholar
  5. 5.
    Durante, D., Dunson, D.B.: Bayesian inference and testing of group differences in brain networks. Bayesian Anal. 13(1), 29–58 (2018)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Poldrack, R., Mumford, J., Nichols, T.: Handbook of fMRI Data Analysis. Cambridge University Press (2011)Google Scholar
  7. 7.
    Handwerker, D.A., Gonzalez-Castillo, J., D’Esposito, M., Bandettini, P.A.: The continuing challenge of understanding and modeling hemodynamic variation in fMRI. NeuroImage 62(2), 1017–1023 (2012)Google Scholar
  8. 8.
    Handwerker, D.A., Ollinger, J.M., D’Esposito, M.: Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. NeuroImage 21(4), 1639–1651 (2004)Google Scholar
  9. 9.
    Rangaprakash, D., Wu, G.R., Marinazzo, D., Hu, X., Deshpande, G.: Hemodynamic response function (HRF) variability confounds resting-state fMRI functional connectivity. Magn. Reson. Med. (2018)Google Scholar
  10. 10.
    Wu, G.R., Liao, W., Stramaglia, S., Ding, J.R., Chen, H., Marinazzo, D.: A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Med. Image Anal. 17(3), 365–374 (2013)Google Scholar
  11. 11.
    Friston, K.: Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2, 56–78 (1994)Google Scholar
  12. 12.
    Andersen, A., Gash, D., Avison, M.: Principal component analysis of the dynamic response measured by fMRI: a generalized linear systems framework. Magn. Reson. Imaging 17(6), 795–815 (1999)Google Scholar
  13. 13.
    Calhoun, V., Adali, T., Pearlson, G., Pekar, J.: A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 14(3), 140–151 (2001)Google Scholar
  14. 14.
    Varoquaux, G., Gramfort, A., Poline, J., Thirion, B., Zemel, R., Shawe-Taylor, J.: Brain covariance selection: better individual functional connectivity models using population prior. Adv. Neural Inf. Process. Syst. (2010)Google Scholar
  15. 15.
    Bowman, F., Caffo, B., Bassett, S., Kilts, C.: A Bayesian hierarchical framework for spatial modeling of fMRI data. NeuroImage 39(1), 146–156 (2008)Google Scholar
  16. 16.
    Zhang, L., Guindani, M., Versace, F., Vannucci, M.: A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses. NeuroImage 95, 162–175 (2014)Google Scholar
  17. 17.
    Ferguson, T.S.: A Bayesian analysis of some nonparametric problems. Ann. Stat., 209–230 (1973)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186–198 (2009)Google Scholar
  19. 19.
    van den Heuvel, M.P., Sporns, O.: Network hubs in the human brain. Trends Cogn. Sci. 17(12), 683–696 (2013)Google Scholar
  20. 20.
    Stam, C.J., Reijneveld, J.C.: Graph theoretical analysis of complex networks in the brain. Nonlinear Biomed. Phys. 1, 3–3 (2007)Google Scholar
  21. 21.
    Ginestet, C.E., Li, J., Balachandran, P., Rosenberg, S., Kolaczyk, E.D.: Hypothesis testing for network data in functional neuroimaging. Ann. Appl. Stat. 11(2), 725–750 (2017)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Friston, K.J., Frith, C.D., Frackowiak, R.S.J.: Time-dependent changes in effective connectivity measured with pet. Hum. Brain Mapp. 1(1), 69–79 (1993).  https://doi.org/10.1002/hbm.460010108Google Scholar
  23. 23.
    Büchel, C., Friston, K.: Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cereb. Cortex 7(8), 768–778 (1997)Google Scholar
  24. 24.
    Mclntosh, A., Gonzalez-Lima, F.: Structural equation modeling and its application to network analysis in functional brain imaging. Hum. Brain Mapp. 2(1), 2–22 (1994)Google Scholar
  25. 25.
    Friston, K., Harrison, L., Penny, W.: Dynamic causal modelling. NeuroImage 19(4), 1273–1302 (2003)Google Scholar
  26. 26.
    Harrison, L., Penny, W., Friston, K.: Multivariate autoregressive modeling of fMRI time series. NeuroImage 19(4), 1477–1491 (2003)Google Scholar
  27. 27.
    Goebel, R., Roebroeck, A., Kim, D., Formisano, E.: Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magn. Reson. Imaging 21(10), 1251–1261 (2003)Google Scholar
  28. 28.
    Zheng, X., Rajapakse, J.: Learning functional structure from fMR images. NeuroImage 31(4), 1601–1613 (2006)Google Scholar
  29. 29.
    Yu, Z., Pluta, D., Shen, T., Chen, C., Xue, G., Ombao, H.: Statistical challenges in modeling big brain signals. ArXiv e-prints (2018)Google Scholar
  30. 30.
    Gorrostieta, C., Fiecas, M., Ombao, H., Burke, E., Cramer, S.: Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity. Front. Comput. Neurosci. 7 (2013)Google Scholar
  31. 31.
    Yu, Z., Prado, R., Quinlan, E.B., Cramer, S.C., Ombao, H.: Understanding the impact of stroke on brain motor function: a hierarchical Bayesian approach. J. Am. Stat. Assoc. 111(514), 549–563 (2016)MathSciNetGoogle Scholar
  32. 32.
    Friston, K.: Functional and effective connectivity: a review. Brain Connect. 1(1), 13–36 (2011)MathSciNetGoogle Scholar
  33. 33.
    Bowman, F.: Brain imaging analysis. Annu. Rev. Stat. Its Appl. 1, 61–85 (2014)Google Scholar
  34. 34.
    Enno, S.K., J., F.K.: Analyzing effective connectivity with functional magnetic resonance imaging. Wiley Interdiscip. Rev. Cogn. Sci. 1(3), 446–459 (2010)Google Scholar
  35. 35.
    Savitz, J.B., Rauch, S.L., Drevets, W.C.: Clinical application of brain imaging for the diagnosis of mood disorders: the current state of play. Mol. Psychiatry 18, 528 EP (2013)Google Scholar
  36. 36.
    Insel, T., Cuthbert, B.: Brain disorders? Precisely. Science 348(6234) (2015)Google Scholar
  37. 37.
    Paulus, M.P., Stein, M.B.: Role of functional magnetic resonance imaging in drug discovery. Neuropsychol. Rev. 17(2), 179–188 (2007)Google Scholar
  38. 38.
    Kaufman, J., Gelernter, J., Hudziak, J.J., Tyrka, A.R., Coplan, J.D.: The Research Domain Criteria (RDoC) project and studies of risk and resilience in maltreated children. J. Am. Acad. Child Adolesc. Psychiatry 54(8), 617–625 (2015)Google Scholar
  39. 39.
    Kose, S., M., C.: The research domain criteria framework: transitioning from dimensional systems to integrating neuroscience and psychopathology. Psychiatry Clin. Psychopharmacol. 27(1), 1–5 (2017)Google Scholar
  40. 40.
    Johnson, T., Liu, Z., Bartsch, A., Nichols, T.: A Bayesian non-parametric Potts model with application to pre-surgical fMRI data. Stat. Methods Med. Res. 22(4), 364–381 (2013)MathSciNetGoogle Scholar
  41. 41.
    Kim, S., Smyth, P., Stern, H.: A nonparametric Bayesian approach to detecting spatial activation patterns in fMRI data. Med. Image Comput. Comput. Assist. Interv., 217–224 (2006)Google Scholar
  42. 42.
    Jbabdi, S., Woolrich, M., Behrens, T.: Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models. NeuroImage 44(2), 373–384 (2009)Google Scholar
  43. 43.
    Xu, L., Johnson, T., Nichols, T., Nee, D.: Modeling inter-subject variability in fMRI activation location: a Bayesian hierarchical spatial model. Biometrics 65(4), 1041–1051 (2009)MathSciNetzbMATHGoogle Scholar
  44. 44.
    Zhang, L., Guindani, M., Versace, F., Engelmann, J.M., Vannucci, M.: A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data. Ann. Appl. Stat. 10(2), 638–666 (2016)MathSciNetzbMATHGoogle Scholar
  45. 45.
    Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Hierarchical dirichlet processes. J. Am. Stat. Assoc. 101(476), 1566–1581 (2006)MathSciNetzbMATHGoogle Scholar
  46. 46.
    Flandin, G., Penny, W.: Bayesian fMRI data analysis with sparse spatial basis function priors. NeuroImage 34(3), 1108–1125 (2007)Google Scholar
  47. 47.
    Harrison, L., Green, G.: A Bayesian spatiotemporal model for very large data sets. NeuroImage 50(3), 1126–1141 (2010)Google Scholar
  48. 48.
    Penny, W., Kiebel, S., Friston, K.: Variational Bayesian inference for fMRI time series. NeuroImage 19(3), 727–741 (2003)Google Scholar
  49. 49.
    Penny, W., Trujillo-Barreto, N., Friston, K.: Bayesian fMRI time series analysis with spatial priors. NeuroImage 24(2), 350–362 (2005)Google Scholar
  50. 50.
    Woolrich, M., Behrens, T., Smith, S.: Constrained linear basis sets for HRF modelling using variational Bayes. NeuroImage 21(4), 1748–1761 (2004b)Google Scholar
  51. 51.
    Kook, J.H., Guindani, M., Zhang, L., Vannucci, M.: NPBayes-fMRI: Non-parametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data (2017, in press)Google Scholar
  52. 52.
    Muschelli, J., Gherman, A., Fortin, J.P., Avants, B., Whitcher, B., Clayden, J.D., Caffo, B.S., Crainiceanu, C.M.: Neuroconductor: an R platform for medical imaging analysis (2018, in press)Google Scholar
  53. 53.
    Fornito, A., Zalesky, A., Pantelis, C., Bullmore, E.T.: Schizophrenia, neuroimaging and connectomics. NeuroImage 62(4), 2296–2314 (2012)Google Scholar
  54. 54.
    Li, J., Wang, Z., Palmer, S., McKeown, M.: Dynamic Bayesian network modeling of fMRI: a comparison of group-analysis methods. NeuroImage 41(2), 398–407 (2008)Google Scholar
  55. 55.
    Hutchison, R.M., Womelsdorf, T., Allen, E.A., Bandettini, P.A., Calhoun, V.D., Corbetta, M., Penna, S.D., Duyn, J.H., Glover, G.H., Gonzalez-Castillo, J., Handwerker, D.A., Keilholz, S., Kiviniemi, V., Leopold, D.A., de Pasquale, F., Sporns, O., Walter, M., Chang, C.: Dynamic functional connectivity: promise, issues, and interpretations. NeuroImage 80(0), 360–378 (2013). Mapping the ConnectomeGoogle Scholar
  56. 56.
    Allen, E.A., Damaraju, E., Plis, S.M., Erhardt, E.B., Eichele, T., Calhoun, V.D.: Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex (2012)Google Scholar
  57. 57.
    Lindquist, M.A., Xu, Y., Nebel, M.B., Caffo, B.S.: Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach. NeuroImage (2014)Google Scholar
  58. 58.
    Cribben, I., Haraldsdottir, R., Atlas, L., Wager, T., Lindquist, M.: Dynamic connectivity regression: determining state-related changes in brain connectivity. NeuroImage 61, 907–920 (2012)Google Scholar
  59. 59.
    Xu, Y., Lindquist, M.A.: Dynamic connectivity detection: an algorithm for determining functional connectivity change points in fMRI data. Front. Neurosci. 9(285) (2015)Google Scholar
  60. 60.
    Chiang, S., Cassese, A., Guindani, M., Vannucci, M., Yeh, H.J., Haneef, Z., Stern, J.M.: Time-dependence of graph theory metrics in functional connectivity analysis. NeuroImage 125, 601–615 (2015)Google Scholar
  61. 61.
    Warnick, R., Guindani, M., Erhardt, E., Allen, E., Calhoun, V., Vannucci, M.: A Bayesian approach for estimating dynamic functional network connectivity in fMRI data. J. Am. Stat. Assoc. 113(521), 134–151 (2018)MathSciNetzbMATHGoogle Scholar
  62. 62.
    Dobra, A., Lenkoski, A., Rodriguez, A.: Bayesian inference for general gaussian graphical models with application to multivariate lattice data. J. Am. Stat. Assoc. 106(496), 1418–1433 (2011)MathSciNetzbMATHGoogle Scholar
  63. 63.
    Roverato, A.: Hyper inverse Wishart distribution for non-decomposable graphs and its application to Bayesian inference for Gaussian graphical models. Scand. J. Stat. 29(3), 391–411 (2002)MathSciNetzbMATHGoogle Scholar
  64. 64.
    Baker, A., Brookes, M., Rezek, A., Smith, S., Behrens, T., Penny, J., Smith, R., Woolrich, M.: Fast transient networks in spontaneous human brain activity. eLife 3(3), 1–18 (2014)Google Scholar
  65. 65.
    Balqis-Samdin, S., Ting, C.M., Ombao, H., Salleh, S.H.: A unified estimation framework for state-related changes in effective brain connectivity. IEEE Trans. Biomed. Eng. 64(4), 844–858 (2017)Google Scholar
  66. 66.
    Peterson, C., Stingo, F.C., Vannucci, M.: Bayesian inference of multiple gaussian graphical models. J. Am. Stat. Assoc. 110(509), 159–174 (2015)MathSciNetzbMATHGoogle Scholar
  67. 67.
    Chiang, S., Guindani, M., Yeh, H.J., Dewar, S., Haneef, Z., Stern, J.M., Vannucci, M.: A hierarchical Bayesian model for the identification of pet markers associated to the prediction of surgical outcome after anterior temporal lobe resection. Front. Neurosci. 11, 669 (2017)Google Scholar
  68. 68.
    Haynes, J.D., Rees, G.: Predicting the stream of consciousness from activity in human visual cortex. Curr. Biol. 15(14), 1301–1307 (2005)Google Scholar
  69. 69.
    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)Google Scholar
  70. 70.
    Mitchell, T., Hutchinson, R., Niculescu, R., Pereira, F., Wang, X., Just, M., Newman, S.: Learning to decode cognitive states from brain images. Mach. Learn. 57(1–2), 145–175 (2004)zbMATHGoogle Scholar
  71. 71.
    Arribas, J., Calhoun, V.D., Adali, T.: Automatic Bayesian classification of healthy controls, bipolar disorder, and schizophrenia using intrinsic connectivity maps from fMRI data. IEEE Trans. Biomed. Eng. 57(12) (2010)Google Scholar
  72. 72.
    Burge, J., Lane, T., Link, H., Qiu, S., Clark, V.P.: Discrete dynamic Bayesian network analysis of fMRI data. Hum. Brain Mapp. 30, 122–137 (2009)Google Scholar
  73. 73.
    Zhang, L., Guindani, M., Vannucci, M.: Bayesian models for functional magnetic resonance imaging data analysis. Wiley Interdiscip. Rev. Comput. Stat. 7(1), 21–41 (2015)MathSciNetGoogle Scholar
  74. 74.
    Uludag, K., Roebroeck, A.: General overview on the merits of multimodal neuroimaging data fusion. NeuroImage 102, 3–10 (2014)Google Scholar
  75. 75.
    Valdes-Sosa, P.A., Kotter, R., Friston, K.J.: Introduction: multimodal neuroimaging of brain connectivity. Philos. Trans. R. Soc. B Biol. Sci. 360(1457), 865–867 (2005)Google Scholar
  76. 76.
    Biessmann, F., Plis, S., Meinecke, F.C., Eichele, T., Muller, K.R.: Analysis of multimodal neuroimaging data. IEEE Rev. Biomed. Eng. 4, 26–58 (2011)Google Scholar
  77. 77.
    Jorge, J., van der Zwaag, W., Figueiredo, P.: EEG-fMRI integration for the study of human brain function. NeuroImage 102, 24–34 (2014)Google Scholar
  78. 78.
    Kalus, S., Sämann, P., Czisch, M., Fahrmeir, L.: fMRI activation detection with EEG priors. Technical report, University of Munich (2013)Google Scholar
  79. 79.
    Chiang, S., Guindani, M., Yeh, H.J., Haneef, Z., Stern, J.M., Vannucci, M.: Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data. Hum. Brain Mapp. 38(3), 1311–1332 (2016)Google Scholar
  80. 80.
    Nathoo, F., Kong, L., Zhu, H.: A review of statistical methods in imaging genetics. Technical report, ArXiv (2018)Google Scholar
  81. 81.
    Stingo, F.C., Guindani, M., Vannucci, M., Calhoun, V.D.: An integrative Bayesian modeling approach to imaging genetics. J. Am. Stat. Assoc. 108(503), 876–891 (2013)MathSciNetzbMATHGoogle Scholar
  82. 82.
    Greenlaw, K., Szefer, E., Graham, J., Lesperance, M., Nathoo, F.S., The Alzheimer’s Disease Neuroimaging Initiative: A Bayesian group sparse multi-task regression model for imaging genetics. Bioinformatics 33(16), 2513–2522 (2017)Google Scholar
  83. 83.
    Wang, H., Nie, F., Huang, H., Risacher, S.L., Saykin, A.J., Shen, L., The Alzheimer’s Disease Neuroimaging Initiative: Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning. Bioinformatics 28(12), i127–i136 (2012)Google Scholar
  84. 84.
    Chekouo, T., Stingo, F.C., Guindani, M., Do, K.A.: A Bayesian predictive model for imaging genetics with application to schizophrenia. Ann. Appl. Stat. 10(3), 1547–1571 (2016)MathSciNetzbMATHGoogle Scholar
  85. 85.
    Yu, C.H., Prado, R., Ombao, H., Rowe, D.: A Bayesian variable selection approach yields improved detection of brain activation from complex-valued fMRI. J. Am. Stat. Assoc., 1–61 (2018)Google Scholar
  86. 86.
    Rockova, V., George, E.I.: EMVS: the EM approach to Bayesian variable selection. J. Am. Stat. Assoc. 109(506), 828–846 (2014)MathSciNetzbMATHGoogle Scholar
  87. 87.
    Boto, E., Holmes, N., Leggett, J., Roberts, G., Shah, V., Meyer, S.S., Muñoz, L.D., Mullinger, K.J., Tierney, T.M., Bestmann, S., Barnes, G.R., Bowtell, R., Brookes, M.J.: Moving magnetoencephalography towards real-world applications with a wearable system. Nature 555, 657 EP (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of CaliforniaIrvineUSA
  2. 2.Department of StatisticsRice UniversityHoustonUSA

Personalised recommendations