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3D Multi-voxel Pattern Based Machine Learning for Multi-center fMRI Data Normalization

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Computer Vision and Image Processing (CVIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1568))

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Abstract

Multi-center fMRI studies help accumulate significant number of subjects to increase the statistical power of data analyses. However, the seemingly ambitious gain is hindered by the fact that differences between centers have significant effects on the imaging results. We present a novel machine learning (ML) based technique, which uses non-linear regression with multi-voxel based anatomically informed contextual information, to help normalize multi-center fMRI data to a chosen reference center. Accuracy graphs were obtained by thresholding the estimated maps at high p-values of \(p < 0.001\) after kernel density estimation. Results indicate significant reduction in spurious activations and more importantly, enhancement of the genuine activation clusters. Group level ROI based analysis reveals changes in activation pattern of clusters that are consistent with their role in cognitive function. Furthermore, as the mapping functions exhibit the tendency to induce sensitivity to the regions associated with the task they can help identify small but significant activations which could otherwise be lost due to population based inferences across centers.

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References

  1. Ogawa, S., Lee, T.M., Kay, A.R., Tank, D.W.: Brain magnetic resonance imaging with contrast dependent on blood oxygenation 87(24), 9868–9872 (1990). http://www.pnas.org/content/87/24/9868

  2. Parrish, T.B., Gitelman, D.R., LaBar, K.S., Mesulam, M., et al.: Impact of signal-to-noise on functional MRI. Magn. Reson. Med. 44(6), 925–932 (2000)

    Article  Google Scholar 

  3. Beckmann, C.F., Jenkinson, M., Smith, S.M.: General multilevel linear modeling for group analysis in fMRI. Neuroimage 20(2), 1052–1063 (2003)

    Article  Google Scholar 

  4. Horn, J.D.V., Grafton, S.T., Rockmore, D., Gazzaniga, M.S.: Sharing neuroimaging studies of human cognition 7(5), 473–481 (2004). https://doi.org/10.1038/nn1231. http://www.nature.com/neuro/journal/v7/n5/abs/nn1231.html

  5. Kelemen, A., Liang, Y.: Multi-center correction functions for magnetization transfer ratios of MRI scans. J. Health Med. Inform. 3, 2 (2011)

    Google Scholar 

  6. Machielsen, W.C., Rombouts, S.A., Barkhof, F., Scheltens, P., Witter, M.P.: fMRI of visual encoding: Reproducibility of activation. Hum. Brain Mapp. 9(3), 156–164 (2000). https://doi.org/10.1002/(SICI)1097-0193(200003)9:3<156::AID-HBM4>3.0.CO;2-Q

  7. Keator, D.B., et al.: The function biomedical informatics research network data repository. Neuroimage 124, 1074–1079 (2016)

    Article  Google Scholar 

  8. Gountouna, V.-E., et al.: Functional magnetic resonance imaging (fMRI) reproducibility and variance components across visits and scanning sites with a finger tapping task, NeuroImage 49(1), 552–560 (2010). https://doi.org/10.1016/j.neuroimage.2009.07.026. http://www.sciencedirect.com/science/article/pii/S1053811909007988

  9. Gorgolewski, K., et al.: Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front. Neuroinform. 5, 13 (2011)

    Article  Google Scholar 

  10. Cox, R.W.: AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29(3), 162–173 (1996)

    Article  Google Scholar 

  11. Worsley, K.J., Friston, K.J.: Analysis of fMRI time-series revisited-again. Neuroimage 2(3), 173–181 (1995)

    Article  Google Scholar 

  12. Smith, S.M., et al.: Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, S208–S219 (2004)

    Article  Google Scholar 

  13. Pereira, J., Acosta-Cabronero, J., Pengas, G., Xiong, L., Nestor, P., Williams, G.: VBM with viscous fluid registration of gray matter segments in SPM. Front. Aging Neurosci. 5, 30 (2012)

    Google Scholar 

  14. Worsley, K., et al.: A general statistical analysis for fMRI data 15(1), 1–15 (2002) . https://doi.org/10.1006/nimg.2001.0933. http://www.sciencedirect.com/science/article/pii/S1053811901909334

  15. Poldrack, R.A., Mumford, J.A., Nichols, T.E.: Handbook of Functional MRI Data Analysis. Cambridge University Press, Cambridge (2011)

    Book  Google Scholar 

  16. Theano Development Team: Theano: a Python framework for fast computation of mathematical expressions. arXiv e-prints \(\rm {abs}\)/1605.02688. http://arxiv.org/abs/1605.02688

  17. Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: Proceedings of the 30th International Conference on Machine Learning (ICML 2013), pp. 1139–1147 (2013)

    Google Scholar 

  18. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  19. Friedman, L., Glover, G.H.: The FBIRN consortium, reducing interscanner variability of activation in a multicenter fMRI study: controlling for signal-to-fluctuation-noise-ratio (SFNR) differences. NeuroImage 33(2), 471–481 (2006). https://doi.org/10.1016/j.neuroimage.2006.07.012. http://www.sciencedirect.com/science/article/pii/S1053811906007944

  20. Friedman, L., Glover, G.H., Krenz, D., Magnotta, V.: Reducing inter-scanner variability of activation in a multicenter fMRI study: role of smoothness equalization. NeuroImage 32(4), 1656–1668 (2006). https://doi.org/10.1016/j.neuroimage.2006.03.062. http://www.sciencedirect.com/science/article/pii/S1053811906004435

  21. Thomas, A.J., Bathula, D.: Reducing inter-scanner variability in multi-site fMRI data: exploring choice of reference activation map and use of correction functions. In: 2015 International Conference on Computing, Communication & Automation (ICCCA), pp. 1187–1192. IEEE (2015)

    Google Scholar 

  22. Thomas, A.J., Bathula, D.: Reducing inter-scanner variability in multi-site fMRI activations using correction functions: a preliminary study. In: Singh, R., Vatsa, M., Majumdar, A., Kumar, A. (eds.) Machine Intelligence and Signal Processing. AISC, vol. 390, pp. 109–117. Springer, New Delhi (2016). https://doi.org/10.1007/978-81-322-2625-3_10

    Chapter  Google Scholar 

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Acknowledgements

Financial assistance from DST (grant no. SB/FTP/ETA-353/2013) New Delhi, India to DRB. The data used in this study was acquired through and provided by the Biomedical Informatics Research Network under the following support: U24-RR021992, Function BIRN and U24 GM104203, Bio-Informatics Research Network Coordinating Center (BIRN-CC).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Information Technology Tiruchirappalli, Tiruchirappalli, 620012, Tamil Nadu, India

    Anoop Jacob Thomas

  2. Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, 140001, Punjab, India

    Deepti R. Bathula

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  1. Anoop Jacob Thomas
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  2. Deepti R. Bathula
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Correspondence to Anoop Jacob Thomas .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  2. Indian Institute of Technology Ropar, Ropar, India

    Subrahmanyam Murala

  3. Jadavpur University, Kolkata, India

    Ananda Chowdhury

  4. Indian Institute of Technology Ropar, Ropar, India

    Abhinav Dhall

  5. Indian Institute of Technology Ropar, Ropar, India

    Puneet Goyal

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Thomas, A.J., Bathula, D.R. (2022). 3D Multi-voxel Pattern Based Machine Learning for Multi-center fMRI Data Normalization. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_45

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  • DOI: https://doi.org/10.1007/978-3-031-11349-9_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11348-2

  • Online ISBN: 978-3-031-11349-9

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