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Disentanglement of Session and Plasticity Effects in Longitudinal fMRI Studies

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Machine Learning in Medical Imaging (MLMI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8184))

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Abstract

In longitudinal studies, such as those about neurocognitive rehabilitation or brain plasticity, functional MRI sessions are subsequently recorded to detect how the patterns of activation differs through time. Usually, consecutive recordings are interleaved by a period of treatment or training. The general purpose of fMRI data analysis is to compute a brain map accounting for plasticity effects on BOLD response. Specifically, the challenge of longitudinal studies analysis is to disentangle plain session effect from the plasticity one. To date, a commonly used practice is to separately compute these two effects and then combine results through a comparison. The brain map for session effect is obtained with a univariate analysis of the contrast between two subsequent recordings. We argue that such a brain map includes also the bias of the coregistration error related to the alignment of two sessions. Here we do not propose a method to reduce the coregistration error but a method to reduce the effect of this bias when estimating the session effect. We show how, combining functional hyperalignment and multivariate pattern analysis, it is possible to summarize a single brain map that accounts for plasticity effect without the session effect.

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References

  1. Binder, J.R., Frost, J.A., Hammeke, T.A., Cox, R.W., Rao, S.M., Prieto, T.: Human brain language areas identified by functional magnetic resonance imaging. The Journal of Neuroscience: the Official Journal of the Society for Neuroscience 17(1), 353–362 (1997)

    Google Scholar 

  2. Cox, R.W.: AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, an International Journal 29(3), 162–173 (1996)

    Article  Google Scholar 

  3. Haxby, J.V., Guntupalli, S.S., Connolly, A.C., Halchenko, Y.O., Conroy, B.R., Gobbini, I.I., Hanke, M., Ramadge, P.J.: A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron 72(2), 404–416 (2011)

    Article  Google Scholar 

  4. Meinzer, M., Flaisch, T., Breitenstein, C., Wienbruch, C., Elbert, T., Rockstroh, B.: Functional re-recruitment of dysfunctional brain areas predicts language recovery in chronic aphasia. NeuroImage 39(4), 2038–2046 (2008)

    Article  Google Scholar 

  5. Meltzer, J.A., Postman-Caucheteux, W.A., McArdle, J.J., Braun, A.R.: Strategies for longitudinal neuroimaging studies of overt language production. NeuroImage 47(2), 745–755 (2009)

    Article  Google Scholar 

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

    Google Scholar 

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© 2013 Springer International Publishing Switzerland

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Iacovella, V., Avesani, P., Miceli, G. (2013). Disentanglement of Session and Plasticity Effects in Longitudinal fMRI Studies. In: Wu, G., Zhang, D., Shen, D., Yan, P., Suzuki, K., Wang, F. (eds) Machine Learning in Medical Imaging. MLMI 2013. Lecture Notes in Computer Science, vol 8184. Springer, Cham. https://doi.org/10.1007/978-3-319-02267-3_20

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  • DOI: https://doi.org/10.1007/978-3-319-02267-3_20

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02266-6

  • Online ISBN: 978-3-319-02267-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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