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Multi-Task Learning for Interpretation of Brain Decoding Models

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Machine Learning and Interpretation in Neuroimaging (MLINI 2013, MLINI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9444))

Abstract

Improving the interpretability of multivariate models is of primary interest for many neuroimaging studies. In this study, we present an application of multi-task learning (MTL) to enhance the interpretability of linear classifiers once applied to neuroimaging data. To attain our goal, we propose to divide the data into spatial fractions and define the temporal data of each spatial unit as a task in MTL paradigm. Our result on magnetoencephalography (MEG) data reveals preliminary evidence that, (1) dividing the brain recordings into spatial fractions based on spatial units of data and (2) considering each spatial fraction as a task, are two factors that provide more stability and consequently more interpretability for brain decoding models.

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Notes

  1. 1.

    The full dataset is publicly available at ftp://ftp.mrc-cbu.cam.ac.uk/personal/rik.henson/wakemandg_hensonrn/.

  2. 2.

    The competition data are available at http://www.kaggle.com/c/decoding-the-human-brain.

  3. 3.

    The preprocessing scripts in python and MATLAB are available at: https://github.com/FBK-NILab/DecMeg2014.

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Author information

Authors and Affiliations

  1. University of Trento, Trento, Italy

    Seyed Mostafa Kia

  2. NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation, Trento, Italy

    Seyed Mostafa Kia, Sandro Vega-Pons, Emanuele Olivetti & Paolo Avesani

  3. Centro Interdipartimentale Mente e Cervello (CIMeC), University of Trento, Trento, Italy

    Seyed Mostafa Kia, Sandro Vega-Pons, Emanuele Olivetti & Paolo Avesani

Authors
  1. Seyed Mostafa Kia
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  2. Sandro Vega-Pons
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  3. Emanuele Olivetti
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  4. Paolo Avesani
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Corresponding author

Correspondence to Seyed Mostafa Kia .

Editor information

Editors and Affiliations

  1. IBM T.J. Watson Research Center , Yorktown Heights, New York, USA

    Irina Rish

  2. Medical University of Vienna , Vienna, Austria

    Georg Langs

  3. University of California, Berkeley, California, USA

    Leila Wehbe

  4. T.J. Watson Research Center , Yorktown Heights, New York, USA

    Guillermo Cecchi

  5. Carnegie Mellon University , Pittsburgh, Pennsylvania, USA

    Kai-min Kevin Chang

  6. Queen's University Belfast , Belfast, United Kingdom

    Brian Murphy

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Kia, S.M., Vega-Pons, S., Olivetti, E., Avesani, P. (2016). Multi-Task Learning for Interpretation of Brain Decoding Models. In: Rish, I., Langs, G., Wehbe, L., Cecchi, G., Chang, Km., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. MLINI MLINI 2013 2014. Lecture Notes in Computer Science(), vol 9444. Springer, Cham. https://doi.org/10.1007/978-3-319-45174-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-45174-9_1

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-45174-9

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