Multi-Task Learning for Interpretation of Brain Decoding Models

  • Seyed Mostafa Kia
  • Sandro Vega-Pons
  • Emanuele Olivetti
  • Paolo Avesani
Conference paper

DOI: 10.1007/978-3-319-45174-9_1

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9444)
Cite this paper as:
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 K., Murphy B. (eds) Machine Learning and Interpretation in Neuroimaging. MLINI 2014, MLINI 2013. Lecture Notes in Computer Science, vol 9444. Springer, Cham

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.

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Seyed Mostafa Kia
    • 1
    • 2
    • 3
  • Sandro Vega-Pons
    • 2
    • 3
  • Emanuele Olivetti
    • 2
    • 3
  • Paolo Avesani
    • 2
    • 3
  1. 1.University of TrentoTrentoItaly
  2. 2.NeuroInformatics Laboratory (NILab)Bruno Kessler FoundationTrentoItaly
  3. 3.Centro Interdipartimentale Mente e Cervello (CIMeC)University of TrentoTrentoItaly

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