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Towards XAI: Interpretable Shallow Neural Network Used to Model HCP’s fMRI Motor Paradigm Data

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Bioinformatics and Biomedical Engineering (IWBBIO 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13347))

Abstract

Under the concept of explainable artificial intelligence (XAI), this study explores the usage of shallow neural networks (SNN) to model and predict motor processes in the brain. Two main goals are considered: the suitability of independent component analysis (ICA) for data dimension reduction; and the capability of the SNN to have its underlying processes explained while retaining accurate predictions.

Thirty subjects from the HCP Young Adult database are used. A traditional GLM-based data analysis is carried out aiming to establish a ground for comparison, besides founded neuroscientific knowledge. ICA is used for input data dimensionality reduction, which feeds an SNN with one hidden layer containing 10 nodes. Accuracies range from 67.5% to 92.5%, and precisions from 64.3% to 97.2%, per stimulus. The analysis of the weights yields independent components (ICs), i.e. inputs, that encompass motor areas. Even though the ICs’ spatial resolution is not optimal, the SNN predicts well above the chance level.

The motor cortex-containing ICs, i.e. the main inputs, are in accordance with the founded neuroscientific knowledge and the GLM-based data analysis results, allowing for the interpretability of the SNN underlying processes.

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Notes

  1. 1.

    https://www.humanconnectome.org/study/hcp-young-adult.

  2. 2.

    https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL.

  3. 3.

    https://www.r-project.org/.

  4. 4.

    https://www.rstudio.com/.

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Acknowledgements

This work was partially financially supported by Base Funding - UIDB/00027/2020 of the Artificial Intelligence and Computer Science Laboratory – LIACC - funded by national funds through the FCT/MCTES (PIDDAC).

Author information

Authors and Affiliations

  1. Faculty of Engineering, University of Porto, R. Dr Roberto Frias, 4200-465, Porto, Portugal

    José Diogo Marques dos Santos

  2. Abel Salazar Biomedical Sciences Institute, University of Porto, R. Jorge de Viterbo Ferreira, 4050-313, Porto, Portugal

    José Diogo Marques dos Santos

  3. University of Maia, Av. Carlos de Oliveira Campos, 4475-690, Maia, Portugal

    José Paulo Marques dos Santos

  4. LIACC - Artificial Intelligence and Computer Science Laboratory, University of Porto, R. Dr Roberto Frias, 4200-465, Porto, Portugal

    José Paulo Marques dos Santos

  5. Unit of Experimental Biology, Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319, Porto, Portugal

    José Paulo Marques dos Santos

Authors
  1. José Diogo Marques dos Santos
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  2. José Paulo Marques dos Santos
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Corresponding author

Correspondence to José Paulo Marques dos Santos .

Editor information

Editors and Affiliations

  1. Marcelina Siebold Guest Relations Dept., University of Granada, Granada, Spain

    Ignacio Rojas

  2. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  3. ETSIIT. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. ETSIIT, University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Marques dos Santos, J.D., Marques dos Santos, J.P. (2022). Towards XAI: Interpretable Shallow Neural Network Used to Model HCP’s fMRI Motor Paradigm Data. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13347. Springer, Cham. https://doi.org/10.1007/978-3-031-07802-6_22

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  • DOI: https://doi.org/10.1007/978-3-031-07802-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07801-9

  • Online ISBN: 978-3-031-07802-6

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