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On Machine Learning for Autism Prediction from Functional Connectivity

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Progress in Image Processing, Pattern Recognition and Communication Systems (CORES 2021, IP&C 2021, ACS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 255))

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Abstract

Autism Spectrum Disorder (ASD) has claimed big attention in order to find biomarkers in brain connectivity alterations detected in resting state functional magnetic resonance imaging (rs-fMRI) data by the induction of classification models from functional connectivities. In this paper we provide a comprehensive exploration of the impact of feature extraction/selection methods, classification model, connectivity measure and atlas parcelation on the predictive performance. We find, that principal component analysis (PCA) and factor analysis (FA) methods provide a significant improvement.

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Notes

  1. 1.

    https://github.com/mmscnet/Impact-feature-extraction-in-Autism.

  2. 2.

    http://fcon_1000.projects.nitrc.org/indi/abide/.

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Author information

Authors and Affiliations

  1. Universidad Mayor de San Andrés, La Paz, Bolivia

    Moises Silva

  2. Computational Intelligence Group UPV/EHU, San Sebastian, Spain

    Manuel Graña

Authors
  1. Moises Silva
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  2. Manuel Graña
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Editor information

Editors and Affiliations

  1. Institute of Computer Science and Telecommunications, University of Science and Technlogy, Bydgoszcz, Poland

    Michal Choraś

  2. Institute of Computer Science and Telecommunications, University of Science and Technology, Bydgoszcz, Poland

    Ryszard S. Choraś

  3. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Marek Kurzyński

  4. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Paweł Trajdos

  5. West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Jerzy Pejaś

  6. West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Tomasz Hyla

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Silva, M., Graña, M. (2022). On Machine Learning for Autism Prediction from Functional Connectivity. In: Choraś, M., Choraś, R.S., Kurzyński, M., Trajdos, P., Pejaś, J., Hyla, T. (eds) Progress in Image Processing, Pattern Recognition and Communication Systems. CORES IP&C ACS 2021 2021 2021. Lecture Notes in Networks and Systems, vol 255. Springer, Cham. https://doi.org/10.1007/978-3-030-81523-3_16

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