Analysis of Functional Connectome Pipelines for the Diagnosis of Autism Spectrum Disorders

  1. Clara Jiménez-Valverde 1
  2. Rosa María Maza-Quiroga 1
  3. Domingo López-Rodríguez 1
  4. Karl Thurnhofer-Hemsi 1
  5. Ezequiel López-Rubio 1
  6. Rafael Marcos Luque-Baena 1
  1. 1 Universidad de Málaga
    info
    Universidad de Málaga

    Málaga, España

    ROR https://ror.org/036b2ww28

    Geographic location of the organization Universidad de Málaga
Book:
Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence: 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Puerto de la Cruz, Tenerife, Spain, May 31 – June 3, 2022, Proceedings, Part II
  1. José Manuel Ferrández Vicente (dir. congr.)
  2. José Ramón Alvarez Sánchez (dir. congr.)
  3. Félix de la Paz López (dir. congr.)
  4. Hojjat Adeli

Publisher: Springer Suiza

ISBN: 978-3-031-06527-9

Year of publication: 2022

Pages: 213-222

Type: Book chapter

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Abstract

This paper explores the effect of using different pipelines to compute connectomes (matrices representing brain connections) and use them to train machine learning models with the goal of diagnosing Autism Spectrum Disorder. Five different pipelines are used to train six different ML models, splitting the data into female, male and all subsets so we can also research the effect of considering male and female patients separately. Our results conclude that pipeline and model choice impact results, along with using general or specific models.

Data source: Dialnet