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

Clara Jiménez-Valverde ^[1] ; Rosa María Maza-Quiroga ^[1] ; Domingo López-Rodríguez ^[1] ; Karl Thurnhofer-Hemsi ^[1] ; Ezequiel López-Rubio ^[1] ; Rafael Marcos Luque-Baena ^[1]
1. [1] Universidad de Málaga
  
  Universidad de Málaga
  
  Málaga, España
Localización: 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 / José Manuel Ferrández Vicente (dir. congr.), José Ramón Álvarez Sánchez (dir. congr.), Félix de la Paz López (dir. congr.), Hojjat Adeli (aut.), 2022, ISBN 978-3-031-06527-9, págs. 213-222
Idioma: inglés
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Resumen
- 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.