Contributions to the study of Austism Spectrum Brain conectivity

  1. SILVA CHOQUE, MOISES MARTIN
Supervised by:
  1. Manuel Graña Romay Director

Defence university: Universidad del País Vasco - Euskal Herriko Unibertsitatea

Fecha de defensa: 17 June 2021

Committee:
  1. Alicia Emilia D'Anjou D'Anjou Chair
  2. Javier de Lope Asiaín Secretary
  3. Sebastian Rios Perez Committee member
Department:
  1. Ciencia de la Computación e Inteligencia Artificial

Type: Thesis

Teseo: 155250 DIALNET lock_openADDI editor

Abstract

Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines.