ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-06202022-170435


Tipo di tesi
Tesi di laurea magistrale
Autore
CAMPO, FEDERICO
URN
etd-06202022-170435
Titolo
Identification of alterations in functional brain connectivity with explainable Artificial Intelligence analysis of multicenter MRI data
Dipartimento
FISICA
Corso di studi
FISICA
Relatori
relatore Prof.ssa Retico, Alessandra
correlatore Prof. Oliva, Piernicola
Parole chiave
  • explainable AI
  • fMRI
  • harmonization
  • multi-center
  • machine learning
  • Pearson
  • wavelet
  • SHAP
Data inizio appello
21/07/2022
Consultabilità
Completa
Riassunto
This thesis concerns the study of brain functional connectivity using machine learning (ML) models aimed to the investigation of Autism Spectrum Disorders (ASD) using MRI data.
Data are collected in different medical centers, thus they suffer the so-called batch effect.
This effects should be removed through harmonization procedures to avoid biases during the learning process of ML algorithms.
Two harmonization procedures are implemented and compared, one analytical and the other based on a deep neural network.
From fMRI data, functional connectivity is obtained by using a Pearson-based correlation measure and a time-frequency analysis by means of wavelet transforms of brain areas time series.
The two approaches have been compared to assess which one allow a deep neural network to better differentiate between control subjects and ASD patients.
Explainable Artificial Intelligence method, namely the SHAP approach have been employed to decode the outputs of machine learning models.
With this analysis it was possible to identify altered functional connections and determine what brain areas are mainly involved in ASD.
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