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Títol: Automatic classification of attention-deficit/hyperactivity disorder using brain activation
Autor: Vila Muñoz, Maria del Mar
Tutor/director/avaluador: Igual Muñoz, Laura
Universitat: Universitat Politècnica de Catalunya
Matèries: Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Machine learning
Brain--Imaging
Attention-deficit hiperactivity disorder
Aprenentatge automàtic
Cervell--Imatges
Trastorns de l'atenció
Data: 4-set-2012
Tipus de document: Master thesis
Resum: Nowadays, there is an active fi eld of research in neuroscience trying to fi nd relations between neurofunctional abnormalities of brain structures and neurological disorders. Previous statistical studies on brain functional Magnetic Resonance Images (fMRI) have found Attention Defi cit Hyperactivity Disorder (ADHD) patients are characterized by reduced activity in the inferior frontal gyrus (IFG) during response inhibition tasks and in the Ventral Striatum (VStr) during reward anticipation tasks. Interpreting brain image experiments using fMRI requires analysis of complex data and diff erent univariate or multivariate approaches can be chosen. Recently, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to discriminate abnormal behavior or other variables of interest from fMRI data. The purpose of this work is to apply machine learning techniques to perform fMRI group analysis in an adult population. We propose a multivariate classifi er using diff erent discriminative features. Furthermore, we show how temporal information of fMRI data can be taken into account to improve the discrimination. We demonstrate that our new approach is able to diagnose the ADHD characteristics based on the activation in the executive functions. Previous results on the response inhibition task did not find di fferences between activation responses. Opposite to these results, we achieve accurate classifi cation performance for this task. Moreover, in this case, we show that classi fication rates can be signi cantly improved by incorporating temporal information into the classi fier.
URI: http://hdl.handle.net/2099.1/16505
Condicions d'accés: Open Access
Apareix a les col·leccions:Master in Artificial Intelligence - MAI (Pla 2006)
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