# name
Estudio de imágenes de resonancia magnética funcional en reposo para la predicción de variables personales

# metadata.dc.description[].value
Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2016, Director: Laura Igual Muñoz

# metadata.dc.description.abstract[].value
This	project is focused on the	creation of a classification system that separates a	 group of	subjects according to their gender based on data from magnetic resonance images (MRI) in a resting state. 	
The	images from MRI in a resting state are a tool to measure the brain connectivity or functioning that is currently being used for many neuroscience studies. 	
This	project, in particular, uses the representation of facts based on the Network in a resting state to characterize the functional connectivity of the	subjects for the visualization of the obtained results. 	
As well as	 evaluating the	 accuracy	of the classification system developed, another objective of the project is to determine which of the cerebral networks	are more discriminative in the	task	of separating men and women. 	
The mothodology utilized combines two types of automatic learning: unsupervised learning, as in the Independent Componentes Analysis and the Principal Components Analysis, and supervised learning, as is  the K-NN and SVM classifiers.			
The results obtained are promising, because it finds a RSN that discriminates both sex and	we also note that the Principal Component Analysis does not affect when classifying .

# metadata.dc.rights[].value
memòria: cc-by-nc-sa (c) Juan Luis Moral Pérez, 2016

# metadata.dc.rights[].value
codi: GPL (c) Juan Luis Moral Pérez, 2016

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Enginyeria Informàtica

# metadata.dc.title[].value
Estudio de imágenes de resonancia magnética funcional en reposo para la predicción de variables personales