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               <dc:title>The use of whole-brain models and variational autoencoders for the low-dimensional representation of psychosis and its perturbational landscape</dc:title>
               <dc:creator>Garcés de Marcilla Lappin, Iraïs</dc:creator>
               <dc:subject>Psychosis</dc:subject>
               <dc:subject>Psychosis relapsing</dc:subject>
               <dc:subject>Deep learning</dc:subject>
               <dc:subject>Variational autoencoder</dc:subject>
               <dc:subject>WholeBrain dynamics</dc:subject>
               <dc:subject>Classification</dc:subject>
               <dc:description>Tutors: Dr. Gustavo Deco, Yonatan Sanz.&#xd;
Treball de fi de grau en Biomèdica</dc:description>
               <dc:description>Psychosis can be described as an alteration in brain connectivity that leads to an&#xd;
impairment of cognition and the speed at which the information gets processed,&#xd;
what causes a diversity of psychiatric symptoms. This symptomatology is characterized by changes in the brain activity in certain areas, which can be detected&#xd;
by Functional Magnetic Resonance Imaging (fMRI) as it registers changes in the&#xd;
brain associated with blood flow, and this allows us to measure brain activity and&#xd;
connectivity between regions. Furthermore, the state of these alterations may differ&#xd;
between patients depending on the severity of their condition and the number of&#xd;
episodes they have had or may suffer. This study focuses on the use of the connectivity and structural information extracted from fMRIs and a whole-brain model to&#xd;
generate synthetic data with enough resemblance to the original dataset cases to&#xd;
train a Variational Autoencoder architecture for the creation of a low dimensional&#xd;
space in which the cases where patients have had one psychotic episode (remitting)&#xd;
or multiple (relapsing) are represented, and therefore a classification model can be&#xd;
trained to distinguish them. A dimensionality analysis has been performed to find&#xd;
the most optimal dimension of this space that allow us to distinguish between remitting and relapsing cases with high enough accuracy. Moreover, perturbations&#xd;
were introduced in the original model to generate new data which was reclassified&#xd;
in the low dimensional space to find which alterations could produce changes in the&#xd;
classification of the psychotic stage.</dc:description>
               <dc:date>2023-09-22T17:11:50Z</dc:date>
               <dc:date>2023-09-22T17:11:50Z</dc:date>
               <dc:date>2023-09-22</dc:date>
               <dc:type>info:eu-repo/semantics/masterThesis</dc:type>
               <dc:rights>Llicència CC Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional (CC BY-NC-ND 4.0)</dc:rights>
               <dc:rights>https://creativecommons.org/licenses/by-nc-nd/4.0/deed.ca</dc:rights>
               <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
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