2026-04-17T20:41:55Zhttps://recercat.cat/oai/requestoai:recercat.cat:10230/579462025-12-23T20:54:10Zcom_2072_6col_2072_452954
RECERCAT
author
Garcés de Marcilla Lappin, Iraïs
2023-09-22T17:11:50Z2023-09-22T17:11:50Z2023-09-22
Tutors: Dr. Gustavo Deco, Yonatan Sanz.
Treball de fi de grau en BiomèdicaPsychosis can be described as an alteration in brain connectivity that leads to an
impairment of cognition and the speed at which the information gets processed,
what causes a diversity of psychiatric symptoms. This symptomatology is characterized by changes in the brain activity in certain areas, which can be detected
by Functional Magnetic Resonance Imaging (fMRI) as it registers changes in the
brain associated with blood flow, and this allows us to measure brain activity and
connectivity between regions. Furthermore, the state of these alterations may differ
between patients depending on the severity of their condition and the number of
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
generate synthetic data with enough resemblance to the original dataset cases to
train a Variational Autoencoder architecture for the creation of a low dimensional
space in which the cases where patients have had one psychotic episode (remitting)
or multiple (relapsing) are represented, and therefore a classification model can be
trained to distinguish them. A dimensionality analysis has been performed to find
the most optimal dimension of this space that allow us to distinguish between remitting and relapsing cases with high enough accuracy. Moreover, perturbations
were introduced in the original model to generate new data which was reclassified
in the low dimensional space to find which alterations could produce changes in the
classification of the psychotic stage.
Llicència CC Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/deed.ca info:eu-repo/semantics/openAccess
Psychosis
Psychosis relapsing
Deep learning
Variational autoencoder
WholeBrain dynamics
Classification
The use of whole-brain models and variational autoencoders for the low-dimensional representation of psychosis and its perturbational landscape
info:eu-repo/semantics/masterThesis