Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/173009
Title: Parkinson’s Disease Brain States and Functional Connectivity: A Machine Learning Analysis of Neuroimaging Data
Author: Montabes García, Lluís
Director/Tutor: Cos Aguilera, Ignasi
Keywords: Aprenentatge automàtic
Algorismes computacionals
Programari
Treballs de fi de grau
Neurociència computacional
Electroencefalografia
Machine learning
Computer algorithms
Computer software
Computational neuroscience
Electroencephalography
Bachelor's thesis
Issue Date: 13-Sep-2020
Abstract: [en] The goal of this study is to identify and characterize brain states as a function of the motivation with which the task was performed (the presence of avatars and their skill at performing the task). To this end, we developed a series of machine learning algorithms capable of capturing differences between the EEG data recorded at each condition. We used metrics of local activity, such as electrode power, of similarity (correlation between electrodes), and of network functional connectivity (co-variance across electrodes) and use them to cluster brain states and to identify network connectivity patterns typical of each motivated state. Studies in the field of computational neuroscience involve the analysis of brain dynamics across specific brain areas to study the mechanisms underlying brain activity. This particular study aims at discovering how brain activity is affected by social motivation by computational means. To this end, we analyzed a dataset of electro-encephalographic (EEG) data recorded previously during a reward-driven decision-making experiment performed by Parkinson patients. The goal of the experiment was to select and perform a reaching movement from an origin cue to one of two possible wide rectangular targets. Reward was contingent upon arrival precision. Social motivation was manipulated by simulating avatar partners of varying skill with whom our participants played. Competition with the avatar was explicitly discouraged. Our results show that the presence of different avatars yielded distinct brain states, characterized by means of functional connectivity and local activity. Specifically, we observed that motivation related states were best identified for the highest frequency band (gamma band) of the EEGs. In summary, this study has shown that brain states can be characterized by level of motivation with a high degree of accuracy, independently of the presence of medication.
Note: Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Ignasi Cos Aguilera
URI: http://hdl.handle.net/2445/173009
Appears in Collections:Treballs Finals de Grau (TFG) - Enginyeria Informàtica
Programari - Treballs de l'alumnat

Files in This Item:
File Description SizeFormat 
codi.zipCodi font22.08 kBzipView/Open
mem173009.pdfMemòria3.5 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons