Publicação
Modelling Transitions between Brain States: from rest to motor execution
| dc.contributor.author | Lima, David Gonçalo de Nóbrega Pereira de | |
| dc.contributor.institution | Faculty of Sciences | |
| dc.contributor.institution | Department of Physics | |
| dc.contributor.supervisor | Andrade, Alexandre da Rocha Freire de | |
| dc.contributor.supervisor | Fernandes, Sofia Rita Cardoso | |
| dc.date.accessioned | 2026-02-09T18:30:01Z | |
| dc.date.available | 2026-02-09T18:30:01Z | |
| dc.date.issued | 2026 | |
| dc.description | Tese de mestrado, Engenharia Física, 2026, Universidade de Lisboa, Faculdade de Ciências | |
| dc.description.abstract | This thesis is part of an ongoing research project at the Instituto de Biofísica e Engenharia Biomédica (IBEB), dedicated to developing software for constructing personalized brain models. These models aim to predict neuronal responses to electrical stimulation using electroencephalography (EEG) data, ultimately contributing to optimized therapeutic protocols for neurological disorders affecting motor functions. The main objective is to develop a brain network model capable of representing brain states associated with rest and motor execution. The EEG data used was collected from healthy right-handed participants during five conditions: rest, left and right-hand motor execution, and left and right-hand motor imagery. The EEG signals were preprocessed, transformed into cortical space using eLORETA and the fsaverage head model, and then parcellated using the Desikan–Killiany atlas. Functional connectivity (FC) was quantified through three metrics: Coherence (COH), phase locking value (PLV), and amplitude envelope correlation (Pearson). Outside the FC, other relevant metrics used were global coherence and metastability as well as the Probabilistic Metastable Substates (PMS) resulting from Leading Eigenvector Dynamics Analysis (LEiDA). Analyzing the empirical data revealed that all brain states are very similar. Global coherence, metastability and PMS showed no significant differences between states. The FC reveals some slight differences which were most pronounced between motor tasks, particularly opposite-hand movements showed the highest differences. The final model accurately reproduces empirical FC across tasks and frequencies, capturing not only overall similarities but also task-specific differences with high sensitivity and precision. Fitting global coherence showed that right-handed movements may require a higher degree of synchronization. While the model can reproduce the empirical dynamic measures (metastability and PMS), they cannot be fitted precisely. | en |
| dc.format | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10400.5/116945 | |
| dc.language.iso | eng | |
| dc.subject | Functional Connectivity | |
| dc.subject | Brain network model | |
| dc.subject | Brain states | |
| dc.title | Modelling Transitions between Brain States: from rest to motor execution | en |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess |
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