Develop a whole brain model personalized for patients with epilepsy
Epilepsy, a neurological disorder affecting millions worldwide, presents significant challenges in its drug-resistant form, where standard treatments often fail. The GALVANI project aims to address these challenges by developing personalized whole-brain models to optimize therapeutic strategies such...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/430886 |
| Acceso en línea: | https://hdl.handle.net/2117/430886 |
| Access Level: | acceso abierto |
| Palabra clave: | Epilepsy Electroencephalography Nervous system -- Surgery Epilepsy, Neural mass model, EEG, Forward method Epilèpsia Electroencefalografia Sistema nerviós -- Cirurgia Àrees temàtiques de la UPC::Enginyeria biomèdica::Aparells mèdics |
| Sumario: | Epilepsy, a neurological disorder affecting millions worldwide, presents significant challenges in its drug-resistant form, where standard treatments often fail. The GALVANI project aims to address these challenges by developing personalized whole-brain models to optimize therapeutic strategies such as transcranial current stimulation and surgery. These models integrate structural, functional, and clinical data to simulate seizure dynamics and predict treatment outcomes. This thesis focuses on advancing the pipeline for whole-brain modeling by transitioning from invasive stereo-electroencephalography (SEEG) to non-invasive scalp electroencephalography (EEG). Using neural mass models (NMMs) and structural connectivity derived from diffusion MRI, the study develops a framework to personalize whole-brain models using EEG data. Key steps include generating synthetic EEG via forward modeling, comparing it to empirical EEG, and optimizing model parameters such as excitability. Validation results highlight the feasibility of capturing seizure propagation patterns using EEGbased models. In particular, topographic amplitude patterns (topographic maps) show a strong spatial correspondence between synthetic and real EEG, especially in regions identified clinically as seizure onset zones. Although functional connectivity comparisons show some qualitative similarities, further work is needed to improve their quantitative alignment. Frequency-specific analyses underscore the relevance of tailoring the model to epileptically meaningful bands, such as theta and gamma, which appear most sensitive to ictal activity. By reducing the invasiveness of data acquisition while maintaining biologically plausible seizure propagation dynamics, this work contributes to the development of clinically applicable, patientspecific simulations for epilepsy treatment. Future directions include integrating multimodal data and extending the framework to other neurological disorders. |
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