Μοντελοποίηση της ανθρώπινης εγκεφαλικής δραστηριότητας μακράς διάρκειας: Είναι τα μοντέλα έγκυρα, δεδομένων των σφαλμάτων μέτρησης

Ζορπαλά, Κοντεσσα - Ιωάννα; Zorpala, Kontessa Ioanna

dc.contributor.author	Ζορπαλά, Κοντεσσα - Ιωάννα	el
dc.contributor.author	Zorpala, Kontessa Ioanna	en
dc.date.accessioned	2025-04-28T08:05:06Z
dc.date.available	2025-04-28T08:05:06Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/61783
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.29479
dc.description	Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) "Μεταφραστική Βιοϊατρική Μηχανικής και Επιστήμης"
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	Whole Brain Emulation	en
dc.subject	Measurement Error	en
dc.subject	Neural Simulation	en
dc.subject	Connectome	en
dc.subject	Προσομοίωση εγκεφάλου	el
dc.subject	Σφάλμα μέτρησης	el
dc.subject	Συνδεσιμότητα νευρώνων	el
dc.subject	Νευρωνική προσομοίωση	el
dc.subject	Plasticity	en
dc.subject	Νευρωνική πλαστικότητα	el
dc.title	Μοντελοποίηση της ανθρώπινης εγκεφαλικής δραστηριότητας μακράς διάρκειας: Είναι τα μοντέλα έγκυρα, δεδομένων των σφαλμάτων μέτρησης	el
dc.title	Modelling long-term human brain activity: Are models still valid, given errors in measurement?	en
heal.type	masterThesis
heal.classification	Νευροεπιστήμες	el
heal.classification	Neuroscience	en
heal.classification	Βιοϊατρική Μηχανική	el
heal.classification	Biomedical Engineering	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2024-10-14
heal.abstract	Whole Brain Emulation (WBE) represents one of the most ambitious objectives in the contemprorary computational neu roscience, aimingtoreplicate humanbrainactivitywithinacom putational model. This study investigates the role of measure ment errors during the data acquisition phase and their subse quent impact on neural simulations, focusing on the Kuramoto and Izhikevich models. Both models were employed to simu late the dynamics of different brain regions, particularly focus ing on the introduction of noise that mimics errors originating from brain imaging techniques. Our analysis begins with the observation of how noise affects neuraldynamicsbysegmentingthesimulationsintothreephases: (1) the control phase before noise introduction, representing a brain’s natural state; (2) the branching point, where noise is in troduced as a representation of data acquisition errors in WBE; and (3) the simulation of both control data (undisturbed brain function) and noisy data (the behavior of a brain replica im pacted by measurement errors). Akeyfindingofthis work is the clear correlation between noise levels and the total error in both models, confirming that higher noise results in greater error. This underscores the critical im portance of using precise measurement techniques during the data acquisition phase and suggests the need for developing error-correction mechanisms to mitigate the impact of noise. We also investigated the impact of connectivity strength in specific brain regions, revealing distinct differences between the mod els. In the Kuramoto model, regions with higher connectivity contributed more to final error, while in the Izhikevich model, these same regions tended to reduce error as their connectivity increased. These findings are significant because they highlight the need for further investigation into measurement error in WBE, in ad dition to ongoing work on computational and hardware aspects of brain emulation. As demonstrated, noise introduced by data acquisition has a profound impact on neural simulation accu racy, and addressing this challenge is essential for achieving re liable WBE. Additionally, while the models employed in this study lack certain biological realism, including synaptic plastic ity and adaptive behavior, they still offer valuable insights into how noise and learning mechanisms may influence neural dy namics in computational models. This work lays the foundation for future research aimed at improving both the fidelity of neu ral simulations and the accuracy of data acquisition techniques.	en
heal.sponsor	Ίδρυμα Λοχαγού Φανουράκη	el
heal.sponsor	Captain Fanourakis Foundation	en
heal.advisorName	Νικήτα, Κωνσταντίνα	el
heal.advisorName	Nikita, Konstantina	en
heal.committeeMemberName	Βουλόδημος, Αθανάσιος	el
heal.committeeMemberName	Voulodimos, Athanasios	en
heal.committeeMemberName	Νικήτα, Κωνσταντίνα	el
heal.committeeMemberName	Nikita, Konstantina	en
heal.committeeMemberName	Kaiser, Marcus	en
heal.committeeMemberName	Κάιζερ, Μάρκους	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	48 σ.	el
heal.fullTextAvailability	false