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Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning

  • This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.

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Verfasserangaben:Simon Wein, Gustavo Deco, Ana Maria ToméORCiD, Markus GoldhackerORCiD, Wilhelm M. Malloni, Mark W. GreenleeORCiD, Elmar Wolfgang LangORCiD
DOI:https://doi.org/10.1155/2021/5573740
Pubmed-Id:https://pubmed.ncbi.nlm.nih.gov/34135951
Titel des übergeordneten Werkes (Englisch):Computational intelligence and neuroscience
Verlag:Hindawi
Dokumentart:Artikel aus einer Zeitschrift/Zeitung
Sprache der Veröffentlichung:Englisch
Jahr der Veröffentlichung:2021
Datum der Freischaltung:19.07.2022
Freies Schlagwort / Tag:Brain Mapping; Brain/diagnostic imaging; Diffusion Tensor Imaging; Machine Learning; Magnetic Resonance Imaging; Nerve Net/diagnostic imaging; Neural Pathways/diagnostic imaging; Structure-Activity Relationship
Aufsatznummer:5573740
Erste Seite:1
Letzte Seite:31
Fakultäten / Institute / Einrichtungen:Fakultät Maschinenbau
Begutachtungsstatus:peer-reviewed
Publikationsherkunft:Externe Publikationen
Forschungsschwerpunkt:Digitalisierung
Lizenz (Deutsch):Creative Commons - CC BY - Namensnennung 4.0 International