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

Autor:	Wein S; CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany.; Experimental Psychology, University of Regensburg, Regensburg 93040, Germany., Deco G; Center for Brain and Cognition, Department of Technology and Information, University Pompeu Fabra, Carrer Tanger, 122-140, Barcelona 08018, Spain.; Institució Catalana de la Recerca i Estudis Avançats, University Barcelona, Passeig Lluís Companys 23, Barcelona 08010, Spain., Tomé AM; IEETA/DETI, University de Aveiro, Aveiro 3810-193, Portugal., Goldhacker M; CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany.; Experimental Psychology, University of Regensburg, Regensburg 93040, Germany., Malloni WM; Experimental Psychology, University of Regensburg, Regensburg 93040, Germany., Greenlee MW; Experimental Psychology, University of Regensburg, Regensburg 93040, Germany., Lang EW; CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany.
Jazyk:	angličtina
Zdroj:	Computational intelligence and neuroscience [Comput Intell Neurosci] 2021 May 27; Vol. 2021, pp. 5573740. Date of Electronic Publication: 2021 May 27 (Print Publication: 2021).
DOI:	10.1155/2021/5573740
Abstrakt:	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. Competing Interests: The authors declare that they have no conflicts of interest. (Copyright © 2021 Simon Wein et al.)
Databáze:	MEDLINE
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