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

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 5573740
• Main Authors: Wein, Simon, Deco, Gustavo, Tomé, Ana Maria, Goldhacker, Markus, Malloni, Wilhelm M., Greenlee, Mark W., Lang, Elmar W.
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Brain; Brain - diagnostic imaging; Brain Mapping; Brain research; Computational neuroscience; Diffusion Tensor Imaging; Electroencephalography; Functional anatomy; Functional magnetic resonance imaging; Graph theory; Imaging techniques; Information processing; Learning algorithms; Machine Learning; Magnetic Resonance Imaging; Medical imaging; Molecular biology; Nerve Net - diagnostic imaging; Network topologies; Neural networks; Neural Pathways - diagnostic imaging; Neuroimaging; Neurons; Neurosciences; Review; Structure-Activity Relationship; Structure-function relationships; Substrates; Tensors; Tracks (paths); Germany
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/5573740

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.