TY - JOUR A1 - Wein, Simon A1 - Deco, Gustavo A1 - Tomé, Ana Maria A1 - Goldhacker, Markus A1 - Malloni, Wilhelm M. A1 - Greenlee, Mark W. A1 - Lang, Elmar Wolfgang T1 - Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning JF - Computational intelligence and neuroscience N2 - 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. KW - Brain Mapping KW - Brain/diagnostic imaging KW - Diffusion Tensor Imaging KW - Machine Learning KW - Magnetic Resonance Imaging KW - Nerve Net/diagnostic imaging KW - Neural Pathways/diagnostic imaging KW - Structure-Activity Relationship Y1 - 2021 U6 - https://doi.org/10.1155/2021/5573740 SP - 1 EP - 31 PB - Hindawi ER - TY - JOUR A1 - Wein, Simon A1 - Tomé, Ana Maria A1 - Goldhacker, Markus A1 - Greenlee, Mark W. A1 - Lang, Elmar Wolfgang T1 - A Constrained ICA-EMD Model for Group Level fMRI Analysis JF - Frontiers in Neuroscience N2 - Independent component analysis (ICA), being a data-driven method, has been shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is that it is not, in general, compatible with the analysis of group data. Various techniques have been proposed to overcome this limitation of ICA. In this paper, a novel ICA-based workflow for extracting resting-state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used, in a data-driven manner, to generate reference signals that can be incorporated into a constrained version of ICA (cICA), thereby eliminating the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this study, we demonstrate that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA. This approach yields typical resting-state patterns that are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline yields comparable activity patterns across subjects in a mathematically transparent manner. Our approach provides a user-friendly tool to adjust the trade-off between a high similarity across subjects and preserving individual subject features of the independent components. Y1 - 2020 U6 - https://doi.org/10.3389/fnins.2020.00221 SN - 1662-453X SN - 1662-4548 VL - 14 SP - 1 EP - 10 PB - frontiers ER -