@article{WeinDecoTomeetal., author = {Wein, Simon and Deco, Gustavo and Tom{\´e}, Ana Maria and Goldhacker, Markus and Malloni, Wilhelm M. and Greenlee, Mark W. and Lang, Elmar Wolfgang}, title = {Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning}, series = {Computational intelligence and neuroscience}, journal = {Computational intelligence and neuroscience}, publisher = {Hindawi}, doi = {10.1155/2021/5573740}, pages = {1 -- 31}, abstract = {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.}, language = {en} } @inproceedings{WeinTomeGoldhackeretal., author = {Wein, S. and Tom{\´e}, Ana Maria and Goldhacker, Markus and Greenlee, Mark W. and Lang, Elmar Wolfgang}, title = {Hybridizing EMD with cICA for fMRI Analysis of Patient Groups}, series = {2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): 23-27 July 2019, Berlin, Germany}, booktitle = {2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): 23-27 July 2019, Berlin, Germany}, publisher = {IEEE}, doi = {10.1109/EMBC.2019.8856355}, pages = {194 -- 197}, abstract = {Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is naturally not convenient for analysis of group studies. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to overcome the inherent ambiguities. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach. It is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. This novel processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge, and also the trade-off between similarity across subjects and preserving individual features can be well adjusted and adapted for different requirements in the new work-flow.}, language = {en} } @article{WeinTomeGoldhackeretal., author = {Wein, Simon and Tom{\´e}, Ana Maria and Goldhacker, Markus and Greenlee, Mark W. and Lang, Elmar Wolfgang}, title = {A Constrained ICA-EMD Model for Group Level fMRI Analysis}, series = {Frontiers in Neuroscience}, volume = {14}, journal = {Frontiers in Neuroscience}, publisher = {frontiers}, issn = {1662-453X}, doi = {10.3389/fnins.2020.00221}, pages = {1 -- 10}, abstract = {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.}, language = {en} } @article{GoldhackerAlSubariAlBaddaietal., author = {Goldhacker, Markus and Al-Subari, Karema and Al-Baddai, Saad and Tom{\´e}, Ana Maria and Faltermeier, Rupert and Lang, Elmar Wolfgang}, title = {EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition}, series = {Journal of Neuroscience Methods}, volume = {253}, journal = {Journal of Neuroscience Methods}, number = {September}, publisher = {Elsevier}, address = {AMsterdam}, doi = {10.1016/j.jneumeth.2015.06.020}, pages = {193 -- 205}, abstract = {Background: Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field. New method: EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis. Results: EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox. Comparison with existing methods: EMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal. Conclusions: EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.}, language = {en} } @inproceedings{GoldhackerTomeGreenleeetal., author = {Goldhacker, Markus and Tom{\´e}, Ana Maria and Greenlee, Mark W. and Lang, Elmar Wolfgang}, title = {Early meta-level: deeper understanding of connectivity-states and consequences for state definition}, series = {21st Annual Meeting of the Organization for Human Brain Mapping, June 14-18, 2015, Honolulu, Hawaii}, booktitle = {21st Annual Meeting of the Organization for Human Brain Mapping, June 14-18, 2015, Honolulu, Hawaii}, publisher = {Academic Press}, address = {San Diego, CA}, doi = {10.13140/RG.2.1.2561.3929}, language = {en} } @misc{AlSubariAlBaddaiTomeetal., author = {Al-Subari, Karema and Al-Baddai, Saad and Tom{\´e}, Ana Maria and Goldhacker, Markus and Faltermeier, Rupert and Lang, Elmar Wolfgang}, title = {EMDLAB-toolbox- tutorial video}, language = {en} }