@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{GoldhackerTomeGreenleeetal., author = {Goldhacker, Markus and Tom{\´e}, Ana Maria and Greenlee, Mark W. and Lang, Elmar Wolfgang}, title = {Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States}, series = {Frontiers in Human Neuroscience}, volume = {12}, journal = {Frontiers in Human Neuroscience}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1662-5161}, doi = {10.3389/fnhum.2018.00253}, pages = {1 -- 16}, abstract = {Investigating temporal variability of functional connectivity is an emerging field in connectomics. Entering dynamic functional connectivity by applying sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged from this topic. We introduce frequency-resolved dynamic functional connectivity (frdFC) by means of multivariate empirical mode decomposition (MEMD) followed up by filter-bank investigations. In general, we find that MEMD is capable of generating time courses to perform frdFC and we discover that the structure of connectivity-states is robust over frequency scales and even becomes more evident with decreasing frequency. This scale-stability varies with the number of extracted clusters when applying k-means. We find a scale-stability drop-off from k = 4 to k = 5 extracted connectivity-states, which is corroborated by null-models, simulations, theoretical considerations, filter-banks, and scale-adjusted windows. Our filter-bank studies show that filter design is more delicate in the rs-fMRI than in the simulated case. Besides offering a baseline for further frdFC research, we suggest and demonstrate the use of scale-stability as a possible quality criterion for connectivity-state and model selection. We present first evidence showing that connectivity-states are both a multivariate, and a multiscale phenomenon. A data repository of our frequency-resolved time-series is provided.}, language = {en} } @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{GoldhackerTomeGreenleeetal., author = {Goldhacker, Markus and Tom{\´e}, Ana Maria and Greenlee, Mark W. and Lang, Elmar W.}, 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} } @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{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} } @article{GoldhackerKeckIgeletal., author = {Goldhacker, Markus and Keck, P. and Igel, A. and Lang, Elmar Wolfgang and Tom{\´e}, Ana Maria}, title = {A multi-variate blind source separation algorithm}, series = {Computer Methods and Programs in Biomedicine}, volume = {151}, journal = {Computer Methods and Programs in Biomedicine}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1872-7565}, doi = {10.1016/j.cmpb.2017.08.019}, pages = {91 -- 99}, abstract = {Background and objective The study follows the proposal of decomposing a given data matrix into a product of independent spatial and temporal component matrices. A multi-variate decomposition approach is presented, based on an approximate diagonalization of a set of matrices computed using a latent space representation. Methods The proposed methodology follows an algebraic approach, which is common to space, temporal or spatiotemporal blind source separation algorithms. More specifically, the algebraic approach relies on singular value decomposition techniques, which avoids computationally costly and numerically instable matrix inversion. The method is equally applicable to correlation matrices determined from second order correlations or by considering fourth order correlations. Results The resulting algorithms are applied to fMRI data sets either to extract the underlying fMRI components or to extract connectivity maps from resting state fMRI data collected for a dynamic functional connectivity analysis. Intriguingly, our algorithm shows increased spatial specificity compared to common approaches, while temporal precision stays similar. Conclusion The study presents a novel spatiotemporal blind source separation algorithm, which is both robust and avoids parameters that are difficult to fine tune. Applied on experimental data sets, the new method yields highly confined and focused areas with least spatial extent in the retinotopy case, and similar results in the dynamic functional connectivity analyses compared to other blind source separation algorithms. Therefore, we conclude that our novel algorithm is highly competitive and yields results, which are superior or at least similar to existing approaches.}, language = {en} }