@article{GoldhackerRosengarthPlanketal., author = {Goldhacker, Markus and Rosengarth, Katharina and Plank, Tina and Greenlee, Mark W.}, title = {The effect of feedback on performance and brain activation during perceptual learning}, series = {Vision research}, volume = {99}, journal = {Vision research}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1878-5646}, doi = {10.1016/j.visres.2013.11.010}, pages = {99 -- 110}, abstract = {We investigated the role of informative feedback on the neural correlates of perceptual learning in a coherent-motion detection paradigm. Stimulus displays consisted of four patches of moving dots briefly (500 ms) presented simultaneously, one patch in each visual quadrant. The coherence level was varied in the target patch from near threshold to high, while the other three patches contained only noise. The participants judged whether coherent motion was present or absent in the target patch. To guarantee central fixation, a secondary RSVP digit-detection task was performed at fixation. Over six training sessions subjects learned to detect coherent motion in a predefined quadrant (i.e., the learned location). Half of our subjects were randomly assigned to the feedback group, where they received informative feedback after each response during training, whereas the other group received non-informative feedback during training that a response button was pressed. We investigated whether the presence of informative feedback during training had an influence on the learning success and on the resulting BOLD response. Behavioral data of 24 subjects showed improved performance with increasing practice. Informative feedback promoted learning for motion displays with high coherence levels, whereas it had little effect on learning for displays with near-threshold coherence levels. Learning enhanced fMRI responses in early visual cortex and motion-sensitive area MT+ and these changes were most pronounced for high coherence levels. Activation in the insular and cingulate cortex was mainly influenced by coherence level and trained location. We conclude that feedback modulates behavioral performance and, to a lesser extent, brain activation in areas responsible for monitoring perceptual learning.}, 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{GoldhackerFellnerVolbergetal., author = {Goldhacker, Markus and Fellner, Marie-Christin and Volberg, G. and Mullinger, K. J. and Greenlee, Mark W. and Hanslmayr, Simon}, title = {Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement}, series = {NeuroImage}, volume = {133}, journal = {NeuroImage}, number = {June}, publisher = {Elsevier}, doi = {10.1016/j.neuroimage.2016.03.031}, pages = {354 -- 366}, abstract = {Simultaneous EEG-fMRI provides an increasingly attractive research tool to investigate cognitive processes with high temporal and spatial resolution. However, artifacts in EEG data introduced by the MR scanner still remain a major obstacle. This study, employing commonly used artifact correction steps, shows that head motion, one overlooked major source of artifacts in EEG-fMRI data, can cause plausible EEG effects and EEG-BOLD correlations. Specifically, low-frequency EEG (< 20 Hz) is strongly correlated with in-scanner movement. Accordingly, minor head motion (< 0.2 mm) induces spurious effects in a twofold manner: Small differences in task-correlated motion elicit spurious low-frequency effects, and, as motion concurrently influences fMRI data, EEG-BOLD correlations closely match motion-fMRI correlations. We demonstrate these effects in a memory encoding experiment showing that obtained theta power (~ 3-7 Hz) effects and channel-level theta-BOLD correlations reflect motion in the scanner. These findings highlight an important caveat that needs to be addressed by future EEG-fMRI studies.}, 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} } @article{GreenleeRosengarthSchmalhoferetal., author = {Greenlee, Mark W. and Rosengarth, Katharina and Schmalhofer, Carolin and Goldhacker, Markus and Brandl-R{\"u}hle, Sabine and Plank, Tina}, title = {Perceptual learning in patients with central scotomata due to hereditary and age-related macular dystrophy}, series = {Journal of Vision}, volume = {14}, journal = {Journal of Vision}, number = {10}, publisher = {ARVO}, issn = {1468-4233}, doi = {10.1167/14.10.666}, abstract = {Hereditary and age-related forms of macular dystrophy (MD) are characterized by loss of cone function in the fovea, leading to central scotomata and eccentric fixation at the so-called preferred retinal locus (PRL). We investigated whether perceptual learning enhances visual abilities at the PRL. We also determined the neural correlates (3-Tesla fMRI) of learning success. Twelve MD patients (eight with age-related macular dystrophy, four with hereditary macular dystrophies) were trained on a texture discrimination task (TDT) over six days. Patients underwent three fMRI sessions (before, during and after training) while performing the TDT (target at PRL or opposite PRL). Reading speed, visual acuity (Vernier task) and contrast sensitivity were also assessed before and after training. With one exception, all patients showed improved performance (i.e. significant decrease in stimulus onset asynchronies and reaction times, significant increase in hit rates) on the TDT. Eight patients also showed moderate increases in reading speed, six patients showed improved thresholds in contrast sensitivity and nine patients showed improved thresholds in a vernier visual acuity task after TDT training. We found an increase in BOLD response in the projections zone of the PRL in the primary visual cortex in nine of twelve patients after training. The change in fMRI signal correlated (r = .8; p = .02) with the patients{\^a}€™ performance enhancements when the target was in the PRL. The results suggest that perceptual learning can enhance eccentric vision and cortical processing in MD patients.}, language = {en} } @article{GoldhackerRosengarthAnstisetal., author = {Goldhacker, Markus and Rosengarth, Katharina and Anstis, Stuart and Wirth, Anna and Plank, Tina and Greenlee, Mark W.}, title = {FMRI evidence for perceptual filling-in in patients with macular dystrophy}, series = {Perception}, volume = {42}, journal = {Perception}, issn = {1468-4233}, pages = {72 -- 73}, language = {en} }