Entwicklung und Vergleich von Methoden der multimodalen Bildgebung basierend auf EEG und (f)MRT sowie deren Anwendung in den kognitiven Neurowissenschaften
Zusammenfassung der Projektergebnisse
Multimodal analyses of neuroimaging data promise to substantially foster our understanding of the neural underpinnings of normal and deviant behavior. In cognitive brain imaging one important goal of such analyses is to achieve both a high spatial and a high temporal resolution at the same time, thus overcoming limitations of unimodal analyses. Two techniques that complement each other very well in this respect are functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). However, the lack of stringent comparisons between the various uni- and multimodal approaches used for data processing hinders an integrative interpretation and the common use of these advanced methods. Within this project a taxonomy for the classification of methods for fMRI/EEG integration was developed, which differentiates between procedures relying on biophysical modeling, symmetric data fusion by means of machine learning algorithms, and asymmetric integration methods that bias the processing of one modality by information of the other. Focusing on the latter class, the project assessed the strengths and weaknesses of fMRI-informed EEG on the one, and EEG-informed fMRI analyses on the other hand, which altogether represent the most common applications for EEG/fMRI integration. Several experiments, focusing on the study of cognitive control processes, were conducted to compare uni- and multimodal analysis schemes to assess the degree of convergence and divergence of asymmetric integration methods. It was found that EEG may capture neural processes not necessarily represented in fMRI, thereby potentially causing distortions in fMRI-informed EEG, i.e. the guidance of EEG source analyses by means of fMRI activation maps. EEG-informed fMRI, where fMRI time courses are predicted based on fluctuations derived from an EEG feature, however, relies on the application of filter mechanisms to reliably extract activity patterns at the level of single trials. Therefore, data decomposition by means of independent component analysis is applied regularly to the data of single subjects. A common problem in this context is to extract and identify those components that across subjects represent the exact same neurocognitive process. Procedures were developed and tested to address these problems, e.g. by applying group-level decomposition of EEG data for feature extraction prior to EEG-informed fMRI analyses, a procedure that generates features with high signal-to-noise ratios while concurrently guaranteeing the matching of features across subjects.
Projektbezogene Publikationen (Auswahl)
- Methods for simultaneous EEG-fMRI: an introductory review. J Neurosci. 2012; 32(18):6053-60
Huster RJ, Debener S, Eichele T, Herrmann CS
(Siehe online unter https://dx.doi.org/10.1523/JNEUROSCI.0447-12.2012) - A large N400 but no BOLD effect--comparing source activations of semantic priming in simultaneous EEG-fMRI. PLoS One. 2013; 8(12):e84029
Geukes S, Huster RJ, Wollbrink A, Junghöfer M, Zwitserlood P, Dobel C
(Siehe online unter https://doi.org/10.1371/journal.pone.0084029) - Function-structure associations of the brain: evidence from multimodal connectivity and covariance studies. Neuroimage. 2014; 102:11-23
Sui J, Huster R, Yu Q, Segall JM, Calhoun VD
(Siehe online unter https://doi.org/10.1016/j.neuroimage.2013.09.044) - Functional and effective connectivity of stopping. Neuroimage. 2014; 94:120-8
Huster RJ, Plis SM, Lavallee CF, Calhoun VD, Herrmann CS
(Siehe online unter https://doi.org/10.1016/j.neuroimage.2014.02.034) - Stimulus-response mappings shape inhibition processes: a combined EEG-fMRI study of contextual stopping. PLoS One. 2014; 9(4):e96159
Lavallee CF, Herrmann CS, Weerda R, Huster RJ
(Siehe online unter https://doi.org/10.1371/journal.pone.0096159) - When holding your horses meets the deer in the headlights: time-frequency characteristics of global and selective stopping under conditions of proactive and reactive control. Front Hum Neurosci. 2014; 8:994
Lavallee CF, Meemken MT, Herrmann CS, Huster RJ
(Siehe online unter https://doi.org/10.3389/fnhum.2014.00994) - Group-level component analyses of EEG: validation and evaluation. Front Neurosci. 2015; 9:254
Huster RJ, Plis SM, Calhoun VD
(Siehe online unter https://doi.org/10.3389/fnins.2015.00254)
