Article Figures & Data
Figures
- Figure 1.
Illustration of fMRI-informed EEG source reconstruction. To estimate the location and activity of active cortical patches in the brain that lead to measurable EEG signal changes on the scalp, forward or head models are constructed from individual MR images. Here, volumes representing skin, skull, and brain tissue have been extracted. Based on such a model and the EEG time courses as well as corresponding scalp topographies (the pattern of EEG activity as recorded on a participant's head), EEG sources can be inferred (inverse modeling). Statistical maps from a standard fMRI analysis are used to further constrain possible source constellations. The procedure used here for inverse modeling computes a high number of dipolar sources distributed across the brain, each of which is characterized by its position, orientation (pointing direction of an arrow), and strength (as indicated by coloring).
- Figure 2.
Schematic of EEG-informed fMRI. For an event of interest (e.g., the occurrence of a response error), a parameter value of an EEG feature (e.g., the amplitude of an ERP) is extracted from every trial that includes this event. With respect to fMRI, the onsets of these events during the course of the experiment are known, and the fMRI signal changes caused by hemodynamic responses following the events are mathematically modeled. This is done via convolution: the multiplication and summation of a vector of zeros and ones representing the event onsets and the hemodynamic response function (mathematically modeled fMRI signal changes following an event). The result is the predicted time course of fMRI signal changes which can then be statistically compared with the observed fMRI signal for each voxel (volume element) of a brain scan. In the case of EEG-informed fMRI, not only is this model determined by event onsets and the hemodynamic response function (blue model prediction), but the expected hemodynamic responses are additionally parameterized using the extracted EEG feature: signal changes following events on trials with a large EEG response are scaled up as compared with trials with smaller EEG responses (green model prediction).
- Figure 3.
Illustration of neurogenerative models for EEG-fMRI data analysis. This approach relies on mathematically modeling the dynamics of neural ensembles at various scales ranging from gross connectivity patterns to cellular events. In addition, biophysical forward models must specify the transformation of neural events to the measured EEG and fMRI signals. Based on these data, the result of a predefined neural activity pattern can be simulated (forward modeling) or, given a multimodal EEG-fMRI dataset, the most likely neural events can be inferred based on the observed EEG-fMRI signal properties (inverse modeling).
- Figure 4.
Schematic illustration of joint ICA. EEG and fMRI for a set of subjects are concatenated within the same matrix and subjected to a joint ICA. This procedure helps to capture the variance in brain responses across subjects and modalities on which basis a decomposition to independent components can be computed. The resulting components represent spatial (fMRI) and temporal (EEG) characteristics of brain responses. Here, the lower row shows the averaged ERP (blue) and time courses of two components (red) alongside their associated spatial maps.
