Trial-by-Trial Coupling of Concurrent Electroencephalogram and Functional Magnetic Resonance Imaging Identifies the Dynamics of Performance Monitoring

By Stefan Debener, Markus Ullsperger, Markus Siegel, Katja Fiehler, D. von Cramon, Andreas K. Engel. Journal of Neuroscience, 2005

1,075 citations recorded by the source corpus.

Goal-directed behavior requires the continuous monitoring and dynamic adjustment of ongoing actions. Here, we report a direct coupling between the event-related electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), and behavioral measures of performance monitoring in humans. By applying independent component analysis to EEG signals recorded simultaneously with fMRI, we found the single-trial error-related negativity of the EEG to be systematically related to behavior in the subsequent trial, thereby reflecting immediate behavioral adjustments of a cognitive performance monitoring system. Moreover, this trial-by-trial EEG measure of performance monitoring predicted the fMRI activity in the rostral cingulate zone, a brain region thought to play a key role in processing of response errors. We conclude that investigations of the dynamic coupling between EEG and fMRI provide a powerful approach for the study of higher order brain functions.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/0e47f42a3b22eb95670a700cf1a1ea707dce9c81

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In Cognitive Neuroscience

Citations in the corpus

Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions (2012, methodology): “…investigating the function of different component systems using single methodologies (e.g., resting state; Deshpande, Santhanam, & Hu, 2011) or combined methodologies (e.g., EEG and fMRI; Debener et al., 2005) have the potential to elucidated the complex brain dynamics underlying cognitive control.”
Neurophysiology of performance monitoring and adaptive behavior. (2014, methodology): “In several experiments, the individual size of PES was predicted by the individual’s ERN amplitude (136, 198, 575, 576), by their midfrontal theta power increase in error trials (79), or by their BOLD response in pMFC (87, 196, 291, 296).”
The Phase of Ongoing EEG Oscillations Predicts Visual Perception (2009, background): “These areas have previously been discussed as generators of EEG theta rhythms (Debener et al., 2005; Onton et al., 2005; Tsujimoto et al., 2006), which are involved in a variety of cognitive tasks (for review, see Mitchell et al.”
Choice, uncertainty and value in prefrontal and cingulate cortex (2008, background): “A negative-going event-related potential, the error- or feedback-related negativity (ERN or FRN), which has a dipole source in the ACC, can be recorded over the frontal midline scalp when errors are mad”
Imaging human EEG dynamics using independent component analysis. (2006, background): “This may be an effective approach for quite stereotyped evoked activity when the relevant time window is known and the ERP is composed mainly of a single ICA component (Debener et al., 2005).”
Electrophysiological Imaging of Brain Activity and Connectivity – Challenges and Opportunities (2011, background): “Such superior joint spatial and temporal resolution would be highly desirable to delineate complex neural networks related to cognitive function, such as error processing [56], learning [57], and decision making [58], allowing answering the question of “where” as well as the question of “when.”
Human brain mapping: hemodynamic response and electrophysiology. (2008, methodology): “As an example of the simultaneous use of EEG and fMRI, Debener et al. (2005) recorded EEG during functional MR scanning in an experimental paradigm called speeded flanker task for recording the so-called errorrelated brain activity.”
Theta Lingua Franca: A Common Mid-Frontal Substrate for Action Monitoring Processes (2011, neutral): “…et al., 2007), and these oscillations have been linked to multiple processes, including memory, attention, learning, and action selection (Cohen, Ridderinkhof, Haupt, Elger, & Fell, 2008; Debener et al., 2005; Marco-Pallares et al., 2008; Onton, Delorme, & Makeig, 2005; Wang et al., 2005).”
The Cognitive Neuroscience of Metacognition (2014, methodology): “2) [15], would one think to offer qualitatively different low and high-level interpretations of the behavioral data.”
Event-related EEG time-frequency analysis: an overview of measures and an analysis of early gamma band phase locking in schizophrenia. (2008, methodology): “A baseline period should begin in the epoch after at least one-half the length of the temporal window used in time-frequency decomposition in order to avoid ‘‘edge effects’’ (ie, distortions resulting from convolution of the temporal windowing function with a data time series that does not extend ov”
Prediction of human errors by maladaptive changes in event-related brain networks (2008, methodology): “We have previously demonstrated the role of the pMFC in monitoring errors and influencing subsequent response time adjustments using an EEG-informed fMRI analysis of these data (36).”
Trial-by-trial coupling between EEG and BOLD identifies networks related to alpha and theta EEG power increases during working memory maintenance (2009, background): “Successful single trial coupling between EEG measures (ERP's) and the BOLD signal has been demonstrated by Eichele et al. (2005) and Debener et al. (2005).”