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Coupling electrophysiological and hemodynamic responses to errors.

ABSTRACT: Performance errors are associated with distinct electrophysiological and hemodynamic signatures: a fronto-central error-related negativity (ERN) is seen in the event-related potentials and a network of activations including medio-frontal, parietal, and insular cortex is revealed by functional magnetic resonance imaging. We used simultaneous electroencephalography and functional magnetic resonance imaging (fMRI) to characterize the relationship between the electrophysiological and hemodynamic responses to errors. Participants performed a modified Flanker task. When analyzed independently, we found the ERN and hemodynamic activations in dorsal anterior cingulate cortex, superior frontal gyrus, precentral gyrus, inferior frontal gyrus, and inferior parietal lobule. fMRI-informed dipole modeling and joint independent component analysis (ICA) were used to couple electrophysiological and hemodynamic data. Both techniques revealed a temporal evolution of the areas found in the fMRI analysis, with the right hemisphere activations peaking before the left hemisphere. However, joint ICA added information, revealing a number of cortical and subcortical areas that had not been shown with parametric mapping. This technique also uncovered how these areas evolve over time. All together, these analyses provide a more detailed picture of the spatiotemporal dynamics of the processing of performance errors.

SUBMITTER: Donamayor N

PROVIDER: S-EPMC6870069 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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Coupling electrophysiological and hemodynamic responses to errors.

Doñamayor Nuria N Heilbronner Urs U Münte Thomas F TF

Human brain mapping 20110526 7

Performance errors are associated with distinct electrophysiological and hemodynamic signatures: a fronto-central error-related negativity (ERN) is seen in the event-related potentials and a network of activations including medio-frontal, parietal, and insular cortex is revealed by functional magnetic resonance imaging. We used simultaneous electroencephalography and functional magnetic resonance imaging (fMRI) to characterize the relationship between the electrophysiological and hemodynamic res ...[more]

PMID: 21618663

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Project description:OBJECTIVE:Epilepsy causes measurable irregularity over a range of brain signal frequencies, as well as autonomic nervous system functions that modulate heart and respiratory rate variability. Imaging dynamic neuronal signals utilizing simultaneously acquired ultra-fast 10 Hz magnetic resonance encephalography (MREG), direct current electroencephalography (DC-EEG), and near-infrared spectroscopy (NIRS) can provide a more comprehensive picture of human brain function. Spectral entropy (SE) is a nonlinear method to summarize signal power irregularity over measured frequencies. SE was used as a joint measure to study whether spectral signal irregularity over a range of brain signal frequencies based on synchronous multimodal brain signals could provide new insights in the neural underpinnings of epileptiform activity. METHODS:Ten patients with focal drug-resistant epilepsy (DRE) and ten healthy controls (HC) were scanned with 10 Hz MREG sequence in combination with EEG, NIRS (measuring oxygenated, deoxygenated, and total hemoglobin: HbO, Hb, and HbT, respectively), and cardiorespiratory signals. After pre-processing, voxelwise SEMREG was estimated from MREG data. Different neurophysiological and physiological subfrequency band signals were further estimated from MREG, DC-EEG, and NIRS: fullband (0-5 Hz, FB), near FB (0.08-5 Hz, NFB), brain pulsations in very-low (0.009-0.08 Hz, VLFP), respiratory (0.12-0.4 Hz, RFP), and cardiac (0.7-1.6 Hz, CFP) frequency bands. Global dynamic fluctuations in MREG and NIRS were analyzed in windows of 2 min with 50% overlap. RESULTS:Right thalamus, cingulate gyrus, inferior frontal gyrus, and frontal pole showed significantly higher SEMREG in DRE patients compared to HC. In DRE patients, SE of cortical Hb was significantly reduced in FB (p = .045), NFB (p = .017), and CFP (p = .038), while both HbO and HbT were significantly reduced in RFP (p = .038, p = .045, respectively). Dynamic SE of HbT was reduced in DRE patients in RFP during minutes 2 to 6. Fitting to the frontal MREG and NIRS results, DRE patients showed a significant increase in SEEEG in FB in fronto-central and parieto-occipital regions, in VLFP in parieto-central region, accompanied with a significant decrease in RFP in frontal pole and parietal and occipital (O2, Oz) regions. CONCLUSION:This is the first study to show altered spectral entropy from synchronous MREG, EEG, and NIRS in DRE patients. Higher SEMREG in DRE patients in anterior cingulate gyrus together with SEEEG and SENIRS results in 0.12-0.4 Hz can be linked to altered parasympathetic function and respiratory pulsations in the brain. Higher SEMREG in thalamus in DRE patients is connected to disturbances in anatomical and functional connections in epilepsy. Findings suggest that spectral irregularity of both electrophysiological and hemodynamic signals are altered in specific way depending on the physiological frequency range.

Project description:Phase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase-amplitude coupling (PAC) or by n:m-cross-frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that interareal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state (RS) brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at mesoscale resolution from stereoelectroencephalography (SEEG) and at macroscale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel, to our knowledge, graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from nonsinusoidal signals ubiquitous in neuronal activity. We show that genuine interareal CFC is present in human RS activity in both SEEG and MEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high-frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for interareal CFS and PAC being 2 distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.

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Coupling electrophysiological and hemodynamic responses to errors.

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Coupling electrophysiological and hemodynamic responses to errors.

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