Dataset Information

Dynamic EEG-informed fMRI modeling of the pain matrix using 20-ms root mean square segments.

ABSTRACT: Previous studies on the spatio-temporal dynamics of cortical pain processing using electroencephalography (EEG), magnetoencephalography (MEG), or intracranial recordings point towards a high degree of parallelism, e.g. parallel instead of sequential activation of primary and secondary somatosensory areas or simultaneous activation of somatosensory areas and the mid-cingulate cortex. However, because of the inverse problem, EEG and MEG provide only limited spatial resolution and certainty about the generators of cortical pain-induced electromagnetic activity, especially when multiple sources are simultaneously active. On the other hand, intracranial recordings are invasive and do not provide whole-brain coverage. In this study, we thought to investigate the spatio-temporal dynamics of cortical pain processing in 10 healthy subjects using simultaneous EEG/functional magnetic resonance imaging (fMRI). Voltages of 20 ms segments of the EEG root mean square (a global, largely reference-free measure of event-related EEG activity) in a time window 0-400 ms poststimulus were used to model trial-to-trial fluctuations in the fMRI blood oxygen level dependent (BOLD) signal. EEG-derived regressors explained additional variance in the BOLD signal from 140 ms poststimulus onward. According to this analysis, the contralateral parietal operculum was the first cortical area to become activated upon painful laser stimulation. The activation pattern in BOLD analyses informed by subsequent EEG-time windows suggests largely parallel signal processing in the bilateral operculo-insular and mid-cingulate cortices. In that regard, our data are in line with previous reports. However, the approach presented here is noninvasive and bypasses the inverse problem using only temporal information from the EEG.

SUBMITTER: Brinkmeyer J

PROVIDER: S-EPMC6871058 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

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Dynamic EEG-informed fMRI modeling of the pain matrix using 20-ms root mean square segments.

Brinkmeyer Juergen J Mobascher Arian A Warbrick Tracy T Musso Francesco F Wittsack Hans-Jörg HJ Saleh Andreas A Schnitzler Alfons A Winterer Georg G

Human brain mapping 20101101 11

Previous studies on the spatio-temporal dynamics of cortical pain processing using electroencephalography (EEG), magnetoencephalography (MEG), or intracranial recordings point towards a high degree of parallelism, e.g. parallel instead of sequential activation of primary and secondary somatosensory areas or simultaneous activation of somatosensory areas and the mid-cingulate cortex. However, because of the inverse problem, EEG and MEG provide only limited spatial resolution and certainty about t ...[more]

PMID: 20162596

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Project description:Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME.

Dataset Information

Dynamic EEG-informed fMRI modeling of the pain matrix using 20-ms root mean square segments.

Publications

Dynamic EEG-informed fMRI modeling of the pain matrix using 20-ms root mean square segments.

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