The Cortical Network of Emotion Regulation: Insights From Advanced EEG-fMRI Integration Analysis

Nguyen, Thinh; Zhou, Tiantong; Potter, Thomas; Zou, Ling; Zhang, Yinchun

doi:10.1109/tmi.2019.2900978

Cited by 45 publications

(25 citation statements)

References 80 publications

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“…Similar result was found in a previous emotion and working memory study [74]. Although we found different cortical activations and connectivity patterns in the three emotions, it is very important to involve negative emotions to establish functional connectivity as an index for emotion processing and regulation [75].…”

Section: Summary and Discussionsupporting

confidence: 90%

Emotion Recognition Based on Fusion of Local Cortical Activations and Dynamic Functional Networks Connectivity: An EEG Study

et al. 2019

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In this paper, we present a method to improve emotion recognition based on the fusion of local cortical activations and dynamic functional network patterns. We estimate the cortical activations using power spectral density (PSD) with the Burg autoregressive model. On the other hand, we estimate the functional connectivity networks by utilizing the phase locking value (PLV). The results of cortical activations and connectivity networks show different patterns across three emotions at all frequency bands. Similarly, the results of fusion significantly improve the classification rate in terms of accuracy, sensitivity, specificity and the area under the receiver operator characteristics curve (AROC), p < 0.05. The average improvement with fusion in all evaluation metrics are 6.84% and 4.1% when compared to PSD and PLV alone, respectively. The results clearly demonstrate the advantage of fusion of cortical activations with dynamic functional networks for developing human-computer interaction system in real-world applications.

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Section: Summary and Discussionsupporting

confidence: 90%

Emotion Recognition Based on Fusion of Local Cortical Activations and Dynamic Functional Networks Connectivity: An EEG Study

et al. 2019

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“…In this study, the reconstructed time series of each brain ROI was first filtered into different frequency bands, including theta (4-8 Hz), low alpha (8-10 Hz), high alpha (10-13 Hz), and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). The wPLI between the time series of any ROI pair was then computed for each frequency band.…”

Section: Functional Connectivity and Graph Theory Analysismentioning

confidence: 99%

“…Prior studies have shown that, using high spatial prior information acquired by fMRI, people are able to estimate brain activity with high spatiotemporal resolution in both computer simulations and real experiments involving visual and motor tasks. [22][23][24][25] However, fMRI is typically limited by the high sensitivity to motion artifacts, poor portability, and high costs, rendering it less appropriate for investigating brain activity related to human motor control in realistic situations (eg, in sitting or standing positions). 26 One alternative solution for overcoming this limitation is to use functional near-infrared spectroscopy (fNIRS) as a supplement to EEG source localization.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Neuroimaging Using Concurrent EEG/fNIRS for Poststroke Recovery Assessment: An Exploratory Study

Roh

et al. 2020

Neurorehabil Neural Repair

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Background Persistent motor deficits are very common in poststroke survivors and often lead to disability. Current clinical measures for profiling motor impairment and assessing poststroke recovery are largely subjective and lack precision. Objective A multimodal neuroimaging approach was developed based on concurrent functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to identify biomarkers associated with motor function recovery and document the poststroke cortical reorganization. Methods EEG and fNIRS data were simultaneously recorded from 9 healthy controls and 18 stroke patients during a hand-clenching task. A novel fNIRS-informed EEG source imaging approach was developed to estimate cortical activity and functional connectivity. Subsequently, graph theory analysis was performed to identify network features for monitoring and predicting motor function recovery during a 4-week intervention. Results The task-evoked strength at ipsilesional primary somatosensory cortex was significantly lower in stroke patients compared with healthy controls ( P < .001). In addition, across the 4-week rehabilitation intervention, the strength at ipsilesional premotor cortex (PMC) ( R = 0.895, P = .006) and the connectivity between bilateral primary motor cortices (M1) ( R = 0.9, P = .007) increased in parallel with the improvement of motor function. Furthermore, a higher baseline strength at ipsilesional PMC was associated with a better motor function recovery ( R = 0.768, P = .007), while a higher baseline connectivity between ipsilesional supplementary motor cortex (SMA)–M1 implied a worse motor function recovery ( R = −0.745, P = .009). Conclusion The proposed multimodal EEG/fNIRS technique demonstrates a preliminary potential for monitoring and predicting poststroke motor recovery. We expect such findings can be further validated in future study.

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“…Therefore, we cannot definitively say whether our ERP dependent findings corroborate these results, since Detachment elicited weaker instead of stronger amplitudes compared to Active Viewing. ER strategies rely on those prefrontal regions of cognitive control 89 and a study using combined fMRI and EEG to investigate ER proposed an influence of prefrontal regions on the LPP 90 . The authors found that an accumulation of current density in posterior regions might directly or indirectly be influenced by the ventrolateral prefrontal cortex, reducing the LPP amplitude in reappraisal conditions.…”

Section: Discussionmentioning

confidence: 99%

EEG microstate analysis of emotion regulation reveals no sequential processing of valence and emotional arousal

Zerna

Strobel

Scheffel

2021

Sci Rep

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In electroencephalography (EEG), microstates are distributions of activity across the scalp that persist for several tens of milliseconds before changing into a different pattern. Microstate analysis is a way of utilizing EEG as both temporal and spatial imaging tool, but has rarely been applied to task-based data. This study aimed to conceptually replicate microstate findings of valence and emotional arousal processing and investigate the effects of emotion regulation on microstates, using data of an EEG paradigm with 107 healthy adults who actively viewed emotional pictures, cognitively detached from them, or suppressed facial reactions. Within the first 600 ms after stimulus onset only the comparison of viewing positive and negative pictures yielded significant results, caused by different electrodes depending on the microstate. Since the microstates associated with more and less emotionally arousing pictures did not differ, sequential processing could not be replicated. When extending the analysis to 2000 ms after stimulus onset, differences were exclusive to the comparison of viewing and detaching from negative pictures. Intriguingly, we observed the novel phenomenon of a microstate difference that could not be attributed to single electrodes. This suggests that microstate analysis can detect differences beyond those detected by event-related potential analysis.

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The Cortical Network of Emotion Regulation: Insights From Advanced EEG-fMRI Integration Analysis

Cited by 45 publications

References 80 publications

Emotion Recognition Based on Fusion of Local Cortical Activations and Dynamic Functional Networks Connectivity: An EEG Study

Emotion Recognition Based on Fusion of Local Cortical Activations and Dynamic Functional Networks Connectivity: An EEG Study

Multimodal Neuroimaging Using Concurrent EEG/fNIRS for Poststroke Recovery Assessment: An Exploratory Study

EEG microstate analysis of emotion regulation reveals no sequential processing of valence and emotional arousal

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