Probing fMRI brain connectivity and activity changes during emotion regulation by EEG neurofeedback

Dehghani, Amin; Hamid, Soltanian-Zadeh,; Hossein-Zadeh, Gholam-Ali

doi:10.48550/arxiv.2006.06829

Cited by 3 publications

(4 citation statements)

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“…The extracted brain regions by CCMTF are in limbic, frontal, temporal, and occipital regions. The involvement of these regions justifies according to emotion regulation models in previous studies (Kohn et al, 2014;Dehghani et al, 2020b). As a short description, limbic and deep brain regions like the amygdala, insula, thalamus, caudate, putamen, and palladium play a key role in emotion regulation/generation, recalling positive and negative memories and they connect with several regions involved in emotion regulation especially prefrontal and frontal regions.…”

Section: Results Of the Correlated Coupled Tensor Matrix Factorizatio...mentioning

confidence: 89%

“…Before applying the CCMTF method, it is necessary to choose the common profile between EEG and fMRI data. Preprocessing pipline of EEG and fMRI were completely described in previous study (Dehghani et al, 2020b). For EEG data, several steps, e.g., removing fMRI artifact, detecting the QRS complexes from an ECG channel, removing the pulse (ballistocardiography/BCG), and finally ICA for removing eye blinking, head movement, and cardioballistic or BCG residual were done.…”

Section: Correlated Coupled Tensor Matrix Factorization For Neurofeed...mentioning

confidence: 99%

“…EEG and fMRI are two neuroimaging modalities used for emotion regulation neurofeedback. EEG frontal asymmetry was used in several previous studies for emotion regulation using the power spectrum of frontal EEG channels in the special frequency band (Peeters et al, 2014;Allen and Reznik, 2015;Quaedflieg et al, 2015;Kelley et al, 2017;Mennella et al, 2017;Reznik and Allen, 2018;Dehghani et al, 2020b). For emotion regulation based on fMRI neurofeedback, amygdala activity as feedback was used in several previous neurofeedback studies (Zotev et al, 2016;Barreiros et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Dynamic functional connectivity estimation for neurofeedback emotion regulation paradigm with simultaneous EEG-fMRI analysis

Mosayebi

Dehghani

Hossein-Zadeh

2022

Front. Hum. Neurosci.

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Joint Analysis of EEG and fMRI datasets can bring new insight into brain mechanisms. In this paper, we employed the recently introduced Correlated Coupled Tensor Matrix Factorization (CCMTF) method for analysis of the emotion regulation paradigm based on EEG frontal asymmetry neurofeedback in the alpha frequency band with simultaneous fMRI. CCMTF method assumes that the co-variations of the common dimension (temporal dimension) between EEG and fMRI are correlated and not necessarily identical. The results of the CCMTF method suggested that EEG and fMRI had similar covariations during the transition of brain activities from resting states to task (view and upregulation) states and these covariations followed an increasing trend. The fMRI shared spatial component showed activations in the limbic system, DLPFC, OFC, and VLPC regions, which were consistent with the previous studies and were linked to EEG frequency patterns in the range of 1–15 Hz with a correlation value close to 0.75. The estimated regions from the CCMTF method were then used as the candidate nodes for dynamic functional connectivity (dFC) analysis, in which the changes in connectivity from view to upregulation states were examined. The results of the dFC analysis were compared with a Normalized Mutual information (NMI) based approach in two different frequency ranges (1–15 and 15–40 Hz) as the NMI method was applied to the vectors of dFC nodes of EEG and fMRI data. The results of the two methods illustrated that the relation between EEG and fMRI datasets was mostly in the frequency range of 1–15 Hz. These relations were both in the brain activations and the dFCs between the two modalities. This paper suggests that the CCMTF method is a capable approach for extracting the shared information between EEG and fMRI data and can reveal new information about brain functions and their connectivity without solving the EEG inverse problem or analyzing different frequency bands.

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Section: Results Of the Correlated Coupled Tensor Matrix Factorizatio...mentioning

confidence: 89%

Section: Correlated Coupled Tensor Matrix Factorization For Neurofeed...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic functional connectivity estimation for neurofeedback emotion regulation paradigm with simultaneous EEG-fMRI analysis

Mosayebi

Dehghani

Hossein-Zadeh

2022

Front. Hum. Neurosci.

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“…Traditionally, passive elicitation methods place the user as a mere observer, ignoring the importance of personal meaning in the emotional experience. Active mechanisms are characterised by high ecological validity (meaning the ability of experimental results to be generalisable to the real world outside the laboratory [18]), immersiveness and interactivity.…”

Section: Introductionmentioning

confidence: 99%

Virtual Reality Enhances EEG-Based Neurofeedback for Emotional Self-regulation

et al. 2022

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A pilot study to investigate possible differences between a virtual reality-based neurofeedback and a traditional neurofeedback is presented. Neurofeedback training aimed to strengthen the emotional regulation capacity. The neurofeedback task is to down-regulate negative emotions by decreasing the beta band power measured in the midline areas of the scalp (i.e., Fcz-Cpz). Negative International Affective Picture System images were chosen as eliciting stimuli. Three healthy subjects participated in the experimental activities. Each of them underwent three VR-based neurofeedback sessions and three neurofeedback sessions delivered on a traditional 2D screen. The neurofeedback training session was preceded by a calibration phase allowing to record the rest and the baseline values to adapt the neurofeedback system to the user. For the majority of sessions, the average value of the high beta band power during the neurofeedback training remained below the baseline, as expected. In compliance with previous studies, future works should investigate the virtual reality-based neurofeedback efficacy in physiological responses and behavioral performance.

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Source localization and functional network analysis in emotion cognitive reappraisal with EEG-fMRI integration

Zhang

Jiang

et al. 2022

Front. Hum. Neurosci.

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BackgroundThe neural activity and functional networks of emotion-based cognitive reappraisal have been widely investigated using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). However, single-mode neuroimaging techniques are limited in exploring the regulation process with high temporal and spatial resolution.ObjectivesWe proposed a source localization method with multimodal integration of EEG and fMRI and tested it in the source-level functional network analysis of emotion cognitive reappraisal.MethodsEEG and fMRI data were simultaneously recorded when 15 subjects were performing the emotional cognitive reappraisal task. Fused priori weighted minimum norm estimation (FWMNE) with sliding windows was proposed to trace the dynamics of EEG source activities, and the phase lag index (PLI) was used to construct the functional brain network associated with the process of downregulating negative affect using the reappraisal strategy.ResultsThe functional networks were constructed with the measure of PLI, in which the important regions were indicated. In the gamma band source-level network analysis, the cuneus, the lateral orbitofrontal cortex, the superior parietal cortex, the postcentral gyrus, and the pars opercularis were identified as important regions in reappraisal with high betweenness centrality.ConclusionThe proposed multimodal integration method for source localization identified the key cortices involved in emotion regulation, and the network analysis demonstrated the important brain regions involved in the cognitive control of reappraisal. It shows promise in the utility in the clinical setting for affective disorders.

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Probing fMRI brain connectivity and activity changes during emotion regulation by EEG neurofeedback

Cited by 3 publications

References 0 publications

Dynamic functional connectivity estimation for neurofeedback emotion regulation paradigm with simultaneous EEG-fMRI analysis

Dynamic functional connectivity estimation for neurofeedback emotion regulation paradigm with simultaneous EEG-fMRI analysis

Virtual Reality Enhances EEG-Based Neurofeedback for Emotional Self-regulation

Source localization and functional network analysis in emotion cognitive reappraisal with EEG-fMRI integration

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