Altered Amygdala Information Flow during rt-fMRI Neurofeedback Training of Emotion Regulation

We, He; Bu, Haibing; Gao, Hui; Li, Tong; Wang, Linyuan; Li, Zhong-Lin; Yan, Bin

doi:10.12783/dtcse/smce2017/12436

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…With the development of medical imaging technology, fMRI can be used to reveal the abnormalities of brain functional connectivity of MDD patients. By treating the human brain as a comprehensive network of functionally interacting brain regions and associating it with human behavior, we can make an understanding of how brain tissue changes in psychiatric disorders, which may effectively contribute to MDD diagnosis approaches ( He et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

The classification of brain network for major depressive disorder patients based on deep graph convolutional neural network

Zhu

Yu²,

He³

2023

Front. Hum. Neurosci.

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IntroductionThe early diagnosis of major depressive disorder (MDD) is very important for patients that suffer from severe and irreversible consequences of depression. It has been indicated that functional connectivity (FC) analysis based on functional magnetic resonance imaging (fMRI) data can provide valuable biomarkers for clinical diagnosis. However, previous studies mainly focus on brain disease classification in small sample sizes, which may lead to dramatic divergences in classification accuracy.MethodsThis paper attempts to address this limitation by applying the deep graph convolutional neural network (DGCNN) method on a large multi-site MDD dataset. The resting-state fMRI data are acquired from 830 MDD patients and 771 normal controls (NC) shared by the REST-meta-MDD consortium.ResultsThe DGCNN model trained with the binary network after thresholding, identified MDD patients from normal controls and achieved an accuracy of 72.1% with 10-fold cross-validation, which is 12.4%, 9.8%, and 7.6% higher than SVM, RF, and GCN, respectively. Moreover, the process of dataset reading and model training is faster. Therefore, it demonstrates the advantages of the DGCNN model with low time complexity and sound classification performance.DiscussionBased on a large, multi-site dataset from MDD patients, the results expressed that DGCNN is not an extremely accurate method for MDD diagnosis. However, there is an improvement over previous methods with our goal of better understanding brain function and ultimately providing a biomarker or diagnostic capability for MDD diagnosis.

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Section: Introductionmentioning

confidence: 99%

The classification of brain network for major depressive disorder patients based on deep graph convolutional neural network

Zhu

Yu²,

He³

2023

Front. Hum. Neurosci.

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

Dehghani

Soltanian‐Zadeh

Hossein-Zadeh

2023

Front. Hum. Neurosci.

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Despite the existence of several emotion regulation studies using neurofeedback, interactions among a small number of regions were evaluated, and therefore, further investigation is needed to understand the interactions of the brain regions involved in emotion regulation. We implemented electroencephalography (EEG) neurofeedback with simultaneous functional magnetic resonance imaging (fMRI) using a modified happiness-inducing task through autobiographical memories to upregulate positive emotion. Then, an explorative analysis of whole brain regions was done to understand the effect of neurofeedback on brain activity and the interaction of whole brain regions involved in emotion regulation. The participants in the control and experimental groups were asked to do emotion regulation while viewing positive images of autobiographical memories and getting sham or real (based on alpha asymmetry) EEG neurofeedback, respectively. The proposed multimodal approach quantified the effects of EEG neurofeedback in changing EEG alpha power, fMRI blood oxygenation level-dependent (BOLD) activity of prefrontal, occipital, parietal, and limbic regions (up to 1.9% increase), and functional connectivity in/between prefrontal, parietal, limbic system, and insula in the experimental group. New connectivity links were identified by comparing the brain functional connectivity between experimental conditions (Upregulation and View blocks) and also by comparing the brain connectivity of the experimental and control groups. Psychometric assessments confirmed significant changes in positive and negative mood states in the experimental group by neurofeedback. Based on the exploratory analysis of activity and connectivity among all brain regions involved in emotion regions, we found significant BOLD and functional connectivity increases due to EEG neurofeedback in the experimental group, but no learning effect was observed in the control group. The results reveal several new connections among brain regions as a result of EEG neurofeedback which can be justified according to emotion regulation models and the role of those regions in emotion regulation and recalling positive autobiographical memories.

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Altered Amygdala Information Flow during rt-fMRI Neurofeedback Training of Emotion Regulation

Cited by 2 publications

References 23 publications

The classification of brain network for major depressive disorder patients based on deep graph convolutional neural network

The classification of brain network for major depressive disorder patients based on deep graph convolutional neural network

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

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