Altered resting-state regional homogeneity after real-time fMRI emotion self-regulation training

Real-time functional MRI neurofeedback (rtfMRI-NF) is a noninvasive technique that extracts concurrent brain states and provides feedback to subjects in an online method. Our study aims to investigate the effect of rtfMRI-NF on amygdala-based emotion self-regulation by analyzing resting-state functional connectivity. We conducted a task experiment to train subjects in self-regulating amygdala activity in response to emotional stimuli. Twenty subjects were divided into two groups. The up-regulate group (URG) viewed positive stimulus, while the down-regulate group (DRG) viewed negative stimulus. The rtfMRI-NF experiment paradigm consisted of three conditions. The URG’s percent amplitude fluctuation (PerAF) scores are significant, indicating that positive emotions may be a partial side effect, with increased activity in the left hemisphere. Resting-state functional connectivity was analyzed via a paired-sample t-test before and after neurofeedback training. Brain network properties and functional connectivity analysis showed a significant difference between the default mode network (DMN) and the brain region associated with the limbic system. These results reveal to some extent the mechanism of neurofeedback training to improve individuals’ emotional regulate regulation ability. Our study has shown that rtfMRI-neurofeedback training can effectively enhance the ability to voluntarily control brain responses. Furthermore, the results of the functional analysis have revealed distinct changes in the amygdala functional connectivity circuits following rtfMRI-neurofeedback training. These findings may suggest the potential clinical applications of rtfMRI-neurofeedback as a new therapy for emotionally related mental disorders.

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“…An echoplanar imaging (EPI) sequence was used to acquire functional MRI data. Head motion was lightly restricted using soft pads [23].…”

Section: Data Acquisitionmentioning

confidence: 99%

Altered amygdala functional connectivity after real-time functional MRI emotion self-regulation training

et al. 2023

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“…Chen et al (2012),Dong, Huang, and Du (2012),Fang et al (2013),Gnanadas, Sathishbabu, and Vijayakarthik (2017),Liang et al (2011),Liu et al (2010),Peng et al (2011),Qin et al (2017),Qing, Dong, Li, Zang, and Liu (2015),Qiu et al (2019),Shukla, Keehn, and Müller (2010),Song, Zhang, and Liu (2014),Wang, Song, Jiang, Zhang, and Yu (2011),Wu et al (2007),Wu et al (2009),Yang et al (2018),,Zang, Jiang, Lu, He, and Tian (2004),and Zeng, Pizarro, Nair, , Huang, Lin, and Biswal (2013),Dong, Guo, Zhang, Fu, and Shi (2010),Guo, Dong, Zhang, Zhang, and Yin (2010),Hu et al (2018), Khodaee, Hossein-Zadeh, and Ananloo (2015), Liang, P. (2014),,Meng, Zhang, Fan, and Li (2018),Turner et al (2012),Yan, Zhuo, Wang, and Wang (2011),,,Yu-Feng et al (2007),Zhang et al (2015),Zou et al (2008), andYu et al (2014) …”

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confidence: 99%

The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review

Rashid

Singh²,

Goyal

2020

Expert Systems

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Functional Magnetic Resonance Imaging (fMRI) is presently one of the most popular techniques for analysing the dynamic states in brain images using various kinds of algorithms. From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature. It takes much time for each researcher to know about the various approaches and algorithms which are in use for fMRI data. This paper provides a review in a systematic manner for the present literature of fMRI data that makes use of the machine and deep learning algorithms. The major goals of this review paper are to (a) identify machine learning and deep learning research trends for the implementation of fMRI; (b) identify usage of Machine Learning Algorithms and deep learning in fMRI, and (c) help new researchers based on fMRI to put their new findings appropriately in existing domain of fMRI research. The results of this systematic review identified various fMRI studies and classified them based on fMRI types, mental diseases, use of machine learning and deep learning algorithms. The authors have provided the studies with the best performance of machine learning and deep learning algorithms used in fMRI. The authors believe that this systematic review will help incoming researchers on fMRI in their future works.

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Resting-State Regional Homogeneity Analysis on Real-Time fMRI Emotion Self-Regulation Training

Yang

Qin

Wang

et al. 2018

2018 International Conference on Control, Automation and Information Sciences (ICCAIS)

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Altered resting-state regional homogeneity after real-time fMRI emotion self-regulation training

Cited by 3 publications

References 10 publications

Altered amygdala functional connectivity after real-time functional MRI emotion self-regulation training

Altered amygdala functional connectivity after real-time functional MRI emotion self-regulation training

The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review

Resting-State Regional Homogeneity Analysis on Real-Time fMRI Emotion Self-Regulation Training

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