Resting-state fMRI Study of Neurofeedback Emotion Regulation in DLPFC
Poster No:
79
Submission Type:
Abstract Submission
Authors:
Hui Gao1, Chi Zhang1, Li Tong1, Zhonglin Li2, Tianyuan Liu1, Bao Li1, Panpan Chen1, Kai Yang1
Institutions:
1PLA Strategic Support Force Information Engineering University, Zhengzhou, China, 2Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
First Author:
Co-Author(s):
Zhonglin Li
Henan Provincial People's Hospital, People's Hospital of Zhengzhou University
Zhengzhou, China
Henan Provincial People's Hospital, People's Hospital of Zhengzhou University
Zhengzhou, China
Introduction:
Real-time functional MRI neurofeedback (rtfMRI-NF) is a safe non-pharmacological intervention (Weiskopf, N. 2012). This technique has been successfully applied to enhance the ability for autonomous emotion regulation. Although the dorsolateral prefrontal cortex (DLPFC) is associated with both emotion and cognition. the mechanism of emotional regulation through stimulating DLPFC remains incompletely understood. Our purpose is to investigate alterations in resting-state function before and after DLPFC regulation through neurofeedback training, so as to get a better understanding of the brain's mechanisms of emotional regulation and cognitive control, and provide new methods for future neurofeedback therapy.
Methods:
All fMRI data were collected on a 3T Siemens Prisma of Henan Key Laboratory of Imaging and Intelligent Processing using the 64-channel head coil. Real time online data processing was performed on the OpenNFT system (Koush, Y., et al. 2017). We designed a rt-fMRI neurofeedback experiment based on regulation of left DLPFC activity in Healthy Human. Twenty-seven healthy young people participated in the experiment. The subjects completed two visits with an interval of 5-10 days (Figure 1A). The rtfMRI-NF experiment paradigm consisted of two sessions. First NF session included a resting state run (Rest1, 6 min 40 s) before training, a pre-training run (6 min 2 s) during which the person could adapt to NF training and then the three NF runs (each 6min 2s). Second NF session included the three NF runs (each 6 min 2 s), a transfer run (6 min 2 s) to observe whether the patient had mastered the regulation strategy, and then a resting state run (Rest2, 6 min 40 s) after training. Each NF run consisted of 12s for experiment preparation and alternating 18 s negative emotion stimulus block, 40 s feedback block, 12 s rest with cycle for 5 times. At stimulus blocks, subjects saw three negative emotion pictures (Lu Bai, et al. 2005), each lasting for 6 seconds. At rest blocks, subjects were asked to calm the mind and during feedback blocks, subjects were instructed to regulate feedback score in the screen as high as possible (Figure 1B). feedback blocks were designed to provide feedback of left DLPFC activity in real time and instructed subjects to voluntarily control the feedback signal by recalling a positive autobiographical memory. The resting-state fMRI data were performed using SPM12 (www.fil.ion.ucl.ac.uk/spm) and DPABI (Chaogan Yan, et al. 2010). ReHo and ALFF (YuFeng Zang, et al. 2004, 2007) were analyzed using a paired-sample t-test before and after NF training.
·(A) rt-fMRI NF experimental procedure of two visits and procedure of two NF sessions. (B) Design of NF training runs.
Results:
In this paper, we explored alterations in resting-state before and after NF training in subjects (Figure 2A, 2B). We observed an increase in the ReHo map score following the training in the right middle temporal gyrus, and a decrease in the ReHo map score in the left lingual, left cuneus, bilateral calcarine, and insula. Additionally, we identified significantly increased ALFF in the left inferior occipital gyrus and right middle temporal gyrus, and reduced ALFF in the left Rolandic operculum. Furthermore, we found a significant reduction in rumination, state anxiety, and Beck Depression Scale scores following neurofeedback training (Figure 2C).
·(A) Regions with altered ALFF score after rtfMRI-NF training (B) Regions with altered ReHo score after rtfMRI-NF training (GRF corrected voxel p<0.005). (C) Significantly different scale scores.
Conclusions:
Through rt-fMRI NF training based on the left DLPFC, alterations in resting-state ReHo and ALFF in emotional and cognitive brain regions may be related to enhanced cognitive function and improved emotional regulation abilities. Furthermore, rt-fMRI NF has demonstrated effectiveness in modulating negative effects and is anticipated to serve as a potential adjunct for clinical treatment in the future.
Brain Stimulation:
Non-Invasive Stimulation Methods Other 1
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Emotions
FUNCTIONAL MRI
NORMAL HUMAN
1|2Indicates the priority used for review
Provide references using author date format
2. Koush, Y., et al. (2017), 'OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis', Neuroimage, vol. 156, p. 489-503.
3. Lu Bai, et al. (2005), 'Development of the Chinese Affective Picture System: A Pilot Study in 46 Chinese College Students', Chinese Mental Health Journal, 19(11), p. 719-722.
4. Chaogan Yan, et al. (2010), 'DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI', Frontiers in Systems Neuroscience, 4(13), p. 13.
5. YuFeng Zang, et al. (2004), 'Regional Homogeneity Approach to fMRI Data Analysis', NeuroImage, vol. 22, p. 394-400.
6. YuFeng Zang, et al. (2007), 'Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI', Brain Dev. vol. 29, p. 83–91.