Poster No:
1336
Submission Type:
Abstract Submission
Authors:
Aika Osano1, Yuki Tsuji2, Sotaro Shimada1
Institutions:
1Department of Electronics and Bioinformatics, School of Science and Technology, Meiji University, Kawasaki, Kanagawa, 2Organization for the Strategic Coordination of Research and Intellectual Properties, Meiji Univ., Kawasaki, Kanagawa
First Author:
Aika Osano
Department of Electronics and Bioinformatics, School of Science and Technology, Meiji University
Kawasaki, Kanagawa
Co-Author(s):
Yuki Tsuji
Organization for the Strategic Coordination of Research and Intellectual Properties, Meiji Univ.
Kawasaki, Kanagawa
Sotaro Shimada
Department of Electronics and Bioinformatics, School of Science and Technology, Meiji University
Kawasaki, Kanagawa
Introduction:
Chronic stress increases risks of mental and physical health issues (Sapolsky, 2004). Mindfulness meditation and dietary management offer promising stress-reduction strategies (Kabat-Zinn, 1990). Comparative studies on the effects of these interventions on brain networks remain limited, but electroencephalography (EEG) is widely recognized as a powerful tool for analyzing stress-related neural activity (Rubinov, 2010). We conducted the training of the mindfulness meditation or the diet management program for four consecutive weeks and compared the stress reduction effects using EEG-based graph theoretical measures.
Methods:
Twenty healthy volunteers (10 females, aged 19-23) with no prior mindfulness meditation experience participated and randomly assigned to either the mindfulness meditation group (MG, n = 12) or the diet management group (control group: CG, n = 8). To examine the levels of mindfulness and depression, each participant filled out the two psychological scales (the Five Facet Mindfulness Questionnaire (FFMQ) and Beck Depression Inventory-II (BDI-II)) before training and every weekend during the program.
EEG data were recorded pre- and post-training under three conditions by using EEG (LiveAmp, Brain Products GmbH, Germany): resting state (5 min), acute stress task (mental arithmetic task (Yao, 2016): 10 min), and a second resting state (5 min). EEG signals were sampled at 500 Hz. Functional connectivity (phase synchronization) was analyzed using low-resolution brain electromagnetic tomography with thirty-six regions of interest defined as network nodes, and connectivity values served as edges for graph theoretical analysis. The graph theoretic properties including diameter, shortest path length, clustering coefficient, and efficiency measures, were calculated.
To assess the effect of training, a two-way analysis of variance (ANOVA) with aligned rank transform (ART) was performed with group (MG or CG) as the between-subjects factor and session (pre- or post-training) as the within-subject for the graph theoretic properties, and a two-way ANOVA with ART was performed with group (MG or CG) as the between-subjects factor and week (pre-training, week1, week2, week3, or week4) as the within-subject for the psychological scales.
Results:
For the psychological scales, we found significant interactions between group and week (FFMQ total score (F(4,76) = 3.06, p = 0.021) and FFMQ nonjudging (F(4,76) = 4.12, p = 0.0046)). Post-hoc analysis for these interactions revealed that the FFMQ total score was significantly higher in MG than CG in week 2 and week 3 (Wilcoxon signed-rank sum, week 2: p = 0.045, week 3: p = 0.024). For the FFMQ nonjudging score, the value at week 2 was significantly higher than at that pre-training in MG (Wilcoxon rank-sum, p = 0.0049). No other significant main effect and interaction were observed.
For the graph theoretic properties, two-way ANOVA revealed significant interactions between group and session in diameter (F(1,19) = 5.21, p = 0.034) and shortest path length (F(1,19) = 6.22, p = 0.022). Post-hoc analysis revealed that the diameter at post-training was significantly longer than that at pre-training in MG (Wilcoxon rank-sum, p = 0.019). For the shortest path length, no significant differences were observed between sessions (pre-training; p = 0.067, post-training; p = 0.778) or between groups (CG; p = 0.695, MG; p = 0.083), despite the presence of an interaction.
Conclusions:
Our results demonstrated that the mindfulness meditation program significantly improved FFMQ scores and increased the diameter of brain networks. In contrast, similar changes were not observed in the dietary management program. These findings suggest that the improvement in FFMQ scores induced by mindfulness meditation may facilitate a transition in brain network functional structure to be more organized (Boersma, 2011).
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Novel Imaging Acquisition Methods:
EEG 2
Keywords:
Electroencephaolography (EEG)
Other - mindfulness; meditation; stress; brain networks; graph theory; functional connectivity
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Not applicable
Please indicate which methods were used in your research:
EEG/ERP
Behavior
Which processing packages did you use for your study?
Other, Please list
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MATLAB(EEGLAB); LORETA; Python
Provide references using APA citation style.
1. Boersma, M. (2011). Network analysis of resting state EEG in the developing young brain: Structure comes with maturation. Human Brain Mapping, 32(3), 413–425.
2. Kabat-Zinn, J. (1990). Full catastrophe living: Using the wisdom of your body and mind to face stress, pain, and illness. New York, NY: Delacorte.
3. Rubinov, M. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(4), 1059–1069.
4. Sapolsky, R. M. (2004). Stress and Cognition. In M. S. Gazzaniga (Ed.), The cognitive neurosciences, 3,1031–1042. Boston Review.
5. Yao, Z. (2016). Stronger cortisol response to acute psychosocial stress is correlated with larger decrease in temporal sensitivity. PeerJ, 4, e2061.
No