Silence Practice Modulates the Resting State Functional Connectivity of Language Network with Default Mode and Dorsal Attention Networks in Long-Term Meditators
MINDFULNESS(2024)
Boston University | University of California | Massachusetts General Hospital
Abstract
The practice of silence is integral to some meditation traditions. Research is lacking on how silence practice affects brain connectivity. We hypothesized that silent, retreat-based meditation practice would reduce the connection between the language network from core cognitive networks such as the dorsal attention network (DAN) and default mode network (DMN). In a retrospective study, we analyzed resting state functional MRI (rsfMRI) data in 13 long-term Vipassana meditators (LTM) ( 11,000 average hours of lifetime meditation experience) and healthy controls (n = 34) with no experience in meditation. We also compared our results with a large-scale dataset—Human Connectome Project (n = 169) (HCP). We compared the within and across functional connectivity among the three networks and correlated meditation experience and days spent in silence with the network connectivities. We found that the meditators have decoupled functional connectivity strengths (F(2,204) = 10.27, p < 0.01) between the DMN and language network (M = − 0.05, SD = 0.19) as compared to HCP controls (M = 0.14, SD = 0.14). The DAN had a negatively correlated connectivity strength with the language network in meditators (r = − 0.20) as compared to both control groups (r = 0.02) and a strong inverse relation (r = − 0.54) was found between DAN-language connectivity and the number of days spent in silent retreat. Our study finds a potential role of silence training in changing the connectivities of three cognitive networks, DMN, DAN, and language network, resulting in reduced thoughts during meditation and a deeper experience of meditation. This study is not preregistered.
MoreTranslated text
Key words
Meditators,Silence,fMRI,Resting state functional connectivity,Default mode network,Language network,Dorsal attention network
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2000
被引用6716 | 浏览
2012
被引用91 | 浏览
2017
被引用1863 | 浏览
2016
被引用1567 | 浏览
2019
被引用10 | 浏览
2019
被引用24 | 浏览
2019
被引用46 | 浏览
2021
被引用14 | 浏览
2022
被引用75 | 浏览
2022
被引用23 | 浏览
2022
被引用59 | 浏览
2018
被引用107 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper