Many studies demonstrate that meditation improves executive control, which includes many cognitive functions such as mental set shifting, information updating and monitoring and the inhibition of pre-potent response. However it is not clear why and how meditation is related to the enhancement of such cognitive functions. As such, the understanding of precise mechanisms behind meditation, or even the duration of the effect of meditation, are both lacking. The aims of this study include: (1) to use stop-signal task to uncover whether meditation facilitates inhibitory control; this will be the first attempt in the field. (2) To compare meditators’ improvement of cognitive functions from stop-signal task, attention network test, visual short-term memory (VSTM) and working memory task. (3) To record electroencephalography (EEG), (4) electrocardiography (EKG) and (5) magnetic resonance imaging (MRI) during meditation and while performing cognitive tasks, and (6) to analyze these data via multiscale entropy as a biological index of complexity. (7) Subjects will also complete surveys reflecting their status of anxiety and quality of meditation. We will integrate comprehensive tools to reveal the mechanisms of meditation.The innovation and importance of this study include: (1) There is no study using stop-signal task to examine meditators’ performance of inhibitory control. (2) No study has used comprehensive tools and designs to reveal the neural mechanisms of meditation. We will integrate fMRI, EEG and EKG to discuss biological and cognitive functions during meditation and cognitive tasks. (3) This is the first meditation study analyzed with multiscale entropy, which is a new and an insightful index to reveal how meditation would modulate the neural dynamics of the cognitive functions and the complexity. Results and applications of this study would be highly beneficial to patients that are suffering from major depression, addiction, ADHD and Autism.
|Effective start/end date||1/01/15 → 31/12/15|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
- inhibitory control
- stop-signal task
- Attention Network Test
- visual working memory
- Electroencephalography (EEG)
- Electrocardiography (EKG)
- functional magnetic resonance imaging (fMRI)
- heart rate variability (HRV)
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