Meditation skills, or paying attention to internal mental states, are thought to improve people's health. This study is developing a new brain measure of meditation skills, called the EMBODY Task, using functional magnetic resonance imaging (fMRI). The investigators are testing whether pattern recognition methods can be applied to fMRI data to identify mental states during meditation, including attention to the body and to thoughts. This task is being developed in meditation practitioners and non-meditators. The goal is to understand what people are paying attention to during meditation using brain data. The investigators hypothesize that pattern recognition technology will be able to identify different mental states that occur during meditation.
The investigators are developing a new functional magnetic resonance imaging (fMRI) task (the EMBODY Task) to measure mental states during meditation using pattern recognition or machine learning technology. This task is being piloted and validated in 20 meditators and 20 control participants, in two waves of pilot testing. Meditators will have practiced meditation for at least the 5 years, at least 90 minutes weekly. Control participants will have little to no meditation experience and will be age- and gender-matched to each meditator. All participants will be MRI-compatible, healthy with no health conditions that affect breathing, have no current psychiatric disorder, and not be taking psychotropic medications. In the EMBODY Task, participants will be instructed to pay attention to areas of the body, their thoughts, sounds in the scanner, and to stop paying attention, in short intervals (16-45s). They will also meditate on their breath for 10 minutes. The investigators will determine whether pattern recognition technology can distinguish 5 mental states, and whether these brain patterns can be used to identify mental states during meditation. The investigators hypothesize that all 5 mental states will be distinguished by pattern recognition in the meditators, and potentially in the controls. Investigators also hypothesize that meditators should pay attention to their breath longer during meditation compared to controls.
Study Type
OBSERVATIONAL
Enrollment
16
Osher Center for Integrative Medicine
San Francisco, California, United States
Classification accuracy of brain patterns from EMBODY Task
The EMBODY Task is a new brain-based measure using functional magnetic resonance imaging (fMRI) to measure meditation skills. The primary outcome measure of the EMBODY Task is whether brain patterns are recognized by pattern classification algorithms for the 5 conditions in the study (attention to breath, body, mind wandering, thoughts, and sounds) above chance levels (20% for 5 conditions, using a one-sample t-test for each condition in the entire sample). Classification accuracy is a standard outcome measure in studies that use brain pattern classification. This will demonstrate that brain patterns associated with internal attention are indeed differentiable by pattern classification methods. These brain patterns will then be used to identify the focus of attention during breath meditation.
Time frame: Outcome measure will be assessed once at the baseline fMRI scan to develop the pilot fMRI task.
Percentage time paying attention to breath during meditation
Using EMBODY Task metrics, investigators will calculate how much time people pay attention to their breath during meditation
Time frame: Outcome measure will be assessed once at the baseline fMRI scan to develop the pilot fMRI task.
Percentage time spent mind wandering during meditation
Using EMBODY Task metrics, investigators will calculate how much time people were mind wandering during meditation
Time frame: Outcome measure will be assessed once at the baseline fMRI scan to develop the pilot fMRI task.
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