Bayesian inversion and data science methods to identify changes in brain activity during meditation from MEG measurements

Abstract: Meditation as a potential alternative for pharmaceutical intervention to mitigate conditions such as chronic pain or clinical depression continues to obtain significant attention. One of the problems is that often the positive effects of meditation that have been reported are anecdotal or are based on self reporting. To quantify the effects of meditation, it is therefore important to develop methods based on medical imaging to identify brain regions that are involved in the meditation practice. In this talk, we review some recent results about this topic, addressed by using magnetoencephalography (MEG) to map brain activity during meditation. One of the difficulties here is that the data are less sensitive to activity taking place in the deep brain regions, including the limbic system that is believed to play an important role in meditation. The MEG inverse problem is addressed by using novel Bayesian methods combined with advanced numerical techniques, applied on data from professional Buddhist meditators. The reconstructed activity is then analyzed using data science techniques to distill the information about the activation changes during meditation.

Mathematics

Audience: researchers in the topic

International Zoom Inverse Problems Seminar, UC Irvine