Math Colloquia

Bayes meets data science to identify changes in brain activity during meditation from MEG measurements

by Erkki Somersalo (Case Western Reserve University, USA)



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. 


The seminar will be held in the Main Auditorium and it will also be streamed online via zoom at this link:

Please notice that, in accordance to GSSI regulations, your Green Pass will be checked at the entrance.

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