Aims

Meditation training has been shown to improve attention and emotion regulation. However, the mechanisms responsible for these effects are largely unknown. In order to make further progress, a rigorous interdisciplinary approach that combines both empirical and theoretical experiments is required. We used data collected as part of the larger project (the Shamatha project), more info about it here . These data were collected as part of a longitudinal wait-list controlled study of intensive meditation training. Retreat participants practiced focused attention (FA) meditation techniques for three months during an initial retreat. Wait-list participants later undertook formally identical training during a second retreat.

Here, we used an interdisciplinary approach to understand mechanisms underlying intensive meditation training. In the first phase, we characterized the longitudinal changes in scalp-recorded EEG associated with intensive meditation training (~6 hrs/day for 3 months). Dense-array scalp-recorded electroencephalogram (EEG) data were collected during 6 min of mindfulness of breathing meditation at three assessment points during each retreat. Second-order blind source separation, along with a novel semi-automatic artifact removal tool (SMART), was used for data preprocessing. We observed replicable reductions in meditative state-related beta-band power bilaterally over anteriocentral and posterior scalp regions. In addition, individual alpha frequency (IAF) decreased across both retreats and in direct relation to the amount of meditative practice. These findings provide evidence for replicable longitudinal changes in brain oscillatory activity during meditation and increase our understanding of the cortical processes engaged during meditation that may support long-term improvements in cognition.

In the second phase, we used a computational modeling approach that can realistically simulate neurophysiological data while conforming to basic anatomical and physiological constraints can provide a unique opportunity to generate concrete and testable hypotheses about the mechanisms supporting complex cognitive tasks such as meditation. Here, we applied the mean-field computational modeling approach using the scalp-recorded electroencephalogram (EEG) collected at three assessment points from meditating participants during two separate 3-month-long shamatha meditation retreats. We modeled cortical, corticothalamic, and intrathalamic interactions to generate a simulation of EEG signals recorded across the scalp. We also present two novel extensions to the mean-field approach that allow for: (a) non-parametric analysis of changes in model parameter values across all channels and assessments; and (b) examination of variation in modeled thalamic reticular nucleus (TRN) connectivity over the retreat period. After successfully fitting whole-brain EEG data across three assessment points within each retreat, two model parameters were found to replicably change across both meditation retreats. First, after training, we observed an increased temporal delay between modeled cortical and thalamic cells. This increase provides a putative neural mechanism for a previously observed reduction in individual alpha frequency in these same participants. Second, we found decreased inhibitory connection strength between the TRN and secondary relay nuclei (SRN) of the modeled thalamus after training. This reduction in inhibitory strength was found to be associated with increased dynamical stability of the model. Altogether, this paper presents the first computational approach, taking core aspects of physiology and anatomy into account, to formally model brain processes associated with intensive meditation training. The observed changes in model parameters inform theoretical accounts of attention training through meditation, and may motivate future study on the use of meditation in a variety of clinical populations.

Highlights

• Brain mechanisms associated with shamatha meditation training are modeled.
• A novel approach to analyze longitudinal changes in model parameters is presented.
• A new method to model lateral connectivity in thalamic reticular nucleus is shown.
• Modeled intrathalamic gain & corticothalamic delay change with meditation training.

Presentations/Papers

1. Saggar, M., Zanesco, A. P., King, B. G., Bridwell, D. A., MacLean, K. A., Aichele, S. R., Jacobs, T.L., Wallace, B.A., Saron, C.D., Miikkulainen, R. (2015) Mean-field thalamocortical modeling of longitudinal EEG acquired during intensive meditation training. NeuroImage, 114, 88–104
2. Saggar, M., King, B. G., Zanesco, A. P., MacLean, K. A., Aichele, S. R., Jacobs, T. L., Bridwell, D.A., Shaver, P.R., Rosenberg, E.L., Sahdra, B.K., Ferrer, E., Tang, A.C., Mangun, G.R., Wallace, B.A., Miikkulainen, R., Saron, C.D. (2012) Intensive training induces longitudinal changes in meditation state-related EEG oscillatory activity. Frontiers in Human Neuroscience , 6, 256

Next steps

Drop us a line, if you are interested in exploring following or other related ideas

• In the future, we are interested in examining whether meditation training can enhance creative capacity
• We are also interested in examining the effects of meditation practice done alone vs. in group settings
• Lastly, we are also motivated to examine the effects of meditation training on brain dynamics at both short and long term scales.

Funding

M.S. received Francisco J. Varela Memorial Award from the Mind & Life Institute (2006) for this work.