Tuesday 25 February 2014

Gamma and self-reference

The concept of anatta or not-self is central to Buddhism. Meditation is a project designed to loosen attachment to the self.  A number of neuroimaging studies have focused on regions of the brain associated with self-reference (Brewer et al, 2011; Farb et al, 2007; Heatherington, 2006; Kelley et al, 2002; 2009; Qi & Northoff, 2011). Most of these studies used fMRI, but a study by Berkowitz-Ohana and associates (Berkovich-Ohana et al, 2011) used EEG to investigate the relationship of mindfulness meditation to activity related to self-reference.  The authors focused specifically on gamma power over frontal and midline areas that reflect default mode network (DMN) activity.

This study is interesting for a number of reasons. First, the authors differentiated between two types of self-reference (narrative self-reference and experiential self-reference) and the areas of the brain in which they occur. Second, they suggested that gamma power has an entirely different significance for self- reference in different parts of the brain.  Third, because they studied mindfulness meditation practitioners at three different levels of experience, they were able to illuminate the point at which their practices loosened their attachment to self.

The EEG changes associated with the transition from a resting state to a simple time production task were lower gamma power over frontal and midline regions.  This effect was similar in practitioners with three levels of experience and controls who had not previously meditated.  When the three groups of practitioners were compared to matched controls during a resting state, the practitioners exhibited lower frontal gamma activity, mainly right lateralized, thought to be related to narrative self-reference and default mode activity, and higher gamma in the right parietal-occipital region, possibly related to a more experiential focus with increased awareness of internal and external stimuli.  This suggests the practitioners had made a transition from one form of self-reference to another.  Curiously, these changes were found irrespective of expertise level suggesting that these changes occurred in the earlier stages of practice, ranging from 180 to 1430 hours of practice.
This study also highlights the risks of associating any given EEG band with meditation.  For years, alpha was the go-to band for meditation states.  More recently, mid-line frontal theta has enjoyed a hay day.  Studies of meditators doing compassion meditation (Lutz et al, 2004) and vipassana meditation (Cahn et al, 2010) indicated increased gamma activation.  But meditation is clearly complex and different bands at different locations have different significance.

Berkovich-Ohana, A., Glicksohn, J., & Goldstein, A. (2012). Mindfulness-induced changes in gamma band activity–implications for the default mode network, self-reference and attention. Clinical Neurophysiology, 123(4), 700-710.
Brewer, J. A., Worhunsky, P. D., Gray, J. R., Tang, Y. Y., Weber, J., & Kober, H. (2011). Meditation experience is associated with differences in default mode network activity and connectivity. Proceedings of the National Academy of Sciences, 108(50), 20254-20259.
Cahn, B. R., Delorme, A., & Polich, J. (2010). Occipital gamma activation during Vipassana meditation. Cognitive Processing, 11(1), 39-56.
Farb, N. A., Segal, Z. V., Mayberg, H., Bean, J., McKeon, D., Fatima, Z., & Anderson, A. K. (2007). Attending to the present: mindfulness meditation reveals distinct neural modes of self-reference. Social cognitive and affective neuroscience, 2(4), 313-322.
Heatherton, T. F., Wyland, C. L., Macrae, C. N., Demos, K. E., Denny, B. T., & Kelley, W. M. (2006). Medial prefrontal activity differentiates self from close others. Social Cognitive and Affective Neuroscience, 1(1), 18-25.
Kelley, W. M., Macrae, C. N., Wyland, C. L., Caglar, S., Inati, S., & Heatherton, T. F. (2002). Finding the self? An event-related fMRI study. Journal of cognitive neuroscience, 14(5), 785-794.
Lutz, A., Greischar, L. L., Rawlings, N. B., Ricard, M., & Davidson, R. J. (2004). Long-term meditators self-induce high-amplitude gamma synchrony during mental practice. Proceedings of the National Academy of Sciences of the United States of America, 101(46), 16369-16373.
Trapnell, P. D., & Campbell, J. D. (1999). Private self-consciousness and the five-factor model of personality: distinguishing rumination from reflection. Journal of personality and social psychology, 76(2), 284.
Qin, P., & Northoff, G. (2011). How is our self related to midline regions and the default-mode network?. NeuroImage, 57, 1221-1233.

Monday 17 February 2014

Meditation and neurofeedback

A recent article echoes many of the ideas that I have been discussion on this blog:  Brandmeyer, T., & Delorme, A. (2013). Meditation and neurofeedback. Frontiers in psychology4.  It can be downloaded from this site:  

Brandmeyer and Delorme focus on the possibility that "machine assisted programs such as neurofeedback may help individuals develop their meditation practice more rapidly."  They point out that many of the disorders that neurofeedback has been used to treat also benefit from meditation training.  Neurofeedback and meditation are both methods of training mental states.  Both techniques facilitate and improve concentration and emotional regulation.  The focus of training in most cognitive enhancement neurofeedback protocols share similarities with the EEG frequency bands that show the most significant change during the early stages of meditation practice.

They assume that reliable and reproducible EEG signatures associated with specific meditation practices can be identified and that it might be possible to train users to make their EEG brainwaves similar to the brainwaves of expert practitioners.  While these assumption appear plausible, the diversity of meditation techniques and the complexity of brain activity during meditation may prove challenging.  If these challenges could be overcome, the objective would not be to replace meditation practice but provide feedback to practitioners on how well they are doing.

Another type of neurofeedback program could help detect mind-wandering, setting off an alarm to users when their minds start to wander.  The authors suggest that the feedback should be subtle so as not to disturb the subjects' meditation.  Once again this would not replace meditation practice, but rather "facilitate and support it in its early to middle states of practice."

They conclude with suggestions that neurofeedback could be especially effective for beginners who are struggling with meditation practice.  They point to smartphones and apps and social networking as opening up possibilities for the widespread adoption of neurofeedback as an aid to meditation.

Many of the points made by the authors have been made in previous posts on this blog.  The biggest challenge, as they acknowledge, is identifying EEG signatures for meditation.  In my own experience this is highly problematic.  For example, slow alpha or high end theta is seen in experienced practitioners, but these slow waves are also associated with mind-wandering and drowsiness.  There is some suggestion that combinations of slow waves and very fast waves in the gamma range may characterize some forms of meditation, but due to problems with artifact and the limited capabilities of low cost equipment, it may not be possible to train these combinations.  There is a risk that, in the absence of very sophisticated and in-depth research on EEG signatures, training using this approach may do little good or even some harm.

The BrainBot app promises to provide warnings of mind-wandering.  The developers seem to be on the right track, but how effective the app is remains to be seen.  As I have described in previous posts, I have experimented with a similar approach.  However, my experiences don't really count as research.

Brandmeyer and Delorme have identified the issues in their article and opened up to a wider audience what promises to be a fruitful discussion.

Monday 10 February 2014