Practitioners’ sleep structure parameters.

<p>Times (average across participants ± standard deviation) are expressed in minutes.</p

Meditation-naïve individuals recorded at the same time points as practitioners to control for aspecific effect of adaptation to the lab environment did not show changes in scalp EEG between sessions.

<p>Average NREM sleep scalp topographies across cycles in control participants at the time points corresponding to baseline (B) and meditation sessions (M, pooled) for practitioners. The naïve individuals did not undergo day of practice. The first 3 sleep cycles are indexed as 1, 2, and 3. For each cycle, topographical maps of t-values (T) are plotted in the same [-5 5] scale across frequency bins and cycles. SnPM statistics confirmed the absence of changes between time points.</p

The meditation related increase depicted in Fig 1 survived correction for the multiple comparisons ensuing from testing 185 electrodes, 39 frequency bins, and 3 sleep cycles.

<p>The significant cluster (N = 2161, p = 0.046) is shown in pink over white topographical maps (Statistical non Parametric Mapping, SnPM). E indicates the number of significant electrodes for each sleep cycle and frequency bin. S stands for statistical map. The first 3 sleep cycles are indexed as 1, 2, and 3. Traditional frequency bands [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148961#pone.0148961.ref045" target="_blank">45</a>] are reported on the left.</p

Univariate, repeated-measures ANOVAs showed that sleep structure parameters did not change across conditions.

<p>Greenhouse-Geisser correction has been applied to p values and degrees of freedom.</p

Lifetime open monitoring meditation experience correlated with the topography-specific changes in low-frequency activity following intense daylong meditation practice.

<p>The significant cluster (N = 885, p = 0.048) is shown in pink over white topographical maps (Statistical non Parametric Mapping, SnPM). E indicates the number of significant electrodes for each sleep cycle and frequency bin. S stands for statistical map. The first 3 sleep cycles are indexed as 1, 2, and 3. Traditional frequency bands [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148961#pone.0148961.ref045" target="_blank">45</a>] are reported on the left.</p

The between groups comparison of the changes relative to baseline shows a pattern similar to the meditation-related changes depicted in Fig 1.

<p>For each of the first 3 sleep cycles (indexed as 1, 2, and 3), topographical maps of t-values (T) deriving from the comparison between baseline subtracted meditation sessions (pooled) data in practitioners and corresponding time points in meditation-naïve individuals are plotted in the same [-5 5] scale across frequency bins and cycles. SnPM statistics showed a cluster largely overlapping the one in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148961#pone.0148961.g002" target="_blank">Fig 2</a> at a trend level (N = 1929, p = 0.057).</p

8-h of intense mindfulness and compassion meditation induced an increase in prefrontal and left parietal low-frequency activity (1–12 Hz) in long-term practitioners that extended to high frequencies (25–40Hz) at the end of the sleep night.

<p>Average NREM sleep scalp topographies across cycles at baseline (B) and following a daylong meditation session (M) in mindfulness and compassion practice styles (pooled). The first 3 sleep cycles are indexed as 1, 2, and 3. For each cycle, topographical maps of t-values (T) are plotted in the same [-5 5] scale across frequency bins and cycles.</p

Experienced Mindfulness Meditators Exhibit Higher Parietal-Occipital EEG Gamma Activity during NREM Sleep

<div><p>Over the past several years meditation practice has gained increasing attention as a non-pharmacological intervention to provide health related benefits, from promoting general wellness to alleviating the symptoms of a variety of medical conditions. However, the effects of meditation training on brain activity still need to be fully characterized. Sleep provides a unique approach to explore the meditation-related plastic changes in brain function. In this study we performed sleep high-density electroencephalographic (hdEEG) recordings in long-term meditators (LTM) of Buddhist meditation practices (approximately 8700 mean hours of life practice) and meditation naive individuals. We found that LTM had increased parietal-occipital EEG gamma power during NREM sleep. This increase was specific for the gamma range (25–40 Hz), was not related to the level of spontaneous arousal during NREM and was positively correlated with the length of lifetime daily meditation practice. Altogether, these findings indicate that meditation practice produces measurable changes in spontaneous brain activity, and suggest that EEG gamma activity during sleep represents a sensitive measure of the long-lasting, plastic effects of meditative training on brain function.</p></div

Information about long-term meditators' (LTM) practice history: In our convention, Focused Attention meditation encompasses concentrative practices such as Theravada Jhana, or breath awareness meditation.

<p>Open Monitoring meditation encompasses practices such as Vipassana meditation.</p

NREM Gamma increase in LTM compared to meditation naives had a large effect size (ES = 0.8, Panel A), and was significantly correlated with the length of meditation daily practice (rho = 0.475, p = 0.017, Panel B).

<p>NREM Gamma increase in LTM compared to meditation naives had a large effect size (ES = 0.8, Panel A), and was significantly correlated with the length of meditation daily practice (rho = 0.475, p = 0.017, Panel B).</p