  Meditation practices have been claimed to have a positive effect on the
regulation of mood and emotion for quite some time by practitioners, and in
recent times there has been a sustained effort to provide a more precise
description of the changes induced by meditation on human brain. Longitudinal
studies have reported morphological changes in cortical thickness and volume in
selected brain regions due to meditation practice, which is interpreted as
evidence for effectiveness of it beyond the subjective self reporting. Evidence
based on real time monitoring of meditating brain by functional imaging
modalities such as MEG or EEG remains a challenge. In this article we consider
MEG data collected during meditation sessions of experienced Buddhist monks
practicing focused attention (Samatha) and open monitoring (Vipassana)
meditation, contrasted by resting state with eyes closed. The MEG data is first
mapped to time series of brain activity averaged over brain regions
corresponding to a standard Destrieux brain atlas, and further by bootstrapping
and spectral analysis to data matrices representing a random sample of power
spectral densities over bandwidths corresponding to $\alpha$, $\beta$,
$\gamma$, and $\theta$ bands in the spectral range. We demonstrate using linear
discriminant analysis (LDA) that the samples corresponding to different
meditative or resting states contain enough fingerprints of the brain state to
allow a separation between different states, and we identify the brain regions
that appear to contribute to the separation. Our findings suggest that
cingulate cortex, insular cortex and some of the internal structures, most
notably accumbens, caudate and putamen nuclei, thalamus and amygdalae stand out
as separating regions, which seems to correlate well with earlier findings
based on longitudinal studies.
