Unique Brain Activity Registers Internal Attentional States During Meditation
By John M. de Castro, Ph.D.
“Your brain is actually shaped by your thoughts and your behaviors. . . meditation can help boost attention and keep the brain sharp. . . mindful breath awareness may improve attention and help curb impulsive behavior” – Grace Bullock
There has accumulated a large amount of research demonstrating that mindfulness has significant benefits for psychological, physical, and spiritual wellbeing. It even improves high level thinking known as executive function and emotion regulation and compassion. One of the primary effects of mindfulness training is an improvement in the ability to pay attention to the task at hand and ignore interfering stimuli. This is an important consequence of mindfulness training and produces improvements in thinking, reasoning, and creativity. The importance of heightened attentional ability to the individual’s ability to navigate the demands of complex modern life cannot be overstated. It helps in school, at work, in relationships, or simply driving a car. As important as attention is, it’s surprising that little is known about the mechanisms by which mindfulness improves attention.
There is evidence that mindfulness training improves attention by altering the brain. It appears That mindfulness training increases the size, connectivity, and activity of areas of the brain that are involved in paying attention. But there are various states of attention including meditation-related states: breath attention, mind wandering, and self-referential processing, and control states e.g. attention to feet and listening to ambient sounds. It is not known what changes occur in the brain during these five different modes and if they can be used to better discriminate the nature of attentional changes during meditation.
In today’s Research News article “Focus on the Breath: Brain Decoding Reveals Internal States of Attention During Meditation.” (See summary below or view the full text of the study at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7483757/ ) Weng and colleagues recruited healthy adult meditators (at least 5 years of experience) and non-meditators. They were given a series of tasks while having their brains scanned with functional Magnetic Resonance Imaging (fMRI). They were asked for 16-50 seconds to 1) pay attention to their breath, 2) let the mind wander, 3) think about past events, 4) pay attention to their feet, and 5) pay attention to ambient sounds. The 5 conditions were repeated multiple times in random orders. They then performed a 10-minute breath following meditation followed by a repeat of the premeditation tasks. Artificial intelligence was employed to determine unique neural activity associated with each of the 5 mental states for each participant.
They found unique individual brain activity patterns for each participant and could reliably distinguish different individual patterns for the 5 mental states. They then used these individualized patterns in an attempt to determine mental state during the breath focused meditation. They found that the individualized patterns identified for following the breath were present a greater percentage of time than the mind wandering or self-referential states when engaging in breath focused meditation. Further they found that the greater the amount of time for each participant in the breath following brain pattern the larger the rating by the participant of their engagement with breath following.
This was a proof of concept study. But it successfully demonstrated that unique individual patterns of brain activity can be identified for 5 mental states. These could be reliably differentiated. It also showed that these patterns could be used to identify breath following during breath following meditation. This suggests that this method may be used to identify mental states during ongoing meditation sessions. This could be a powerful research tool for future investigations of the mental states occurring during meditation.
So, unique brain activity registers internal attentional states during meditation.
“Mindfulness training can help change patterns of brain activity because the synapses within these attentional networks can strengthen or weaken with use. So, join a mindful meditation class or download a mindful meditation app and train your brain to get out of the default mode network and be present!” – Mclean Bolton
CMCS – Center for Mindfulness and Contemplative Studies
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Weng, H. Y., Lewis-Peacock, J. A., Hecht, F. M., Uncapher, M. R., Ziegler, D. A., Farb, N., Goldman, V., Skinner, S., Duncan, L. G., Chao, M. T., & Gazzaley, A. (2020). Focus on the Breath: Brain Decoding Reveals Internal States of Attention During Meditation. Frontiers in Human Neuroscience, 14, 336. https://doi.org/10.3389/fnhum.2020.00336
Meditation practices are often used to cultivate interoception or internally-oriented attention to bodily sensations, which may improve health via cognitive and emotional regulation of bodily signals. However, it remains unclear how meditation impacts internal attention (IA) states due to lack of measurement tools that can objectively assess mental states during meditation practice itself, and produce time estimates of internal focus at individual or group levels. To address these measurement gaps, we tested the feasibility of applying multi-voxel pattern analysis (MVPA) to single-subject fMRI data to: (1) learn and recognize internal attentional states relevant for meditation during a directed IA task; and (2) decode or estimate the presence of those IA states during an independent meditation session. Within a mixed sample of experienced meditators and novice controls (N = 16), we first used MVPA to develop single-subject brain classifiers for five modes of attention during an IA task in which subjects were specifically instructed to engage in one of five states [i.e., meditation-related states: breath attention, mind wandering (MW), and self-referential processing, and control states: attention to feet and sounds]. Using standard cross-validation procedures, MVPA classifiers were trained in five of six IA blocks for each subject, and predictive accuracy was tested on the independent sixth block (iterated until all volumes were tested, N = 2,160). Across participants, all five IA states were significantly recognized well above chance (>41% vs. 20% chance). At the individual level, IA states were recognized in most participants (87.5%), suggesting that recognition of IA neural patterns may be generalizable for most participants, particularly experienced meditators. Next, for those who showed accurate IA neural patterns, the originally trained classifiers were applied to a separate meditation run (10-min) to make an inference about the percentage time engaged in each IA state (breath attention, MW, or self-referential processing). Preliminary group-level analyses demonstrated that during meditation practice, participants spent more time attending to breath compared to MW or self-referential processing. This paradigm established the feasibility of using MVPA classifiers to objectively assess mental states during meditation at the participant level, which holds promise for improved measurement of internal attention states cultivated by meditation.