Controlling the Temporal Structure of Brain Oscillations by Focused Attention Meditation

Controlling the Temporal Structure of Brain Oscillations by Focused Attention Meditation Our focus of attention naturally fluctuates between different sources of information even when we desire to focus on a single object. Focused attention (FA) meditation is associated with greater control over this process, yet the neuronal mechanisms underlying this ability are not entirely understood. Here, we hypothesize that the capacity of attention to transiently focus and swiftly change relates to the critical dynamics emerging when neuronal systems balance at a point of instability between order and disorder. In FA meditation, however, the ability to stay focused is trained, which may be associated with a more homogeneous brain state. To test this hypothesis, we applied analytical tools from criticality theory to EEG in meditation practitioners and meditation‐naïve participants from two independent labs. We show that in practitioners—but not in controls—FA meditation strongly suppressed long‐range temporal correlations (LRTC) of neuronal oscillations relative to eyes‐closed rest with remarkable consistency across frequency bands and scalp locations. The ability to reduce LRTC during meditation increased after one year of additional training and was associated with the subjective experience of fully engaging one's attentional resources, also known as absorption. Sustained practice also affected normal waking brain dynamics as reflected in increased LRTC during an eyes‐closed rest state, indicating that brain dynamics are altered beyond the meditative state. Taken together, our findings suggest that the framework of critical brain dynamics is promising for understanding neuronal mechanisms of meditative states and, specifically, we have identified a clear electrophysiological correlate of the FA meditation state. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Human Brain Mapping Wiley

Controlling the Temporal Structure of Brain Oscillations by Focused Attention Meditation

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018 Wiley Periodicals, Inc.
ISSN
1065-9471
eISSN
1097-0193
D.O.I.
10.1002/hbm.23971
Publisher site
See Article on Publisher Site

Abstract

Our focus of attention naturally fluctuates between different sources of information even when we desire to focus on a single object. Focused attention (FA) meditation is associated with greater control over this process, yet the neuronal mechanisms underlying this ability are not entirely understood. Here, we hypothesize that the capacity of attention to transiently focus and swiftly change relates to the critical dynamics emerging when neuronal systems balance at a point of instability between order and disorder. In FA meditation, however, the ability to stay focused is trained, which may be associated with a more homogeneous brain state. To test this hypothesis, we applied analytical tools from criticality theory to EEG in meditation practitioners and meditation‐naïve participants from two independent labs. We show that in practitioners—but not in controls—FA meditation strongly suppressed long‐range temporal correlations (LRTC) of neuronal oscillations relative to eyes‐closed rest with remarkable consistency across frequency bands and scalp locations. The ability to reduce LRTC during meditation increased after one year of additional training and was associated with the subjective experience of fully engaging one's attentional resources, also known as absorption. Sustained practice also affected normal waking brain dynamics as reflected in increased LRTC during an eyes‐closed rest state, indicating that brain dynamics are altered beyond the meditative state. Taken together, our findings suggest that the framework of critical brain dynamics is promising for understanding neuronal mechanisms of meditative states and, specifically, we have identified a clear electrophysiological correlate of the FA meditation state.

Journal

Human Brain MappingWiley

Published: Jan 1, 2018

Keywords: ; ; ;

References

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