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State of the art in EEG signal features of mindfulness-based treatments for chronic pain.

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Title: State of the art in EEG signal features of mindfulness-based treatments for chronic pain.
Authors: Duran, D. (AUTHOR), Arpaia, P. (AUTHOR), D'Errico, G. (AUTHOR), Grazzi, L. (AUTHOR), Lanteri, P. (AUTHOR), Moccaldi, N. (AUTHOR), Raggi, A. (AUTHOR), Robbio, R. (AUTHOR), Visani, E. (AUTHOR)
Source: Neurological Sciences. Aug2025, Vol. 46 Issue 8, p3537-3545. 9p.
Subjects: Evoked potentials (Electrophysiology), Mindfulness, Chronic pain, Search engines, Pain management
Abstract: A systematic review of electroencephalographic (EEG) correlates of Mindfulness- based treatment for chronic pain is presented. Recent technological advances have made EEG acquisition more accessible and also reliable. EEG monitoring before, during, and after treatment might support efficacy assessment and enable real- time adaptive intervention. The preliminary research extracted 131 papers from 6 scientific search engines. The application of the exclusion criteria led to the selection of 4 papers, indicating that the topic is still unexplored and further investigations are required. The collected papers exhibited great variability making challenging the comparison, nevertheless promising EEG correlates emerged. In particular, pain-related evoked potentials correlate with Mindfulness-Based treatment. EEG source analysis revealed the prevalent involvement of regions modulating emotional responses. In addition, higher baseline theta power was associated with greater improvement in depression when Mindfulness-based treatments are administered. This last result makes EEG also suitable for evaluating which patients can benefit most from mindfulness-based treatments. [ABSTRACT FROM AUTHOR]
Copyright of Neurological Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: <searchLink fieldCode="DE" term="%22Evoked+potentials+%28Electrophysiology%29%22">Evoked potentials (Electrophysiology)</searchLink><br /><searchLink fieldCode="DE" term="%22Mindfulness%22">Mindfulness</searchLink><br /><searchLink fieldCode="DE" term="%22Chronic+pain%22">Chronic pain</searchLink><br /><searchLink fieldCode="DE" term="%22Search+engines%22">Search engines</searchLink><br /><searchLink fieldCode="DE" term="%22Pain+management%22">Pain management</searchLink>
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  Data: A systematic review of electroencephalographic (EEG) correlates of Mindfulness- based treatment for chronic pain is presented. Recent technological advances have made EEG acquisition more accessible and also reliable. EEG monitoring before, during, and after treatment might support efficacy assessment and enable real- time adaptive intervention. The preliminary research extracted 131 papers from 6 scientific search engines. The application of the exclusion criteria led to the selection of 4 papers, indicating that the topic is still unexplored and further investigations are required. The collected papers exhibited great variability making challenging the comparison, nevertheless promising EEG correlates emerged. In particular, pain-related evoked potentials correlate with Mindfulness-Based treatment. EEG source analysis revealed the prevalent involvement of regions modulating emotional responses. In addition, higher baseline theta power was associated with greater improvement in depression when Mindfulness-based treatments are administered. This last result makes EEG also suitable for evaluating which patients can benefit most from mindfulness-based treatments. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neurological Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=186678218
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