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In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?

Rockholt, Mika LU orcid ; Kenefati, George ; Doan, Lisa V. ; Sage Chen, Zhe and Wang, Jing (2023) In Frontiers in Neuroscience 17. p.1-19
Abstract
Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and... (More)
Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives. (Less)
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author
; ; ; and
publishing date
type
Contribution to journal
publication status
published
in
Frontiers in Neuroscience
volume
17
article number
1186418
pages
1 - 19
publisher
Frontiers Media S. A.
external identifiers
  • pmid:37389362
  • scopus:85163856721
ISSN
1662-4548
DOI
10.3389/fnins.2023.1186418
language
English
LU publication?
no
id
682a4bbe-3d6b-4372-a7c6-756093646cec
date added to LUP
2023-10-03 18:00:22
date last changed
2023-10-04 07:54:09
@article{682a4bbe-3d6b-4372-a7c6-756093646cec,
  abstract     = {{Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.}},
  author       = {{Rockholt, Mika and Kenefati, George and Doan, Lisa V. and Sage Chen, Zhe and Wang, Jing}},
  issn         = {{1662-4548}},
  language     = {{eng}},
  month        = {{06}},
  pages        = {{1--19}},
  publisher    = {{Frontiers Media S. A.}},
  series       = {{Frontiers in Neuroscience}},
  title        = {{In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?}},
  url          = {{http://dx.doi.org/10.3389/fnins.2023.1186418}},
  doi          = {{10.3389/fnins.2023.1186418}},
  volume       = {{17}},
  year         = {{2023}},
}