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Construction And Analysis Of The Time-Evolving Pain-Related Brain Network Using Literature Mining

Authors Oh J, Bae H, Kim CE

Received 24 May 2019

Accepted for publication 17 September 2019

Published 16 October 2019 Volume 2019:12 Pages 2891—2903

DOI https://doi.org/10.2147/JPR.S217036

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Melinda Thomas

Peer reviewer comments 3

Editor who approved publication: Dr E Alfonso Romero-Sandoval


Jihong Oh, Hyojin Bae, Chang-Eop Kim

Department of Physiology, College of Korean Medicine, Gachon University, Seongnam 13120, Republic of Korea

Correspondence: Chang-Eop Kim
Department of Physiology, College of Korean Medicine, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Repubic of Korea
Tel/fax +82-31-750-5416
Email eopchang@gachon.ac.kr

Purpose: We aimed to quantitatively investigate how the neuroscience field developed over time in terms of its concept on how pain is represented in the brain and compare the research trends of pain with those of mental disorders through literature mining of accumulated published articles.
Methods: The abstracts and publication years of 137,525 pain-related articles were retrieved from the PubMed database. We defined 22 pain-related brain regions that appeared more than 100 times in the retrieved abstracts. Time-evolving networks of pain-related brain regions were constructed using the co-occurrence frequency. The state-space model was implemented to capture the trend patterns of the pain-related brain regions and the patterns were compared with those of mental disorders.
Results: The number of pain-related abstracts including brain areas steadily increased; however, the relative frequency of each brain region showed different patterns. According to the chronological patterns of relative frequencies, pain-related brain regions were clustered into three groups: rising, falling, and consistent. The network of pain-related brain regions extended over time from localized regions (mainly including brain stem and diencephalon) to wider cortical/subcortical regions. In the state-space model, the relative frequency trajectory of pain-related brain regions gradually became closer to that of mental disorder-related brain regions.
Conclusion: Temporal changes of pain-related brain regions in the abstracts indicate that emotional/cognitive aspects of pain have been gradually emphasized. The networks of pain-related brain regions imply perspective changes on pain from the simple percept to the multidimensional experience. Based on the notable occurrence patterns of the cerebellum and motor cortex, we suggest that motor-related areas will be actively explored in pain studies.

Keywords: pain, pain-related brain regions, pain-related brain networks, pain research trend analysis, literature mining, text mining, mental disorders and pain
 

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