Literature DB >> 33642978

Predicting Individual Pain Thresholds From Morphological Connectivity Using Structural MRI: A Multivariate Analysis Study.

Rushi Zou1,2,3, Linling Li1,2,3, Li Zhang1,2,3, Gan Huang1,2,3, Zhen Liang1,2,3, Zhiguo Zhang1,2,3,4.   

Abstract

Pain sensitivity is highly variable among individuals, and it is clinically important to predict an individual's pain sensitivity for individualized diagnosis and management of pain. Literature has shown that pain sensitivity is associated with regional structural features of the brain, but it remains unclear whether pain sensitivity is also related to structural brain connectivity. In the present study, we investigated the relationship between pain thresholds and morphological connectivity (MC) inferred from structural MRI based on data of 221 healthy participants. We found that MC was highly predictive of an individual's pain thresholds and, importantly, it had a better prediction performance than regional structural features. We also identified a number of most predictive MC features and confirmed the crucial role of the prefrontal cortex in the determination of pain sensitivity. These results suggest the potential of using structural MRI-based MC to predict an individual's pain sensitivity in clinical settings, and hence this study has important implications for diagnosis and treatment of pain.
Copyright © 2021 Zou, Li, Zhang, Huang, Liang and Zhang.

Entities:  

Keywords:  individual difference; morphological connectivity; multivariate analysis; pain sensitivity; structural MRI

Year:  2021        PMID: 33642978      PMCID: PMC7902866          DOI: 10.3389/fnins.2021.615944

Source DB:  PubMed          Journal:  Front Neurosci        ISSN: 1662-453X            Impact factor:   4.677


  48 in total

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  1 in total

1.  The brain's structural differences between postherpetic neuralgia and lower back pain.

Authors:  Jianxing Qiu; Mengjiao Du; Jing Liu; Zhiguo Zhang; Junzhe Yang; Zengmao Lin; Naishan Qin; Xiaowei Sun; Linling Li; Rushi Zou; Juan Wei; Bing Wu
Journal:  Sci Rep       Date:  2021-11-17       Impact factor: 4.379

  1 in total

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