4.6 Article

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

期刊

FRONTIERS IN NEUROSCIENCE
卷 15, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2021.615944

关键词

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

资金

  1. National Natural Science Foundation of China [81871443]
  2. Science, Technology and Innovation Commission of Shenzhen Municipality Technology Fund [JCYJ20170818093322718]
  3. Shenzhen Peacock Plan [KQTD2016053112051497]

向作者/读者索取更多资源

Predicting individual pain sensitivity is clinically important, and structural MRI-based morphological connectivity (MC) has been found to be a more accurate method for predicting pain thresholds. The prefrontal cortex plays a crucial role in determining pain sensitivity.
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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据