Literature DB >> 31394144

Distinct dynamic functional connectivity patterns of pain and touch thresholds: A resting-state fMRI study.

Yueming Yuan1, Li Zhang1, Linling Li1, Gan Huang1, Ahmed Anter1, Zhen Liang1, Zhiguo Zhang2.   

Abstract

Dynamic functional connectivity (dFC) analysis based on resting-state functional magnetic resonance imaging (fMRI) has gained popularity in recent years. Despite many studies have linked dFC patterns to various mental diseases and cognitive functions, little research has used dFC in the investigation of low-level sensory perception. The present study is aimed to explore resting-state fMRI dFC patterns correlated with thresholds of two types of perception, pain and touch, on an individual basis. We collected and analyzed resting-state fMRI data and thresholds of pain and touch from 80 healthy participants. dFC states were identified by using independent component analysis, sliding window correlation, and clustering, and then the thresholds of pain and touch are correlated with the occurrence frequencies of dFC states. A new permutation analysis is developed to make identified dFC states more interpretable. We found that the occurrence frequency of a default mode network (DMN)-dominated state was positively correlated with the pain threshold, while the occurrence frequency of a static functional connectivity (sFC)-like state was negatively correlated with the touch threshold. This study showed that the thresholds of pain and touch have distinct dFC correlates, suggesting different influences of baseline brain states on different types of sensory perception. This study also showed that dFC could serve as an indicator of an individual's pain sensitivity, which can be potentially used for pain management.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Dynamic functional connectivity; Functional magnetic resonance imaging; Pain sensitivity; Pain threshold; Touch threshold

Mesh:

Year:  2019        PMID: 31394144     DOI: 10.1016/j.bbr.2019.112142

Source DB:  PubMed          Journal:  Behav Brain Res        ISSN: 0166-4328            Impact factor:   3.332


  3 in total

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Authors:  Jocelyn M Powers; Gabriela Ioachim; Patrick W Stroman
Journal:  Front Neurosci       Date:  2022-05-26       Impact factor: 5.152

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

Authors:  Rushi Zou; Linling Li; Li Zhang; Gan Huang; Zhen Liang; Zhiguo Zhang
Journal:  Front Neurosci       Date:  2021-02-10       Impact factor: 4.677

3.  Case report: The promising application of dynamic functional connectivity analysis on an individual with failed back surgery syndrome.

Authors:  Jingya Miao; Isaiah Ailes; Laura Krisa; Kristen Fleming; Devon Middleton; Kiran Talekar; Peter Natale; Feroze B Mohamed; Kevin Hines; Caio M Matias; Mahdi Alizadeh
Journal:  Front Neurosci       Date:  2022-09-23       Impact factor: 5.152

  3 in total

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