Literature DB >> 31349067

Identifying inter-individual differences in pain threshold using brain connectome: a test-retest reproducible study.

Yiheng Tu1, Binlong Zhang1, Jin Cao1, Georgia Wilson1, Zhiguo Zhang2, Jian Kong3.   

Abstract

Individuals are unique in terms of brain and behavior. Some are very sensitive to pain, while others have a high tolerance. However, how inter-individual intrinsic differences in the brain are related to pain is unknown. Here, we performed longitudinal test-retest analyses to investigate pain threshold variability among individuals using a resting-state fMRI brain connectome. Twenty-four healthy subjects who received four MRI sessions separated by at least 7 days were included in the data analysis. Subjects' pain thresholds were measured using two modalities of experimental pain (heat and pressure) on two different locations (heat pain: leg and arm; pressure pain: leg and thumbnail). Behavioral results showed strong inter-individual variability and strong within-individual stability in pain threshold. Resting state fMRI data analyses showed that functional connectivity profiles can accurately identify subjects across four sessions, indicating that an individual's connectivity profile may be intrinsic and unique. By using multivariate pattern analyses, we found that connectivity profiles could be used to predict an individual's pain threshold at both within-session and between-session levels, with the most predictive contribution from medial-frontal and frontal-parietal networks. These results demonstrate the potential of using a resting-state fMRI brain connectome to build a 'neural trait' for characterizing an individual's pain-related behavior, and such a 'neural trait' may eventually be used to personalize clinical assessments.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Inter-individual variability; Neural trait; Pain threshold; Within-individual stability; fMRI brain connectome

Year:  2019        PMID: 31349067      PMCID: PMC6819259          DOI: 10.1016/j.neuroimage.2019.116049

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  10 in total

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  10 in total

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