Combining Regional and Connectivity Metrics of Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging for Individualized Prediction of Pain Sensitivity.

Rushi Zou,Lizu Xiao,Linling Li,Zhen Liang,Zhiguo Zhang,Gan Huang,Li Zhang

doi:10.3389/fnmol.2022.844146

Abstract

Characterization and prediction of individual difference of pain sensitivity are of great importance in clinical practice. MRI techniques, such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), have been popularly used to predict an individual’s pain sensitivity, but existing studies are limited by using one single imaging modality (fMRI or DTI) and/or using one type of metrics (regional or connectivity features). As a result, pain-relevant information in MRI has not been fully revealed and the associations among different imaging modalities and different features have not been fully explored for elucidating pain sensitivity. In this study, we investigated the predictive capability of multi-features (regional and connectivity metrics) of multimodal MRI (fMRI and DTI) in the prediction of pain sensitivity using data from 210 healthy subjects. We found that fusing fMRI-DTI and regional-connectivity features are capable of more accurately predicting an individual’s pain sensitivity than only using one type of feature or using one imaging modality. These results revealed rich information regarding individual pain sensitivity from the brain’s both structural and functional perspectives as well as from both regional and connectivity metrics. Hence, this study provided a more comprehensive characterization of the neural correlates of individual pain sensitivity, which holds a great potential for clinical pain management.

Highlights

Pain is a subjective, complex, and multidimensional sensory experience that exhibits huge intersubject variability (Rainville, 2002; Nielsen et al, 2009; Coghill, 2010)
The prediction performances in terms of Pearson’s correlation coefficients (PCC) of the models based on two type features (i.e., Regional Model, Connectivity Model, functional magnetic resonance imaging (fMRI) Model, and diffusion tensor imaging (DTI) Model) are higher than models which only used one type of feature
The correlation result of fMRI Model is significantly better than functional connectivity (FC) Model (p = 0.007), and PCC of DTI Model is significantly better than structural connectivity (SC) model (p = 4.19×10−5)

Summary

Introduction

Complex, and multidimensional sensory experience that exhibits huge intersubject variability (Rainville, 2002; Nielsen et al, 2009; Coghill, 2010). The study of individual differences in pain sensitivity is of great importance in clinical practice (Werner et al, 2010; Abrishami et al, 2011) and in pharmaceutical research (Chizh et al, 2009; Angst et al, 2012). With the fast development of neuroimaging technologies and associated data analytics, using neural images and signals, such as magnetic resonance imaging (MRI) and electroencephalography (EEG), to probe the neural mechanisms of pain has been widely adopted in pain researches, which include the studies of momentary (acute or chronic) pain experience (Apkarian et al, 2005) and pain sensitivity (Coghill et al, 2003; Zunhammer et al, 2016). Several studies have revealed that individual differences in pain sensitivity are reflected in differences in brain structure and function by using different MRI modalities (Geisler et al, 2021; Niddam et al, 2021)

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Discussion

Conclusion