Distinct thalamocortical network dynamics are associated with the pathophysiology of chronic low back pain

Yiheng Tu,Thomas Liu,Vitaly Napadow,Ted J Kaptchuk,Joel Park,Vince Calhoun,Cuiping Mao,Randy L Gollub,Jessica Gerber,Zening Fu,Suk-Tak Chan,Bruce Rosen,Robert R Edwards,Maryam Falahpour,Georgia Wilson,Jian Kong

doi:10.1038/s41467-020-17788-z

Abstract

Thalamocortical dysrhythmia is a key pathology of chronic neuropathic pain, but few studies have investigated thalamocortical networks in chronic low back pain (cLBP) given its non-specific etiology and complexity. Using fMRI, we propose an analytical pipeline to identify abnormal thalamocortical network dynamics in cLBP patients and validate the findings in two independent cohorts. We first identify two reoccurring dynamic connectivity states and their associations with chronic and temporary pain. Further analyses show that cLBP patients have abnormal connectivity between the ventral lateral/posterolateral nucleus (VL/VPL) and postcentral gyrus (PoCG) and between the dorsal/ventral medial nucleus and insula in the less frequent connectivity state, and temporary pain exacerbation alters connectivity between the VL/VPL and PoCG and the default mode network in the more frequent connectivity state. These results extend current findings on thalamocortical dysfunction and dysrhythmia in chronic pain and demonstrate that cLBP pathophysiology and clinical pain intensity are associated with distinct thalamocortical network dynamics.

Highlights

Thalamocortical dysrhythmia is a key pathology of chronic neuropathic pain, but few studies have investigated thalamocortical networks in chronic low back pain given its nonspecific etiology and complexity
Patients underwent two resting-state functional magnetic resonance imaging (fMRI) scans before and after physical maneuvers aimed to exacerbate their spontaneous LBP. This allowed us to separate brain patterns associated with chronic pain pathophysiology and temporal intensity alternations of clinical pain[19,20] since previous studies have suggested that neural dynamics discriminating chronic low back pain (cLBP) patients from healthy controls (HCs) may be distinct from neural dynamics sensitive to pain intensity changes[20,21]
In a further analysis of relationships between dynamic functional network connectivity (dFNC) and clinical symptoms in cLBP patients, we found that the fraction rate and dwell time of State 2 were significantly correlated with pain severity/intensity of cLBP patients in the past 7 days (Fig. 3b), indicating that more severe cLBP may result in a higher occurrence rate of State 2

Summary

Introduction

Thalamocortical dysrhythmia is a key pathology of chronic neuropathic pain, but few studies have investigated thalamocortical networks in chronic low back pain (cLBP) given its nonspecific etiology and complexity. Further analyses show that cLBP patients have abnormal connectivity between the ventral lateral/posterolateral nucleus (VL/VPL) and postcentral gyrus (PoCG) and between the dorsal/ventral medial nucleus and insula in the less frequent connectivity state, and temporary pain exacerbation alters connectivity between the VL/VPL and PoCG and the default mode network in the more frequent connectivity state These results extend current findings on thalamocortical dysfunction and dysrhythmia in chronic pain and demonstrate that cLBP pathophysiology and clinical pain intensity are associated with distinct thalamocortical network dynamics. This allowed us to separate brain patterns associated with chronic pain pathophysiology (as compared with HCs) and temporal intensity alternations of clinical pain (high pain vs low pain)[19,20] since previous studies have suggested that neural dynamics discriminating cLBP patients from HCs may be distinct from neural dynamics sensitive to pain intensity changes[20,21]. We replicated the findings from a dataset of 25 cLBP patients and 25 HCs with a similar attention/vigilance level, as demonstrated by a multisource interference task (MSIT), to rule out the confounding effect of vigilance

Methods

Results

Conclusion