Dataset Information

Somatotopically specific primary somatosensory connectivity to salience and default mode networks encodes clinical pain.

ABSTRACT: Although several studies have found that chronic pain is characterized by increased cross-network connectivity between salience network, sensorimotor network, and default mode network (DMN), a large sample-size investigation allowing for a more reliable evaluation of somatotopic specificity and subgroup analyses with linkage to clinical pain intensity has been lacking. We enrolled healthy adults and a large cohort of patients (N = 181) suffering from chronic low back pain (cLBP). To specifically link brain connectivity with clinical pain intensity, patients were scanned at baseline and after performing physical maneuvers that exacerbated pain. Compared with healthy adults, patients with cLBP demonstrated increased connectivity between the functionally localized back representation in the primary somatosensory cortex (S1back) and both salience network and DMN. Pain exacerbation maneuvers increased S1back connectivity to salience network regions, but decreased connectivity to DMN, with greater pain intensity increase associated with greater shifts in these connectivity patterns. Furthermore, only in patients with cLBP reporting high pain catastrophizing, DMN connectivity was increased to a cardinal node of the salience network, anterior insula cortex, which was correlated with increased postmaneuver pain in this cLBP subgroup. Hence, increased information transfer between salience processing regions, particularly anterior insula, and DMN may be strongly influenced by pain catastrophizing. Increased information transfer between the salience network and S1 likely plays an important role in shifting nociceptive afference away from self-referential processing, reallocating attentional focus, and affective coding of nociceptive afference from specific body areas. These results demonstrate S1 somatotopic specificity for cross-network connectivity in encoding clinical back pain and moderating influence of catastrophizing for DMN/insula connectivity.

SUBMITTER: Kim J

PROVIDER: S-EPMC6586503 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Somatotopically specific primary somatosensory connectivity to salience and default mode networks encodes clinical pain.

Kim Jieun J Mawla Ishtiaq I Kong Jian J Lee Jeungchan J Gerber Jessica J Ortiz Ana A Kim Hyungjun H Chan Suk-Tak ST Loggia Marco L ML Wasan Ajay D AD Edwards Robert R RR Gollub Randy L RL Rosen Bruce R BR Napadow Vitaly V

Pain 20190701 7

Although several studies have found that chronic pain is characterized by increased cross-network connectivity between salience network, sensorimotor network, and default mode network (DMN), a large sample-size investigation allowing for a more reliable evaluation of somatotopic specificity and subgroup analyses with linkage to clinical pain intensity has been lacking. We enrolled healthy adults and a large cohort of patients (N = 181) suffering from chronic low back pain (cLBP). To specifically ...[more]

PMID: 30839429

Similar Datasets

Project description:Schizophrenia and autism spectrum disorder (ASD) are two prevalent neurodevelopmental disorders sharing some similar genetic basis and clinical features. The extent to which they share common neural substrates remains unclear. Resting-state fMRI data were collected from 35 drug-naïve adolescent participants with first-episode schizophrenia (15.6 ± 1.8 years old) and 31 healthy controls (15.4 ± 1.6 years old). Data from 22 participants with ASD (13.1 ± 3.1 years old) and 21 healthy controls (12.9 ± 2.9 years old) were downloaded from the Autism Brain Imaging Data Exchange. Resting-state functional networks were constructed using predefined regions of interest. Multivariate pattern analysis combined with multi-task regression feature selection methods were conducted in two datasets separately. Classification between individuals with disorders and controls was achieved with high accuracy (schizophrenia dataset: accuracy = 83%; ASD dataset: accuracy = 80%). Shared atypical brain connections contributing to classification were mostly present in the default mode network (DMN) and salience network (SN). These functional connections were further related to severity of social deficits in ASD (p = 0.002). Distinct atypical connections were also more related to the DMN and SN, but showed different atypical connectivity patterns between the two disorders. These results suggest some common neural mechanisms contributing to schizophrenia and ASD, and may aid in understanding the pathology of these two neurodevelopmental disorders. Autism Res 2017, 10: 1776-1786. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.Lay summaryAutism spectrum disorder (ASD) and schizophrenia are two common neurodevelopmental disorders which share several genetic and behavioral features. The present study identified common neural mechanisms contributing to ASD and schizophrenia using resting-state functional MRI data. The results may help to understand the pathology of these two neurodevelopmental disorders.

Dataset Information

Somatotopically specific primary somatosensory connectivity to salience and default mode networks encodes clinical pain.

Publications

Somatotopically specific primary somatosensory connectivity to salience and default mode networks encodes clinical pain.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets