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Central Nervous System Mechanisms of Nausea in Gastroparesis: An fMRI-Based Case–Control Study

  • Phillip Snodgrass
  • Hugo Sandoval
  • Vince D. Calhoun
  • Luis Ramos-Duran
  • Gengqing Song
  • Yan Sun
  • Ben Alvarado
  • Mohammad Bashashati
  • Irene SarosiekEmail author
  • Richard W. McCallum
Original Article
  • 32 Downloads

Abstract

Background/Aims

Nausea is a major complaint of gastroparesis (GP), and the pathophysiology of this condition is poorly understood. Therefore, this study utilized fMRI to investigate the possible central nervous system (CNS) mechanisms of nausea in 10 GP patients versus 8 healthy controls (HCs).

Methods

Nausea severity was assessed on a 0–10 scale and presented as mean ± SD. Nausea was increased from baseline utilizing up to 30 min of visual stimulation (VS). Functional network connectivity was measured with fMRI at baseline and after 30 min of VS. fMRI data were preprocessed using statistical parametric mapping software. Thirty-four independent components were identified as meaningful resting-state networks (RSNs) by group independent component analysis. The Functional Network Connectivity (FNC) among 5 RSNs considered important in CNS nausea mechanisms was calculated as the Pearson’s pairwise correlation.

Results

Baseline nausea score in GP patients was 2.7 ± 2.0 and increased to 7.0 ± 1.5 after stimulation (P < 0.01). In HCs nausea scores did not increase from baseline after stimulus (0.3 ± 0.5). When comparing GP patients to HCs after VS, a significant reduction (P < 0.001) in bilateral insula network connectivity compared to the right insula network was detected. No significant differences in connectivity were noted among the other RSNs. Additionally, the average gray matter volume was non-significantly reduced in the insula in GP patients compared to HC.

Conclusions

The insula connectivity network is impaired in nauseated GP patients. This phenomenon could explain the susceptibility of GP patients to nausea or may have resulted from a state of chronic nausea.

Keywords

Central nervous system fMRI Functional network connectivity Gastroparesis Nausea 

Abbreviations

fMRI

Functional magnetic resonance imaging

FNC

Functional network connectivity

GP

Gastroparesis

GICA

Group independent component analysis

HC

Healthy control

IC

Independent component

RSN

Resting-state network

VS

Visual stimulation

Notes

Funding

The funding for this work was provided by Texas Tech University Health Sciences Center of El Paso Internal Medicine Department Seed Grant and Texas Tech University Health Sciences Center of El Paso, Paul L. Foster School of Medicine Scholarly Activity and Research Program Mini Grant.

Compliance with Ethical Standards

Conflict of interest

Dr. Richard McCallum was supported by CinRx Pharma (Consultant or advisor), GI Stimultation-Patent on “gastric pacing” (owner of patent), Allergan Pharma, Takeda Pharma, Vanda Pharma, (Research Support), Salix Pharma (Speaker’s Bureau, Takeda Pharma (Publications, Steering Committee). The other authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Phillip Snodgrass
    • 3
  • Hugo Sandoval
    • 2
  • Vince D. Calhoun
    • 4
    • 5
  • Luis Ramos-Duran
    • 2
  • Gengqing Song
    • 1
  • Yan Sun
    • 1
  • Ben Alvarado
    • 1
  • Mohammad Bashashati
    • 1
  • Irene Sarosiek
    • 1
    Email author
  • Richard W. McCallum
    • 1
  1. 1.Department of Internal Medicine, Paul L. Foster School of MedicineTexas Tech University Health Sciences CenterEl PasoUSA
  2. 2.Department of Radiology, Paul L. Foster School of MedicineTexas Tech University Health Sciences CenterEl PasoUSA
  3. 3.Paul L. Foster School of MedicineTexas Tech University Health Sciences CenterEl PasoUSA
  4. 4.Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaUSA
  5. 5.The Department of Electrical and Computer EngineeringAlbuquerqueUSA

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