Graph theory analysis reveals how sickle cell disease impacts neural networks of patients with more severe disease

Michelle Case, Sina Shirinpour, Vishal Vijayakumar, Huishi Zhang, Yvonne H Datta, Stephen Nelson, Paola Pergami, Deepika S. Darbari, Kalpna Gupta, Bin He

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Sickle cell disease (SCD) is a hereditary blood disorder associated with many life-threatening comorbidities including cerebral stroke and chronic pain. The long-term effects of this disease may therefore affect the global brain network which is not clearly understood. We performed graph theory analysis of functional networks using non-invasive fMRI and high resolution EEG on thirty-one SCD patients and sixteen healthy controls. Resting state data were analyzed to determine differences between controls and patients with less severe and more severe sickle cell related pain. fMRI results showed that patients with higher pain severity had lower clustering coefficients and local efficiency. The neural network of the more severe patient group behaved like a random network when performing a targeted attack network analysis. EEG results showed the beta1 band had similar results to fMRI resting state data. Our data show that SCD affects the brain on a global level and that graph theory analysis can differentiate between patients with different levels of pain severity.

Original languageEnglish (US)
Article number101599
JournalNeuroImage: Clinical
Volume21
DOIs
StatePublished - 2019

Bibliographical note

Funding Information:
This work was supported by NIH U01 HL117664, R01 NS096761, R01 EB021027, R01 AT009263, RF1 MH114233, T32EB008389, S10 OD021721, and by NSF DGE-1069104 and CBET-1450956. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health and National Science Foundation.

Funding Information:
This work was supported by NIH U01 HL117664 , R01 NS096761 , R01 EB021027 , R01 AT009263 , RF1 MH114233 , T32EB008389 , S10 OD021721 , and by NSF DGE-1069104 and CBET-1450956 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health and National Science Foundation.

Publisher Copyright:
© 2018 The Authors

Keywords

  • Electroencephalography (EEG)
  • Functional magnetic resonance imaging (fMRI)
  • Graph theory
  • Resting state
  • Sickle cell disease

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