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Identifying the neural marker of chronic sciatica using multimodal neuroimaging and machine learning analyses.

Xiaoya Wei1, Liqiong Wang1, Fangting Yu1

  • 1International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture- Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China.

Frontiers in Neuroscience
|December 19, 2022
PubMed
Summary

Chronic sciatica (CS) patients exhibit brain structural and functional changes. Multimodal neuroimaging and AI accurately identified CS patients, suggesting potential for AI in chronic pain research.

Keywords:
ALFFbrain networkschronic painchronic sciaticacortical surface areafMRIsupport vector machines

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Area of Science:

  • Neuroimaging
  • Brain Structure and Function
  • Artificial Intelligence in Medicine

Background:

  • Chronic sciatica (CS) is a pain disorder often linked to herniated discs compressing nerves.
  • Functional brain abnormalities are noted in CS, but structural markers and classification value of multimodal neuroimaging are unclear.

Purpose of the Study:

  • Investigate brain structural and functional differences in CS patients compared to healthy controls (HCs).
  • Assess the classification value of multimodal neuroimaging features for differentiating CS patients.

Main Methods:

  • Acquired structural and resting-state functional MRI (fMRI) data from 34 CS patients and 36 HCs.
  • Analyzed cortical surface area, thickness, ALFF, REHO, and functional connectivity (FC).
  • Utilized a support vector machine (SVM) algorithm with multimodal features for classification.

Main Results:

  • CS patients showed larger cortical surface area and higher ALFF in specific brain regions compared to HCs.
  • Enhanced FC was observed between somatomotor and ventral attention networks in CS patients.
  • A classification accuracy of 90.00% was achieved using three multimodal neuroimaging features and SVM.

Conclusions:

  • CS involves extensive reorganization of local brain function, surface area, and network metrics.
  • AI combined with multimodal neuroimaging shows promise for identifying chronic pain patients and advancing research.