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Related Experiment Video

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Brain entropy changes in classical trigeminal neuralgia.

Xiang Liu1, Xiuhong Ge1, Xue Tang2

  • 1Department of Radiology, Hangzhou First People's Hospital, Hangzhou, Zhejiang, China.

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|December 11, 2023
PubMed
Summary

This study reveals brain entropy (BEN) alterations in classical trigeminal neuralgia (CTN) patients, specifically in the thalamus, brainstem, and inferior semilunar lobule. These BEN changes can effectively differentiate CTN patients from healthy controls using machine learning.

Keywords:
brain entropyclassical trigeminal neuralgiacross-validationmachine learningresting-state functional magnetic resonance imaging

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Neurology

Background:

  • Classical trigeminal neuralgia (CTN) is a debilitating chronic neuropathic facial pain disorder with incompletely understood pathological mechanisms.
  • Resting-state functional magnetic resonance imaging (rs-fMRI) offers insights into functional brain changes in CTN.
  • The precise spatial distribution of neural complexity alterations in CTN remains unclear.

Purpose of the Study:

  • To investigate the spatial distribution of brain entropy (BEN) alterations in CTN patients.
  • To explore the relationship between BEN changes and clinical variables in CTN.
  • To evaluate the efficacy of machine learning in classifying CTN patients based on BEN patterns.

Main Methods:

  • rs-fMRI data from 85 CTN patients and 79 healthy controls (HCs) were analyzed.
  • Brain entropy (BEN) was calculated to assess neural complexity.
  • Sixteen machine learning algorithms were employed for classification, with the best-performing model selected.

Main Results:

  • CTN patients showed increased BEN in the thalamus and brainstem, and decreased BEN in the inferior semilunar lobule compared to HCs.
  • A low positive correlation was observed between thalamic BEN values and neuropsychological assessments.
  • The Conditional Mutual Information Maximization-Random Forest (CMIM-RF) model achieved the highest classification accuracy (AUC = 0.801).

Conclusions:

  • BEN alterations in the thalamus, pons, and inferior semilunar lobule are associated with CTN.
  • Machine learning effectively classifies CTN patients from HCs using BEN data.
  • These findings offer novel insights into CTN neuropathology and potential diagnostic/therapeutic targets.