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Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent.

Yunfan Wu1, Xiaofen Ma1, Zhihua Zhou2

  • 1Department of Medical Imaging, Guangdong Second Provincial General Hospital, Shiliugang Rd, Haizhu District, Guangzhou, 510317, PR China.

Journal of Psychiatric Research
|September 5, 2020
PubMed
Summary
This summary is machine-generated.

Codeine-containing cough syrup dependence in adolescents can be predicted using brain connectome analysis. Machine learning models accurately identified dependent users based on functional network abnormalities.

Keywords:
CoughHuman connectomeImpulsive behaviorMachine learningSyrup

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Area of Science:

  • Neuroscience
  • Addiction Research
  • Machine Learning in Medicine

Background:

  • Codeine-containing cough syrup (CCS) is a prevalent substance of abuse among adolescents globally.
  • Accurate identification and prediction of codeine-containing cough syrup dependence (CCSD) are critical for intervention.
  • Understanding the neurobiological underpinnings of CCSD is essential for developing effective treatments.

Purpose of the Study:

  • To identify a brain-connectome-based predictor for codeine-containing cough syrup dependence (CCSD).
  • To develop and validate a machine learning model for classifying CCSD users.
  • To explore the relationship between brain network abnormalities and clinical characteristics in CCSD.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) was used to construct functional brain networks in 40 CCSD users and 40 healthy controls (HC).
  • Partial correlation analysis examined relationships between network metrics and clinical variables (BIS scores, abuse duration, dose).
  • A ten-fold cross-validation logistic regression (LR) classifier was employed to distinguish CCSD users from HC subjects.

Main Results:

  • CCSD users exhibited significant abnormalities in brain nodes and connections across multiple regions, including the prefrontal cortex, parietal lobe, and cerebellum.
  • Global network topologies in CCSD showed higher characteristic path length and lower clustering coefficient (Cp) and local efficiency (Eloc).
  • The LR classifier achieved an accuracy of 82.5%, with sensitivity, specificity, and AUC also at 82.5%, indicating robust predictive capability.

Conclusions:

  • Abnormalities in the functional brain connectome are closely associated with the clinical characteristics of CCSD.
  • Functional connectome-based biomarkers show promise for the personalized diagnosis of CCSD.
  • This study highlights the potential of neuroimaging and machine learning in understanding and diagnosing adolescent substance use disorders.