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Abnormal resting-state functional connectome in methamphetamine-dependent patients and its application in

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Methamphetamine (MA) dependence alters brain functional connectivity, shifting networks towards randomness and increasing sensitivity to drug cues. Graph theory analysis of resting-state functional connectivity (rsFC) effectively classifies MA dependence with high accuracy.

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Area of Science:

  • Neuroscience
  • Neuroimaging
  • Addiction Research

Background:

  • Substance use disorders (SUDs), particularly methamphetamine (MA) dependence, are often studied using resting-state functional connectivity (rsFC).
  • Traditional Pearson correlation analysis for rsFC cannot distinguish direct from indirect neural pathways.
  • Limited research has applied graph theory to analyze rsFC in MA dependence.

Purpose of the Study:

  • To evaluate alterations in Tikhonov regularization-based rsFC and graph theory topological attributes in individuals with MA dependence.
  • To explore correlations between rsFC topological attributes and clinical variables in MA dependence.
  • To develop a support vector machine (SVM)-based classifier for MA dependence using selected rsFC topological attributes.

Main Methods:

  • Analysis of Tikhonov regularization-based rsFC and graph theory metrics in 46 MA-dependent patients.
  • Utilized least absolute shrinkage and selection operator (LASSO) for feature selection.
  • Constructed an SVM classifier to predict MA dependence based on selected topological attributes.

Main Results:

  • The MA group exhibited a subnetwork with increased rsFC (reward circuit overactivation) and decreased rsFC (orbitofrontal cortex system dysfunction).
  • Significant decreases in clustering coefficient, shortest path length, modularity, and small-worldness, alongside increases in global efficiency and network strength, indicate a shift towards random networks in MA dependence.
  • An SVM classifier using 36 LASSO-selected topological features achieved high cross-validated performance (AUC 99.03 ± 1.79%), demonstrating the efficacy of rsFC attributes in classifying MA dependence.

Conclusions:

  • rsFC alterations, particularly a shift towards random network topology, are characteristic of MA dependence.
  • rsFC-based topological attributes are sensitive to clinical variables, such as psychiatric symptoms.
  • rsFC topological attributes provide effective biomarkers for developing high-efficacy diagnostic classifiers for MA dependence.