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Nonlinear EEG Complexity as a Marker of Maladaptive Brain Plasticity in Substance Use Disorders: A Multi-Group

Mashal Fatima¹, Faraz Akram¹, Imran Khan Niazi^2,3,4

¹Department of Biomedical Engineering, Riphah International University, Islamabad 44035, Pakistan.

|June 26, 2026

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Nonlinear EEG complexity measures reveal reduced neural adaptability in substance use disorder (SUD). These findings offer potential for objective assessment and monitoring of brain plasticity in SUD patients.

Area of Science:

Neuroscience
Electrophysiology
Addiction Research

Background:

Chronic substance exposure causes maladaptive brain plasticity with lasting neural dynamic changes.
Electrophysiological signatures, especially nonlinear electroencephalography (EEG) complexity, are underexplored in diverse substance use profiles.
Substance Use Disorder (SUD) involves complex alterations in brain function.

Purpose of the Study:

Investigate nonlinear EEG complexity as a biomarker for maladaptive plasticity in SUD.
Explore EEG complexity across various substance use categories.
Determine if EEG complexity can differentiate SUD individuals from controls.

Main Methods:

Included 350 participants across seven groups (6 SUD, 1 control).

Keywords:

EEG frontal cortex maladaptive brain plasticity nonlinear EEG complexity substance use disorder

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Recorded resting-state EEG using an eight-channel system.

Extracted nonlinear features: Largest Lyapunov Exponent (LLE), Fractal Dimension (FD), Hurst Exponent (HE), Kolmogorov Complexity (KC).

Utilized two-way ANOVA for statistical analysis and K Nearest Neighbour (KNN) for classification.

Main Results:

Significant differences (p < 0.05) in all nonlinear EEG features across groups.
Controls exhibited higher complexity than SUD groups, indicating reduced neural variability.
Frontal and central cortical areas, crucial for motor control, were notably affected.
KNN achieved high accuracy (98.4%), sensitivity (100%), and specificity (96.8%) in classification.

Conclusions:

Nonlinear EEG complexity serves as a robust marker for substance-induced maladaptive brain plasticity.
Reduced complexity signifies impaired neural adaptability, particularly in motor control networks.
EEG complexity metrics show promise for objective assessment, classification, and neurorehabilitation monitoring in SUD.