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Prefrontal connectivity alterations and oscillatory dynamics in cannabis use disorder.

Nattanon Bunrodchu, Ekkasit Kumarnsit, Wan Khairunizam Wan Ahmad, Nifareeda Samerphob

Biological Psychology July 9, 2026 DOI: 10.1016/j.biopsycho.2026.109344 (opens in new tab)

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Study at a glance

AI-extracted from the abstract
Characteristics Case-control study Peer reviewed
Sample size 46
Population Participants with cannabis use disorder and healthy controls
Duration Five-minute resting-state EEG recording with eyes open and closed
Topics Cannabis
Keywords Frontal cortical networks Multilayer perceptron Neural oscillations Resting-State EEG
Key findings Delta-high gamma and beta-high gamma coupling in prefrontal regions, along with heightened frontal-parietal and frontal-temporal coherence during eye closure, distinguished cannabis users from controls.

Abstract

Long-term cannabis use can result in the development of cannabis use disorder (CUD) and dependence via the endocannabinoid system pathway. A brain network that has been consistently linked to various stages of addiction and various substances is the default mode network (DMN). By comparing EEG features between cannabis users and healthy participants, this study sought to identify EEG-based markers in altered DMN function. A resting-state EEG recording was conducted with 23 CUD participants and 23 healthy participants for five minutes with their eyes open and closed. Data were analyzed using power spectral density (PSD), phase-amplitude coupling (PAC), functional connectivity, and multidimensional signal analysis to extract features. The results showed that a delta-high gamma and beta-high gamma coupling in the prefrontal regions highly specific to classify the top features of CUD participants when the eyes were open. Signaling adaptive modulation of neural oscillations of cannabis users was relatively stable coupling in both states. In addition, it was found that frontal-parietal and frontal-temporal coherence in CUD participants were linked at heightened levels during eye closure according to functional connectivity analysis. Using these multidimensional EEG features, the Multilayer perceptron (MLP) classifier was able to distinguish cannabis users from controls. Overall, these findings highlight altered DMN dynamics and cross-frequency interactions as potential electrophysiological markers of long-term cannabis use and demonstrate the utility of machine learning approaches for identifying neural signatures of cannabis-related brain changes.

Comparable studies

Other observational and cohort studies on cannabis, most cited first.

Study Year Design Participants
High-potency cannabis and the risk of psychosis People with first episode of psychosis and healthy controls from the local population 2009 Case-control study n = 454
Ayahuasca-Assisted Therapy for Addiction: Results from a Preliminary Observational Study in Canada Rural aboriginal population 2013 Observational study
Impaired cognitive performance in drug free users of recreational ecstasy (MDMA) Otherwise healthy young people 2000 Observational
Continued cannabis use and risk of incidence and persistence of psychotic symptoms: 10 year follow-up cohort study Individuals from the general population in Germany aged 14-24 at baseline 2011 Observational cohort n = 1,923
Cannabis-induced psychosis and subsequent schizophrenia-spectrum disorders: follow-up study of 535 incident cases Patients treated for cannabis-induced psychotic symptoms in Denmark between 1994 and... 2005 Observational cohort n = 535

Citations in the library

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Cannabis Addiction Default mode network