Functional Connectivity of the Default Mode Network During Opioid Use and Cessation

Background

Opioid abuse has become a global public health issue, particularly in the United States, where opioid overdose deaths have tripled since 1999. Opioids not only lead to addiction but are also associated with chronic pain, immune system suppression, and neurocognitive impairments (e.g., attention, memory, and executive function). Despite various treatment options, the relapse rate among individuals with opioid use disorder (OUD) remains high, prompting researchers to explore new interventions, particularly at different stages of addiction.

Functional neuroimaging offers a new perspective for studying the neural mechanisms of addiction. The Default Mode Network (DMN), a brain network active during rest, is involved in self-monitoring and mental simulation. Research suggests that the DMN plays a significant role in substance use disorders (SUDs), especially during addiction and withdrawal. However, studies on DMN functional connectivity in opioid use disorder (OUD) remain limited. Therefore, this study aims to investigate changes in DMN functional connectivity during opioid use and cessation and assess whether these changes can predict relapse.

Source of the Paper

This paper was co-authored by Jade Dandurand, Michael Stein, and other researchers from the Department of Psychology at the University of Georgia, the School of Public Health at Boston University, the Department of Psychiatry and Human Behavior at Brown University, and other institutions. The paper was published in 2025 in the European Journal of Neuroscience under the title Resting State Functional Connectivity of the Default Mode Network During Opioid Use and Cessation in Treatment-Seeking Persons.

Research Process

Participants and Design

The study recruited 20 treatment-seeking individuals diagnosed with OUD according to DSM-5 criteria. Ultimately, 11 participants completed all study procedures (7 males, average age 30.9 years). Participants underwent two functional magnetic resonance imaging (fMRI) scans—one during active opioid use and another during withdrawal, approximately 3 days apart. The withdrawal scan was conducted after participants initiated buprenorphine treatment.

Methods

1. fMRI Scanning: Resting-state fMRI scans were performed using a Siemens Tim Trio 3.0T scanner, with each scan lasting 6 minutes. Participants were instructed to fixate on a crosshair during the scan.
2. Image Processing: fMRI data were processed using AFNI software, including steps such as time correction, denoising, alignment, and spatial smoothing. Time series of seed regions for the DMN, Salience Network (SN), and Executive Control Network (ECN) were extracted.
3. Functional Connectivity Analysis: Functional connectivity strength was quantified by calculating the correlation between seed regions and whole-brain voxels. Correlation coefficients were converted to z-scores using Fisher’s transformation, and group-level analyses were conducted.

Data Analysis

1. Withdrawal Symptom Assessment: The Subjective Opiate Withdrawal Scale (SOWS) was used to assess the severity of withdrawal symptoms.
2. Functional Connectivity Comparison: Paired-sample t-tests were used to compare DMN functional connectivity strength during active use and withdrawal.
3. Relapse Prediction: Independent-sample t-tests were used to compare DMN functional connectivity differences between relapsers and non-relapsers during withdrawal.

Key Findings

1. Enhanced DMN Functional Connectivity During Withdrawal: The study found that overall DMN functional connectivity significantly increased during withdrawal, particularly between the posterior cingulate cortex (PCC) and bilateral angular gyrus (AG). This result supports the hypothesis that the DMN may be involved in self-referential processing and future thinking during withdrawal.
2. Correlation Between Withdrawal Symptoms and Functional Connectivity: The severity of withdrawal symptoms was positively correlated with increased DMN functional connectivity, particularly in the right angular gyrus (RAG). Additionally, functional connectivity in the Executive Control Network (ECN) was also correlated with withdrawal symptom severity.
3. Relapse Prediction: Although DMN functional connectivity increased during withdrawal, no significant association was found between functional connectivity strength and relapse rates.

Conclusions and Significance

This study is the first to explore changes in DMN functional connectivity during opioid use and cessation in individuals with OUD. The results indicate that increased DMN functional connectivity during withdrawal may be related to the severity of withdrawal symptoms, particularly in self-referential processing and future thinking. This finding provides new insights into the neural mechanisms of opioid addiction and suggests that DMN functional connectivity may serve as a marker of neural repair during withdrawal.

However, the small sample size (n=11) may limit the generalizability of the results. Future studies should expand the sample size and further investigate the interactions between the DMN and other brain networks (e.g., SN and ECN) to better understand the neural mechanisms of addiction and develop more effective interventions.

Research Highlights

1. First Exploration of DMN Functional Connectivity in OUD: This study fills a gap in research on DMN functional connectivity in opioid addiction.
2. Enhanced DMN Functional Connectivity During Withdrawal: This finding provides new evidence for understanding the neural mechanisms of withdrawal.
3. Correlation Between Withdrawal Symptoms and Functional Connectivity: The study reveals a relationship between DMN functional connectivity and the severity of withdrawal symptoms, suggesting its potential as a marker of neural repair during withdrawal.