A widespread internal brain state for fentanyl withdrawal.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Addiction Research

Background:

• Opioid addiction involves escalating drug use, partly due to withdrawal's negative reinforcement.
• The neural mechanisms linking opioid withdrawal to increased drug-seeking are not well understood.

Purpose of the Study:

• To identify large-scale brain networks involved in opioid exposure and withdrawal.
• To understand the neural basis of negative reinforcement in opioid addiction.

Main Methods:

• Multisite local field potential recordings in mice.
• Interpretable machine learning to analyze brain network activity.
• Fentanyl self-administration paradigms.

Main Results:

• Repeated fentanyl exposure created a ramping network of beta and gamma oscillations.
• A distinct network, EN-Withdrawal, characterized by gamma and delta/theta oscillations, selectively encoded withdrawal emergence and severity.
• EN-Withdrawal activity predicted withdrawal across diverse conditions and persisted for days.
• Network activity scaled with individual withdrawal severity, suggesting an internal state.
• EN-Withdrawal activity predicted escalation of fentanyl self-administration in experienced animals.

Conclusions:

• Opioid withdrawal involves specific large-scale brain network dynamics.
• The EN-Withdrawal network provides a neurophysiological substrate for the negative reinforcement cycle in addiction.
• These findings may explain individual differences in vulnerability to opioid addiction.