Opioid-Specific Brain Connectivity Dynamics Distinguish Analgesia from Secondary Effects

This repository contains the Python package and scripts for analysis and visualization of the Opioids dataset, from the article "Opioid-Specific Brain Connectivity Dynamics Distinguish Analgesia from Secondary Effects".

Abstract

The µ-opioid receptor (MOP) is a critical pharmaceutical target that mediates both the therapeutic benefits and adverse effects of opioid drugs. However, the large-scale neural circuit dynamics underlying key opioid effects, such as analgesia and respiratory depression, remain poorly understood, hindering the development of safer analgesics. Here, we present a multimodal experimental framework that integrates functional ultrasound imaging (fUSI) through the intact skull with behavioral and molecular analyses to investigate opioid-induced large-scale functional responses and their physiological relevance in awake, behaving mice. Administration of major opioids—morphine, fentanyl, methadone, and buprenorphine—elicited robust, dose- and time-dependent reorganization of functional brain connectivity (FC) patterns, with magnitude scaling according to MOP agonist efficacy. This opioid-specific functional fingerprint is marked by decreased FC between the somatosensory cortex and hippocampal/thalamic regions and increased bilateral subcortical FC within the somatosensory cortex. Notably, this fingerprint was attenuated following tolerance induction and abolished by pharmacological or genetic MOP inactivation. Through power Doppler spectral analysis and lagged correlation measurements, we show that morphine perturbs temporal FC dynamics and the propagation of brain-wide oscillatory activity, disrupting critical-state dynamics. Importantly, we identify a dissociation between fast, transient processes—such as cerebral blood volume (CBV) changes, locomotion, and respiratory depression—and slower processes driving FC reorganization, analgesia, and sustained MOP activation. This study provides mechanistic insights into opioid-induced network reorganization, establishes FC alterations as a reliable biomarker of opioid efficacy, and offers a framework for advancing the development of analgesic compounds with improved therapeutic windows and reduced side effects.

Installation

This project uses uv for Python dependency management. You may also find installing just useful to run the provided recipes.

Repository Structure

• src/opioids_analysis/: Python package containing analysis modules
  • pearson.py: Pearson correlation analysis functions
  • cross_correlation.py: Cross-correlation analysis functions
  • multimodal.py: Multimodal data processing utilities
  • plotting.py: Plotting utilities for figures
• article-figures/: Scripts to generate publication figures
  • 01_figure_pearson.py: Pearson correlation analysis and figures
  • 02_figure_fc_vs_analgesia.py: Functional connectivity vs analgesia scatter plots
  • 03_figure_multimodal.py: Multimodal time series and correlation matrices
  • 04_figure_rcbv_rasterplots.py: Relative CBV rasterplots
  • 05_figure_xcorr.py: Cross-correlation analysis and figures
• params/: Analysis parameters and ROI masks

Generating Figures

The scripts in article-figures/ generate the publication sub-figures. They must be run in order due to dependencies:

# Run all figure generation scripts in order
just figures

# Or run individual scripts with uv
uv run article-figures/01_figure_pearson.py
uv run article-figures/02_figure_fc_vs_analgesia.py
# ... and so on

Note

The analysis scripts require access to the preprocessed dataset, which is not included in this repository. Please download it from the Zenodo archive.