Motivated by clinical needs, we conducted a comprehensive study to explore the effects of anesthesia on cortical functional connectivity at an individual level. Our study involved individualized functional analyses of resting-state fMRI data from 14 healthy participants, encompassing both awake and anesthetized states. Importantly, each participant underwent extensive 96-minute fMRI scanning, enabling us to capture robust anesthesia-induced changes at the individual level.
We observed reduced connectivity during anesthesia compared to the awake state, particularly within unimodal networks and across various between-network interactions, indicating a weakened functional integration. Although anesthesia diminished individual differences in functional connectivity, we found that subject-specific functional connectivity fingerprints were well-preserved. To mitigate the effects of anesthesia on brain connectivity, we developed a predictive model that accurately reconstructs functional connectomes in the awake state using data collected under anesthesia. We demonstrated the model's ability to identify disease-specific dysfunctions using data from 29 anesthetized children with autism spectrum disorder (ASD).
The package is supported for Linux and macOS. The package has been tested on Linux operating systems.
- Linux : Ubuntu (20.04.5)
- macOS
Some softwares should be installed and setted up.
BrainSector® Cloud：preprocess, you can also use other pipelines to deal with the rsfMRI data
FreeSurfer: v6.0.0, for registration
FSL: v6.0 for registration
Connectome Workbench: v1.5.0, for visualization
Matlab: 2020a, for statistical analysis
This repository contains a general pipeline for analysis of the publication
Dissimilarity analysis: For each vertex on the cortical surface, we compared its connectivity profiles between awake and anesthetized states while controlling for normal variations.
Variability analysis: To understand how anesthesia affects individual differences in brain functional activity, we utilized a previously reported method to derive a map of inter-individual variability in RSFC while controlling for intra-individual variability.
HBM model: Anesthetic effects on functional connectivity could obscure the effect of diseases. To address this, we developed a predictive model to estimate the individual functional connectome in the awake state using data collected during the anesthetized state. The model incorporates a nonlinear fitting model and a Hierarchical Bayesian Model (HBM), leveraging prior knowledge about the anesthetic effect on functional networks and inter-individual variability, respectively.
Results： you can find the data and results used in this research, including Similarity,Dissimilarity,all FC matrix and Abnormality.
This project is covered under the Apache 2.0 License.