Introduction: Brain connectomes are insightful models that describe the connectivity of different regions throughout the brain. These connectomes are traditionally generated through correlation of oxygen data from fMRI scans. Photoacoustic ultrasound (PAU) is also able to detect oxygen levels, yet it is much more accessible and cost efficient. We propose the use of PAU to generate brain connectomes as an alternative to fMRI.
Methods: In this study we successfully developed a pipeline for processing PAU data from mice models and found that the connectomes it produced were comparable to those generated by fMRI, particularly in detecting connections previously documented in the literature. We used PAU imaging to capture brain activity in CD1 mice and generated brain connectomes based on regional oxygenation levels. PAU was performed using the Vevo F2 LAZR-X Imaging System, and data were processed using a custom MATLAB pipeline. For comparison, connectomes were also generated using fMRI, performed on a 9.4 T MRI system. Correlation matrices from PAU and fMRI were analyzed and compared to assess functional connectivity.
Results: Our findings suggest that PAU offers a promising alternative to fMRI for brain connectome analysis, with advantages in sensitivity and broader physical range, making it a valuable tool for future research in brain connectivity.
Conclusion : PAU provides a promising alternative to fMRI for generating brain connectomes, offering a more accessible and versatile imaging modality. Future work should focus on validating PAU-detected connections, optimizing sampling rates, and further comparing PAU with fMRI to ensure the accuracy and reliability of the detected networks.
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