This study illustrates the potential of non-invasive Photoacoustic Microscopy (PAM) to monitor functional changes in a squirrel monkey brain due to peripheral mechanical stimulation. Our unique approach employs a deep Fully Convolutional Neural Network (FCNN) to significantly enhance PAM image quality, improving signal-to-noise ratio and structural similarity index. Notably, functional changes induced by peripheral mechanical stimulation were effectively observed. The study showcases the potential of PAM in neurological applications, advancing our understanding of brain hemodynamics, and the transformative effect of machine learning techniques on PAM image quality, opening new possibilities for future neuroscientific research.
The feasibility of using photoacoustic imaging (PAI) to measure electrically-evoked hemodynamic responses in a squirrel monkey brain in vivo was examined. A linear-array photoacoustic computed tomography (PACT) system and a high-resolution photoacoustic microscopy (PAM) system were built for imaging subcortical and cortical brain regions, respectively. The hemodynamic responses at multiple cortices, including premotor, primary motor, and primary somatosensory cortices, were monitored. The variations could be observed in all cortices and their underlying cortical and subcortical brain regions. The results from this study validated the potential of PAI technique for multiscale and multi-resolution functional brain mapping for non-human primates.
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