Use of pattern recognition in photoacoustic imaging to identify neuronal ensembles in the prefrontal cortex of rats undergoing conditioned fear learning

Abstract

Pattern recognition analysis in brain research has improved understanding of sensory processing and led to the identification of default brain networks in neuroimaging studies. The current study uses pattern recognition analysis to extend our previous findings showing conditioned fear learning and novelty-exposure (i.e. sham conditioning) equally increase Fos-dependent neuronal ensemble signal intensity in the prefrontal cortex (PFC) of rats as quantified by photoacoustic imaging (e.g. functional/molecular photoacoustic tomography). Here we use similarity metrics-based pattern recognition analysis to determine if neuronal ensemble activation patterns in the PFC are unique fear-conditioned compared to sham-conditioned rats. Our results show that a qualitatively-unique pattern in signal intensity exists only for the fear-conditioned group compared to sham-conditioned, behaviourally-naïve, or fear-conditioned vehicle control groups. These findings suggest that while the PFC is involved equally in novelty-exposure and fear learning, only highly coordinated behavioral tasks engage the PFC in a homogenous pattern of activity. This study also highlights the use of pattern recognition analysis using photoacoustic imaging data leading the way for future use of this computational approach to brain imaging.