Prefrontal engrams of long-term fear memory perpetuate pain perception.

A painful episode can lead to a life-long increase in an individual's experience of pain. Fearful anticipation of imminent pain could play a role in this phenomenon, but the neurobiological underpinnings are unclear because fear can both suppress and enhance pain. Here, we show in mice that long-term associative fear memory stored in neuronal engrams in the prefrontal cortex determines whether a painful episode shapes pain experience later in life. Furthermore, under conditions of inflammatory and neuropathic pain, prefrontal fear engrams expand to encompass neurons representing nociception and tactile sensation, leading to pronounced changes in prefrontal connectivity to fear-relevant brain areas. Conversely, silencing prefrontal fear engrams reverses chronically established hyperalgesia and allodynia. These results reveal that a discrete subset of prefrontal cortex neurons can account for the debilitating comorbidity of fear and chronic pain and show that attenuating the fear memory of pain can alleviate chronic pain itself.

Quanty-cFOS, a Novel ImageJ/Fiji Algorithm for Automated Counting of Immunoreactive Cells in Tissue Sections.

Analysis of neural encoding and plasticity processes frequently relies on studying spatial patterns of activity-induced immediate early genes' expression, such as c-fos. Quantitatively analyzing the numbers of cells expressing the Fos protein or c-fos mRNA is a major challenge owing to large human bias, subjectivity and variability in baseline and activity-induced expression. Here, we describe a novel open-source ImageJ/Fiji tool, called 'Quanty-cFOS', with an easy-to-use, streamlined pipeline for the automated or semi-automated counting of cells positive for the Fos protein and/or c-fos mRNA on images derived from tissue sections. The algorithms compute the intensity cutoff for positive cells on a user-specified number of images and apply this on all the images to process. This allows for the overcoming of variations in the data and the deriving of cell counts registered to specific brain areas in a highly time-efficient and reliable manner. We validated the tool using data from brain sections in response to somatosensory stimuli in a user-interactive manner. Here, we demonstrate the application of the tool in a step-by-step manner, with video tutorials, making it easy for novice users to implement. Quanty-cFOS facilitates a rapid, accurate and unbiased spatial mapping of neural activity and can also be easily extended to count other types of labelled cells.