Abstract This project aims to develop the first Inverse Activity Marker (IAM) for detecting neuronal inhibition (broadly defined as the decrease of neuronal activities). The transcription of immediate early genes (IEGs) like c-Fos and Arc has been the most widely used for translating neuronal activity into stable, trackable histological labels to allow structural and functional interrogations. Existing activity targeting methods, either through direct detection of IEGs or engineered IEG promoters, are optimized for detecting the sustained increase of neural activity. However, they are generally less effective for labeling the inhibition of neuronal activity. Therefore, to better understand the bi-directional brain activities, it is important to have a set of new markers to label the decrease of neuronal activity opposite to the conventional IEGs, which we propose here as the Inverse Activity Marker. We aim to develop IAMs based on protein post-translational modifications (PTMs), which are known to be rapid, bi-directional, and trackable. We hypothesize that if we can identify PTMs inversely correlated with neuronal activation through unbiased screens, these changes could be developed into IAMs to report neural inhibition in behaving animals. We established an original optogenetic- proteomics screening platform, from which we discovered that the phosphorylation of pyruvate dehydrogenase E1 subunit Alpha 1 or pPDH inversely correlated with neuronal activity. Our central hypothesis is to test whether pPDH can serve as the first IAM to reflect the inhibition of neural activity in vitro and in vivo. The method development goal is to integrate IAMs with whole-brain clearing, lightsheet imaging, and multiplexed labeling to enable a cell-ID compatible tool for unbiased profiling of brain-wide inhibition. We assembled a team of investigators with well-recognized expertise in activity-dependent tool development, circuit mapping, electrophysiology, and proteomics and behaviors. The development and dissemination of these novel tools will bring new perspectives to understanding the circuit dynamics of the brain.