To avoid user-bias, the threshold is selected via an automated procedure which initially analyses a small portion of the movie with different thresholds and identifies the one that produces the best clustering. The evaluation of clustering quality is based on the silhouette, a commonly used metric in clustering algorithms.(62) Briefly, the silhouette compare the correlation of the pixel under treatment with the pixel belonging to the same cluster with the correlation of the same pixel with other clusters, determining how well the pixel fits into its attributed cluster. A mathematical definition and more detailed explanation of the silhouette score is outlined in Supporting Note II. Upon applying CLIM, several key outputs are obtained. The first one is the correlation map (Figure ). It is calculated by averaging the correlation of each pixel with its direct neighbors. The correlation map clearly reveals a correlation maximum at the position where the simulated emitters are located, enabling the observation of features that are unrecognizable in the raw PL image. The second output is the cluster map (Figure ), illustrating how the algorithm divides the image into clusters of highly correlated pixels. This allows for the extraction of the size of the clusters/functional domains and the integration of PL intensity to further study time-dependent PL fluctuation dynamics.