Fig. 1: Methodological workflow of ACE.

a–c, Intact whole brains immunolabeled, cleared and imaged with LSFM were used as input to the ACE pipeline. a, Whole-brain LSFM data are passed to ACE’s segmentation module, consisting of ViT- and CNN-based DL models, to generate binary segmentation maps in addition to a voxel-wise uncertainty map for estimation of model confidence. b, The autofluorescence channel of data is passed to the registration module, consisting of MIRACL registration algorithms, to register to a template brain such as the Allen Mouse Brain Reference Atlas (ARA). High-resolution segmentation maps are then voxelized using a convolution filter and warped to the ARA (10 µm) using deformations obtained from registration. c, Voxelized and warped segmentation maps are passed to ACE’s statistics module. Group-wise heatmaps of neuronal density are obtained by subtracting the average of warped and voxelized segmentation maps in each group to identify neural activity hotspots. To identify significant localized group-wise differences in neuronal activity in an atlas-agnostic manner, a cluster-wise, threshold-free cluster enhancement permutation analysis (using group-wise ANOVA) is conducted. The resulting P value map represents clusters showing significant differences between groups. Correspondingly, ACE outputs a table summarizing these clusters, including their volumes and the portion of each brain region included in each cluster. Significant clusters are then passed to the ACE native space cluster validation module, where clusters are warped to the native space of each subject. Utilizing warped clusters and ACE segmentation maps, the number of neurons within each cluster is calculated and a post hoc nonparametric test is applied between counts within each cluster across two groups. The top left panel of the figure was created using BioRender.com.