Fig. 1: Flow chart illustrating data analysis pipeline.

A Sliding window approach for age grouping: Participants from the ASD and TDC groups were divided into overlapping age bins, each spanning 2 years with a 1-year overlap. For each age group, regional gray matter volume (GMV) maps were extracted separately for ASD and TDC individuals. B Estimation of distribution deviation (DEV): For each region of interest (ROI), the probability density functions (PDFs) of GMV values were estimated for both ASD and TDC groups, and using the Kullback–Leibler (KL) divergence and expected values to quantified DEV between the two distributions. The top 10% of regions with the highest DEV values were identified as atypical regions for each age group, forming age-specific variation maps. C Functional connectivity-constrained DEV diffusion modeling: To examine whether observed developmental changes in atypicality patterns are shaped by intrinsic brain connectivity, a network-based diffusion model (NDM) was applied. Using functional connectivity (FC) networks, atypical regions from a given age window served as seed inputs to simulate the spread of atypicality over time. The model fitting was performed by comparing simulated maps to observed DEV patterns in the subsequent age window. yrs refers to the year.