Structural connectomes are commonly used to investigate connectivity changes related to various disorders. However, small sample sizes in individual studies and highly heterogeneous disorder-related manifestations underscore the need to pool datasets across multiple studies to identify coherent and generalizable patterns linked to disorders. Yet, combining datasets introduces site bias due to variations in scanner hardware or acquisitions. This highlights the necessity for data harmonization to mitigate site bias while preserving the biological integrity associated with participant demographics and the disorders. While several paradigms exist for harmonizing normally distributed imaging data, this study represents the first effort to establish a harmonization framework specifically for structural connectomes.