Diffusion MRI (dMRI) provides a superior characterization of white matter and connectivity compared to other MRI modalities, and is routinely included in studies of disorders with atypical brain connectivity like autism spectrum disorder (ASD). The field could benefit tremendously from combining studies, to have comprehensive representation of the underlying heterogeneity in connectivity-based disorders. This is rendered challenging by dMRI being very sensitive to acquisition parameters, needing sophisticated statistical harmonization tools due to the complicated effect of scanner related changes. This also calls for a robust automated quality control (QC) protocol prior to data harmonization. Thus, in this proposal, we will develop tools to facilitate integration of dMRI data across studies. In Aim 1, we will develop and validate a deep learning based tool for automating QC for dMRI data that will identify different data artifacts (caused by multiple sources like scanner, coil, scan parameters, motion etc), and the appropriate action that needs to be taken (like motion and eddy correction). In Aim 2, we will develop a suite of tools for harmonizing dMRI measures to remove acquisition differences. The effectiveness of our proposed tools will be demonstrated by harmonizing ~1500 datasets (ages 6-32 years) from 11 ASD studies. These large harmonized datasets create the need for a subject-wise characterization of the sample and for diagnostic markers that harness the imaging heterogeneity of the larger harmonized sample. To address this new need, we will develop additional connectomic analysis tools, that will be adapted to ASD to create the CHARM (Connectomic Heterogeneity in Autism Research through Multi-site dMRI harmonization) suite comprising of a generalizable biomarker of ASD, as well as a dimensional connectomic coordinate system. In Aim 3, we will characterize each subject using a connectivity phenotype, cluster the integrated ASD sample based on this connectivity-phenotype, define a classifier for each cluster; and create a connectivity-based ensemble biomarker of ASD, called the CHARM-marker, combining these cluster-specific classifier decisions. Finally, in Aim 4, we will create a subject-wise characterization of ASD by designing a multi-dimensional connectomic coordinate system using metric learning, to quantify the dissimilarity of each subject from the harmonized healthy controls. We will elucidate the link of these CHARM-coordinates to ASD constructs, by correlating core ASD symptoms with the CHARM coordinates in the harmonized/combined sample. This will enable the ASD community to associate informative connectomic dimensions with each subject, facilitating subject-wise longitudinal assessment, paving the way for precision medicine. Such a group- based and subject-wise characterization of ASD could not have been possible without data integration. Additionally, the neuroimaging community will have new dMRI harmonization and connectomic analysis tools enabling the integration of studies for a more comprehensive connectomic investigation of existing data. It will pave the way for such studies in other connectivity-related disorders that affect mental health.