The study of large-scale brain connectivity is increasingly adopting unsupervised approaches that derive low-dimensional spatial representations from high-dimensional connectomes, referred to as gradient analysis. When translating this approach to study interindividual variations in connectivity, one technical issue pertains to the selection of an appropriate group-level template to which individual gradients are aligned. Here, we compared different group-level template construction strategies using functional and structural connectome data from neurotypical controls and individuals with autism spectrum disorder (ASD) to identify between-group differences. We studied multimodal magnetic resonance imaging data obtained from the Autism Brain Imaging Data Exchange (ABIDE) Initiative II and the Human Connectome Project (HCP). We designed six template construction strategies that varied in whether: (1) they included typical controls in addition to ASD; or (2) they mapped from one dataset onto another. We found that aligning a combined subject template of the ASD and control subjects from the ABIDE Initiative onto the HCP template exhibited the most pronounced effect size. This strategy showed robust identification of ASD-related brain regions for both functional and structural gradients across different study settings. Replicating the findings on focal epilepsy demonstrated the generalizability of our approach. Our findings will contribute to improving gradient-based connectivity research.

Gradient-based connectivity analysis provides a compact understanding of complex connectivity patterns across the brain. One issue of the gradient analysis is to choose an appropriate group-level template in which individual gradients are aligned. Here, we assessed six different strategies for constructing group-level gradient templates, including those based on combined data from individuals with psychiatric conditions and control subjects, and data from an independent dataset of young, healthy adults. The choice of template significantly influences the outcomes of gradient analyses, with templates combining data from both groups or aligning to a high-quality independent dataset, providing more balanced results. This study emphasizes the importance of template selection in brain connectivity studies, contributing to more reliable gradient analyses.

This content is only available as a PDF.

Author notes

Handling Editor: Angie Laird

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit

Article PDF first page preview

Article PDF first page preview

Supplementary data