Choice of parcellation atlas might not be too critical for connectomic analysis

Connectomics has been a rapidly growing discipline in neuroimaging and neuroscience that evolved our understanding of the brain. Connectomics involves representing the brain as a network of regions, where the parcellation of the brain into regions using a template atlas is an integral part of the analysis. Over developmental and young adult cohorts of healthy individuals, we investigated how choosing parcellation atlases at certain resolutions affect sex classification and age prediction tasks performed using deep learning on structural connectomes. Datasets were processed on a total of 35 parcellations, where the only significant difference was observed for age prediction on the developmental cohort with a slight improvement on higher resolutions. This indicates that choice of parcellation scheme is generally not critical for deep learningbased age prediction and sex classification. Therefore, results between studies using different parcellation schemes could be comparable and repeating analyses on multiple atlases might be unnecessary.