Before revision:  We have processed and parcellated the 8 fMRI conditions for the 1200 from subjects part of the HCP-YA datasetinto to the Schaefer parcellations, which are further subdivisions of the resting state functional networks. We also provide a detailed fingerprinting analysis of these parcellated connectomes and timeseries data using the differential identifiability framework on three different cohorts: Unrelated subjects, monozygotic twins, and dizygotic twins. Overall, results indicate that combining fine-grained parcellations with the differential identifiability framework uncovers fingerprints for all cohorts and fMRI conditions evaluated .
After revision:  We have processed and parcellated the 8 fMRI conditions for the 1200 from subjects part of the HCP-YA datasetinto to the Schaefer Atlas scheme, which has multiple levels of resolution. Our differential identifiability results show that the fingerprint gradients based on genetic and environmental similarities are indeed present when comparing FCs for all parcellations and fMRI conditions. Importantly, only when assessing optimally reconstructed FCs, we fully uncover fingerprints present in higher resolution atlases. We also study the effect of scanning length on subject fingerprint of resting-state FCs to analyze the effect of scanning length and parcellation. In the pursuit of open science, we have also made available the processed and parcellated FCs and timeseries for all conditions for ~1200 subjects part of the HCP-YA dataset to the scientific community .