Large openly available neuroimaging datasets, although highly valuable, do not typically provide the community with subject-level whole-brain connectivity matrices that are ready to be used brain connectivity analysis. In the pursuit of open science, we have made available fully processed and parcellated connectomes and timeseries for the Human Connectome Project - Young Adult (HCP-YA)dataset to the neuroimaging and computational neuroscience community . 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 .
The assessment of brain fingerprints has emerged in the recent years as an important tool to study individual differences and to infer quality of neuroimaging datasets. Studies so far have mainly focused on connectivity fingerprints between different brain scans of the same individual. Here, we extend the concept of brain connectivity fingerprints beyond test/retest and assess fingerprint gradients in young adults by developing an extension of the differential identifiability framework. To do so, we look at the similarity between not only the multiple scans of an individual (subject fingerprint), but also between the scans of monozygotic and dizygotic twins (twin fingerprint) . We have carried out this analysis on the 8 fMRI conditions present in the Human Connectome Project -- Young Adult dataset, which we processed into functional connectomes (FCs) and timeseries parcellated according 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 .
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Large openly available neuroimaging datasets, although highly valuable, do not typically provide the community with subject-level whole-brain connectivity matrices that are ready to be used brain connectivity analysis. In the pursuit of open science, we have made available fully processed and parcellated connectomes and timeseries for the Human Connectome Project - Young Adult (HCP-YA)dataset to the neuroimaging and computational neuroscience community
The assessment of brain fingerprints has emerged in the recent years as an important tool to study individual differences and to infer quality of neuroimaging datasets. Studies so far have mainly focused on connectivity fingerprints between different brain scans of the same individual. Here, we extend the concept of brain connectivity fingerprints beyond test/retest and assess fingerprint gradients in young adults by developing an extension of the differential identifiability framework. To do so, we look at the similarity between not only the multiple scans of an individual (subject fingerprint), but also between the scans of monozygotic and dizygotic twins (twin fingerprint)
R
processed and parcellated
carried out this analysis on
R
for the 1200 from subjects part of the HCP-YA datasetinto
present in the Human Connectome Project -- Young Adult dataset, which we processed into functional connectomes (FCs) and timeseries parcellated according
R
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
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