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Neuroimage. 2018 Apr 1;169:172-175. doi: 10.1016/j.neuroimage.2017.12.045. Epub 2017 Dec 15.

Considering factors affecting the connectome-based identification process: Comment on Waller et al.

Author information

1
Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA. Electronic address: corey.horien@yale.edu.
2
Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA.
3
Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA; Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, USA.
4
Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.
5
Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA; The Child Study Center, Yale University School of Medicine, New Haven, CT, USA.
6
Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA; Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, USA.

Abstract

A recent study by Waller and colleagues evaluated the reliability, specificity, and generalizability of using functional connectivity data to identify individuals from a group. The authors note they were able to replicate identification rates in a larger version of the original Human Connectome Project (HCP) dataset. However, they also report lower identification accuracies when using historical neuroimaging acquisitions with low spatial and temporal resolution. The authors suggest that their results indicate connectomes derived from historical imaging data may be similar across individuals, to the extent that this connectome-based approach may be inappropriate for precision psychiatry and the goal of drawing inferences based on subject-level data. Here we note that the authors did not take into account factors affecting data quality and hence identification rates, independent of whether a low spatiotemporal resolution acquisition or a high spatiotemporal resolution acquisition is used. Specifically, we show here that the amount of data collected per subject and in-scanner motion are the predominant factors influencing identification rates, not the spatiotemporal resolution of the acquisition. To do this, we investigated identification rates in the HCP dataset as a function of the amount of data and motion. Using a dataset from the Consortium for Reliability and Reproducibility (CoRR), we investigated the impact of multiband versus non-multiband imaging parameters; that is, high spatiotemporal resolution versus low spatiotemporal resolution acquisitions. We show scan length and motion affect identification, whereas the imaging protocol does not affect these rates. Our results suggest that motion and amount of data per subject are the primary factors impacting individual connectivity profiles, but that within these constraints, individual differences in the connectome are readily observable.

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