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Neuroimage. 2019 Apr 1;189:676-687. doi: 10.1016/j.neuroimage.2019.02.002. Epub 2019 Feb 2.

The individual functional connectome is unique and stable over months to years.

Author information

1
Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA. Electronic address: corey.horien@yale.edu.
2
Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.
3
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.
4
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

Functional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions of the entire brain. Using four resting-state fMRI datasets with a wide range of ages, we show that individual differences of the functional connectome are stable across 3 months to 1-2 years (and even detectable at above-chance levels across 3 years). Medial frontal and frontoparietal networks appear to be both unique and stable, resulting in high ID rates, as did a combination of these two networks. We conduct analyses demonstrating that these results are not driven by head motion. We also show that edges contributing the most to a successful ID tend to connect nodes in the frontal and parietal cortices, while edges contributing the least tend to connect cross-hemispheric homologs. Our results demonstrate that the functional connectome is stable across years and that high ID rates are not an idiosyncratic aspect of a specific dataset, but rather reflect stable individual differences in the functional connectivity of the brain.

PMID:
30721751
PMCID:
PMC6422733
[Available on 2020-04-01]
DOI:
10.1016/j.neuroimage.2019.02.002
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