期刊
PLOS COMPUTATIONAL BIOLOGY
卷 12, 期 11, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1005203
关键词
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资金
- Army Research Laboratory
- NSF BIGDATA [1247658]
- Human Connectome Project, WU-Minn Consortium [1U54MH091657]
- Ruentex Group
- Ministry of Economic Affairs, Taiwan [101-EC-17-A-19-S1-175]
- National Institutes of Health [R01 DK095172]
- [W911NF-10-2-0022]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1247658] Funding Source: National Science Foundation
Quantifying differences or similarities in connectomes has been a challenge due to the immense complexity of global brain networks. Here we introduce a noninvasive method that uses diffusion MRI to characterize whole-brain white matter architecture as a single local connectome fingerprint that allows for a direct comparison between structural connectomes. In four independently acquired data sets with repeated scans (total N = 213), we show that the local connectome fingerprint is highly specific to an individual, allowing for an accurate self-versus-others classification that achieved 100% accuracy across 17,398 identification tests. The estimated classification error was approximately one thousand times smaller than fingerprints derived from diffusivity-based measures or region-to-region connectivity patterns for repeat scans acquired within 3 months. The local connectome fingerprint also revealed neuroplasticity within an individual reflected as a decreasing trend in self-similarity across time, whereas this change was not observed in the diffusivity measures. Moreover, the local connectome fingerprint can be used as a phenotypic marker, revealing 12.51% similarity between monozygotic twins, 5.14% between dizygotic twins, and 4.51% between none-twin siblings, relative to differences between unrelated subjects. This novel approach opens a new door for probing the influence of pathological, genetic, social, or environmental factors on the unique configuration of the human connectome.
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