Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints.
PLoS Comput Biol
; 12(11): e1005203, 2016 Nov.
Article
em En
| MEDLINE
| ID: mdl-27846212
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Encéfalo
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Interpretação de Imagem Assistida por Computador
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Técnica de Subtração
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Imagem de Tensor de Difusão
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Conectoma
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Substância Branca
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Adult
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Female
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Humans
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Male
Idioma:
En
Ano de publicação:
2016
Tipo de documento:
Article