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Sina Mansour L, Ye Tian, B.T. Thomas Yeo, Vanessa Cropley, Andrew Zalesky (2021). High-resolution connectomic fingerprints: Mapping neural identity and behavior. NeuroImage, 229, 117695. Elsevier BV.

Date de publication
avril 2021
Identifiant
10.1016/j.neuroimage.2020.117695
Auteurs
Sina Mansour L, Ye Tian, B.T. Thomas Yeo, Vanessa Cropley, Andrew Zalesky
Source
NeuroImage
Détails
229, 117695
Type de référence
article
Éditeur
Elsevier BV
Source de métadonnées
crossref

Résumé

Connectomes are typically mapped at low resolution based on a specific brain parcellation atlas. Here, we investigate high-resolution connectomes independent of any atlas, propose new methodologies to facilitate their mapping and demonstrate their utility in predicting behavior and identifying individuals. Using structural, functional and diffusion-weighted MRI acquired in 1000 healthy adults, we aimed to map the cortical correlates of identity and behavior at ultra-high spatial resolution. Using methods based on sparse matrix representations, we propose a computationally feasible high-resolution connectomic approach that improves neural fingerprinting and behavior prediction. Using this high-resolution approach, we find that the multimodal cortical gradients of individual uniqueness reside in the association cortices. Furthermore, our analyses identified a striking dichotomy between the facets of a person's neural identity that best predict their behavior and cognition, compared to those that best differentiate them from other individuals. Functional connectivity was one of the most accurate predictors of behavior, yet resided among the weakest differentiators of identity; whereas the converse was found for morphological properties, such as cortical curvature. This study provides new insights into the neural basis of personal identity and new tools to facilitate ultra-high-resolution connectomics.

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