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• W3119000187 endingPage "117695" @default.
• W3119000187 startingPage "117695" @default.
• W3119000187 abstract "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." @default.
• W3119000187 created "2021-01-18" @default.
• W3119000187 creator A5016198416 @default.
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• W3119000187 creator A5056249695 @default.
• W3119000187 creator A5070934699 @default.
• W3119000187 creator A5076799707 @default.
• W3119000187 date "2021-04-01" @default.
• W3119000187 modified "2023-09-30" @default.
• W3119000187 title "High-resolution connectomic fingerprints: Mapping neural identity and behavior" @default.
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• W3119000187 doi "https://doi.org/10.1016/j.neuroimage.2020.117695" @default.
• W3119000187 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33422711" @default.
• W3119000187 hasPublicationYear "2021" @default.

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