High-resolution connectomic fingerprints: Mapping neural identity and behavior
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 indiv...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-04-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811920311800 |
| _version_ | 1819146332026175488 |
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| author | Sina Mansour L Ye Tian B.T. Thomas Yeo Vanessa Cropley Andrew Zalesky |
| author_facet | Sina Mansour L Ye Tian B.T. Thomas Yeo Vanessa Cropley Andrew Zalesky |
| author_sort | Sina Mansour L |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T13:12:14Z |
| format | Article |
| id | doaj.art-48cdfecd3bd3441389056f8a1cc5651b |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-22T13:12:14Z |
| publishDate | 2021-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-48cdfecd3bd3441389056f8a1cc5651b2022-12-21T18:24:44ZengElsevierNeuroImage1095-95722021-04-01229117695High-resolution connectomic fingerprints: Mapping neural identity and behaviorSina Mansour L0Ye Tian1B.T. Thomas Yeo2Vanessa Cropley3Andrew Zalesky4Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia; Corresponding author.Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Victoria, AustraliaDepartment of Electrical and Computer Engineering and Yong Loo Lin School of Medicine, National University of Singapore, SingaporeMelbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Victoria, AustraliaDepartment of Biomedical Engineering, The University of Melbourne, Victoria, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Victoria, AustraliaConnectomes 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.http://www.sciencedirect.com/science/article/pii/S1053811920311800Brain connectivityHuman connectome projectConnectome fingerprintingMulti-modal dataNeural behavior predictionCortical gradients |
| spellingShingle | Sina Mansour L Ye Tian B.T. Thomas Yeo Vanessa Cropley Andrew Zalesky High-resolution connectomic fingerprints: Mapping neural identity and behavior NeuroImage Brain connectivity Human connectome project Connectome fingerprinting Multi-modal data Neural behavior prediction Cortical gradients |
| title | High-resolution connectomic fingerprints: Mapping neural identity and behavior |
| title_full | High-resolution connectomic fingerprints: Mapping neural identity and behavior |
| title_fullStr | High-resolution connectomic fingerprints: Mapping neural identity and behavior |
| title_full_unstemmed | High-resolution connectomic fingerprints: Mapping neural identity and behavior |
| title_short | High-resolution connectomic fingerprints: Mapping neural identity and behavior |
| title_sort | high resolution connectomic fingerprints mapping neural identity and behavior |
| topic | Brain connectivity Human connectome project Connectome fingerprinting Multi-modal data Neural behavior prediction Cortical gradients |
| url | http://www.sciencedirect.com/science/article/pii/S1053811920311800 |
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