| Autor: |
Karl-Heinz Nenning, Ting Xu, Arielle Tambini, Alexandre R. Franco, Daniel S. Margulies, Stanley J. Colcombe, Michael P. Milham |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Communications Biology, Vol 7, Iss 1, Pp 1-7 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2399-3642 |
| DOI: |
10.1038/s42003-024-06401-4 |
| Popis: |
Abstract Brain connectome analysis suffers from the high dimensionality of connectivity data, often forcing a reduced representation of the brain at a lower spatial resolution or parcellation. This is particularly true for graph-based representations, which are increasingly used to characterize connectivity gradients, capturing patterns of systematic spatial variation in the functional connectivity structure. However, maintaining a high spatial resolution is crucial for enabling fine-grained topographical analysis and preserving subtle individual differences that might otherwise be lost. Here we introduce a computationally efficient approach to establish spatially fine-grained connectivity gradients. At its core, it leverages a set of landmarks to approximate the underlying connectivity structure at the full spatial resolution without requiring a full-scale vertex-by-vertex connectivity matrix. We show that this approach reduces computational time and memory usage while preserving informative individual features and demonstrate its application in improving brain-behavior predictions. Overall, its efficiency can remove computational barriers and enable the widespread application of connectivity gradients to capture spatial signatures of the connectome. Importantly, maintaining a spatially fine-grained resolution facilitates to characterize the spatial transitions inherent in the core concept of gradients of brain organization. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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