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- Title
Fast connectivity gradient approximation: maintaining spatially fine-grained connectivity gradients while reducing computational costs.
- Authors
Nenning, Karl-Heinz; Xu, Ting; Tambini, Arielle; Franco, Alexandre R.; Margulies, Daniel S.; Colcombe, Stanley J.; Milham, Michael P.
- 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. This work presents a computationally efficient approach to establish spatially fine-grained connectivity gradients, reducing computational costs while preserving informative individual features and improving brain-behavior predictions.
- Subjects
SPATIAL resolution; FUNCTIONAL connectivity; SPATIAL variation; INDIVIDUAL differences; COST
- Publication
Communications Biology, 2024, Vol 7, Issue 1, p1
- ISSN
2399-3642
- Publication type
Article
- DOI
10.1038/s42003-024-06401-4