Fast Connectivity Gradient Approximation: Maintaining spatially fine-grained connectivity gradients while reducing computational costs

Nenning, K.-H.; Xu, T.; Tambini, A.; Franco, A. R.; Margulies, D. S.; Colcombe, S. J.; Milham, M. P.

doi:10.1101/2023.07.22.550017

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bioRxiv · 10.1101/2023.07.22.550017

Fast Connectivity Gradient Approximation: Maintaining spatially fine-grained connectivity gradients while reducing computational costs

Nenning, K.-H.·Xu, T.·Tambini, A.·Franco, A. R.·Margulies, D. S.·Colcombe, S. J.·Milham, M. P.

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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. However, maintaining high spatial resolution can both allow fine-grained topographical analysis and preserve subtle individual differences otherwise lost. This work presents a computationally efficient approach to estimate spatially fine-grained connectivity gradients and demonstrates its application in improving brain-behavior predictions.

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Nenning, K.-H., Xu, T., Tambini, A., Franco, A. R., Margulies, D. S., Colcombe, S. J., Milham, M. P.. 2023-07-25. Fast Connectivity Gradient Approximation: Maintaining spatially fine-grained connectivity gradients while reducing computational costs. https://doi.org/10.1101/2023.07.22.550017

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