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GPU-accelerated connectome discovery at scale

Sreenivasan, V and Kumar, S and Pestilli, F and Talukdar, P and Sridharan, D (2022) GPU-accelerated connectome discovery at scale. In: Nature Computational Science, 2 (5). pp. 298-306.

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Official URL: https://doi.org/10.1038/s43588-022-00250-z


Diffusion magnetic resonance imaging and tractography enable the estimation of anatomical connectivity in the human brain, in vivo. Yet, without ground-truth validation, different tractography algorithms can yield widely varying connectivity estimates. Although streamline pruning techniques mitigate this challenge, slow compute times preclude their use in big-data applications. We present ‘Regularized, Accelerated, Linear Fascicle Evaluation’ (ReAl-LiFE), a GPU-based implementation of a state-of-the-art streamline pruning algorithm (LiFE), which achieves >100× speedups over previous CPU-based implementations. Leveraging these speedups, we overcome key limitations with LiFE’s algorithm to generate sparser and more accurate connectomes. We showcase ReAl-LiFE’s ability to estimate connections with superlative test–retest reliability, while outperforming competing approaches. Moreover, we predicted inter-individual variations in multiple cognitive scores with ReAl-LiFE connectome features. We propose ReAl-LiFE as a timely tool, surpassing the state of the art, for accurate discovery of individualized brain connectomes at scale. Finally, our GPU-accelerated implementation of a popular non-negative least-squares optimization algorithm is widely applicable to many real-world problems.

Item Type: Journal Article
Publication: Nature Computational Science
Publisher: Springer Nature
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Magnetic resonance imaging, Big data applications; Connectomes; Diffusion magnetic resonance imaging; GPU-accelerated; Ground truth; Human brain; In-vivo; Pruning techniques; State of the art; Tractography, Graphics processing unit
Department/Centre: Division of Biological Sciences > Centre for Neuroscience
Division of Electrical Sciences > Computer Science & Automation
Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 16 Sep 2022 09:21
Last Modified: 16 Sep 2022 09:21
URI: https://eprints.iisc.ac.in/id/eprint/76550

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