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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Abstract
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 |
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| 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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