ReAl-life: Accelerating the discovery of individualized brain connectomes on GPUs

Kumar, S and Sreenivasan, V and Talukdar, P and Pestilli, F and Sridharan, D (2019) ReAl-life: Accelerating the discovery of individualized brain connectomes on GPUs. In: 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, 27 January 2019through 1 February 2019, Honolulu, pp. 630-638.

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Abstract

Diffusion imaging and tractography enable mapping structural connections in the human brain, in-vivo. Linear Fascicle Evaluation (LiFE) is a state-of-the-art approach for pruning spurious connections in the estimated structural connectome, by optimizing its fit to the measured diffusion data. Yet, LiFE imposes heavy demands on computing time, precluding its use in analyses of large connectome databases. Here, we introduce a GPU-based implementation of LiFE that achieves 50-100x speedups over conventional CPU-based implementations for connectome sizes of up to several million fibers. Briefly, the algorithm accelerates generalized matrix multiplications on a compressed tensor through efficient GPU kernels, while ensuring favorable memory access patterns. Leveraging these speedups, we advance LiFE's algorithm by imposing a regularization constraint on estimated fiber weights during connectome pruning. Our regularized, accelerated, LiFE algorithm (“ReAl-LiFE”) estimates sparser connectomes that also provide more accurate fits to the underlying diffusion signal. We demonstrate the utility of our approach by classifying pathological signatures of structural connectivity in patients with Alzheimer's Disease (AD). We estimated million fiber whole-brain connectomes, followed by pruning with ReAl-LiFE, for 90 individuals (45 AD patients and 45 healthy controls). Linear classifiers, based on support vector machines, achieved over 80% accuracy in classifying AD patients from healthy controls based on their ReAl-LiFE pruned structural connectomes alone. Moreover, classification based on the ReAl-LiFE pruned connectome outperformed both the unpruned connectome, as well as the LiFE pruned connectome, in terms of accuracy. We propose our GPU-accelerated approach as a widely relevant tool for non-negative least squares optimization, across many domains.

Item Type:	Conference Paper
Publication:	33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
Publisher:	AAAI Press
Additional Information:	The copyright for this article belongs to the Association for the Advancement of Artificial Intelligence.
Keywords:	Diffusion; Neurodegenerative diseases; Program processors; Support vector machines, Alzheimer's disease; Generalized matrix; Least-squares optimization; Linear classifiers; Memory access patterns; State-of-the-art approach; Structural connections; Structural connectivity, Brain mapping
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:	05 Dec 2022 10:12
Last Modified:	05 Dec 2022 10:12
URI:	https://eprints.iisc.ac.in/id/eprint/78259

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