Computing Univariate Neurodegenerative Biomarkers with Volumetric Optimal Transportation: A Pilot Study
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ORIGINAL ARTICLE
Computing Univariate Neurodegenerative Biomarkers with Volumetric Optimal Transportation: A Pilot Study Yanshuai Tu 1 & Liang Mi 1 & Wen Zhang 1 & Haomeng Zhang 1 & Junwei Zhang 2 & Yonghui Fan 1 & Dhruman Goradia 3 & Kewei Chen 3 & Richard J. Caselli 4 & Eric M. Reiman 3 & Xianfeng Gu 2 & Yalin Wang 1 & The Alzheimer’s Disease Neuroimaging Initiative
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Changes in cognitive performance due to neurodegenerative diseases such as Alzheimer’s disease (AD) are closely correlated to the brain structure alteration. A univariate and personalized neurodegenerative biomarker with strong statistical power based on magnetic resonance imaging (MRI) will benefit clinical diagnosis and prognosis of neurodegenerative diseases. However, few biomarkers of this type have been developed, especially those that are robust to image noise and applicable to clinical analyses. In this paper, we introduce a variational framework to compute optimal transportation (OT) on brain structural MRI volumes and develop a univariate neuroimaging index based on OT to quantify neurodegenerative alterations. Specifically, we compute the OT from each image to a template and measure the Wasserstein distance between them. The obtained Wasserstein distance, Wasserstein Index (WI) for short to specify the distance to a template, is concise, informative and robust to random noise. Comparing to the popular linear programming-based OT computation method, our framework makes use of Newton’s method, which makes it possible to compute WI in large-scale datasets. Experimental results, on 314 subjects (140 Aβ + AD and 174 Aβnormal controls) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) baseline dataset, provide preliminary evidence that the proposed WI is correlated with a clinical cognitive measure (the Mini-Mental State Examination (MMSE) score), and it is able to identify group difference and achieve a good classification accuracy, outperforming two other popular univariate indices including hippocampal volume and entorhinal cortex thickness. The current pilot work suggests the application of WI as a potential univariate neurodegenerative biomarker. Keywords Alzheimer’s disease . Neurodegenerative biomarker . Optimal transport . Wasserstein distance Yanshuai Tu and Liang Mi contributed equally to this work. Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf * Yalin Wang [email protected] 1
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ 85287, USA
2
Department of Computer
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