Abstract #3487

Deep Learning Identifies Neuroimaging Signatures of Alzheimer’s Disease Using Structural and Artificial Functional MRI Data

Nanyan Zhu^1,2,3, Chen Liu^2,3,4, Sabrina Gjerswold-Selleck⁵, Xinyang Feng⁵, Dipika Sikka⁵, Scott A. Small^2,6,7, and Jia Guo^3,8

¹Biological Science, Columbia University, New York, NY, United States, ²Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, United States, ³Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States, ⁴Electrical Engineering, Columbia University, New York, NY, United States, ⁵Biomedical Engineering, Columbia University, New York, NY, United States, ⁶Radiology, Columbia University, New York, NY, United States, ⁷Gertrude H. Sergievsky Center, Columbia University, New York, NY, United States, ⁸Psychiatry, Columbia University, New York, NY, United States

Alzheimer’s disease (AD) is a neurodegenerative disorder where functional deficits precede structural deformations. Various studies have demonstrated the efficacy of deep learning in diagnosing AD using imaging data, and that functional modalities are more helpful than structural counterparts over comparable sample size. To deal with the lack of large-scale functional data in the real world, we used a structure-to-function translation network to artificially generate a previously non-existent spatially-matched functional neuroimaging dataset from existing large-scale structural data. The artificial functional data, generated with little cost, complemented the authentic structural data to further improve the performance of AD classification.

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