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Abstract #2745

Classification of Different Episodic Memory Tasks by Time Points using a Deep Neural Network

Zhengshi Yang1, Xiaowei Zhuang1, Karthik Sreenivasan1, Virendra Mishra1, Christopher Bird1, Tim Curran2, Sarah J Banks1, and Dietmar Cordes1,2

1Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, 2University of Colorado, Boulder, CO, United States

Classification of different episodic memory tasks by time points is challenging because the signal-to-noise ratio in affected brain regions of the medial temporal lobes is low and similar brain regions (such as the hippocampus) contribute to memory activation. No studies have implemented a deep neural network (DNN) to classify memory tasks at each fMRI time point using whole-brain data. We have implemented a region-of-interest based DNN framework and applied it to classify three different episodic memory tasks. Results indicate that this DNN classifier can accurately discriminate between all these tasks.

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