Abstract #2225

An Interpretable Deep Learning Approach for Identifying Working Memory-related Regions in fMRI using Three Large Cohorts

Tianyun Zhao^1,2, Philip N Tubiolo^2,3, John C Williams^2,3, Jared X Van Snellenberg^2,3,4, and Chuan Huang^1,2,5

¹Radiology and Imaging Science, Emory University School of Medicine, Atlanta, GA, United States, ²Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, ³Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, United States, ⁴Psychology, Stony Brook University, Stony Brook, NY, United States, ⁵Biomedical Engineering, Georgia Institute of Technology, Atalnta, GA, United States

Synopsis

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, fMRI, Working memory

Motivation: fMRI is allows studying human brain activity in vivo, but standard analyzing fMRI cannot capture nonlinear relationships between activity and variables. Utlizing deep learning (DL) models may capture such relationships, providing new insight into mechanisms underlying human health and disease.

Goal(s): To evaluate our interpretable DL pipeline in fMRI analysis using three large cohorts to demonstrate its generalizability and reproducibility.

Approach: We built a VGG-like network to predict task performance and generate saliency maps that can show brain regions important for task performance using three independent datasets.

Results: The DL generated saliency maps are consistent between each dataset.

Impact: We demonstrated that interpretable deep learning can be used as a reliable and generalizable tool to gain insight into brain regions whose activation impacts task performance.

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