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Abstract #1385

Visualizing sex differences in deep learning models for neuroimaging using neuroanatomically guided salient source separation

Ishaan Batta1, Anees Abrol1, Yuda Bi1, and Vince D. Calhoun1
1Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Emory University, Georgia Institute of Technology, Atlanta, GA, United States

Synopsis

Keywords: Diagnosis/Prediction, Brain, ML, Deep Learning, Source Separation

Motivation: This work aims at utilizing and interpreting the associative patterns learned by DL models with the goal of aiding data-driven biomarker development for brain disorders.

Goal(s): Automatically source separation of salient brain areas from the high-dimensional predictive encodings of deep learning models for structural neuroimaging data.

Approach: We compute active subspaces in the saliency space of a deep learning network trained using sMRI maps, followed by a spatially-constrained independent component analysis (scICA) step.

Results: Our framework is able to compute multiple salient brain sources that characterize the sex differences encoded by a deep learning model, along with high prediction accuracy.

Impact: This work aims at utilizing and interpreting the associative patterns learned by DL models with the goal of aiding data-driven biomarker development for brain characeristics and disorders.

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Keywords