Learning Robust Hierarchical Patterns of Human Brain across Many fMRI StudiesDownload PDF

21 May 2021, 20:42 (modified: 08 Oct 2021, 05:17)NeurIPS 2021 PosterReaders: Everyone
Keywords: Hierarchical Latent Factor Modeling, Matrix Factorization, Domain Adaptation, fMRI analysis
Abstract: Multi-site fMRI studies face the challenge that the pooling introduces systematic non-biological site-specific variance due to hardware, software, and environment. In this paper, we propose to reduce site-specific variance in the estimation of hierarchical Sparsity Connectivity Patterns (hSCPs) in fMRI data via a simple yet effective matrix factorization while preserving biologically relevant variations. Our method leverages unsupervised adversarial learning to improve the reproducibility of the components. Experiments on simulated datasets display that the proposed method can estimate components with higher accuracy and reproducibility, while preserving age-related variation on a multi-center clinical data set.
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