Shared Space Transfer Learning for analyzing multi-site fMRI data

• URL: http://arxiv.org/abs/2010.15594v1
• Date: Sat, 24 Oct 2020 08:50:26 GMT
• Title: Shared Space Transfer Learning for analyzing multi-site fMRI data
• Authors: Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew J. Greenshaw, Russell Greiner
• Abstract summary: Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data. MVPA works best with a well-designed feature set and an adequate sample size. Most fMRI datasets are noisy, high-dimensional, expensive to collect, and with small sample sizes. This paper proposes the Shared Space Transfer Learning (SSTL) as a novel transfer learning approach.
• Score: 83.41324371491774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data, for distinguishing when subjects are performing different cognitive tasks -- e.g., watching movies or making decisions. MVPA works best with a well-designed feature set and an adequate sample size. However, most fMRI datasets are noisy, high-dimensional, expensive to collect, and with small sample sizes. Further, training a robust, generalized predictive model that can analyze homogeneous cognitive tasks provided by multi-site fMRI datasets has additional challenges. This paper proposes the Shared Space Transfer Learning (SSTL) as a novel transfer learning (TL) approach that can functionally align homogeneous multi-site fMRI datasets, and so improve the prediction performance in every site. SSTL first extracts a set of common features for all subjects in each site. It then uses TL to map these site-specific features to a site-independent shared space in order to improve the performance of the MVPA. SSTL uses a scalable optimization procedure that works effectively for high-dimensional fMRI datasets. The optimization procedure extracts the common features for each site by using a single-iteration algorithm and maps these site-specific common features to the site-independent shared space. We evaluate the effectiveness of the proposed method for transferring between various cognitive tasks. Our comprehensive experiments validate that SSTL achieves superior performance to other state-of-the-art analysis techniques.

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