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Shared Space Transfer Learning for analyzing multi-site fMRI data
Tony Muhammad Yousefnezhad · Alessandro Selvitella · Daoqiang Zhang · Andrew Greenshaw · Russell Greiner

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1686

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.

Author Information

Tony Muhammad Yousefnezhad (University of Alberta)

I am a postdoc fellow in the department of computing science and the department of psychiatry at the University of Alberta. My research interests lie in developing machine learning approaches with application to mental health data and neuroimaging such as fMRI, EEG, DTI, etc.

Alessandro Selvitella (Purdue University Fort Wayne)
Daoqiang Zhang (Nanjing University of Aeronautics and Astronautics)
Andrew Greenshaw (University of Alberta)
Russell Greiner (University of Alberta)

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