Technical - Partner Event
AI Seminar – Tony Yousefnezhad
When
Aug. 13, 2021 - Aug. 13, 2021
12:00 PM – 1:00 PM MST
Where
Online
Online
Title: Shared Space Transfer Learning for analyzing multi-site fMRI data
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. In this presentation, we introduce 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 SSTL for transferring between various cognitive tasks. Our comprehensive experiments validate that SSTL achieves superior performance to other state-of-the-art analysis techniques.
For more information, please visit the following link: https://papers.nips.cc/paper/2020/hash/b837305e43f7e535a1506fc263eee3ed-Abstract.html
Short bio: Tony Yousefnezhad is a Postdoctoral Fellow working with the Department of Psychiatry and the Department of Computing Science at the University of Alberta under the supervision of Prof. Andrew Greenshaw and Prof. Russell Greiner. His primary research interests lie in developing machine/deep/reinforcement learning for solving real-world big and complex problems. Specifically, he is working on the intersection of machine learning and computational neuroscience, creating different techniques for decoding patterns of the human brain by exploiting distinctive biomarkers, i.e., fMRI, EEG, MEG, Health Records, etc.
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