Real-time full correlation matrix analysis of fMRI data

Yida Wang, Bryn Keller, Mihai Capota, Michael J. Anderson, Narayanan Sundaram, Jonathan D. Cohen, Kai Li, Nicholas B. Turk-Browne, Theodore L. Willke

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Real-time functional magnetic resonance imaging (rtfMRI) is an emerging approach for studying the functioning of the human brain. Computational challenges combined with high data velocity have to this point restricted rtfMRI analyses to studying regions of the brain independently. However, given that neural processing is accomplished via functional interactions among brain regions, neuroscience could stand to benefit from rtfMRI analyses of full-brain interactions. In this paper, we extend such an offline analysis method, full correlation matrix analysis (FCMA), to enable its use in rtfMRI studies. Specifically, we introduce algorithms capable of processing real-time data for all stages of the FCMA machine learning workflow: incremental feature selection, model updating, and real-time classification. We also present an actor-model based distributed system designed to support FCMA and other rtfMRI analysis methods. Experiments show that our system successfully analyzes a stream of brain volumes and returns neurofeedback with less than 180 ms of lag. Our real-time FCMA implementation provides the same accuracy as an optimized offline FCMA toolbox while running 3.6-6.2x faster.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1242-1251
Number of pages10
ISBN (Electronic)9781467390040
DOIs
StatePublished - Jan 1 2016
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: Dec 5 2016Dec 8 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Other

Other4th IEEE International Conference on Big Data, Big Data 2016
CountryUnited States
CityWashington
Period12/5/1612/8/16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Hardware and Architecture

Keywords

  • big data
  • full correlation matrix analysis
  • machine learning
  • real-time fMRI
  • streaming

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