Modeling task FMRI data via supervised stochastic coordinate coding

Jinglei Lv, Binbin Lin, Wei Zhang, Xi Jiang, Xintao Hu, Junwei Han, Lei Guo, Jieping Ye, Tianming Liu

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations

Abstract

Task functional MRI (fMRI) has been widely employed to assess brain activation and networks. Modeling the rich information from the fMRI time series is challenging because of the lack of ground truth and the intrinsic complexity. Model-driven methods such as the general linear model (GLM) regresses exterior task designs from voxel-wise brain functional activity, which is confined because of ignoring the complexity and diversity of concurrent brain networks. Recently, dictionary learning and sparse coding method has attracted increasing attention in the fMRI analysis field. The major advantage of this methodology is its effectiveness in reconstructing concurrent brain networks automatically and systematically. However, the data-driven strategy is, to some extent, arbitrary due to ignoring the prior knowledge of task design and neuroscience knowledge. In this paper, we proposed a novel supervised stochastic coordinate coding (SCC) algorithm for fMRI data analysis, in which certain brain networks are learned with supervised information such as temporal patterns of task designs and spatial patterns of network templates, while other networks are learned automatically from the data. Its application on two independent fMRI datasets has shown the effectiveness of our methods.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages239-246
Number of pages8
Volume9349
DOIs
StatePublished - 2015
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9349
ISSN (Print)03029743
ISSN (Electronic)16113349

Keywords

  • Brain network
  • Supervised stochastic coordinate coding
  • Task fMRI

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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