Spatio-temporal tensor analysis for whole-brain fMRI classification

Guixiang Ma, Lifang He, Chun Ta Lu, Philip S. Yu, Linlin Shen, Ann B. Ragin

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

17 Citations (Scopus)


Owing to prominence as a research and diagnostic tool in human brain mapping, whole-brain fMRI image analysis has been the focus of intense investigation. Conventionally, input fMRI brain images are converted into vectors or matrices and adapted in kernel based classifiers. fMRI data, however, are inherently coupled with sophisticated spatio-temporal tensor structure (i.e., 3D space x time). Valuable structural information will be lost if the tensors are converted into vectors. Furthermore, time series fMRI data are noisy, involving time shift and low temporal resolution. To address these analytic challenges, more compact and discriminative representations for kernel modeling are needed. In this paper, we propose a novel spatio-temporal tensor kernel (STTK) approach for whole-brain fMRI image analysis. Specifically, we design a volumetric time series extraction approach to model the temporal data, and propose a spatio-temporal tensor based factorization for feature extraction. We further leverage the tensor structure to encode prior knowledge in the kernel. Extensive experiments using real-world datasets demonstrate that our proposed approach effectively boosts the fMRI classification performance in diverse brain disorders (i.e., Alzheimer's disease, ADHD and HIV).

Original languageEnglish
Title of host publication16th SIAM International Conference on Data Mining 2016, SDM 2016
EditorsSanjay Chawla Venkatasubramanian, Wagner Meira
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Electronic)9781510828117
Publication statusPublished - 2016
Externally publishedYes
Event16th SIAM International Conference on Data Mining 2016, SDM 2016 - Miami, United States
Duration: 5 May 20167 May 2016

Publication series

Name16th SIAM International Conference on Data Mining 2016, SDM 2016


Conference16th SIAM International Conference on Data Mining 2016, SDM 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computer Science Applications
  • Software


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