Estimation and classification of BOLD responses over multiple trials

Kush Kapur, Anindya Roy, Dulal K. Bhaumik, Robert D. Gibbons, Nicole A. Lazar, John A. Sweeney, Subhash Aryal, Dave Patterson

Research output: Contribution to journalArticle

Abstract

In this article, we model functional magnetic resonance imaging (fMRI) data for event-related experiment data using a fourth degree spline to fit voxel specific blood oxygenation level-dependent (BOLD) responses. The data are preprocessed for removing long term temporal components such as drifts using wavelet approximations. The spatial dependence is incorporated in the data by the application of 3D Gaussian spatial filter. The methodology assigns an activation score to each trial based on the voxel specific characteristics of the response curve. The proposed procedure has a capability of being fully automated and it produces activation images based on overall scores assigned to each voxel. The methodology is illustrated on real data from an event-related design experiment of visually guided saccades (VGS).

Original languageEnglish (US)
Pages (from-to)3099-3113
Number of pages15
JournalCommunications in Statistics - Theory and Methods
Volume38
Issue number16-17
DOIs
StatePublished - Jan 1 2009

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Keywords

  • Functional magnetic resonance imaging
  • Nearest neighbor
  • Spatial correlation
  • Spline
  • Temporal correlation
  • Wavelets

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Kapur, K., Roy, A., Bhaumik, D. K., Gibbons, R. D., Lazar, N. A., Sweeney, J. A., Aryal, S., & Patterson, D. (2009). Estimation and classification of BOLD responses over multiple trials. Communications in Statistics - Theory and Methods, 38(16-17), 3099-3113. https://doi.org/10.1080/03610920902947576