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A Model-Free De-Drifting Approach for Detecting BOLD Activities in fMRI Data

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Abstract

A model-free method for efficiently capturing drifts in functional magnetic resonance imaging (fMRI) data is presented. The proposed algorithm applies a first order differencing to the fMRI time series samples in order to remove the drift effect. Initially, a consistent hemodynamic response function (HRF) of the fMRI voxel is estimated using linear least-squares. An optimal estimate of the drift is then obtained based on a wavelet thresholding technique applied to the generated residuals after eliminating the induced activation response. Finally, the de-drifted fMRI voxel response is acquired by removing the estimated drift from the fMRI time-series. Its performance is assessed using simulated and motor-task real fMRI data sets obtained from both block and event-related designs. The application results reveal that the proposed method, which avoids the selection of a model to remove the drift component unlike traditional methods, is efficient in de-drifting the fMRI time-series and offers blood oxygen level-dependent (BOLD)-fMRI signal improvement and enhanced activation detection.

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Acknowledgments

The author would like to thank:

1. Dr. Abd-Krim Seghouane (Department of Electrical and Electronic Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia) for insightful comments on this extended work of our previous paper [32].

2. Bio Imaging & Signal Processing Lab of KAIST who shared the fMRI data.

3. The anonymous reviewers for their feedback and insightful comments which greatly helped to improve the content and presentation of this paper.

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Authors and Affiliations

  1. National ICT Australia, Canberra Research Laboratory, PO Box 8001, Canberra, ACT, 2601, Australia

    Adnan Shah

  2. The College of Engineering and Computer Science, Australian National University, Canberra, Australia

    Adnan Shah

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  1. Adnan Shah
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Correspondence to Adnan Shah.

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Shah, A. A Model-Free De-Drifting Approach for Detecting BOLD Activities in fMRI Data. J Sign Process Syst 79, 133–143 (2015). https://doi.org/10.1007/s11265-014-0926-8

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  • DOI: https://doi.org/10.1007/s11265-014-0926-8

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