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Power spectral based detection of brain activation from fMR images

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Abstract

An efficient method for detecting activation on single and multiple epoch functional MRI (fMRI) data based on power spectral density of time-series and hidden Markov model is presented. Conventional methods of analysis of fMRI data are generally based on time-domain correlation analysis concentrating mainly on the multiple epoch data and generally do not provide good results for single epoch data. The main focus of this study is the analysis of single epoch data, constrained by certain experiments such as pain response, sleep, administration of pharmacological agents, which can only have a single or very few stimulus cycles. Further, our method obviates the need to exclusively model the hemodynamic response function and correctly identifies the voxels with delayed activation. We demonstrate the efficacy of our method in detecting brain activation by using both synthetic and real fMRI data.

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Authors and Affiliations

  1. School of EEE, Singapore Polytechnic, Singapore, Singapore

    Arun Kumar

  2. BioInformatics Research Center, Nanyang Technological University, Singapore, Singapore

    Arun Kumar & Jagath C. Rajapakse

  3. Singapore-MIT Alliance, Singapore, Singapore

    Jagath C. Rajapakse

Authors
  1. Arun Kumar
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  2. Jagath C. Rajapakse
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Correspondence to Jagath C. Rajapakse.

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Kumar, A., Rajapakse, J.C. Power spectral based detection of brain activation from fMR images. Neural Comput & Applic 16, 551–557 (2007). https://doi.org/10.1007/s00521-007-0102-1

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  • DOI: https://doi.org/10.1007/s00521-007-0102-1

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