Box-Jenkins intervention analysis of functional magnetic resonance imaging data

G. A. Tagaris, W. Richter, S. G. Kim, Apostolos P Georgopoulos

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Data obtained in functional magnetic resonance imaging (fMRI) typically form a time series of MRI signal collected over a period of time at constant intervals. These data are potentially autocorrelated and may contain time trends. Therefore, any assessment of significant changes in the MRI signal over a certain period of time requires the use of specific statistical techniques. For that purpose we used the Box-Jenkins intervention time series analysis to determine brain activation during task performance. We found that for a substantial number of pixels there was significant autocorrelation and, occasionally, time trends. In these cases, use of the classical t-test would not be appropriate. In contrast, Box-Jenkins intervention analysis, by detrending the series and by explicitly taking into account the correlation structure, provides a more appropriate method to determine the presence of significant activation during the task period in fMRI data.

Original languageEnglish (US)
Pages (from-to)289-294
Number of pages6
JournalNeuroscience Research
Volume27
Issue number3
DOIs
StatePublished - Mar 1997

Bibliographical note

Funding Information:
This work is supported by the USPHS grants NS32919 and RR088079, the US Department of Veterans Affairs and the American Legion Brain Sciences Chair.

Keywords

  • ARIMA
  • Box-Jenkins
  • Data analysis
  • Intervention
  • fMRI

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