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 language||English (US)|
|Number of pages||6|
|State||Published - Mar 1997|
Bibliographical noteFunding 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.
Copyright 2007 Elsevier B.V., All rights reserved.
- Data analysis