Do not throw out the baby with the bath water: choosing an effective baseline for a functional localizer of speech processing
Version of Record online: 17 FEB 2013
© 2013 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Brain and Behavior
Volume 3, Issue 3, pages 211–222, May 2013
How to Cite
Brain and Behavior 2013; 3(3):211–222.
- Issue online: 13 MAY 2013
- Version of Record online: 17 FEB 2013
- Manuscript Accepted: 15 JAN 2013
- Manuscript Revised: 23 DEC 2012
- Manuscript Received: 31 JUL 2012
- Israel Science Foundation. Grant Number: 513/11
- Marie Curie International Reintegration Grant. Grant Number: DNLP 231029
- fMRI ;
- functional localizer;
- reversed speech;
- signal correlated noise;
- speech perception
Speech processing engages multiple cortical regions in the temporal, parietal, and frontal lobes. Isolating speech-sensitive cortex in individual participants is of major clinical and scientific importance. This task is complicated by the fact that responses to sensory and linguistic aspects of speech are tightly packed within the posterior superior temporal cortex. In functional magnetic resonance imaging (fMRI), various baseline conditions are typically used in order to isolate speech-specific from basic auditory responses. Using a short, continuous sampling paradigm, we show that reversed (“backward”) speech, a commonly used auditory baseline for speech processing, removes much of the speech responses in frontal and temporal language regions of adult individuals. On the other hand, signal correlated noise (SCN) serves as an effective baseline for removing primary auditory responses while maintaining strong signals in the same language regions. We show that the response to reversed speech in left inferior frontal gyrus decays significantly faster than the response to speech, thus suggesting that this response reflects bottom-up activation of speech analysis followed up by top-down attenuation once the signal is classified as nonspeech. The results overall favor SCN as an auditory baseline for speech processing.