Detection and Quantification of a Wide Range of fMRI Temporal Responses Using a Physiologically-Motivated Basis Set

Michael P. Harms, Jennifer R. Melcher

Research output: Contribution to journalArticle

43 Scopus citations


The temporal dynamics of fMRI responses can span a broad range, indicating a rich underlying physiology, but also posing a significant challenge for detection. For instance, in human auditory cortex, prolonged sound stimuli (∼30 sec) can evoke responses ranging from sustained to highly phasic (i.e., characterized by prominent peaks just after sound onset and offset). In the present study, we developed a method capable of detecting a wide variety of responses, while simultaneously extracting information about individual response components, which may have different neurophysiological underpinnings. Specifically, we implemented the general linear model using a novel set of basis functions chosen to reflect temporal features of cortical fMRI responses. This physiologically-motivated basis set (the "OSORU" basis set) was tested against (1) the commonly employed "sustained-only" basis "set" (i.e., a single smoothed "boxcar" function), and (2) a sinusoidal basis set, which is capable of detecting a broad range of responses, but lacks a direct relationship to individual response components. On data that included many different temporal responses, the OSORU basis set performed far better overall than the sustained-only set, and as well or better than the sinusoidal basis set. The OSORU basis set also proved effective in exploring brain physiology. As an example, we demonstrate that the OSORU basis functions can be used to spatially map the relative amount of transient vs. sustained activity within auditory cortex. The OSORU basis set provides a powerful means for response detection and quantification that should be broadly applicable to any brain system and to both human and non-human species.

Original languageEnglish
Pages (from-to)168-183
Number of pages16
JournalHuman Brain Mapping
Issue number3
StatePublished - Nov 1 2003
Externally publishedYes


  • Auditory cortex
  • General linear model
  • Temporal dynamics
  • fMRI

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