Validation and optimization of statistical approaches for modeling odorant-induced fMRI signal changes in olfactory-related brain areas.

Recent neuroimaging studies have converged to show that odorant-induced responses to prolonged stimulation in primary olfactory cortex (POC) are characterized by a rapidly habituating time course. Different statistical approaches have effectively modeled this time course. One approach explicitly modeled rapid habituation using an exponentially decaying reference waveform that decreased to baseline levels within 30 to 40 s. A second approach modeled an early transient response by simply shortening the odorant 'ON' period to be less than the actual stimulation period (i.e., 9 of 40 s). The goal of the current study was to validate, compare, and optimize these methodological approaches by applying them to an olfactory fMRI block-design dataset from 10 healthy young subjects presented with odorants for 12 s (ON), alternating with 30 s of clear air (OFF). Both approaches significantly improved sensitivity to odorant-induced signal changes in POC relative to a square-wave model based on the actual stimulation period. Our findings further demonstrate that the 'optimal' model fit to the data was achieved by shortening the odorant 'ON' period to approximately 6 s. These results suggest that sensitivity to odorant-induced POC activity in block-design experiments can be optimized by modeling an early phasic response followed by a precipitous rather than specific exponential decrease to baseline levels. Notably, whole brain voxel-wise analyses further established that modeling rapid habituation in this way is not only sensitive, but also highly specific to odorant-induced activation in a well-established network of olfactory-related brain areas.