Brain imaging with iterated ripple noise

Abstract

In the early 1990's Bill Yost challenged me to test the Auditory Image Model (AIM) with Iterated Ripple Noise (IRN). Bill and Stan Sheft had established the mathematics of IRN using an autocorrelation model. We jointly demonstrated that the pitch algorithms in AIM and their autocorrelation model produced comparable estimates of pitch value and pitch strength. The cochlear simulations in the models both showed that IRNs with similar energy levels but different lags would produce similar spectral and temporal distributions of activity in the cochlea. This led to the intriguing idea that IRN could be used in brain imaging experiments to produce strong neural responses in centers all along the auditory pathway, and so make local imaging contrasts sensitive to aspects of activity beyond gross level, including pitch strength. Around 2000, Tim Griffith's group in London began to employ IRN melodies in PET and fMRI studies. More recently, Andre Rupp's group in Heidelberg has developed MEG imaging with IRN. These and other studies indicate that pitch processing begins in auditory cortex around Heschl's gyrus, bilaterally. These findings, based on Bill's informed use of IRN, are a great tribute to his extensive, inventive research record.