Medial olivocochlear-induced transient-evoked otoacoustic emission amplitude shifts in individual subjects.

Activation of the medial olivocochlear reflex (MOCR) can be assessed indirectly using transient-evoked otoacoustic emissions (TEOAEs). The change in TEOAE amplitudes when the MOCR is activated (medial olivocochlear (MOC) shift) has most often been quantified as the mean value in groups of subjects. The usefulness of MOC shift measurements may be increased by the ability to quantify significant shifts in individuals. This study used statistical resampling to quantify significant MOC shifts in 16 subjects. TEOAEs were obtained using transient stimuli containing energy from 1 to 10 kHz. A nonlinear paradigm was used to extract TEOAEs. Transient stimuli were presented at 30 dB sensation level (SL) with suppressor stimuli presented 12 dB higher. Contralateral white noise, used to activate the MOCR, was presented at 30 dB SL and was interleaved on and off in 30-s intervals during a 7-min recording period. Confounding factors of middle ear muscle reflex and slow amplitude drifts were accounted for. TEOAEs were analyzed in 11 1/3-octave frequency bands. The statistical significance of each individual MOC shift was determined using a bootstrap procedure. The minimum detectable MOC shifts ranged from 0.10 to 3.25 dB and were highly dependent on signal-to-noise ratio at each frequency. Subjects exhibited a wide range of magnitudes of significant MOC shifts in the 1.0-3.2-kHz region (median = 1.94 dB, range = 0.34-6.51 dB). There was considerable overlap between the magnitudes of significant and nonsignificant shifts. While most subjects had significant MOC shifts in one or more frequency bands below 4 kHz, few had significant shifts in all of these bands. Above 4 kHz, few significant shifts were seen, but this may have been due to lower signal-to-noise ratios. The specific frequency bands containing significant shifts were variable across individuals. Further work is needed to determine the clinical usefulness of examining MOC shifts in individuals.