Sensory thresholds obtained from MEG data: cortical psychometric functions

Abstract

Sensory sensitivity is typically measured using behavioural techniques (psychophysics), which rely on observers responding to very large numbers of stimulus presentations. Psychophysics can be problematic when working with special populations, such as children or clinical patients who may lack the compliance or cognitive skills to perform the behavioural tasks. We used an auditory gap-detection paradigm to develop an accurate measure of sensory threshold derived from passively-recorded magnetoencephalographic (MEG) data. Auditory evoked responses were elicited by silent gaps of varying durations in an on-going noise stimulus. Source modelling was used to spatially filter the MEG data and sigmoidal 'cortical psychometric functions' relating response amplitude to gap duration were obtained for each individual participant. Fitting the functions with a curve and estimating the gap duration at which the amplitude of the evoked response exceeded one standard deviation of the prestimulus brain activity provided an excellent prediction of psychophysical threshold. Accurate sensory thresholds can therefore be reliably extracted from MEG data recorded while participants listen passively to a stimulus. Because our paradigm required no behavioural task, the method is suitable for studies of populations where variations in cognitive skills or vigilance make traditional psychophysics unsuitable.