Non-parametric bootstrapping method for measuring the temporal discrimination threshold for XXXX .

@entity1 @entity717 @entity227 @entity3520
OBJECTIVE: Recent studies have proposed that the temporal discrimination threshold (TDT), the shortest detectable time period between two stimuli, is a possible endophenotype for @entity3520 ( @entity3520 ). @entity1 with @entity3520 , the third most common @entity717 , and their first-degree relatives have been shown to have abnormal @entity227 . For this reason it is important to fully characterize each @entity1 's data. To date the TDT has only been reported as a single value. APPROACH: Here, we fit individual @entity1 data with a cumulative Gaussian to extract the mean and standard deviation of the distribution. The mean represents the point of subjective equality (PSE), the inter-stimulus interval at which @entity1 are equally likely to respond that two stimuli are one stimulus (synchronous) or two different stimuli (asynchronous). The standard deviation represents the just noticeable difference (JND) which is how sensitive @entity1 are to changes in temporal asynchrony around the PSE. We extended this method by submitting the data to a non-parametric bootstrapped analysis to get 95% confidence intervals on individual @entity1 data. MAIN RESULTS: Both the JND and PSE correlate with the TDT value but are independent of each other. Hence this suggests that they represent different facets of the TDT. Furthermore, we divided groups by age and compared the TDT, PSE, and JND values. The analysis revealed a statistical difference for the PSE which was only trending for the TDT. SIGNIFICANCE: The analysis method will enable deeper analysis of the TDT to leverage subtle differences within and between control and @entity1 groups, not apparent in the standard TDT measure.
