Abstract
Reliable determination of sensory thresholds is the holy grail of signal detection theory. However, there exists no assumption-independent gold standard for the estimation of thresholds based on neurophysiological parameters, although a reliable estimation method is crucial for both scientific investigations and clinical diagnosis. Whenever it is impossible to communicate with the subjects, as in studies with animals or neonates, thresholds have to be derived from neural recordings or by indirect behavioral tests. Whenever the threshold is estimated based on such measures, the standard approach until now is the subjective setting—either by eye or by statistical means—of the threshold to the value where at least a “clear” signal is detectable. These measures are highly subjective, strongly depend on the noise, and fluctuate due to the low signal-to-noise ratio near the threshold. Here we show a novel method to reliably estimate physiological thresholds based on neurophysiological parameters. Using surrogate data we demonstrate that fitting the responses to different stimulus intensities with a hard sigmoid function, in combination with subsampling, provides a robust threshold value as well as an accurate uncertainty estimate. This method has no systematic dependence on the noise and does not even require samples in the full dynamic range of the sensory system. We prove that this method is universally applicable to all types of sensory systems, ranging from somatosensory stimulus processing in the cortex to auditory processing in the brain stem.
Highlights
Objective, reliable, and reproducible estimation of sensory thresholds is a fundamental problem in neuroscience as well as clinical diagnostics
Common methods for threshold estimation are based on data with low signalto-noise ratio (S/N), where responses may be hard to detect or cannot be detected at all so that thresholds are often assessed too large
A striking example for such severe uncertainty is the evaluation of auditory brainstem responses (ABR) by clinical professionals, where threshold estimates have been demonstrated to differ by up to 60 dB between evaluators (Vidler and Parkert, 2004)
Summary
Reliable, and reproducible estimation of sensory thresholds is a fundamental problem in neuroscience as well as clinical diagnostics. A fully automated method for threshold estimation which is robust against low S/N would be preferable to guarantee objectivity and reproducibility. This challenging problem has been tackled by the implementation of machine learning algorithms such as support vector machines (Acir et al, 2006). Machine learning approaches generate black-box systems with complex internal decision criteria that are not comprehensive to the users (Castelvecchi, 2016) They require huge data sets for training and are neither feasible nor accepted for medical diagnostic purposes (Kononenko, 2001)
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