Observation of nociceptive detection thresholds and cortical evoked potentials: Go/no-go versus two-interval forced choice.

Pain scientists and clinicians search for objective measures of altered nociceptive processing to study and stratify chronic pain patients. Nociceptive processing can be studied by observing a combination of nociceptive detection thresholds and evoked potentials. However, it is unknown whether the nociceptive detection threshold measured using a go-/no-go (GN) procedure can be biased by a response criterion. In this study, we compared nociceptive detection thresholds, psychometric slopes, and central evoked potentials obtained during a GN procedure with those obtained during a two-interval forced choice (2IFC) procedure to determine (1) if the nociceptive detection threshold during a GN procedure is biased by a criterion and (2) to determine if nociceptive evoked potentials observed in response to stimuli around the detection threshold are biased by a criterion. We found that the detection threshold was higher when assessed using a GN procedure in comparison with the 2IFC procedure. During a GN procedure, the average P2 component increased proportionally when averaged with respect to detection probability, but showed on-off behavior when averaged with respect to stimulus detection. During a 2IFC procedure, the average P2 component increased nonlinearly when averaged with respect to detection probability. These data suggest that nociceptive detection thresholds estimated using a GN procedure are subject to a response criterion.

Real-time estimation of perceptual thresholds based on the electroencephalogram using a deep neural network.

BACKGROUND: Perceptual thresholds are measured in scientific and clinical setting to evaluate performance of the nervous system in essential tasks such as vision, hearing, touch, and registration of pain. Current procedures for estimating perceptual thresholds depend on the analysis of pairs of stimuli and participant responses, relying on the commitment and cognitive ability of subjects to respond accurately and consistently to stimulation. Here, we demonstrate that it is possible to measure the threshold for the perception of nociceptive stimuli based on non-invasively recorded brain activity alone using a deep neural network. NEW METHOD: For each stimulus, a trained deep neural network performed a 2-interval forced choice procedure, in which the network had to choose which of two time intervals in the electroencephalogram represented post-stimulus brain activity. Network responses were used to estimate the perceptual threshold in real-time using a psychophysical method of limits. COMPARISON WITH EXISTING METHODS: Network classification was able to match participants in reporting stimulus perception, resulting in average network-estimated perceptual thresholds that matched perceptual thresholds based on participant reports. RESULTS: The neural network successfully separated trials containing brain responses from trials without and could consistently estimate perceptual thresholds in real-time during a Go-/No-Go procedure and a counting task. CONCLUSION: Deep neural networks monitoring non-invasively recorded brain activity are now able to accurately predict stimulus perception and estimate the perceptual threshold in real-time without any verbal or motor response from the participant.