Ocular Surface Sensory Processing and Signal Detection Theory
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Purpose: The main aim of the experiments in this thesis is to evaluate the feasibility of using signal detection theory (SDT) to determine the detectability and bias of various ocular surface pneumatic stimuli. Chapter specific purpose: Chapter 2: To determine the feasibility of using a portable carbon dioxide sensor to calibrate a pneumatic esthesiometer and then to calibrate the chemical stimuli. Chapter 3: i) To evaluate the feasibility of using signal detection theory (SDT) to measure the detectability and bias for nociceptive and non-nociceptive corneal pneumatic stimuli. ii) To compare the detection theory estimates between stimulus types. iii) To test the human corneal psychophysical data from this study against the linking hypotheses based on the non-primate physiology using the Bayesian analysis. Chapter 4: To evaluate the detectability of pneumatic corneal stimuli and response bias using multi-stimuli multi-criterion signal detection theory (MSDT) and analyze the effect of different factors on each detection theory parameter. Also, to evaluate the non-sensory/psychological participant attributes of anxiety and general decision making and determine the relationship between psychological and psychophysical parameters. Methods: Chapter 2: The chemical stimuli in ocular surface experiments, are combinations of medical air and added carbon dioxide (%CO2). These stimuli were calibrated using a portable CO2 sensor (COZIR CM-0041) and data logger, delivered for 90 seconds using the Waterloo Belmonte esthesiometer. The distances between sensor and esthesiometer tip were 0mm (to measure feasibility), 3, 5, and 10mm. In Experiment I, 100% CO2 was tested using 4 different flow rates (50,100,150 and 200 mL/min) at 3 working distances. In Experiment II, flow rates of 20-100 mL/min and concentrations of 20-100%CO2 were tested in 20 steps at 3 working distances. Chapter 3: 30 asymptomatic participants (10 in each experiment) were recruited after screening for ocular surface abnormalities using slit-lamp biomicroscopy. The pneumatic stimuli were delivered from a 5mm working distance to the center of the corneal surface using the Waterloo Belmonte esthesiometer. Initially, corneal thresholds were estimated as a baseline for the SDT experiments using the ascending method of limits, followed by the SDT experiment to estimate detectability (d’) and bias. The signal for the SDT experiment, a supra-threshold stimulus of intensity 1.5x the estimated threshold, was presented with a probability of 0.4 (i.e., 40% signal and 60% catch trials). d’ and bias were estimated for mechanical, chemical, and cold supra-threshold pneumatic stimuli in separate experiments. 100 trials were presented for participants in the mechanical and cold stimuli groups; 50 trials were presented for the chemical stimuli group. The trials were demarcated using automated auditory prompts and participants responded whether they detected the stimulus or not using a button box after each trial. An additional experiment was conducted using the cold stimulus with 60% stimulus probability on a separate study visit. The d’, criterion (c) and likelihood ratio (lnβ) were calculated for each participant from the yes/no responses. Chapter 4: Thirty-six participants were recruited using convenience sampling and grouped based on the symptoms score from the DEQ-5 questionnaire and contact lens usage. Psychological and psychophysical assessments were done sequentially. At the start of the first visit, general decision-making (DM) and trait anxiety were evaluated. DM was assessed using the Melbourne decision-making questionnaire II (MDMQ II) and trait anxiety was assessed using the trait version of the State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA) questionnaire. A Waterloo Belmonte esthesiometer was used to deliver cold, mechanical, and chemical stimuli to the center of the cornea at three separate study visits. The stimulus type was assigned randomly to each visit at the start of the study. The threshold (baseline for detection theory experiment) for the assigned stimulus type was obtained using the ascending method of limits. State anxiety was assessed using the state STICSA questionnaires, which were administered before (pre-) and after (post-) corneal threshold measurements. In the cold and mechanical MSDT experiments, 100 trials (80 signal (20 each for 4 intensities) and 20 catch trials) were presented in randomized order, and participants responded with a 5-point confidence rating to each stimulus. In the chemical MSDT experiments, 50 trials (20 signal trials each for two intensities and 10 catch trials) were presented, and responses were provided using 4-point confidence ratings. Detection theory indices were obtained individually and as groups, which were then analyzed using mixed models and paired t-tests. The relationships between psychological (DM, anxiety) and psychophysical (threshold, detectability, and bias) indices were analyzed using Spearman correlations. Results: Chapter 2: The CO2 sensor correctly reported the esthesiometer extremes of 0% and 100% CO2 when placed at the esthesiometer tip. There were progressive, systematic increases in concentrations reaching/reported by the sensor with increasing flow rates and nominal concentrations, and progressive decreases in measurements with increases in working distance. Chapter 3: The average (±SE) d’ of the supra-threshold cold stimuli was 0.59 ± 0.1 units, while the average d’ of the mechanical and chemical stimuli were 1.65 ± 0.37 and 1.14 ± 0.3 units. The average (±SE) criterion for the mechanical, chemical and cold stimuli were 0.58 ± 0.097, 0.37 ± 0.13 and 0.23 ± 0.1 respectively. The Bayes factor (BF) obtained using the Bayesian ANOVA mildly favored (BF10 = 1.55) a difference between the d’ of the stimulus types, with no support for a difference in the criterion between stimulus types. Further analysis of d’ using multiple comparisons supported the linking hypotheses based on the nociception and nerve conductance. Chapter 4: SDT: da and the area under the curve (Az) were significantly different between stimulus intensities within each stimulus type (all p < 0.001) but were not different between the stimulus types. Receiver operating characteristics (ROC) curves were separable between the scaled intensities for all stimulus types, and no overlaps were observed in the z-ROC space. Bias calculated using the location of criterion (c), as expected, was significantly different between each psychophysical criterion level and between the intensities within a stimulus type (all p < 0.001). For the chemical stimulus, c varied with stimulus intensity and was affected by factors (asymptomatic/symptomatic, non-contact/contact lens wearers, and both, all interaction p < 0.01). In addition, another bias metric, lnβ, depended on stimulus intensity and psychophysical criterion for all stimulus types. Decision-making: The scores for DM components were significantly different from each other (F (3,105) = 121, p < 0.001), and the contrast analysis showed that the DM-vigilance scores were significantly different from other DM-types. Significant positive correlations were observed between procrastination, hypervigilance, and buck-passing scores (p < 0.01). The chemical detection thresholds were negatively correlated with the vigilance scores (p = 0.04), and the buck-passing scores were positively correlated with the da of mechanical threshold stimuli (p = 0.049). There were significant correlations observed between the bias and DM scores, but most of the correlations were observed only for either c1 or c4. The c4 obtained for cold threshold, 1.5x, and 2x threshold stimuli were positively correlated with the buck-passing and procrastination scores (all p < 0.05). Trait anxiety: Cognitive and somatic trait anxiety were significantly different from each other (p < 0.001) and were positively correlated (p < 0.001). A significant interaction of gender was observed in the relationship between cognitive trait anxiety and mechanical detection thresholds (p < 0.05). The d-primes were not correlated with either trait anxiety scores. The bias (c and lnβ), mostly criterion 1 or 4, were significantly correlated with the trait anxiety scores (p < 0.05). The cognitive trait anxiety scores were significantly correlated with their buck-passing, procrastination, and hypervigilance DM scores (all p < 0.05). State anxiety: The somatic component of the state anxiety significantly reduced as the study progressed (p < 0.05), but no significant change was observed in the cognitive component. The state anxiety scores from pre- and post- threshold measurements were not significantly different from each other, There were significant correlations observed between the bias (mostly criterion 1 or 4) and state anxiety scores (all p < 0.05). Conclusion: Chapter 2: CO2 concentrations in pneumatic esthesiometers can be calibrated and as expected, vary with flow rate and distance, highlighting the importance of calibration and standardization of CO2 stimuli in these instruments. Chapter 3: Our experiments were the first to show that it is feasible to use a detection theory approach to examine ocular surface sensory processing. The detectability of the cold stimuli was low compared to the noxious mechanical or chemical stimuli. The participants in this experiment chose a conservative strategy (reporting ‘no’ to trials more commonly), but this strategy might be anticipated considering that the experiment was designed with a relatively large proportion of catch trials. Based on the outcomes, there is a need for a multi-criterion multi-stimulus repeated measures experiment to analyze the d’ and bias characteristic. Chapter 4: It is feasible to use MSDT for analyzing ocular surface sensory processing and the theory provides insight into the possible bias associated with the use of pneumatic stimuli. With noxious and non-noxious pneumatic stimulation, detectability and criteria vary systematically with stimulus intensity, a result that cannot be derived using classical psychophysics.
Cite this version of the work
Varadharajan Jayakumar (2021). Ocular Surface Sensory Processing and Signal Detection Theory. UWSpace. http://hdl.handle.net/10012/17562