Vulnerability to sleep deprivation : a drift diffusion model perspective

• Main Author: Patanaik, Amiya
• Other Authors: Vitali Zagorodnov
• Format: Theses and Dissertations
• Language: English
• Published: 2015
• Subjects: DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
• Online Access: https://hdl.handle.net/10356/62905
• Institution: Nanyang Technological University

Most of us have experienced sleep deprivation (SD) every now and then, and know from experience that lack of sleep can adversely affect cognitive function and vigilance. The neurophysiology of sleep and lack thereof, its association with diseases and general well being has been studied extensively for over a century. The lack of sleep has enormous economic, health and life cost. The loss of performance and attention after SD can lead to industrial and transportation accidents, medical errors and lapses in security. While the decline in performance with SD is well established, it was recently (2004) observed that this decline in performance varies significantly between subjects, with some subjects remaining relatively unaffected while others show considerable decline in performance. This subject specific vulnerability towards SD remains stable in time and shows trait like features, i.e., the relative ranking of individuals according to subject specific performance (on some behavioral task) is maintained over time irrespective of sleep history. This suggests a stable neuro-cognitive basis for between-subjects differences in performance. Being able to predict an individual's vulnerability to performance decline when sleep deprived is therefore of considerable interest. The Psychomotor Vigilance Task (PVT) is a sustained-attention, simple one-choice reaction time (RT) task that measures the speed with which subjects respond to a visual stimulus. It is a proven assay for evaluating vigilance. We use the PVT to evaluate and quantify the degradation of performance with SD on three large independent data sets collected from two different labs over an extended period of time. Instead of looking at the RTs using summary statistics as it is traditionally done, we used the drift diffusion model (DDM) which is a powerful model of perceptual decision making with strong neuro-behavioral underpinnings. Using DDM we tried to address three fundamental questions: (1) How are subjects vulnerable to SD differentially affected compared to resistant subjects. (2) Can these differences measured prior to SD predict performance following SD? (3) If there are measurable differences in DDM parameters between vulnerable and resistant subjects, can these differences be supported and substantiated by neuro-imaging experiments? In addressing these questions, we intended to gain new insights into the neuro-behavioural underpinnings of differential vulnerability to SD and to construct a classification system that can use easily measurable behavioural data collected prior to SD to predict an individual's vulnerability to performance decline when sleep deprived. To be able to reliably and efficiently use the DDM to address our research objectives, considerable improvements had to be made to the DDM which was only recently (Ratcliff, 2011) adapted to the one choice RT task like PVT. The statistical properties of the model were poorly understood. The model further lacked efficient ways to simulate and estimate the model parameters. Furthermore, even if we assume that there are measurable differences between the vulnerable and resistant subjects, it remains to be seen as to how these differences are influenced by experimental conditions. Given the importance of wearable devices and smartphones, the behavioural data may soon come from these portable devices instead of controlled laboratory environments. Therefore, the performance of our model must be ascertained across varying experimental conditions. In this thesis, we made significant improvements in simulation and estimation of the model and understood the statistical limitations of the model. Using the DDM, we showed that the vulnerable subjects had a significantly slower rate of information uptake compared to resistant ones even prior to any SD. This was not anticipated by merely observing PVT performance. We tied these observations to actual brain functions using functional magnetic resonance imaging (fMRI) obtained from subjects in rested wakefulness prior to SD. Finally, we showed the DDM parameters are the most important metrics when it comes to characterizing vulnerability amongst the behavioral metrics. We constructed a classifier that was capable of predicting vulnerability using only behavioral data, taken prior to SD, accurately across data sets taken under varying experimental conditions. We showed that the classification rates were reliable, reproducible and promising. Our results will help clinicians gain a better understanding of differential vulnerability to SD and the classification model has the potential to be used in many practical settings.