Health and Social Development, Faculty of (Okanagan)
Health and Exercise Sciences, School of (Okanagan)
DSpace
UBCO
Copeland, Paige
Master of Science - MSc
University of British Columbia
Sleep deprivation (SD) is a common condition in today’s society. SD can impair cognitive and sensorimotor functioning, including that of the vestibular system, as increases in vestibulo-ocular reflex gain have been documented following SD. Further, SD can influence postural control negatively, though results are not conclusive, and the physiological reasoning behind the observed impairments in standing balance remains unclear. PURPOSE: The purpose was to examine the effect of 24-h SD on the vestibular control of standing balance. It was hypothesized that after 24-h SD, vestibular-evoked balance responses would increase compared to the well-rested control state. METHODS: During a well-rested and SD condition, nine healthy females (22.8 ± 2.9 years) completed two 90-s trials of binaural, bipolar stochastic electrical vestibular stimulation (EVS; 0-25 Hz, root-mean-square = 1.1 mA) and two 120-s trials of quiet stance on a force plate. Mediolateral (ML) ground reaction forces and surface electromyography of the right medial gastrocnemius (MG) were sampled and cross-correlated against the EVS signal to quantify vestibular control of balance within the frequency (gain and coherence) and time (cumulant density) domain. RESULTS: Overall, SD did not alter any quiet stance parameters (p ≥ 0.06). In the time domain, sleep deprivation did not alter the EVS-MG (p=0.51) nor the EVS-ML force (p=0.06) peak-to-peak amplitude. Sleep deprivation had limited effect on the EVS-MG and EVS-ML force coherence, but there were greater normalized gain amplitudes for EVS-MG (<8, 11-24Hz) and EVS-ML force (0.5-9Hz) following SD than the well-rested control. CONCLUSION: Despite a lack of SD-related changes to quiet standing balance parameters, nor changes to the cumulant density estimates and coherence, the gain of the vestibular-evoked balance response increased with SD. The current findings likely indicate that SD leads to higher sensitivity of the central nervous system when transforming vestibular-driven signals for standing balance control.
THE EFFECTS OF 24 HOURS OF SLEEP DEPRIVATION ON THE VESTIBULAR CONTROL OF STANDING BALANCE by Paige Copeland BHK, The University of British Columbia, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Health and Exercise Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) July 2021 © Paige Copeland, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: The effects of 24 hours of sleep deprivation on the vestibular control of standing balance submitted by Paige Copeland in partial fulfillment of the requirements of the degree of Master of Science Dr. Brian Dalton, School of Health and Exercise Science Supervisor Dr. Chris McNeil, School of Health and Exercise Science Supervisory Committee Member Dr. Paul van Donkelaar, School of Healthy and Exercise Science Supervisory Committee Member Dr. Vicki Komisar, School of Engineering University Examiner iii Abstract Sleep deprivation (SD) is a common condition in today’s society. SD can impair cognitive and sensorimotor functioning, including that of the vestibular system, as increases in vestibulo-ocular reflex gain have been documented following SD. Further, SD can influence postural control negatively, though results are not conclusive, and the physiological reasoning behind the observed impairments in standing balance remains unclear. PURPOSE: The purpose was to examine the effect of 24-h SD on the vestibular control of standing balance. It was hypothesized that after 24-h SD, vestibular-evoked balance responses would increase compared to the well-rested control state. METHODS: During a well-rested and SD condition, nine healthy females (22.8 ± 2.9 years) completed two 90-s trials of binaural, bipolar stochastic electrical vestibular stimulation (EVS; 0-25 Hz, root-mean-square amplitude = 1.1 mA) and two 120-s trials of quiet stance on a force plate. Mediolateral (ML) ground reaction forces and surface electromyography of the right medial gastrocnemius (MG) were sampled and cross-correlated against the EVS signal to quantify vestibular control of balance within the frequency (gain and coherence) and time (cumulant density) domain. RESULTS: Overall, SD did not alter any quiet stance parameters (p ≥ 0.06). In the time domain, sleep deprivation did not alter the EVS-MG (p=0.51) nor the EVS-ML force (p=0.06) peak-to-peak amplitude. Sleep deprivation had limited effect on the EVS-MG and EVS-ML force coherence, but there were greater normalized gain amplitudes for EVS-MG (<8, 11-24Hz) and EVS-ML force (0.5-9Hz) following SD than the well-rested control. CONCLUSION: Despite a lack of SD-related changes to quiet standing balance parameters, nor changes to the cumulant density estimates and coherence, the gain of the vestibular-evoked balance response increased with SD. The current findings likely indicate that iv SD leads to higher sensitivity of the central nervous system when transforming vestibular-driven signals for standing balance control. v Lay Summary Sleep deprivation is quite common in today’s society and is known to impact many aspects of human function, including vestibular activity. The vestibular system provides essential sensory information regarding head position and motion to help control balance, an important aspect of activities of daily living. However, it is currently unclear how sleep deprivation affects vestibular function in the context of standing balance. This thesis investigated the effects of 24 hours of sleep deprivation on the vestibular control of standing balance and provides novel insight into how we may rely more on our vestibular signals to maintain balance following a night of sleep deprivation. vi Preface The University of British Columbia’s Clinical Research Ethics Board granted ethical approval for this research on July 19th, 2019 prior to any data collection. The certificate approval number is H19-01274. This certificate includes three studies that were conducted concurrently. One study collected, titled “Neural effects of sleep deprivation on inhibitory control and emotion processing” has been submitted to the Journal of Sleep Research (manuscript ID: JOSR-21-360). The research conducted in the following thesis, to date, has not been submitted for publication. The author declares no conflicts of interest. The composition and findings of this study are original and without dishonest data fabrication, manipulation, or falsification while presented honestly and without bias. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface........... ............................................................................................................................... .vi Table of Contents ....................................................................................................................... viii List of Tables. ............................................................................................................................... ix List of Figures .................................................................................................................................x List of Abbreviations ................................................................................................................... xi Acknowledgements ..................................................................................................................... xii Dedication ................................................................................................................................... xiii Chapter 1: Introduction ................................................................................................................1 1.1 Sleep Deprivation.......................................................................................................... 1 1.1.1 Sleep deprivation, cognition, and sensorimotor function .............................. 2 1.1.2 Sleep deprivation and standing balance ......................................................... 4 1.2 Standing Balance and Vestibular Function ................................................................... 5 1.2.1 Electrical vestibular stimulation & vestibular-evoked balance responses ..... 7 1.3 Purpose and Hypothesis ................................................................................................ 9 Chapter 2: Methods .....................................................................................................................10 2.1 Participants .................................................................................................................. 10 2.2 Study Design and Participation Requirements ........................................................... 10 2.3 Experimental Set-Up and Protocol ............................................................................. 11 2.3.1 EMG and ground reaction forces ................................................................. 13 2.3.2 Vestibular stimulation .................................................................................. 14 2.4 Data and Statistical Analysis ...................................................................................... 14 2.4.1 Centre of pressure analyses .......................................................................... 15 2.4.2 Vestibular-evoked balance responses analyses ............................................ 16 2.4.2.1 Coherence analysis.................................................................................... 16 2.4.2.2 Gain analysis ............................................................................................. 17 2.4.2.3 Cumulant density analysis ........................................................................ 17 Chapter 3: Results........................................................................................................................18 3.1 Participants .................................................................................................................. 18 viii 3.2 Quiet standing balance parameter results ................................................................... 18 3.3 Vestibular control of balance - Frequency domain results ......................................... 19 3.3.1 Coherence .................................................................................................... 19 3.3.2 Gain .............................................................................................................. 20 3.4 Vestibular control of balance - Time domain results .................................................. 22 Chapter 4: Discussion ..................................................................................................................24 4.1 Effect of Sleep Deprivation on Quiet Stance .............................................................. 24 4.2 Effect of Sleep Deprivation on Vestibular-Evoked Balance Responses .................... 26 4.3 Limitations and Future Directions .............................................................................. 28 Chapter 5: Conclusion .................................................................................................................30 Bibliography .................................................................................................................................31 ix List of Tables Table 1. Centre of pressure (COP) parameters ........................................................................19 x List of Figures Figure 1. Experimental set-up (A), example EVS, EMG, and ground reaction force signals (B), representative EVS-EMG coherence and cumulant density responses (C) .......12 Figure 2. Vestibular-evoked coherence responses and difference of coherence (insets) during well-rested control and sleep deprivation session ............................................20 Figure 3. Vestibular-evoked gain responses during well-rested control and sleep deprivation session ...........................................................................................................21 Figure 4. Vestibular-evoked cumulant density responses during well-rested control and sleep deprivation session..................................................................................................22 Figure 5. Vestibular-evoked cumulant density response peak-to-peak amplitudes during well-rested control and sleep deprivation session .........................................................23 xi List of Abbreviations Ag-AgCl silver-silver chloride ANOVA analysis of variance AP anteroposterior COM centre of mass COP centre of pressure cm centimeters EMG electromyography EVS electrical vestibular stimulation h hours MG medial gastrocnemius ML mediolateral ms milliseconds s seconds SD sleep deprivation TA tibialis anterior VOR vestibulo-ocular reflex xii Acknowledgements First and foremost, I would like to thank my supervisor, Dr. Brian Dalton. I like to joke that he taught me everything I know, but in all honesty, he probably did teach me everything I retained. I was fortunate enough to be within the first cohort of students he taught after moving to UBCO and, since then, he has provided constant support and guidance through my academics; because of that, I am a better student, researcher, and person. I would also like to thank my committee members Dr. Chris McNeil and Dr. Paul van Donkelaar for contributing invaluable time and advice towards my thesis, it was truly appreciated. I am forever grateful for my amazing colleagues and lab mates I have had the pleasure of working alongside; Megan, Mathew, Mike, Hogun and Lisa. Thank you for putting up with my endless questions and terrible jokes. I’ve heard grad school can be the best and/or worst time of your life and, thanks to you all, my experience certainly leans towards the former. I look forward to collaborating and celebrating all of our future successes together and apart, wherever we may end up. Last, but definitely not least, I would like to thank my wonderful family and friends who have been there for me through the years. There are too many of you to list, but each and every person in my life has helped me get to where I am today You have supported me academically, emotionally, financially (thanks Mom & Dad) and I can honestly say I don’t know where I’d be without you all. I look forward to continuing to accomplish my goals and making you proud. xiii Dedication To everyone who has helped me along the way And to my friend, Gabe.1 1. Introduction Sleep deprivation (SD) is a common condition often purposefully carried out to significantly prolong wakefulness (Reynolds & Banks, 2010) and can impair cognitive, sensory and motor functioning (Killgore, 2010). One sensory system SD has been shown to affect is the vestibular system, as increases in vestibulo-ocular reflex (VOR) gain have been reported in sleep-deprived individuals during whole-body, mechanical rotation tests (Quarck et al., 2006). In general, standing balance control, as demonstrated by increases in centre of mass (COM) and centre of pressure (COP) kinematics, is impaired following SD (Fabbri et al., 2006; Morad et al., 2007; Schlesinger et al., 1998), with few exceptions (Uimonen et al., 1994). Nevertheless, a lack of consensus exists regarding the physiological origins of these SD-related balance deficits (Batuk et al., 2020; Fabbri et al., 2006; Morad et al., 2007). The neural control of standing balance is a complex phenomenon, which involves the central nervous system (CNS) integrating multiple sensory signals, including those arising from the peripheral vestibular system, to produce whole-body, postural adjustments to keep the body upright. It is possible SD-induced balance impairments may arise from altered functioning within sensory systems used to control standing. Therefore, modified vestibular-driven balance responses due to SD – similar to that observed for VOR – may be evident during quiet standing balance. 1.1 Sleep Deprivation Despite the importance of sleep on overall function, health and wellbeing (Canadian Society for Exercise Physiology, 2020; Magnavita & Garbarino, 2017; Mukherjee et al., 2015; Worley, 2018), up to 50% of Canadian adults are getting inadequate sleep quality, mostly due to problems going to and staying asleep (Statistics Canada, 2019). Though less common than 2 restricted or fragmented sleep, total SD is quite prevalent, and is characterized by the complete lack of sleep for a period of time (Reynolds & Banks, 2010). SD is often purposefully carried out in order to significantly prolong wakefulness, and hence productivity, and can be seen as a consequence of modern society’s tendency to prioritize productivity over one’s health (Killgore, 2010). The effects of SD on human function and performance are wide-ranging and complex. Exposure to insufficient sleep can lead to short- and long-term health-related issues, such as cardiovascular, neurological, and psychological dysfunction (Killgore, 2010; Minkel et al., 2012; Palmer & Alfano, 2017; Van Dongen et al., 2003). These impairments in human function due to inadequate sleep appear to follow a dose-response relationship (Van Dongen et al., 2003) whereby chronic exposure to SD can lead to long-lasting health concerns. While short bouts of sleep loss may seem manageable, acute impairments in human function can still occur and include, but are not limited to, cognitive and sensorimotor decrements (Durmer & Dinges, 2005; Goel et al., 2009). 1.1.1 Sleep deprivation, cognition, and sensorimotor function The effects of SD are often grouped into three main categories: cognitive performance, motor performance, and mood (Goel et al., 2009; Killgore, 2010; Pilcher & Huffcutt, 1996). These effects are thought to be linked to reduced metabolic activity within the brain (Goel et al., 2009; Kusztor et al., 2019; Van Dongen et al., 2003). Specifically, metabolic rates in the frontal and temporal lobes, thalamus, basal ganglia, and cerebellum following acute SD are reduced (Goel et al., 2009). These regional metabolic reductions are thought to explain impairments in aspects of cognitive performance, specifically attention, which appears to be particularly 3 sensitive to SD (Wu et al., 1991). While these neural regions are involved in cognitive performance, it is important to note their involvement in other functions, such as sensorimotor processing required for human movement and balance control. Thus, reduced activity in the aforementioned brain regions may lead to a reduced cognitive processing as well as balance control. Various sensorimotor deficits have been reported following SD. Motor performance impairments include, but are not limited to, increased reaction time (Killgore, 2010), reduced psychomotor vigilance (Lim & Dinges, 2008) and reductions in visual-motor tracking (Dawson & Reid, 1997). Sensory processing impairments are not as well characterized, but visual-spatial perception impairments have been reported (Killgore, 2010; Manly et al., 2005). Similarly, information related to the effect of SD on auditory and cutaneous sensory processing are also under-researched, but potential impairments have been demonstrated (Babkoff et al., 2005; Kundermann et al., 2004). One sensory system that has displayed alterations following SD is the vestibular system, though the results are inconclusive. Quarck and colleagues (2006) examined the VOR and reported the gain of this reflex increased after ~26 hours of SD (Quarck et al., 2006). The authors used two types of whole-body, mechanical rotation tests, and suggested that their results were owing to the intense mode of activating the VOR (i.e., faster head acceleration) they employed, and such changes were due to modified functioning of the temporoparietal cortex, a cortical area whose function is reported to be altered following SD (Drummond & Brown, 2001). These results contrast other studies, which reported no effect of SD on VOR function (Wolfe and Brown, 1968; Collins, 1988). The discrepancies are likely owing to differences in techniques used. For example, Quarck and colleagues (2006) used a more rapid, intense method of 4 activating the vestibular system than the others (Wolfe and Brown, 1968; Collins, 1988). While SD may impair the VOR, its impact on other vestibular functions, such as balance and posture, has yet to be investigated. 1.1.2 Sleep deprivation and standing balance Of the number of studies investigating the effect of SD on postural control, most have found that SD impairs standing balance (Avni et al., 2006; Bougard et al., 2011; Fabbri et al., 2006; Morad et al., 2007; Schlesinger et al., 1998), with one exception (Uimonen et al., 1994). For example, Gribble and colleagues (2004) examined COP area and velocity throughout 48h of SD, whereby increases in one or both dependent variables signify impaired balance control. Gribble et al. (2004) found while COP area was unchanged, COP velocity was greater throughout this period of SD compared to a well-rested state. While increases in COP parameters and, hence, impairments in standing balance are fairly well-documented following acute SD, the physiological underpinnings are unclear. Studies by Fabbri et al. (2006) and Bougard et al. (2011) focused on how visual information altered COP responses following SD. Fabbri et al. (2006) concluded that SD altered the contribution of vision to standing balance, as balance control was impaired more with eyes closed following SD than with eyes open; this study investigated performance of the Romberg’s test, an evaluation of the contribution of vision to posture which uses standing trials with and without vision, during 12hrs of SD (Fabbri et al., 2006). In contrast, Bougard et al. (2011) reported that vision was not involved in the observed postural impairments, because while COP area was significantly larger, performance of the Romberg’s test was unaltered following a night of SD compared to control (Bougard et al., 2011). It is important to note that while Romberg’s test is a clinically available 5 test used to identify various balance-control pathologies in clinical popultations, it may not be sensitive to minor alterations in balance control in healthy young adults. Further, while Romberg’s test is a clinical assesment, it is unable to directly evaluate individual sensory contributions to postural control. Further, Avni and colleagues (2006) examined how 25h of SD influenced different frequencies of postural sway, ranging from 0.01-3Hz, whereby low-frequency sway (i.e., 0.01-0.1 Hz) represents visual contributions, mid-low-frequency sway (i.e., 0.1-0.5 Hz) depicts vestibular control, and sway frequencies between 0.5-1Hz and 1-3 Hz are proposed to reflect lower-limb somatosensory input and muscular stress or CNS dysfunction, respectively. Following SD, Avni et al. (2006) observed the largest impairments in sway frequencies between 0.1-0.5 Hz and suggested the SD-related postural impairments were vestibular in origin. Likewise, Morad et al. (2007) investigated similar sway frequency bands over the course of 26h of SD and reported the vestibular-linked frequency band of sway displayed prominent circadian fluctuations and was greater than baseline after 21h of SD. Both studies mentioned above that proposed a vestibular origin were based on indirect evidence and did not directly test vestibular function. 1.2 Standing Balance and Vestibular Function Adequate standing balance can be described as the ability to maintain COM within the base of support (Forbes et al., 2018). Due to various physiological perturbations, quiet stance still contains small amounts of postural sway. The exact mechanism of sway is not completely understood but appears to be subcortical in origin (Luu, 2010; Murnaghan et al., 2014), although there are cortical influences (Lajoie et al., 1993; Little & Woollacott, 2015; McGeehan et al. 6 2017). Further, while sway is normal and present in healthy individuals, it can be exacerbated by various stressors including, but not limited to, adult aging and concussion (Collins et al., 1995; Prieto et al., 1996; Guskiewicz, 2001; Powers et al., 2014). Postural sway during standing balance is represented by deviations in the COM and is driven by changes in COP. It should be noted, though, that not all fluctuations in COP will result in consequential changes to COM (Lafond et al., 2004). COM displacement, or sway, can be measured using systems such as whole-body, kinematic-tracking devices, while COP displacement is generally assessed using a tri-axial force plate that is more widely available than a motion-analysis system. Within the literature, sway and COP displacement are terms often used interchangeably, and while COP displacement may be used as a proxy for measuring sway, it is important to understand the differences between the two measures. Experimentally, both sway and COP displacement can be quantified using numerous parameters; this thesis focused on COP displacement and the associated velocity of COP displacement and, in this context, more and/or faster COP displacement is considered worse balance control. Further, standing balance is a complex physiological phenomenon which involves the integration of many sensorimotor signals to create coordinated postural adjustments to keep the body upright (Forbes et al., 2018; Peterka, 2002). These sensory inputs include visual, proprioceptive, auditory, somatosensory, and vestibular cues. Directly probing any or all of these sensory pathways provides insight into how each may contribute to standing balance control (Fitzpatrick & Day, 2004; Rasman et al., 2018). One important sensory system for the control of standing balance is the vestibular system. In addition to its role in maintaining balance and posture, the vestibular system also functions to stabilize one’s gaze, via the VOR and to provide an estimate of self-motion (Cullen, 2012). The 7 vestibular system is comprised of end organs, including the otolith organs and the semicircular canals, which sense linear and angular acceleration of the head, respectively and, as such, provide sensory cues related to head position and motion (Cullen, 2012). In the context of the vestibular control of standing balance, the information monitored by the end organs passes through the primary afferents to the vestibular nuclei. This vestibular information is integrated and processed with other sensorimotor signals at various levels of the CNS to afford a clear context of where and how our body is positioned in space. With a reference frame of head-on-feet orientation, the CNS can create proper whole-body adjustments by activating the appropriate muscles to maintain standing balance (Dalton et al., 2017; Fitzpatrick & Day, 2004; Mian & Day, 2009; Wallace et al., 2018). 1.2.1 Electrical vestibular stimulation & vestibular-evoked balance responses Electrical vestibular stimulation (EVS) is a safe and valuable method to investigate the vestibular control of standing balance. As the vestibular end organs rely on mechanoreceptors to sense acceleration (Cullen, 2012), EVS bypasses the end organs and directly stimulates all types of vestibular afferents (Kwan et al., 2019). Providing an ex-afference-like signal that is free of other sensory information is considered a great strength of EVS. Binaural, bipolar EVS uses two small electrodes, a cathode and an anode, placed on the mastoid processes, posterior to each ear. The cathode and anode cause depolarization and hyperpolarization of the underlying vestibular afferents, respectively (Goldberg, 2000; Goldberg et al., 1984). These opposing signals activate our vestibular system in a way which is interpreted by the CNS as the body leaning towards the cathode (Fitzpatrick & Day, 2004), resulting in a compensatory whole-body, postural adjustment towards the anode (Fitzpatrick and Day 200) that 8 is the summation of all muscles responding to the ex-afference-like vestibular error. These distinct whole-body and muscle adjustments are termed vestibular-evoked balance responses and can be characterized using force plate kinetics and electromyographical (EMG) data, respectively. Using continuous, stochastic EVS and an archive of MATLAB scripts (Neurospec) based on multivariate Fourier analyses, vestibular-evoked balance responses can be analyzed within the frequency and time domains (Dakin et al., 2007). To explore the operational frequency bandwidth, coherence and gain of the vestibular-evoked balance responses can be estimated. Coherence represents a linear relationship between an input signal (i.e., EVS) and an output signal (i.e., whole-body, ground reaction forces or EMG amplitude), across a range of relevant frequencies. The vestibulomotor balance responses have been shown to operate primarily within a range of 0-25Hz (Dakin et al., 2007, 2010) for lower limb muscles, which is in line with the operational frequencies of the vestibular nuclei (Armand & Minor, 2001; Huterer & Cullen, 2002). Gain estimates represent the relative magnitude of the output signal in response to the input. To explore the time domain of the vestibular-evoked balance response, cumulant density functions can be examined. Cumulant density functions are a correlation-like measure and represent a time-dependent relationship between an input and an output signal. Cumulant density estimates can be measured using stochastic EVS in lieu of traditional methods of analyzing vestibular responses using square-wave EVS pulses. Indeed, similar timings and amplitudes of vestibular-evoked responses are observed with cumulant density estimates when compared to square-wave EVS responses (Britton et al., 1993; Dakin et al., 2007). Vestibular-evoked cumulant density functions are biphasic, comprised of two distinct peaks termed the short- and 9 medium-latency responses. In lower-limb musculature, the short- and medium-latency responses occur around 50-60ms and 100ms, respectively. Each latency response of the vestibular-evoked reflex is composed of different frequencies, with the short- and medium-latency primarily containing high- (10-25 Hz) and low-frequencies (<10 Hz), respectively (Dalton et al., 2014; Dakin et al., 2007). Because of differences in frequency content, each latency response is suggested to represent different descending pathways (Dakin et al., 2007), such that the short-latency response is proposed to travel to lower-limb musculature via the vestibulospinal pathway, while the low-frequency, medium-latency response is proposed to travel via the reticulospinal pathway (Britton et al. 1993; Welgampola and Colebatch 2002). Furthermore, the coherence, gain, and cumulant density estimates have been shown to effectively quantify differences in the vestibular control of standing balance within and between populations (Horslen et al., 2014; Dalton et al., 2014; Lam et al., 2017; McGeehan et al., 2017; Wallace et al., 2018). 1.3 Purpose and Hypothesis The overall purpose of this thesis was to examine the effect of 24h of SD on the vestibular control of standing balance. Characteristics of whole-body and lower-limb muscle vestibular-evoked balance responses were evaluated across the frequency (coherence and gain) and time (cumulant density) domain. It was hypothesized that following 24h of sleep deprivation, vestibular-evoked balance responses would increase when compared to a well-rested control condition. 10 2. Methods 2.1 Participants Eleven healthy females (age: 22.8 ± 2.9 years, mass: 65.3 ± 9.0 kg, height: 165.2 ± 7.4 cm; a priori sample size calculation, n = 7, power = 0.8) participated in this study, who had no known history of disordered sleep, cardiovascular, neurological, musculoskeletal, nor balance impairments. In an attempt to mitigate any effects of sex hormones (e.g., estrogen, progesterone) may have on postural sway (Lee & Yim, 2016), most participants completed both sessions either a) within the same follicular phase of a single cycle (6 participants), or b) on the same day of their cycle, separated by 28 days (4 participants); the timing of testing relative to the menstrual cycle was not controlled for one participant. Prior to participation, individuals were screened for disordered sleeping and abnormal sleep patterns. Participants were included in the study if they reported consistency within their sleep patterns and followed a regular day-night cycle. Written and oral informed consent were provided by all participants prior to the first testing session. The study was approved by the Clinical Research Ethics Board at the University of British Columbia (H19-01274). 2.2 Study Design and Participation Requirements Participants completed two separate testing days: one control session and one session following 24h of SD. The order of the sessions was pseudo-randomized, and counterbalanced such that nearly half of the participants started with the control session, and half with the SD session. Prior to each experimental session, details (i.e., duration, quality, restfulness) of the individual’s previous three nights of sleep were recorded, and their waking time was logged. The control testing session, which followed a regular night of sleep, was scheduled to begin 1h after 11 the participant’s wake time. On the night prior to the SD testing session, participants arrived at the testing site between 8-10pm, where they remained overnight under the supervision of the investigators. Throughout the night, participants partook in primarily sedentary activities (e.g., watching television, walking to washroom), and were permitted to eat and drink water ad libitum. The SD testing was scheduled to begin 24h after the reported wake time from the previous day. Participants were instructed to avoid caffeine intake for 12 hours prior to the control session and after noon the day prior to the SD session. Between testing days, individuals were asked to refrain from making any drastic alterations to their typical sleep or daily routine, which included changes in exercise and diet. 2.3 Experimental Set-Up and Protocol Participants stood on a tri-axial force plate (OR6-5-1, Advanced Mechanical Technology Inc., Watertown, MA), with the left and right medial malleoli touching and head facing forward (Figure 1). Due to the craniocentricity of the vestibular-evoked balance response, when investigating the responses of the leg, researchers will often have participants position their head perpendicularly to the feet in an attempt to maximize the vestibular-evoked balance response with the action of the plantar flexors (Dakin et al., 2016; Dalton et al., 2014; McGeehan et al., 2017); although, studies have shown that vestibular-evoked responses are still present in the plantar flexors with the head facing forward (Kysar & Dalton, 2019; Reynolds, 2010). To ensure participant’s safety following 24h of SD, we chose to maintain forward head position to allow for a slightly more stable posture. Therefore, for the purposes of this study, all whole-body vestibular analyses were completed using mediolateral ground reaction forces (Fx; ML forces). 12 Fig 1. Panel A depicts the experimental set-up showing participant position on the force plate, with head facing forward. Panel B contains example traces of the stochastic electrical vestibular stimulation (EVS) signal, medial gastrocnemius electromyography (MG EMG) and mediolateral ground reaction forces (Fx; ML forces). Panel C exhibits representative EVS-MG EMG coherence and cumulant density figures from one participant. The black and grey lines represent the responses during the well-rested control and sleep deprivation sessions, respectively. The red bracket denotes the peak-to-peak amplitude of the cumulant density response. The red circle on the first positive spike denotes the short-latency response, while the red circle on the negative spike response denotes the medium-latency response. The horizontal dashed lines represent 95% confidence limits. Consistent foot position was maintained using a chalk outline. Each participant performed four trials in a randomized order, which included two 120-s quiet standing trials, one with eyes open and one blindfolded, and two 90-s standing trials, where vision was occluded with a blindfold and EVS was employed. The quiet standing balance trials allowed for further explanation of the measured vestibular-evoked balance responses, as increased sway is known to increase said responses (Watson & Colebatch, 1998); therefore it is necessary to observe quiet A B C 13 stance separately. During the quiet-standing trials, participants were instructed to stand normally and to keep both hands on their hips, whereas for the EVS trials, participants' arms were relaxed at their sides. In order to optimize the EVS-evoked balance response (Cathers et al., 2005; Peters et al., 2015) the participant’s head was tilted such that Reid’s plane was positioned 19° in relation to horizontal (Fitzpatrick & Day, 2004). Participants were given verbal cues from the experimenter to maintain consistent head position throughout the trials. 2.3.1 EMG and Ground Reaction Forces Surface EMG was sampled from the medial gastrocnemius (MG) of the right leg. Two self-adhesive surface electrodes (Ag-AgCl; 10mm diameter) were placed over the muscle belly in alignment with the muscle fibres and a ~2cm centre-to-centre inter-electrode distance. A ground electrode was positioned over the right lateral malleolus. The skin was cleaned with an isopropyl alcohol swab prior to electrode placement. All EMG signals were pre-amplified (×1000; NeuroLog Digitimer NL844, Welwyn Garden City, UK), amplified (×2 NeuroLog Digitimer NL820A, Welwyn Garden City, UK), and band-pass filtered (30–1000Hz). Ground reaction forces and moments in all three axes were collected using a force plate (OR6-5-1, Advanced Mechanical Technology Inc., Watertown, MA). A data acquisition board (Power1401-3A, Cambridge Electronic Design Limited)) and Spike2 software (version 8; Cambridge Electronic Design Limited) were used to digitize the force plate and EMG data at 2058 Hz, which were stored for analysis. 14 2.3.2 Vestibular stimulation On a PC computer, the EVS signal was generated using MATLAB software (MathWorks Inc., Natick, MA, USA) with freely available code (Forbes et al., 2014). An EVS signal was created from a low-pass filtered (25Hz; 6th order Butterworth) white-noise signal and was presented continuously with a randomly varying amplitude (peak-to-peak amplitude ±4 mA; root-mean-square amplitude 1.1 mA) and frequency (0-25 Hz). Using Spike2 software (Cambridge Electronics Design Limited, Milton, Cambridge, England) and data acquisition interface (Power 1401-3A; Cambridge Electronics Design Limited), the binaural, bipolar EVS signal was delivered via an isolated bipolar current stimulator (DS5, Digitimer LTD, Welwyn Garden City, Hertfordshire, United Kingdom). This signal was passed through carbon-rubber electrodes (9cm2), which were placed over the mastoid processes after being coated with Spectra 360 electrode gel (Parker Laboratories, Fairfield, NJ, USA) and secured with Durapore tape (3M innovations, St. Paul, MN, USA) and an elastic bandage. 2.4 Data and Statistical Analysis Using MATLAB (MathWorks Inc., Natick, MA, USA), all EMG data were digitally high-pass filtered at 30 Hz by a zero-phase shift filter (4th order Butterworth) and full-wave rectified (Dakin et al., 2014), and ground reaction force data were digitally low-pass filtered at 20 Hz by a zero-phase shift filter (4th order Butterworth). Participants’ sleep quality the three nights prior to each lab visit was compared between sessions using a paired sample, two-tailed Student’s t-test to ensure no differences in sleep prior to the testing sessions. All t-tests were completed with significance set a priori at p<0.05. 15 2.4.1 Centre of pressure analyses All COP analyses were completed in Spike2. Following the low-pass digital filtering, the forces and moments collected were used to calculate COP displacement in the mediolateral (ML) and anteroposterior (AP) directions. The following formulas were used to calculate ML and AP COP displacement, respectively. 𝐶𝑂𝑃𝑥 = (−𝑀𝑦 + 𝐹𝑥 × 0.0373)𝐹𝑧 𝐶𝑂𝑃𝑦 =(𝑀𝑥 − 𝐹𝑦 × 0.0373)𝐹𝑧 The COP ML and AP displacement variability was assessed by the standard deviation of COPx and COPy signal, respectively. The COP ML and AP velocity were evaluated using the root-mean-square amplitude of the COPx and COPy velocities. To compare the effects of SD and vision on COP displacement variability and velocity, a two-way, 2 (sleep deprived vs. well-rested) × 2 (vision vs. no vision) repeated measure analysis of variance (ANOVA) was conducted using SPSS version 23 (SPSS Inc., Chicago, IL, USA). Significance was set a priori at 0.05. Effect sizes were evaluated using the partial-eta squared (ηp2) metric. Data are presented as means ± standard deviations. 2.4.2 Vestibular-evoked balance responses analyses Vestibular-evoked balance responses were characterized in the frequency (coherence and gain) and time (cumulant density) domains. Coherence, gain, and cumulant density estimates were derived using an archive of multivariate Fourier analyses (NeuroSpec 2.0: http://www.neurospec.org) MATLAB functions, as previously described by Rosenberg et al. (1989) and Halliday et al. (1995). The surface EMG from the medial gastrocnemius (MG EMG) 16 and ML forces were used to characterize the vestibular-evoked balance responses. All EMG and force plate signals were time-locked to the onset of the EVS in MATLAB. For EVS-MG EMG and EVS-ML forces analyses, each participant contributed 90 (segment length: 0.99514s; resolution: 1.0049 Hz) and 45 (segment length: 1.9903s; resolution: 0.50244 Hz) segments of data for each condition, respectively. 2.4.2.1 Coherence analysis To explore the operational frequency bandwidth of the vestibular-evoked balance responses for the MG and whole-body forces, coherence estimates were determined. Coherence describes a linear relationship between the input (i.e., EVS) and output (i.e., MG EMG, ML forces) across a given range of frequencies, where each frequency point varies in coherence between 0 (non-linear relation) and 1 (perfect linear relation). Coherence was considered significant when values exceeded the 95% confidence limit (Figure 1). The confidence limit was constructed from the total segments per subject to distinguish frequencies that were significantly different than 0. The effect of SD on EVS-MG and EVS-ML force coherence was compared using the difference of coherence subroutine (NeuroSpec 2.0, MATLAB) to determine frequency bandwidths that had significantly different coherence between sleep conditions. This statistical analysis compares the standardized differences between coherence of two data sets (i.e., well-rested control and sleep deprived) and the 95% confidence limits based on Fisher’s transform (tanh-1) of the square root of the coherence values and compared with χ2 (1) distribution (Amjad et al., 1997; Rosenberg et al., 1989). Any significant differences were determined where the standardized difference of coherence frequencies exceeded the 95% confidence limits. 17 2.4.2.2 Gain analysis To further explore the frequencies with significant coherence of the vestibular-evoked balance responses, gain-frequency estimates were derived. Gain estimates represent the relative magnitude of the output signal (i.e., MG EMG, ML ground reaction forces) to the input signal (i.e., EVS). To compare sleep conditions, point-wise 95% confidence limits were calculated (Figure 3). The frequencies at which the respective confidence limits did not overlap were considered statistically different. 2.4.2.3 Cumulant density analysis Cumulant density functions afford the evaluation of the vestibular-evoked balance responses within the time domain, as they represent a correlation-like relationship between an input (i.e., EVS) and an output signal (i.e., EMG or ML forces). To derive the cumulant density estimates, the cross-spectra between the EVS and physiological output signals (EMG or ML forces) was transformed into the time domain. These values were then normalized by the product of the vector norms of the EVS input and EMG and ML force output signals. Essentially, this normalization method transforms the cumulant density values into an equivalent of a cross-correlation, therefore all values are confined between -1 and 1. Thus, cumulant density functions are unitless. The 95% confidence intervals were calculated from the total number of data segments per participant to distinguish frequencies that were significantly different than 0. Paired sample, two-tailed Student’s t-tests were used to compare the peak-to-peak amplitudes of the cumulant density estimates between sleep conditions. 18 3. Results 3.1 Participants Of the eleven participants, two individuals did not tolerate EVS, so they did not participate in the EVS portion of the testing protocol. Data from a separate two participants were excluded from the quiet-stance analysis owing to technical difficulties during the sleep-deprived condition. Therefore, those data points were coded as missing for statistical analysis. Based on a 1-5 Likert scale (with 5 being very restful), the three nights prior to the control session, participants rated their sleep 3.9±0.3 (mean ± standard deviation). Three nights prior to the SD, their sleep was rated 4.0±0.4, which was not different (t(10)= -0.43, p=0.68) than the control. 3.2 Quiet standing balance parameter results For COP ML displacement variability, there was no interaction between sleep and vision (F(1,8) = 0.348, p= 0.572, ηp2 = 0.042), nor a main effect of sleep (F(1,8) = 0.023, p= 0.883, ηp2 = 0.003), but a main effect of vision ((F(1,8) = 17.622, p= 0.003, ηp2 = 0.688) was detected. For COP ML displacement velocity, no interaction (F(1,8) =1.920, p= 0203, ηp2 = 0.194) nor main effect for sleep (F(1,8) = 0.097, p= 0.764, ηp2 = 0.012) was found, but a main effect of vision ((F(1,8) = 36.368, p< 0.001, ηp2 = 0.820) was detected (Table 1). Despite no influence of SD, COP ML displacement variability and velocity increased by 20 and 63%, respectively, in eyes closed compared to open. For COP AP displacement variability, there was no interaction (F(1,8) = 5.020, p= 0.055, ηp2 = 0.386) nor main effect of sleep (F(1,8) = 0.121, p= 0.737, ηp2 = 0.015) detected, but a vision main effect (F(1,8) = 15434, p= 0.004, ηp2 = 0.659) was present. An interaction (F(1,8) = 0.948, p= 0.359, ηp2 = 0.106) and main effect of sleep (F(1,8) = 0.047, p= 0.833, ηp2 = 0.006) 19 were not detected, but there was a main effect of vision ((F(1,8) = 27.779, p< 0.001, ηp2 = 0.776) for COP AP displacement velocity (Table 1). Regardless of sleep condition, the eyes-closed condition led to an 17% increase in variability of COP AP displacement and an 83% increase COP AP displacement velocity when compared to the eyes-open condition. Table 1. COP parameters Characteristic Control SD EO EC EO EC COP ML displacement variability (cm) 0.51 ± 0.14 0.62 ± 0.19* 0.52 ± 0.19 0.61 ± 0.18* COP ML velocity (cm/s) 0.82 ± 0.18 1.28 ± 0.46* 0.79 ±0.26 1.37 ± 0.49* COP AP displacement variability (cm) 0.62 ± 0.21 0.72 ± 0.30* 0.53 ± 0.13 0.76 ± 0.26* COP AP velocity (cm/s) 0.63 ± 0.10 1.10 ± 0.36* 0.60 ±0.10 1.16 ±0.38* Values are means ± standard deviation. COP, centre of pressure; ML, mediolateral; AP, anteroposterior; cm, centimetre; EO, eyes open; EC, eyes closed. * denotes main effect of vision. 3.3 Vestibular control of balance - Frequency domain results 3.3.1 Coherence Coherence reached significance (95% confidence interval) in all subjects for the EVS-MG EMG and EVS-ML forces (Figures 1 and 2). Overall, there were limited differences between sleep conditions across the entire frequency bandwidth, but EVS-MG EMG coherence was less between ~8-10.5 Hz and greater at ~16 Hz during the SD condition compared to the well-rested condition (Figure 2). Coherence also exhibited limited differences between sleep conditions for EVS-ML forces, as coherence was significantly less only at ~12 and ~13 Hz during the SD condition compared to the control. Other than these small portions across the frequency bandwidth, coherence values for EVS-MG EMG and EVS-ML forces were not different between sleep conditions (Figure 2). 20 Fig 2. Vestibular-evoked balance responses as characterized by coherence estimates between the electrical vestibular stimulation (EVS) signal and the medial gastrocnemius (MG) electromyography (EMG) and mediolateral ground reaction forces (ML forces). Black lines represent the coherence during the well-rested conditions and grey lines indicate the sleep-deprived condition. Difference of Coherence (DoC) figures are presented as insets. The dashed lines represent the 95% confidence interval. Any portion of the DoC line which crosses into the red shading is interpreted as significantly higher coherence while sleep deprived, and into the blue shading represents higher coherence during the control. 3.3.2 Gain Compared to the control, the SD condition resulted in greater EVS-MG EMG gain between ~1-8 Hz (Figure 3) and again between ~11-24 Hz. Between ~9-10 Hz, EVS-MG EMG gain was lesser in the SD condition than the control condition. Similarly, following SD, the gain function for EVS-ML forces was greater between ~0.5-9 Hz than the control (Figure 3). 21 Fig. 3 Vestibular-evoked balance responses as characterized by the gain functions in the medial gastrocnemius (MG) and mediolateral ground reaction forces (ML forces). The black and grey shaded areas represent pointwise 95% confidence limits for the sleep-deprived and well-rested control sessions, respectively. The regions in which confidence limits did not overlap were considered statistically different. 22 3.4 Vestibular control of balance - Time domain results Within the time domain, there were no detectable differences in the peak-to-peak amplitude of the cumulant-density responses for the EVS-MG EMG or EVS-ML forces between sleep conditions (t(8)= 0.69, p= 0.512 and t(8)= 2.24, p= 0.056, respectively; Figures 4 and 5) Fig 4. Vestibular-evoked balance responses in the time domain were evaluated using the cumulant-density function from the concatenated data of all participants for the medial gastrocnemius (A) and mediolateral forces (B). The black and grey lines represent the responses observed during the well-rested control and sleep deprived conditions, respectively. Black and grey brackets illustrate the peak-to-peak amplitude of the cumulant-density functions for the well-rested control and sleep-deprived condition, respectively. Red circles denote the short-latency and medium-latency peaks. Dashed horizontal lines represent 95% confidence limits. -200 0 200 400 600 800 1000-0.1-0.08-0.06-0.04-0.0200.020.040.06B A Time (ms) Time (ms) Cumulant Density Amplitude 23 Fig 5. Vestibular-evoked balance responses as depicted by the mean cumulant-density peak-to-peak amplitude for the medial gastrocnemius (MG) and mediolateral ground reaction forces (ML Forces). Black markers represent the responses during the well-rested control condition and grey markers represent the responses following 24h of SD. Circles represent individual data points, while the squares represent group means ± standard deviations. EVS, electrical vestibular stimulation. 00.10.20.3Cumulant Density Peak-to-Peak AmplitudeEVS-MG EVS-ML Forces24 4. Discussion The aim of the present study was to examine the effect of 24h of SD on the vestibular control of standing balance. Stochastic EVS was employed to explore the characteristics of the vestibular-evoked balance response across the time (cumulant density) and frequency (coherence and gain) domains. It was hypothesized that, following 24h of SD, vestibular-evoked balance responses would increase when compared to a well-rested control condition. Following 24h of SD, quiet stance remained unchanged when compared to the well-rested condition. Further, the vestibulomyogenic responses within the MG and the whole-body, vestibular-evoked balance responses were unchanged within the time domain (Figure 5). When considering the frequency domain, there were limited detectable alterations for coherence (Figure 2), but the gain was greater following SD than control for both the EVS-MG EMG and EVS-ML forces (Figure 3). This likely signifies an increased sensitivity of the CNS to vestibular cues during upright stance. Together, our main findings may suggest that, despite 24h of SD, participants demonstrated no change in their ability to maintain standing balance. This ability to maintain standing balance occurs alongside an increase in sensitivity to vestibular-evoked muscle and whole-body postural adjustments during standing. 4.1 Effect of Sleep Deprivation on Quiet Stance The COP displacement variability and velocity was unchanged following 24h of SD. Interestingly, most reports (Avni et al., 2006; Bougard et al., 2011; Fabbri et al., 2006; Morad et al., 2007; Schlesinger et al., 1998), but not all (Uimonen et al., 1994), on SD and standing balance parameters reported a worsening of participants’ postural control following ≥ 24h of SD. 25 While previous research by Gribble and colleagues reported no alterations to COP area throughout 48h of SD, they observed increased COP velocity during the first 24h of SD (Gribble & Hertel, 2004). In the current dataset, there were no detectable differences in COP variability or velocity following 24h of SD. These discrepancies between studies may be due to different activity levels through the period of SD. In the current study, participants were limited to primarily sedentary activities throughout the night, while the other study took place during a 48-h dance marathon and, although participants were not required to dance for the entire 48h, they were required to stay awake and stand the entire time (Gribble & Hertel, 2004). This likely resulted in the participants of that study having increased activity throughout the period of sleep loss and, when combined with SD, this increased activity may have led to their observed postural impairments (Gribble & Hertel, 2004), as exercise-induced fatigue of the lower-limb muscles has been shown to impair standing balance (Corbeil et al., 2003; Lundin et al., 1993). Morad and colleagues (2007) tracked postural changes through 26h of SD and found prominent circadian fluctuations in sway, where impairments peaked in the early morning (after ~21h of SD), but recovered to baseline values around 10:00am (after ~26hrs of SD) the following day. A similar pattern of circadian-related sway fluctuations was observed by Avni and colleagues (2006), whereby participant instability peaked in the early morning (after ~22h of SD), but appeared to return to baseline by 24-25h of SD. While circadian fluctuations in postural stability have been proposed, the physiological mechanisms behind these fluctuations are not clear (Avni et al., 2006; Morad et al., 2007). In the present study, it is possible that the testing session may have taken place during a period of higher postural stability within the participants’ circadian rhythms, leading to limited observable changes in COP measures. 26 Though there was no effect of sleep condition on COP measures in this study, there was an effect of vision, whereby removing vision resulted in greater COP displacement variability in the AP direction, as well as faster COP displacement in both the AP and ML directions compared with vision present. This is consistent with previous literature; it is well documented that removing vision leads to increased postural sway (Day et al., 1993). 4.2 Effect of Sleep Deprivation on Vestibular-Evoked Balance Responses Within the frequency domain, SD had a minimal effect on both the range and amplitude of coherence. Both sleep conditions presented coherence surpassing the 95% confidence interval across the same range of frequencies. Further, there were limited differences of coherence amplitude between sleep conditions, as demonstrated by the difference of coherence test (Figure 2). These findings suggest SD does not significantly alter the operational frequency bandwidth of which the vestibular system influences postural muscle activity. However, SD did lead to a significant increase in gain for both the MG and whole-body vestibular-evoked balance responses across a wide frequency spectrum (Figure 3), which likely signifies an increased sensitivity to vestibular cues during a sleep-deprived state. There are several possible explanations for an increased sensitivity to vestibular information in the current study. First, it has been well-documented that a decrease in metabolic activity in numerous brain regions (i.e., the frontal and parietal lobes, thalamus and cerebellum) occurs following SD (Goel et al., 2009; Kusztor et al., 2019; Van Dongen et al., 2003). As previously stated, these neural regions are involved in various aspects of sensorimotor processing, including standing balance control. It could be assumed that to maintain adequate upright posture, the reduction in activity of several balance-related brain regions may lead to a 27 shift to greater reliance on vestibular-driven modulation of postural muscle activity. A proposed shift towards greater vestibular control of standing balance when sleep deprived compared to well rested may be exacerbated by a lack of attentional capacity with a lack of sleep, as reductions in cognitive-motor performance and attention are well-documented following SD (Wu et al., 1991; Lim & Dinges,2008; Killgore, 2010). For example, divided attention or increased cognitive processing can also increase the vestibular control of balance (McGeehan et al. 2017). Thus, 24h of SD could stress cortical resources, causing them to be unable to contribute to postural control, potentially requiring greater sensitivity of the CNS to vestibular information. Additionally, SD did not alter cumulant-density functions of the muscle or whole-body vestibular-evoked balance responses (Figure 5). This finding suggests SD has no effect on the time-dependent aspect of vestibular responses. Similar to the gain response, cumulant density estimates afford insight into the overall magnitude of the vestibular-evoked balance response. Because cumulant density estimates are time-dependent and composed of all vestibular-relevant frequencies, these responses are less sensitive to specific frequency alterations when compared to frequency-specific analyses (i.e., coherence and gain). This may explain the lack of SD-related changes observed for the cumulant density estimates in the current study, despite SD-related alterations in gain. Quarck and colleagues (2006) examined VOR characteristics following ~26h of SD using two types of mechanical, whole-body rotational tests and reported an increase in VOR gain following a rapid 60° s-1 step velocity test, but no change following a sinusoidal chair rotation at 0.2 Hz. The authors hypothesized the increase in VOR gain during the step test was due to the sudden nature of the acceleration, as it was unexpected at onset and potentially threatening to balance control. In the present study, the method employed to activate the vestibular system, 28 EVS, produces an ex-afference-like signal and causes whole-body adjustments to maintain balance control. Similar to Quarck and colleagues (2006), an increased gain of the vestibular-evoked balance responses were observed in the present study. The lack of change in gain following the sinusoidal chair rotation may be due to 0.2 Hz being too low of a frequency to observe changes; the present study found increased gain of the vestibular-evoked balance responses starting between 0.5-1Hz. It is important to note that the resolution of the frequency analysis of this study does not afford evaluation of frequencies lower than 0.5Hz, so any changes to gain or coherence at lower frequencies are still unknown for the vestibular control of balance. 4.3 Limitations and Future Directions As previously mentioned, not all testing sessions were controlled for phase of menstrual cycle. Though unlikely, it is possible the effect of differing sex-related hormone levels may have altered the results of one participant (Lee & Yim, 2016). Additionally, for this study, participants were asked to abstain from caffeine within 12h of the SD testing session, and prior to the control session. During SD, we allowed participants to eat and drink non-caffeinated drinks ad libitum, and participants partook in primarily sedentary activities throughout the night in an attempt to minimize excess energy expenditure. While these measures were taken in an effort to ensure participant comfort through the night, it may lend the results not entirely comparable to the entire SD literature, as previous SD-related research has varied greatly with the control of various factors throughout the period of participant wakefulness, including activity levels, food/drink intake, and caffeine intake. Factors such as these are known to alter neuromuscular function and may alter participants’ energy levels, mood, motivation, etc. Another limitation may lie within the variability of individuals to tolerate acute sleep loss; while intraparticipant variability in the 29 ability to deal with sleep loss seems relatively limited, interparticipant variability is quite significant (Van Dongen et al., 2004). While all of our participants reported having regular wake-sleep cycles, and good quality sleep prior to the SD session, we are unable to know individual SD tolerance levels. Due to research curtailment resulting from the COVID-19 pandemic, I was unable to collect a matched pool of male participants to evaluate sex-related differences in the vestibular control of standing balance following sleep deprivation. Additionally, when looking at vestibular-evoked balance responses in the leg, it is common to look at both plantar and dorsiflexor muscle groups. Due to inconsistent activity of dorsiflexors in healthy, young adults, we were unable to evaluate vestibular evoked-balance responses in the tibialis anterior in all the participants in the current study. Despite these limitations, this thesis provides novel insight into the vestibular control of balance following 24h of SD. Future studies in the realm of SD, standing balance, and vestibular function could focus on examining how SD may alter the vestibular control of standing balance under different postures. The current study had participants standing with their feet together; while this posture is generally employed in research as it challenges the balance system, more real-world, challenging postures (i.e., single leg, standing on a challenging support surface, etc.) may give a more holistic view on how SD may alter balance control. Another potential research direction could attempt to assess the influence of adding a dual-task paradigm to the current research protocol. Attentional impairments are documented in sleep-deprived individuals (Wu et al., 1991; Killgore et al., 2010); therefore, any additional splitting of attentional resources, as seen in a dual-task paradigm, may further challenge the balance system and reveal potential 30 impairments. This may also lend an ecologically valid view of SD and balance control, as focusing attention elsewhere while standing is common. 5. Conclusion To examine the effect of 24h of SD on the vestibular control of standing balance, stochastic EVS was utilized to examine vestibular-evoked balance responses, using both frequency (coherence and gain) and time (cumulant density) domain approaches. Within the frequency domain, SD had limited effects on the coherence vestibular-evoked balance responses; however, an increase in the gain of these responses was observed. Within the time domain, SD did not alter the cumulant density estimates of the vestibular-evoked balance response. These results suggest a greater sensitivity of our CNS to vestibular cues to maintain standing balance while sleep deprived. In healthy, young females, this increased reliance seems to be adequate to maintain standing balance following 24h of SD. 31 Bibliography Amjad, A. 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Thesis/Dissertation
eng
Health and Exercise Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Dalton, Brian H.
The effects of 24 hours of sleep deprivation on the vestibular control of standing balance
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