Effects of short-term −30° HDT on contrast sensitivity

Abstract

Potential cognitive and physiological alterations due to space environments have been investigated in long-term space flight and various microgravity-like conditions, for example, head-down tilt (HDT), confinement, isolation, and immobilization. However, little is known about the influence of simulated microgravity environments on visual function. Contrast sensitivity (CS), which indicates how much contrast a person requires to see a target, is a fundamental feature of human vision. Here, we investigated how the CS changed by 1-h −30° HDT and determined the corresponding mechanisms with a perceptual template model. A quick contrast sensitivity function procedure was used to assess the CS at ten spatial frequencies and three external noise levels. We found that (1) relative to the + 30° head-up tilt (HUT) position, 1-h −30° HDT significantly deteriorated the CS at intermediate frequencies when external noise was present; (2) CS loss was not detected in zero- or high-noise conditions; (3) HDT-induced CS loss was characterized by impaired perceptual template; and (4) self-reported questionnaires indicated that subjects felt less pleasure and more excitement, less comfort and more fatigued by screen light, less comfort in the area around the eye, and serious symptoms such as piercing pain, blur acid, strain, eye burning, and dizziness after HDT. These findings improve our understanding of the negative effects of simulated microgravity on visual function and elucidate the potential risks of astronauts during space flight.

Keywords

−30° head-down tilt contrast sensitivity external noise spatial frequency perceptual template

Microgravity is a hazardous environment that astronauts encounter during space missions. As a result, long-term space flight might lead to reduced concentration, mental fatigue, and psychological problems (Kanas et al., 2001). In ground laboratories, head-down tilt (HDT) serves as one of the most effective microgravity models. For example, Wang et al. (2017) found that, compared with the baseline, subjects under 7-day −6° HDT condition performed significantly worse on mental rotation tasks, which reflects their spatial cognitive ability (Wang et al., 2017). In addition, responses to threat stimuli were slower after 15 day −6° HDT, indicating that the attentional avoidance of threatening stimuli was impaired (Jiang et al., 2022). Liu and colleagues found poor executive function and emotional responses when subjects were in a −6° HDT position (Liu et al., 2012). Furthermore, Hoffmann et al. indicated that 60 days of HDT bed rest caused cardiovascular deconditioning (Hoffmann et al., 2021).

However, previous studies concentrated on how HDT impacts high-level cognitive function and physiological features. The visual system is critically important for humans. Many researchers have explored spaceflight-associated neuro-ocular syndrome (SANS). Their findings included optic disc edema, optic nerve sheath distension, posterior globe flattening, total and retinal nerve layer thickening, chorioretinal folds, retinal cotton wool spots, and hyperopic refractive shifts (Lee et al., 2020; Mader et al., 2011; Patel et al., 2020). Regarding visual function, visual acuity, color sensitivity and reading accuracy after spaceflight have also been investigated (Ong et al., 2022). As a basic trait of visual perception, contrast sensitivity (CS) reflects an individual's capacity to distinguish luminance variations between neighboring areas (Campbell, 1983; Pelli & Bex, 2013). When the CS is assessed at six to ten spatial frequencies, contrast sensitivity function (CSF) can be drawn, and it represents the foundations of spatial vision and makes significant contributions to clinical applications (Ginsburg, 2003). For example, a previous study found that eyes with age-related geographic atrophy of the macula and good visual acuity have considerably decreased CS (Sunness et al., 1997). Therefore, systemically exploring the negative impact of simulated microgravity on the CSF is the first key point of this study. The CSF may be sufficiently sensitive to detect the changes in visual function induced by short-term HDT.

Traditional CSF tests using preprinted letters or grating charts with classic adaptive methods have low efficiency for clinical applications. For example, sampling CS at seven spatial frequencies with the 3-down/1-up staircase method requires up to 700 trials and therefore is too time consuming. The low efficiency of traditional CSF tests greatly restricts the comprehensive assessment of human vision. Luckily, with Bayesian technique support, scientists have designed a quick CSF (qCSF) algorithm (Lesmes et al., 2010). This algorithm has been validated in amblyopic (Hou et al., 2010), old (Roh et al., 2018), and myopic (Chen et al., 2019) populations. The greatest superiority of qCSF is its precise and accurate estimation of the CSF with fewer trials (e.g., 50 to 100 trials per 10 spatial frequencies). In the current research, the qCSF algorithm was used to survey the negative effect of a short-term HDT on the CS over abundant spatial frequencies.

In daily life and work, people often seek targets with visual noise. In blizzard conditions, pedestrians may have difficulty determining whether there is a car coming. Adding various extents of external noise levels during CSF measurement improves researchers’ understanding of the characteristics of the CSF. The perceptual template model (PTM) includes three parameters or factors that explain the detection performance for specific targets with and without external noise: (1) internal additive noise means magnifying both the signal and noise of visual input; (2) perceptual template has the ability to exclude external noise; and (3) internal multiplicative noise reduces effecting stimulus enhancement and follows Weber's law (Dosher & Lu, 1998). The PTM has been successfully used to determine why the binocular view is better than the monocular view (Zhang et al., 2021), and monetary reward boosts perceptual learning (Zhang et al., 2018). Similarly, the integration of the external noise approach and the PTM is ideal for further investigating the mechanisms of HDT-induced visual function loss.

In summary, this research has two main goals: (1) to inspect whether and how the CS is affected by 1 h of −30° HDT and (2) to determine the relevant mechanisms with the PTM.

Materials and Methods

Participants

Seventeen subjects, aged 18–26 years old, were registered in this research. All subjects received a visual acuity test to confirm normal or corrected-to-normal vision. All subjects were free of psychological or organic dysfunction and signed informed consent. The protocol was approved by the Ethical Review Committee of Hebei Normal University and obeyed the Declaration of Helsinki.

Laboratory Apparatus

All procedures were run on a desktop PC with MATLAB + psychtoolbox 3.0. A luminance-calibrated Apple (CRT) monitor with a 1280 × 1024 resolution, 85 Hz and 34.7 cd/m² background brightness. A mirror was used to reflect the light from the CRT to the subjects’ eyes. All the subjects were anchored to a head-down bed (Figure 1). The experiment was conducted in a dim light room.

Figure 1.

Study design schematic of −30° head down tilt (HDT), + 30° head up tilt position (HUT) with adjustable mirror and desktop PC. The CSF measurement took about 15 min.

Stimuli

In the experiment, the stimuli consisted of signal and noise images. Vertical gratings with one of 10 spatial frequencies (SF: 0.5, 0.67, 1, 1.3, 2, 2.67, 4, 5.3, 8, and 16 cpd) served as the signal and were placed in the center of the visual field. Each grating always had three spatial cycles. Thus, the size of each grating was inversely proportional to its spatial frequency. For example, the sizes of gratings with 0.5 and 16 cpd were 6° and 0.1875°, respectively. In addition, the noise images were generated from Gaussian distributed pixel contrasts with μ = 0 and σ = 0, 0.12, and 0.24 at the zero, low, and high noise levels, respectively. Each noise image consisted of 15 × 15 gray elements. In each trial, the grating and noise images were identical in size, and both were squares. However, to blur the margin, each grating was covered with a truncated Gaussian envelope. Thus, the gratings appeared to be smaller than the noise images. Because spatial frequency was manipulated by changing the size of gratings while keeping the number of cycle fixed and the noise images were scaled up and/or down in the same way as gratings, the spectral relationship between the signal and noise images remained the same in all the spatial frequency conditions (Chen et al., 2014).

Procedure

A typical contrast detection task was used to access the CS (Figure 2). The whole qCSF test included zero, low, and high noise levels. Each noise level produced a CSF at 10 spatial frequencies. Each trial started with a 100 ms fixation. Then, two intervals, separated by a 500 ms blank, were present. In the zero-noise condition, each interval had five 35.3 ms frames of blanks, in which a grating may appear in the middle one. Observers were instructed to note which interval contained the grating by pressing a button. At this time, a beep was sounded. The noise images differed across frames, intervals, and trials. In the qCSF algorithm, the CSF parameters (peak gain; peak spatial frequency; bandwidth, which describes the function's full width at half maximum; and low-frequency truncation level) are estimated via Bayesian adaptive inference (Lesmes et al., 2010).

Figure 2.

Diagram of the classical trial under zero-(left), low-(mid), and high-(right) noise conditions.

A self-assessment manikin (SAM) test was conducted to assess subjective states and comfort (Chen et al., 2021). The whole questionnaire included four parts. In Part 1, three questions were used to assess subjects’ valence, arousal, and dominance level, with 9 for pleasant, excited, and in-control states and 1 for unpleasant, calm, and controlled states. In Part 2, the subjective comfort and eye fatigue when facing the light source on the monitor were rated on a 7-point scale, with 1 indicating discomfort or non-fatigue and 7 indicating comfort or eye fatigue. In Part 3, seven questions were used to evaluate the subjects’ eye symptoms, such as periocular discomfort, eye dryness, eye piercing pain, eye blur, eye acid, eye strain, and eye burning. For these questions, 0, 1, and 5 denoted no symptoms, mild symptoms, and severe symptoms, respectively. In Part 4, the dizziness level of the head was rated, with 0, 1, and 5 denoting no symptoms, mild symptoms, and severe symptoms, respectively. The alertness level was rated, with 1 for very sleepy to 9 for clear-headed.

Design

The entire experiment was carried out in a dark environment. Before participating in the experiment, all 17 subjects completed the visual acuity test. Then, 12 subjects were asked to perform the CSF and SAM tests, during which they sat on a Tatami cushion with a + 30° head-up tilt (HUT). Next, the subjects were directed to maintain a −30° HDT for 1 h but were not allowed to close their eyes or look at their cell phones for a long period. Then, the subjects were instructed to complete CSF and SAM tests again. Another five subjects completed only the SAM test before and after −30° HDT.

Data Analysis

To better evaluate the HDT effect on the CSF, we calculated the area under the log CSF (AULCSF, in log10 units), which represents the overall CSF performance (Koop et al., 1996).

To investigate the possible mechanism of the HDT effect on visual function, the subjects’ performance was fitted by the PTM. A participant's performance was computed by the following equation:

d^{'} = \frac{{(β c)}^{γ}}{\sqrt{{(A_{f} N_{e x t})}^{2 γ} + A_{m}^{2} N_{m u l}^{2} ({(β c)}^{2 γ} + (A_{f} N_{e x t})^{2 γ}) + {(A_{a} N_{a d d})}^{2}}}

(1)

where

d^{'}

is the signal discriminability; internal multiplicative and additive noise are denoted by

N_{m u l}

and

N_{a d d}

, respectively;

N_{e x t}

is the contrast of external noise; γ is the nonlinearity of the visual system; c is the signal contrast; and β is the perceptual template gain. To imitate the impact of the −30° HDT on visual perception,

A_{a}

A_{f}

, and

A_{m}

were added in front of

N_{a d d}

N_{e x t}

, and

N_{m u l}

, respectively. Under + 30° HUT conditions,

A_{a}

A_{f}

, and

A_{m}

were all adjusted to 1. If the slopes of the psychometric function are comparable between the two experimental conditions, the multiplicative noise can be assumed to be constant (Xu et al., 2006; Zhang et al., 2018). If this is true in the present study,

A_{m}

can be removed from Equation (1). Additionally,

N_{a d d}

and β are spatial frequency dependent, while

N_{m}

and γ are not.

The slopes of the psychometric function were computed by the following equation:

P_{i, j} (x) = γ + (1 - γ - λ) (1 - e x p (- 10^{s (l o g_{10} (x) - l o g_{10} (τ_{i, j}))}))

(2)

where P denotes the percent correct. i and j indicate the spatial frequency and external noise conditions, respectively. The guessing rate (γ) and lapse rate (λ) are set to 0.5 and 0.02, respectively. τ is the contrast threshold at the 80.3% correct performance level. s denotes the slope of the psychometric function. In this work, s was estimated by a maximum-likelihood procedure.

Results

Twelve subjects completed the CSF tests in two posture conditions. Figure 3 shows the CSF in three noise conditions with + 30° HUT and −30° HDT. A visual observation suggested that the CS in the + 30° HUT condition was much better than that in the −30° HDT condition when low external noise was added. However, the CS between the two posture conditions was comparable when external noise was absent.

Figure 3.

The contrast sensitivity functions (CSFs) at (A) zero-, (B) low-, and (C) high-noise levels. Black lines with square symbols and red lines with circle symbols indicate contrast sensitivity (CS) under + 30° HUT and −30° HDT, respectively. Data were averaged across subjects. The error bar denotes the standard error.

A limitation of the qCSF method is that although it provides multiple estimates of CS across the function, these estimates are not independent. This lack of independence poses a challenge to the statistics, for example, repeated measures analysis of variance (ANOVA). However, a typical ANOVA still serve its purpose for consistency with those previous studies (Zhang et al., 2018; Zhang et al., 2021). We conducted ANOVA on the CS with spatial frequency, external noise, and posture as within-subject factors. Because the estimated contrast threshold at 16 cpd was close to 1 (floor effect), only nine levels of spatial frequency were included in the analysis. In addition, external noise and posture contained three and two levels, respectively. The results showed that the main effects of external noise level (F(2, 22) = 303.692, p < .001) and spatial frequency (F(8, 88) = 57.746, p < .001) were significant, while the effect of posture was not (F(1, 11) = 0.934, p = .355). The interaction among these three factors was marginally significant (F(16,176) = 1.552, p = .086). The least significant difference (LSD) indicated that (1) under low-noise condition, the CS with −30° HDT was significantly (or marginally) lower than that with + 30° HUT at 1.3 (p = .075), 2 (p = .058), 2.67 (p = .037), and 4 cpd (p = .043); and (2) other comparisons were not significant (all p > .1). In addition, there were significant interactions between noise levels and posture (F(2, 22) = 3.948, p = .034) and between noise levels and spatial frequency (F(16, 176) = 135.046, p < .001). The LSD test of noise levels and postures indicated that in the low noise condition, the CS with −30° HDT was marginally significantly lower than that with + 30° HUT (p = .061). However, in the zero- and high-noise conditions, the differences were not significant (all p > .1). The LSD test of noise level and spatial frequency revealed that (1) at 0.5 to 5.3 cpd, the largest CS was in the zero-noise condition, followed by the low noise condition, and the lowest was in the high-noise condition (all p < .01); and (2) at 8 cpd, the CS in the zero- and low-noise conditions was comparable (p = .618), and they were both larger than that in the high-noise condition (all p < .05).

To better compare the CS across all spatial frequencies between + 30° HUT and −30° HDT, Figure 4 shows the AULCSF. The AULCSF values for + 30° HUT and −30° HDT were 6.120 ± 0.078 and 6.180 ± 0.082 (log₁₀ unit, mean ± SE) under zero-noise condition, respectively; 3.334 ± 0.067 and 2.935 ± 0.077 under low-noise condition, respectively; and 1.801 ± 0.050 and 1.835 ± 0.052 under high-noise condition, respectively. We ran a repeated-measures ANOVA with the noise condition and posture as two within-subject variables. We found a significant main effect of external noise (F(2, 22) = 216.480, p < .001) but not posture (F(1, 11) = 0.681, p = .427). The interaction between the two was also significant (F(2, 22) = 4.205, p = .028). The LSD test indicated that when low noise was added, the −30° HDT decreased the AULCSF (p = .062). However, other comparisons were not significant (all p > .1).

Figure 4.

Area under the log CSF (AULCSF) (log10 units) under (A) zero-, (B) low-, and (C) high-noise conditions. Gray and red bars denote the data from HUT and HDT, respectively. The error bar denotes the standard error.

We also explored whether −30° HDT changed the qCSF parameters. A repeated-measures ANOVA was performed on the peak gain, with noise condition and posture as two within-subject variables. The main effect of posture and the interaction effect between noise and posture reached significant or marginally significant levels (F(2,22) = 585.869, p < .001; F(2,22) = 3.410, p = .051). The main effect of posture was not significant (F(1,11) = 1.277, p = .282). The LSD revealed that the HDT significantly decreased the peak gain in the low-noise condition (p = .028) but not in the zero- or high-noise conditions (all p > .1). The same ANOVAs were performed on the peak spatial frequency, bandwidth, and low-frequency truncation level. Only the main effects of the noise condition were significant (all p < .001). These findings indicate that the peak gain of the CSF is decreased by the HDT in the low-noise condition.

To model the effect of −30° HDT on visual function, the slopes of the psychometric function in the two posture conditions were calculated separately. As mentioned above, because the slope was independent of posture (t(11) = 0.332, p = .754), HDT could not change internal multiplicative noise. Thus, we removed A_m from Equation (1). Two potential mechanisms were considered to clarify the impact of HDT on visual function: increased internal additive noise and/or impaired perceptual template (ability of external noise exclusion). The full model (M0) assumed that HDT increased the internal additive noise and impaired the perceptual template; the reduced model 1 (M1) assumed that HDT increased only the internal additive noise; the reduced model 2 (M2) assumed that HDT impaired only the perceptual template; and the most reduced (M3) model assumed no changes in parameters.

The PTM was fit to the data of an average observer, and the r² values of M0, M1, M2, and M3 were 97.90%, 96.88%, 97.90%, and 96.88%, respectively. The r² of M2 was comparable to that of M0 (p = .990) but significantly better than that of M3 (p < .001). In addition, the r² of M1 was significantly worse than that of M0 (p < .001) but was comparable to that of M3 (p = .540). Thus, M2 with the assumption of an impaired perceptual template after HDT was favored. On average, the $A_{f}$ in M2 was 1.098 ± 0.004 across all spatial frequencies (Figure 5).

Figure 5.

$A_{a}$ , $A_{f}$ , $N_{a d d}$ , $N_{m u l}$ , β, and γ as a function of spatial frequencies.

The SAM questionnaire was accessed before and after −30° HDT in 17 subjects. In Part 1, the subjects became less pleased (p < .001) but more excited (p = .003). In Part 2, the subjects became less comfortable (p = .007) and more fatigued from the screen light (p = .039). In Part 3, the symptoms of piercing pain, blur, acid strain, and burning in the subjects’ eyes became more serious (all p < .05). In addition, the subjects felt more discomfort in the area around the eye (p < .001). In part 4, the subjects became dizzier (p < .001). The scores on other items were not significantly different before and after HDT. See Table 1 for details.

Table 1.

Questionnaire results.

Parts	Item	Mean	SE	p-values	Feeling change (HUT vs. HDT)
Part 1	Valence	2.76	0.398	***	Less pleased
	Arousal	−1.88	0.528	**	More aroused
	Dominance	1.35	0.664	.0584	More dominate
Part 2	Comfort to screen light	1.53	0.493	**	Less comfortable
	Fatigue	−0.882	0.392	*	More fatigued
	Discomfort around	−1.00	0.210	***	More discomfort
	Dry	−0.471	0.299	.134	Drier
Part 3	Piercing pain	−1.18	0.312	**	More piercing
	Blur	−0.941	0.303	**	Blurrier
	Acid	−1.23	0.304	**	More acidic
	Strain	−0.882	0.308	*	More strained
	Burning	−1.18	0.230	**	More burning
Part 4	Dizzy	−1.71	0.306	***	Dizzier
Part 4	Alertness	0.706	0.476	.157	Less alert

Note. Paired samples test of valence, arousal, dominance, comfort to screen light, fatigue, discomfort around, piercing pain, blur, acid, strain, burning, dizziness, and alertness. Mean denotes the average value of the difference. SE denotes standard error.

*p < .05, **p < .01, ***p < .001.

Discussion

Understanding how the human brain adapts to microgravity is essential for space missions. Although there may be a vast concern about the effect of microgravity on the human brain, this is the first study to examine CS changes after a short period of −30° HDT. We found that the CS significantly declined at intermediate spatial frequencies after low external noise was displayed. In addition, the subjects felt less pleasure and more excited in state; less comfort and more fatigued by screen light; less comfort around the eye; and more serious symptoms of piercing pain, blur acid, strain, and burning of the eye, and dizziness.

HDT-dependent visual change has been explored in the literature, but the current design still offers sufficient innovation. On the one hand, the conventional CSF method requires approximately 600–1,000 trials to achieve precision results, which has considerable time costs (Payne et al., 1986). In contrast, with the Bayesian framework (Hou et al., 2010), the qCSF method can assess subtle difference in the CSF before and after HDT. In addition, the qCSF approach can determine the CS over 10 spatial frequencies. Therefore, −30° HDT-induced CS loss can be evaluated by both the CS and AULCSF.

The impact of microgravity exposure on visual function has not been systemically investigated. In 1989, approximately 450 shuttle crewmembers reported declines in visual performance, mainly including near vision, particularly during long-duration space missions. CS provides more fundamental information than a traditional visual acuity test (Sunness et al., 1997). To the best of our knowledge, there are only two studies on the effect of microgravity on CS. One study found that astronauts with normal vision reported evidence of impaired CS (Gibson et al., 2012). Another study found that one day of “dry” immersion significantly increased the CS at low spatial frequencies (Shoshina et al., 2021). These findings seem to be inconsistent with our finding that the CS was not changed after 1 h −30° HDT when external noise was absent. We hypothesize that this may be due to the difference in experimental settings. First, compared to real microgravity in space missions, the 1-h 30° HDT may not be powerful enough to induce a negative influence on the CS. Second, in Irina Shoshina et al.'s (2021) study, the duration of the stimulus was not limited, which may induce a practice effect.

In the current research, the second innovation is the external noise-dependent HDT. According to the AULCSF, the + 30° HUT was greater than the −30° HDT under low noise condition. In contrast, when zero and high noise levels were present, the AULCSF was not significantly different between the + 30° HUT and −30° HDT conditions. An important perceptual ability is to exclude interference information. Infants rapidly develop the ability to exclude noise between 7 and 10 months (Tsui et al., 2011); however, this ability is significantly reduced in older populations (Yan et al., 2020). In the current study, decreased CS was observed at medium spatial frequencies, at which the peak of CSF was located. This indicated that the simulated microgravity impaired the CS, but the defects could be revealed only by low external noise. When high external noise was present, the contrast detection task became too hard, so the subtle difference in the CS between the two postures was masked. The choice of external noise level was based on previous studies (Zhang et al., 2021). First, determining the mechanisms underlying HDT-induced CS loss required at least two external noise levels. Thus, the external noise was sampled at zero, low, and high levels. Second, to avoid subjects’ fatigue, we did not choose four external noise levels. Because the CSF is not a simple function of the gain of the visual system, adding high external noise to gratings can flatten the CSF (Chen et al., 2014; Xu et al., 2006). In our study, high noise level flattened the CSF curves too much, so there was no significant difference on the CSF between −30° HDT and + 30° HUT. At last, with the help of PTM, we determined the corresponding mechanisms underlying HDT-induced CS decline and found that an impaired perceptual template accounted for it.

In the present study, the SAM questionnaire revealed that the subjects felt less pleasant and more arousing at −30° HDT than at + 30° HUT. Previous studies investigated the effects of 30 days of 6° head-down bed rest (HDBR) on affective pictures and assessed valence and arousal levels. However, the results revealed that the self-evaluation of valence and arousal did not differentiate between the normal and HDBR groups (Brauns et al., 2019). The different findings may be due to the HDT angle (−6° vs. −30°). Taken together, our data showed that −30° HDT has adverse effects associated with valence and arousal.

After long-term space exploration, astronauts may suffer from ophthalmic symptoms, including posterior globe flattening, cotton wool spots, papilloedema, and optic nerve sheath distention (Garrett-Bakelman et al., 2019; Marshall-Goebel et al., 2017). Headward fluid shift, which is induced by HDT, is assumed to be one of the key factors of SANS (Gibson et al., 2012). In a spaceflight analog study, HDT posture and elevated ambient carbon dioxide induced optic disc edema in 5 of 11 subjects (McGregor et al., 2021). Although carbon dioxide was not elevated in our study, −30° HDT resulted in several symptoms, including piercing pain, blur acid, strain, eye burning, and dizziness.

In the present study, we did not include a counterbalanced condition in which all participants performed both the + 30° HUT and −30° HDT condition. There were four reasons. First, a counterbalance design usually aims to exclude practice effects. However, the −30° HDT was expected to impair visual function rather than improve it. Second, the CSF is a fundamental feature of human vision and is very difficult to change without intervention. For example, while perceptual learning protocols could improve CS, thousands of training trials are needed (Zhang et al., 2018). Third, the qCSF algorithm was sufficiently rapid, accurate, and precise in measuring the CSF (Hou et al., 2010). Fourth, counterbalance designs were not considered in previous studies on simulated microgravity. Thus, we believe that our design is reasonable.

The time needed for the −30° HDT effects to disappear is an interesting point. However, to address this issue, the CS algorithm and experimental design must meet two requirements. First, the CS measurement must be performed within 1–2 min if researchers want to see the process in detail. Second, the best way to reduce the time costs of CS measurements is to limit the stimuli conditions. However, these limitations reduce the information that can be obtained at various spatial frequencies and external noise levels. Thus, this is a dilemma. We aimed to evaluate the negative effects of −30° HDT on visual function over a broad range of spatial frequencies and external noise levels. The recovery time could be investigated in future work.

We limited the HDT duration to 1 h for three reasons. First, 1-h −30° HDT was used in a previous study (Henderson et al., 2006). Second, in the pilot study, most subjects reported that they were unwilling to lie down for more than 1-h of −30° HDT. Third, according to the self-rated subjective feelings, subjects started to suffer from some symptoms, such as eye discomfort and dizziness, after this time. Thus, according to ethical principles, the −30° HDT duration in the present study was limited to 1 h.

Overall, this study is the first to systemically report CS decline after exposure to −30° HDT, which is of great significance to aerospace. Any subtle impairment in the CS is critical for space flight because even small decreases in precision can probably result in missions compromising serious faults during mission vital tasks. It is very interesting to investigate how to compensate for microgravity-induced CS loss in the future.

Footnotes

Author Contributions

Jing Li: Data curation; Formal analysis; Investigation; Writing – original draft; Writing – review & editing.

Shijie Shang: Formal analysis; Investigation; Writing – original draft; Writing – review & editing.

Pinqing Yue: Formal analysis; Writing – review & editing.

Weicong Ren: Formal analysis; Visualization; Writing – review & editing.

Zeng Wang: Writing – review & editing.

Di Wu: Conceptualization; Formal analysis; Methodology; Supervision; Writing – review & editing.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Social Science Foundation of Hebei Province (HB20JY020, HB14JY031, and HB10VJY032 to ZW), Science and Technology Project of Hebei Province (22556202K to ZW), Social Science Development Project of Hebei Province (20220202304 to ZW), Natural Science Foundation of Hebei Province (C2012205046 to ZW and C2021205005 to PZ), and Science Foundation of Hebei Normal University (L2022B26 to PZ).

ORCID iDs

Weicong Ren

Pan Zhang

Di Wu

References

Brauns

Anika

Hanns-Christian

G. A. M. M. F. D. D.

Alexander

(2019). Electrocortical evidence for impaired affective picture processing after long-term immobilization. Scientific Reports, 9, 16610. https://doi.org/10.1038/s41598-019-52555-1.

Campbell

F. W.

(1983). Why do we measure contrast sensitivity? Behavioural Brain Research, 10, 87–97. https://doi.org/10.1016/0166-4328(83)90154-7

Chen

Hou

Yan

F. F.

Zhang

Zhou

Huang

C. B.

(2014). Noise provides new insights on contrast sensitivity function. PloS One, 9, 1–10.

Chen

Fang

Y. F.

Shuhan

Yifan

Jia

Hua

Bing

H. C.

(2021). The effects of short-term light exposure on subjective affect and comfort are dependent on the lighting time of day. Scientific Reports, 11, 2604. https://doi.org/10.1038/s41598-021-81182-y

Chen

Z.-L.

Hou

Deng

Zhang

(2019). Assessing peripheral visual function in myopia–a qCSF study. Investigative Ophthalmology & Visual Science, 60, 4365–4365.

Dosher, B. A., & Lu, Z.-L. (1998). Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. Proceedings of the National Academy of Sciences, 95, 13988–13993. https://doi.org/10.1073/pnas.95.23.13988

Garrett-Bakelman

F. E.

Darshi

Green

S. J.

Gur

R. C.

Lin

Macias

B. R.

Turek

F. W.

(2019). The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight. Science, 364, eaau8650. https://doi.org/10.1126/science.aau8650

Gibson

C. R.

Mader

T. H.

Schallhorn

S. C.

Pesudovs

Lipsky

Raid

Johnston

S. L.

(2012). Visual stability of laser vision correction in an astronaut on a Soyuz mission to the International Space Station. Journal of Cataract & Refractive Surgery, 38, 1486–1491. https://doi.org/10.1016/j.jcrs.2012.06.012

Ginsburg

A. P.

(2003). Contrast sensitivity and functional vision. International Ophthalmology Clinics, 43, 5–15. https://doi.org/10.1097/00004397-200343020-00004

10.

Henderson

A. C.

Levin

D. L.

Hopkins

S. R.

Olfert

I. M.

Buxton

R. B.

Prisk

G. K.

(2006). Steep head-down tilt has persisting effects on the distribution of pulmonary blood flow. Journal of Applied Physiology, 101, 583–589. https://doi.org/10.1152/japplphysiol.00087.2006

11.

Hoffmann

Rabineau

Mehrkens

Gerlach

D. A.

Moestl

Johannes

B. W.

Tank

(2021). Cardiac adaptations to 60 day head-down-tilt bed rest deconditioning. Findings from the AGBRESA study. ESC Heart Fail, 8, 729–744. https://doi.org/10.1002/ehf2.13103

12.

Hou

Chang-Bing

Luis

Li-Xia

Liming

Yi-Feng

Zhong-Lin

(2010). qCSF in clinical application: efficient characterization and classification of contrast sensitivity functions in amblyopia. Investigative Ophthalmology & Visual Science, 51, 5365–5377. https://doi.org/10.1167/iovs.10-5468.

13.

Jiang

Qian

Y.-M.

Jiang

Cao

Z.-Q.

Xin

Wang

Y.-C.

(2022). Effects of 15 Days-6 head-down bed rest on the attention bias of threatening stimulus. Frontiers in Psychology, 2816. https://doi.org/10.3389/fpsyg.2022.730820

14.

Kanas

Salnitskiy

Gushin

Weiss

D. S.

Grund

E. M.

Flynn

Marmar

C. R.

(2001). Asthenia—does it exist in space? Psychosomatic Medicine, 63, 874–880. https://doi.org/10.1097/00006842-200111000-00004

15.

Koop

Applegate

Howland

(1996). Changes in the area under the log contrast sensitivity function (AULCSF) with myopic refractive error. Paper presented at the Investigative ophthalmology & visual science.

16.

Lee

A. G.

Mader

T. H.

Gibson

C. R.

Tarver

Rabiei

Riascos

R. F.

Brunstetter

(2020). Spaceflight associated neuro-ocular syndrome (SANS) and the neuro-ophthalmologic effects of microgravity: A review and an update. NPJ Microgravity, 6, 1–10. https://doi.org/10.1038/s41526-019-0094-z

17.

Lesmes

L. A.

Z.-L.

Baek

Albright

T. D.

(2010). Bayesian adaptive estimation of the contrast sensitivity function: the quick CSF method. Journal of Vision, 10, 17–17. https://doi.org/10.1167/10.3.17

18.

Liu

Zhou

Chen

Tan

(2012). Effects of head-down bed rest on the executive functions and emotional response. PLoS One, 7, e52160. https://doi.org/10.1371/journal.pone.0052160

19.

Mader

T. H.

Gibson

C. R.

Pass

A. F.

Kramer

L. A.

Lee

A. G.

Fogarty

Sargsyan

(2011). Optic disc edema, globe flattening, choroidal folds, and hyperopic shifts observed in astronauts after long-duration space flight. Ophthalmology, 118, 2058–2069. https://doi.org/10.1016/j.ophtha.2011.06.021

20.

Marshall-Goebel

Robert

Simone

Edwin

Jörn

(2017). Lower body negative pressure reduces optic nerve sheath diameter during head-down tilt. Journal of Applied Physiology (Bethesda, Md. : 1985), 123, 1139–1144. https://doi.org/10.1152/japplphysiol.00256.2017

21.

McGregor

H. R.

Lee

J. K.

Mulder

E. R.

De Dios

Y. E.

Beltran

N. E.

Kofman

I. S.

Seidler

R. D.

(2021). Ophthalmic changes in a spaceflight analog are associated with brain functional reorganization. Human Brain Mapping, 42, 4281–4297. https://doi.org/10.1002/hbm.25546

22.

Ong

Tavakkoli

Zaman

Kamran

S. A.

Waisberg

Gautam

Lee

A. G.

(2022). Terrestrial health applications of visual assessment technology and machine learning in spaceflight associated neuro-ocular syndrome. NPJ Microgravity, 8, 1–12. https://doi.org/10.1038/s41526-022-00222-7

23.

Patel

Z. S.

Brunstetter

T. J.

Tarver

W. J.

Whitmire

A. M.

Zwart

S. R.

Smith

S. M.

Huff

J. L.

(2020). Red risks for a journey to the red planet: The highest priority human health risks for a mission to Mars. NPJ Microgravity, 6, 1–13. https://doi.org/10.1038/s41526-020-00124-6

24.

Payne

Madsen

Harvey

R. C.

Inturrisi

C. E.

(1986). A chronic sheep preparation for the study of drug pharmacokinetics in spinal and ventricular CSF. Journal of Pharmacological Methods, 16, 277–296. https://doi.org/10.1016/0160-5402(86)90032-X

25.

Pelli

D. G.

Bex

(2013). Measuring contrast sensitivity. Vision Research, 90, 10–14. https://doi.org/10.1016/j.visres.2013.04.015

26.

Roh

Selivanova

Shin

H. J.

Miller

J. W.

Jackson

M. L.

(2018). Visual acuity and contrast sensitivity are two important factors affecting vision-related quality of life in advanced age-related macular degeneration. PloS One, 13, e0196481. https://doi.org/10.1371/journal.pone.0196481

27.

Shoshina

Zelenskaya

Karpinskaia

Shilov

Tomilovskaya

(2021). Sensitivity of visual system in 5-day “Dry” immersion with high-frequency electromyostimulation. Frontiers in Neural Circuits, 15, 702792. https://doi.org/10.3389/fncir.2021.702792

28.

Sunness

J. S.

Rubin

G. S.

Applegate

C. A.

Bressler

N. M.

Marsh

M. J.

Hawkins

B. S.

Haselwood

(1997). Visual function abnormalities and prognosis in eyes with age-related geographic atrophy of the macula and good visual acuity. Ophthalmology, 104, 1677-1691. https://doi.org/10.1016/S0161-6420(97)30079-7

29.

Tsui

Chow

H.-M.

Tseng

C.-H.

(2011). Noise exclusion ability in infants. i-Perception, 2, 316-316. https://doi.org/10.1068/ic316

30.

Wang

Jiaobo

Yang

Chuang

Hongzheng

Xufeng

(2017). Objects mental rotation under 7 days simulated weightlessness condition: An ERP study. Frontiers in Human Neuroscience, 11, 553. https://doi.org/10.3389/fnhum.2017.00553

31.

Z.-L.

Qiu

Zhou

(2006). Identify mechanisms of amblyopia in Gabor orientation identification with external noise. Vision Research, 46, 3748-3760. https://doi.org/10.1016/j.visres.2006.06.013

32.

Yan

F.-F.

Fang

Hongyu

Jia

Lijun

Yifan

Chang-Bing

(2020). Aging affects gain and internal noise in the visual system. Scientific Reports, 10, 6768. https://doi.org/10.1038/s41598-020-63053-0.

33.

Zhang

Hou

Yan

F.-F.

Jie

Bo-Rong

Jin

Chang-Bing

(2018). High reward enhances perceptual learning. Journal of Vision, 18. https://doi.org/10.1167/18.8.11

34.

Zhang

Wang

Ren

Chen

(2021). Lower internal additive noise and better perceptual template characterize binocular contrast sensitivity summation. Frontiers in Psychology, 12, 740759. https://doi.org/10.3389/fpsyg.2021.740759