
==== Front
Hum Brain Mapp
Hum Brain Mapp
10.1002/(ISSN)1097-0193
HBM
Human Brain Mapping
1065-9471
1097-0193
John Wiley & Sons, Inc. Hoboken, USA

10.1002/hbm.70013
HBM70013
Research Article
Research Article
Locus coeruleus microstructural integrity is associated with vigilance vulnerability to sleep deprivation
Quan et al.
Quan Peng 1 2
Mao Tianxin 2 3
Zhang Xiaocui 2
Wang Ruosi 3
Lei Hui 2
Wang Jieqiong 2
Liu Wanting 2
Dinges David F. 4
Jiang Caihong 3
Rao Hengyi https://orcid.org/0009-0000-4557-0590
2 3 4 hengyi@pennmedicine.upenn.edu

1 The First Dongguan Affiliated Hospital, School of Humanities and Management Guangdong Medical University Dongguan China
2 Center for Functional Neuroimaging, Department of Neurology University of Pennsylvania Philadelphia Pennsylvania USA
3 Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain‐Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management Shanghai International Studies University Shanghai China
4 Chronobiology and Sleep Institute University of Pennsylvania Philadelphia Pennsylvania USA
* Correspondence
Hengyi Rao, Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Room D502, Richards Medical Research Building, 3700 Hamilton Walk, Philadelphia, PA 19104, USA.
Email: hengyi@pennmedicine.upenn.edu

03 9 2024
9 2024
45 13 10.1002/hbm.v45.13 e7001329 7 2024
26 1 2024
17 8 2024
© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Abstract

Insufficient sleep compromises cognitive performance, diminishes vigilance, and disrupts daily functioning in hundreds of millions of people worldwide. Despite extensive research revealing significant variability in vigilance vulnerability to sleep deprivation, the underlying mechanisms of these individual differences remain elusive. Locus coeruleus (LC) plays a crucial role in the regulation of sleep–wake cycles and has emerged as a potential marker for vigilance vulnerability to sleep deprivation. In this study, we investigate whether LC microstructural integrity, assessed by fractional anisotropy (FA) through diffusion tensor imaging (DTI) at baseline before sleep deprivation, can predict impaired psychomotor vigilance test (PVT) performance during sleep deprivation in a cohort of 60 healthy individuals subjected to a rigorously controlled in‐laboratory sleep study. The findings indicate that individuals with high LC FA experience less vigilance impairment from sleep deprivation compared with those with low LC FA. LC FA accounts for 10.8% of the variance in sleep‐deprived PVT lapses. Importantly, the relationship between LC FA and impaired PVT performance during sleep deprivation is anatomically specific, suggesting that LC microstructural integrity may serve as a biomarker for vigilance vulnerability to sleep loss.

Individuals with high locus coeruleus integrity experience less vigilance impairment from sleep deprivation compared with those with low locus coeruleus integrity.

fractional anisotropy
locus coeruleus
sleep deprivation
vigilance vulnerability
Guangdong Scientific Research Platform and Projects for the Higher‐educational Institution2023WTSCX034 National Natural Science Foundation of China 10.13039/501100001809 32200889 71942003 Shanghai International Studies University Research Projects2021114002 2021KFKT012 Guangdong Philosophy and Social Science PlanningGD23XXL07 National Institutes of Health 10.13039/100000002 CTRC UL1RR024134 P30‐NS045839 R01‐HL102119 R01‐MH107571 R21‐AG051981 source-schema-version-number2.0
cover-dateSeptember 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:03.09.2024
Quan, P. , Mao, T. , Zhang, X. , Wang, R. , Lei, H. , Wang, J. , Liu, W. , Dinges, D. F. , Jiang, C. , & Rao, H. (2024). Locus coeruleus microstructural integrity is associated with vigilance vulnerability to sleep deprivation. Human Brain Mapping, 45 (13 ), e70013. 10.1002/hbm.70013

Peng Quan and Tianxin Mao contributed equally to this work.
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pmc1 INTRODUCTION

Sleep loss is a prevalent public health concern in contemporary society, particularly affecting individuals in demanding professions such as nurses, doctors, and truck drivers (Lieberman et al., 2019). The detrimental effects of sleep loss primarily manifest in impairments across various cognitive functions and vigilant attention (Lim & Dinges, 2010). These impairments elevate the risk of accidents and injuries in industrial settings. Interestingly, not all individuals experience impairment following sleep loss. After sleep deprivation, approximately one third of healthy adults exhibit severe dysfunction, another third experience moderate dysfunction, and the remaining third show little or no dysfunction (Goel et al., 2015).

Further, vulnerability to sleep deprivation is a trait‐like characteristic. Cognitive responses during repeated exposures to sleep loss display a moderate to high level of stability (Goel & Dinges, 2012). Extensive research has delved into the potential factors influencing vulnerability to sleep deprivation. These include demographic characteristics (Saksvik et al., 2011), behavioral factors (Floros et al., 2021), biochemical factors (Sweeten et al., 2019), functional connectivity (Chen et al., 2018), and white matter microstructure (Cui et al., 2015). These factors have all been suggested as potential explanations for the variations in vulnerability to sleep deprivation. However, there is not a single factor that can sufficiently account for the phenotypic vulnerability to the consequences of sleep deprivation.

Neurobiological circuits regulating sleep–wake transition may underlie phenotypic differences in vulnerability to sleep deprivation (Saper et al., 2010). Sleep–wake transition is modulated by homeostatic process (Process S) and circadian process (Process C) (Borbély et al., 2016). Homeostatic and circadian drive accumulate gradually during extended wakefulness (Tkachenko & Dinges, 2018). If no switching mechanism existed, a person would slowly drift between sleep and wakefulness, and be in a twilight state for the majority of the day (Saper et al., 2010). A key mechanism that supports sleep–wake transition is the locus coeruleus (LC)‐norepinephrine (NE) system. LC is a small nucleus located bilaterally in the dorsolateral pontine tegmentum (PT) (Ohm et al., 1997), which is the major source of NE (Petersen & Posner, 2012). The activity of LC‐NE system can anticipate sleep–wake transition. LC neurons fire slowly prior to the transition from waking to sleep, and resume firing at the transition from slow‐wave sleep to wake (Takahashi et al., 2010). There is no doubt that LC is crucial for regulating sleep–wake transition.

Besides, a growing body of evidence suggests a link between LC integrity and various sleep disorders, including nocturnal awakenings (Van Egroo et al., 2021), sleep–wake dysregulation (Van Egroo et al., 2022), sleep–wake disturbance (Elman et al., 2020), and daytime sleep‐related dysfunction (Elman et al., 2021). Furthermore, LC dysfunction is associated with certain chronic health conditions, such as cognitive deficit (Calarco et al., 2022), fatigue (Carandini et al., 2021), and chronic pain (Bell et al., 2024). LC integrity is a crucial nexus linking sleep disorders and chronic health conditions. This evidence suggests a potential correlation between LC integrity and vulnerability to sleep deprivation.

In the current study, we tried to elucidate the relationship between LC integrity and the vigilance vulnerability to sleep deprivation. We examined the neurological properties of LC using three in vivo imaging techniques: diffusion tensor imaging (DTI), voxel‐based morphometry (VBM), and arterial spin‐labeling (ASL). LC integrity was assessed by fractional anisotropy (FA) through DTI. We evaluated the vigilance vulnerability to sleep deprivation through the psychomotor vigilance task (PVT). Vigilant attention is a major component of a wide range of cognitive performance tasks (Hudson et al., 2019). The PVT is one of the most sensitive measures of impaired vigilant attention induced by sleep deprivation, characterized by a decrease in reaction time (RT) and an increase in lapses number. PVT is minimally influenced by aptitude, learning, or practice (Killgore, 2010; Patanaik et al., 2015). By combining magnetic resonance imaging (MRI) with PVT, we hypothesized that the neurological properties of the LC, particularly LC integrity, would predict impaired PVT performance during sleep deprivation. As low FA often indicating impaired cognitive function (Koo et al., 2023), as well as neurological and psychiatric disorders (Kochunov et al., 2015). We hypothesized that lower LC FA would be associated with greater impairment in vigilant attention induced by sleep deprivation, as evidenced by poorer PVT performance during sleep deprivation.

2 MATERIALS AND METHODS

2.1 Participants

This study was approved by the Institutional Review Board of the University of Pennsylvania, and conducted according to the principles in the Declaration of Helsinki. A total of 60 healthy adults (age range 21–50 years, 25 females) participated in a 5‐day and 4‐night in‐lab sleep deprivation study. All participants provided written informed consent before enrolling in the study and got compensation for their time and effort.

Participants were randomly assigned to either the total sleep deprivation (TSD) group (n = 44) or the control group (n = 16). All participants met the following inclusion criteria: (1) Right handed; (2) Normal or corrected‐to‐normal vision; (3) Normal sleep duration between 6.5 and 8 h, with a habitual bedtime between 20:00 and 00:00 h and a regular wake time between 06:00 and 10:00 h. The exclusion criteria were: (1) Contraindication for MRI (MRI incompatible devices); (2) A history of major diseases and neurological, psychiatric, or sleep disorders; (3) A habit of smoking, drinking, or any other substance addiction; (4) Habitual napping or sleep disturbances, as validated by overnight polysomnography and blood oxygen saturation measurements. In the 2 months before the laboratory study, participants were instructed to refrain from engaging in activities involving travel across time zones, shift work, or irregular weekly sleep routines. In the week, both preceding and following the laboratory study, we recorded the bedtime and wake‐up times of the enrolled participants using sleep–wake diaries. Caffeine, alcohol, tobacco, and drugs (except oral contraceptives) were not allowed before and during the laboratory study.

We excluded one participant from the TSD group from further analysis due to an unusually high number of PVT lapses during the baseline state (Day 2, 08:00 h: number of lapses = 29, Z = 6.12). Consequently, 43 participants from the TSD group and 16 from the control group were included in the subsequent analyses. Due to sample attrition, not all participants underwent all three DTI scans (some only underwent one or two scans). Eighteen TSD and five control participants were excluded from the test–retest reliability analysis. However, these 23 participants were still part of the analysis concerning the relationship between LC integrity and vigilance vulnerability to sleep deprivation, as they had baseline DTI scan data. For the reliability analysis of DTI imaging, we included 25 TSD and 11 control participants.

2.2 Study procedure

All participants stayed in the Clinical Translational Research Center at the Hospital of the University of Pennsylvania for five consecutive days and four nights (see Figure 1 for the illustrated protocol). Participants arrived in the afternoon of Day 1 and had 9–10 h time‐in‐bed (TIB) on the first night to ensure they were well‐rested. The first MRI scan was conducted on Day 2. During the TSD night, participants stayed awake. On the regular sleep night, participants had 8 h TIB. All participants underwent their second MRI scan on Day 3 (TSD, awake for 24 h; Control, normal sleep). Following 8 h TIB on nights 3 and 4, participants underwent their third MRI scan on Day 5. All scans were conducted between 07:00 and 09:00 h.

FIGURE 1 The protocol of 5‐day and 4‐night in‐lab sleep deprivation study. Note that the upper part is the total sleep deprivation (TSD) group; the lower part is the control group.

Trained staff continuously monitored the participants during their stay in an environment where brightness and temperature were controlled. Specifically, background lighting was maintained at a constant brightness level (<100 lux) during the scheduled wake periods and turned off during the designated sleep times. The laboratory temperature was maintained within a narrow range of 21 ± 1°C. Participants received meals and snacks at scheduled times. When not undergoing tests, they could read or watch TV but were not allowed to perform physically demanding tasks, use laptops, or leave the study area.

To measure vigilant attention, participants received PVT every 2 h while they were awake. Because of scheduling conflicts with other tests, the TSD group did not complete PVT at 16:00 h on Day 3.

2.3 Psychomotor vigilance task

During the PVT, participants were instructed to concentrate their attention on a red rectangular area positioned at the center of a darkened screen. They were required to press the space bar in response to a yellow millisecond counter that appeared at random (ranging from 2 to 10 s) interstimulus intervals (Lim & Dinges, 2008). Once a response is made, the millisecond counter stops and stays on the screen for 1 s, allowing participants to know their RT. We define “no response within 30 s” as a “timeout” and a nonstimulated button press as a “false start.” If a timeout or false start occurs, a warning message will appear on the screen and remain visible until the end of the trial. Participants were instructed to respond as quickly as possible while avoiding false starts. We utilized two PVT metrics: (a) response speed (RRT, mean reciprocal reaction time based on mean RT excluding lapses, 1000/mean RT); and (b) number of lapses (RT ≥500 ms). Both PVT metrics have high robustness to extreme values (Basner & Dinges, 2011). The PVT administered at 08:00 h on the morning of Day 2 (the first test after wake‐up) was defined as the baseline, while the mean PVT performances between 00:00 and 18:00 h of Day 3 are labeled as the sleep‐deprived state. To explore the effect of extended wakefulness on PVT performances, we subtracted baseline PVT performances to assess the deviations from the baseline state.

2.4 MRI acquisition

Imaging data were acquired with a 3‐Tesla MRI system (Siemens AG, Erlangen, Germany) with an eight‐channel array coil. The standardized imaging protocol included a diffusion‐weighted imaging (DWI) sequence, a 3D magnetization‐prepared rapid gradient‐echo (MPRAGE) sequence, and a pseudo‐continuous ASL (pCASL) sequence. DWIs were acquired using single‐shot echo planar imaging with the following parameters: repetition time (TR)/echo time (TE) = 5000/68 ms, field of view (FOV) = 240 × 240 mm2, voxel size 1.7 × 1.7 × 3.4 mm3 (no gap), 30 gradient directions (b = 1000 s/mm2) and one scan with b = 0. High‐resolution anatomic images were acquired using a T1‐weighted 3D MPRAGE sequence with the following parameters: TR/TE = 1620/3.09 ms, FOV = 187 × 250 mm2, matrix size = 192 × 256, slice thickness = 5 mm, interslice gap = 1 mm. ASL perfusion images were acquired using a pCASL sequence with a 2D gradient echo planar imaging readout with the following parameters: TR = 4 s, TE = 18 ms, FOV = 220 × 220 mm2, matrix = 64 × 64, number of slices = 20, slices thickness = 5 mm, inter‐slice gap = 1 mm, labeling time = 1.5 s, delay time = 1.0 s. Participants were instructed to keep their gaze fixed on the central fixation point throughout the scanning session.

2.5 MRI preprocessing

DTI imaging data were analyzed with FMRIB Software Library (FSL) (fsl.fmrib.ox.ac.uk) (Smith et al., 2004), which included the following steps: (1) Visually checked image quality to remove the gradient direction of signal loss due to excessive motion; (2) Corrected for eddy current distortion using Eddycorrect; (3) Removed skull and nonbrain tissue using BET; (4) Used DTIFit to model the diffusion tensor and obtain FA maps; (5) used FNIRT to align FA maps nonlinearly to MNI152 standard space. Methods using mean FA skeletons such as tract‐based spatial statistics (TBSS) employ a threshold to exclude areas with low FA (Egle et al., 2022; Smith et al., 2006). However, LC is a brain structure located adjacent to the ventricles and not well covered by the FA skeleton (Mäki‐Marttunen & Espeseth, 2021). Therefore, we chose not to use TBSS in order to better capture individual differences in LC FA.

VBM was conducted using CAT12 (Jena University Hospital, Departments of Psychiatry and Neurology, Jena, Germany, www.neuro.uni-jena.de/cat) and SPM12 (Wellcome Department of Cognitive Neurology, London, www.fil.ion.ucl.ac.uk/spm-statistical-parametric-mapping) toolbox, which implemented in MATLAB 2016 (Mathworks Inc., Sherborn, MA, USA). All images are quality controlled for artifacts by visual inspection and passed homogeneity control implemented in the CAT12 toolbox. The structural images were normalized to the Montreal Neurological Institute (MNI) stereotactic space.

ASL data processing and analyses were carried out with Grocer toolbox (www.nitrc.org/projects/fmri_grocer, version 2.12) implemented in MATLAB 2008. The pipeline consisted of correction of head motion, voxel‐wise cerebral blood flow (CBF) quantification, spatial normalization to standard MNI space, and the voxels resampled to 2 × 2 × 2 mm3. The mean CBF map was obtained by averaging preprocessed CBF images across scan time. To eliminate the noise from processing, we created a mask of all absolute CBF‐mean maps above 5 (mL/100 g/min) and below 150 (mL/100 g/min). It is worth mentioning that we analyzed all neurological properties without applying spatial smoothing. This was done to reduce the impact of neighboring brain regions on the observed signal within the specific regions of interest (ROIs) (Murphy et al., 2014).

2.6 ROI analysis

As shown in Figure 2, LC ROI was defined based on a published probability template (1‐SD version; Keren et al., 2009), which has been histologically verified in the MNI space (Keren et al., 2015), and validated in several previous studies (Ciampa et al., 2022; Doppler et al., 2021; Murphy et al., 2014). FA, a scalar value obtained from DTI imaging, characterize the integrity of LC. Additionally, three other DTI metrics were measured: mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Regional brain structural properties were evaluated by gray matter volume (GMV) and white matter volume (WMV). Additionally, CBF, an index derived from ASL, was used to evaluate resting‐state brain function. MarsBaR toolbox (version 0.44) was used to sample the neurological properties of the ROIs.

FIGURE 2 Mean fractional anisotropy image generated from all participants in baseline scan (n = 59). Note that red color refers to a standard locus coeruleus template (1‐SD version) from Keren et al. (2009); blue color refers to the pontine tegmentum; yellow color refers to the thalamus.

In order to validate the anatomical specificity of LC, we chose PT and thalamus as reference ROIs. We selected the PT as one reference ROI because the LC is situated in the dorsolateral PT (Ohm et al., 1997). Additionally, we chose the thalamus as another reference ROI because LC projects directly to the thalamus, and modulate the function of thalamus (Beas et al., 2018). PT was defined according to previous established ROIs (cubic, 4 mm widths, center MNI coordinates [0, −28, −26] mm) (Clewett et al., 2016; Elman et al., 2021; Liu et al., 2019). Thalamus was defined as two combined cubes (4 mm widths, center MNI coordinates [11, −16, 6] and [−11, −16, 6]) (Rolls et al., 2020).

2.7 Statistical analysis

The data were analyzed using SPSS 23.0 and R 3.6.3 unless stated otherwise. Group differences in demographic characteristics were examined using analysis of variance (ANOVA). The reproducibility and reliability of LC FA values were assessed using the coefficients of variations (CoV) and intraclass correlation coefficients (ICC(3,1)) (Dormann, 2013; Koo & Li, 2016; Landis & Koch, 1977). We checked the normal distributions of variables using the Shapiro–Wilk test. To investigate the relationship between LC FA values and PVT performance, as well as that between LC FA values across different scans, we employed Pearson correlation coefficient for normally distributed data and Spearman's rank correlation coefficient for non‐normally distributed data. Stepwise regression analyses were performed to evaluate the contributions of neurobiological properties and demographic variables to the variance in PVT performances. The Benjamini–Hochberg procedure (FDR), was employed to control the expected proportion of false discoveries when multiple tests are performed. A p < .05 was considered statistically significant.

3 RESULTS

Our study examined the extent to which TSD impairs vigilant attention, as indicated by the deviation from the baseline PVT performance. The TSD group and the control group were carefully matched in terms of age, gender, years of education, and body mass index (BMI). As expected, our findings affirmed that TSD had a detrimental effect on vigilant attention. To illustrate the divergent patterns of PVT performance changes over time, we plotted both the number of lapses and response speed against the time points of the tests. For details, please consult Figures S1 and S2. The PVT performances of the TSD group started declining at midnight (Day 3, 00:00 h), reaching its nadir on the next morning (Day 3, 08:00 h), and then gradually recovering. In contrast, the control group demonstrated similar levels of PVT performances across the study. These results confirmed the adverse effects of TSD on PVT performances.

We used FA to assess LC integrity. To be useful, LC FA must be reliable and stable. We firstly assess the reproducibility and reliability of LC FA across all three scans (for the TSD group: Day 2, baseline; Day 3, after sleep deprivation; Day 5, after recovery sleep; for the control group: Day 2, baseline; Day 3, after regular sleep; Day 5, after regular sleep). The scatter plots in Figure 3 illustrated moderate to high correlations. The CoV and ICC(3,1) revealed quite good reproducibility and reliability (see Table 1). These collectively findings suggest that LC FA serves as a reliable and stable metric throughout the current study.

FIGURE 3 The repeatability of locus coeruleus fractional anisotropy. Note that correlation is evaluated by the Pearson correlation coefficient. TSD, total sleep deprivation.

TABLE 1 The reliability and repeatability of LC FA.

		Scan1	Scan2	Scan3	
Control n = 11	Mean ± SD	0.58 ± 0.05	0.58 ± 0.04	0.59 ± 0.05	
CoV	0.08	0.07	0.08	
ICC(3,1)	0.85 [0.70 0.97]	
TSD n = 25	Mean ± SD	0.57 ± 0.04	0.57 ± 0.05	0.57 ± 0.04	
CoV	0.08	0.08	0.06	
ICC(3,1)	0.85 [0.74 0.93]	
Note: Scan1, Day 2, baseline state; Scan2, Day 3, after one night of TSD/normal sleep; Scan3, Day 5, after two nights of recovery sleep/normal sleep. Values in square brackets indicate the 95% confidence interval for ICC.

Abbreviations: CoV, coefficient of variation; ICC(3,1), intraclass correlation coefficient for a single measurement.

To examine the relationship between LC FA and PVT performance, we employed a median split approach. We divided the TSD group into two subgroups based on their LC FA values (median split, 21 participants per group). To minimize the potential physiological impacts of sleep deprivation, LC FA value was obtained on the morning of Day 2, between 07:00 and 09:00 h. A median value of 0.57 served as the criterion for this division. It is worth noting that the three groups (control, high LC FA, low LC FA) demonstrated comparable demographic characteristics and baseline PVT performances (all p > .2, refer to Table 2).

TABLE 2 Demographic characteristics of the control and high/low LC FA group.

	Control (n = 16)	High LC FA (n = 21)	Low LC FA (n = 21)	p‐Value	
Age	35.44 ± 9.54	33.76 ± 7.70	31.71 ± 9.84	.46	
Female, no. (%)	7 (43.8%)	7 (33.3%)	11 (52.4%)	.47	
Years of education	15.00 ± 2.31	14.67 ± 2.27	14.05 ± 1.50	.35	
BMI	25.27 ± 4.28	23.66 ± 2.19	23.68 ± 3.22	.25	
Baseline number of lapses	2.38 ± 3.03	1.67 ± 3.33	2.71 ± 3.36	.58	
Baseline RRT	3.42 ± 0.55	3.66 ± 0.57	3.46 ± 0.53	.36	
Note: The values are expressed as mean ± standard deviation or number (percentage). Baseline PVT was measured at Day 2, 08:00 h.

Abbreviations: BMI, body mass index; FA, fractional anisotropy; LC, locus coeruleus; PVT, psychomotor vigilance test; RRT, reciprocal reaction time.

High LC FA participants exhibited a significant decrease in lapses number when compared with their low LC FA counterparts at multiple time points (see Figure 4a). The same trend was also observed in response speed (see Figure 4b), with less reduction in response speed for the high LC FA participants than the low LC FA participants. Participants with high LC FA demonstrated significantly better performance than those with low LC FA during sleep deprivation. In summary, we have observed an association between LC FA and vigilance vulnerability to sleep deprivation. The high LC FA participants exhibited greater resistance to the adverse effects of sleep deprivation compared with their low LC FA counterparts.

FIGURE 4 The time course of psychomotor vigilance test (PVT) performance when grouped with baseline locus coeruleus (LC) fractional anisotropy (FA)/pontine tegmentum (PT) FA/thalamus FA. (a) lapses number, total sleep deprivation (TSD) subjects were grouped based on LC FA; (b) response speed, TSD subjects were grouped based on LC FA; (c) lapses number, TSD subjects were grouped based on PT FA; (d) response speed, TSD subjects were grouped based on PT FA; (e) lapses number, TSD subjects were grouped based on thalamus FA; (f) response speed, TSD subjects were grouped based on thalamus FA. Note that PVT metrics (means ± SE, the deviation from baseline state) at 2‐h intervals across 34 h. TSD subjects were divided into two subgroups (median split, 21 subjects per group) based on LC FA/PT FA/thalamus FA. Data are plotted against the start times of PVT. The group differences at each time point were determined using Wilcoxon signed‐rank tests (two tailed). Red asterisk: The difference between the high LC FA and low LC FA group was significant; Green asterisk: The difference was significant after Benjamini–Hochberg correction; *p < .05, **p < .01.

We further investigated the association between LC integrity and the PVT performances during sleep‐deprived state (Day 3, 00:00–18:00 h). There were notable associations between them. We observed a negative correlation for number of lapses (r = −.35, p = .022, FDR‐adjusted p‐value = .032) (see Figure 5a), and a positive correlation for response speed (r = .33, p = .032, FDR‐adjusted p‐value = .032; see Figure 5b). We employed G*Power (version 3.1.9.7) to calculate the achieved statistical power (Faul et al., 2009). The statistical power was 77.8% for lapses number and 72.9% for response speed.

FIGURE 5 The relationship between locus coeruleus (LC) fractional anisotropy (FA) and the sleep‐deprived psychomotor vigilance test (PVT) performances. (a) Sleep‐deprived number of lapses plotted as a function of LC FA; (b) sleep‐deprived response speed plotted as a function of LC FA. Note that LC FA was measured during the first scan (Day 2, 07:00–09:00 h). Correlations were assessed using Spearman's rank correlation coefficients.

It's noteworthy that we didn't find any statistically significant correlation between LC FA and PVT performances under other conditions: baseline state (Day 2, 08:00 h, FDR‐adjusted p‐values >.9), or daytime before sleep deprivation (Day 2, 08:00–18:00 h, FDR‐adjusted p‐values >.07).

We further investigated whether the associations between LC integrity and sleep‐deprived PVT performances were specific to a particular anatomical region. Although there was a significant correlation between the LC FA and PT (a LC neighboring region) FA (r = .41, p = .006), no correlation was found between the PT FA and sleep‐deprived PVT performances (all p > .1). In addition, there was no correlation between thalamus FA and sleep‐deprived PVT performances (all p > .1). Furthermore, we conducted median split analyses on PT FA and thalamus FA. We divided the participants in the TSD group into two subgroups based on their PT FA (median value = 0.43), and thalamus FA (median value = 0.31). No significant differences were observed between the two subgroups (refer to Figure 4c,d for PT FA and Figure 4e,f for thalamus FA). In summary, the above findings suggested a rather anatomically specific association between the LC FA and sleep‐deprived PVT performances.

In order to assess the contribution of LC FA to the variations of sleep‐deprived PVT performance, we conducted stepwise regression analyses. We included neurobiological properties (LC FA, PT FA, thalamus FA), and demographic variables (gender, age, years of education, BMI) as independent variables. Remarkably, for sleep‐deprived number of lapses, LC FA emerged as the sole significant predictor, contributing 10.8% of the variance (adjusted R 2). For sleep‐deprived response speed, no independent predictor was found. These results suggest that the contribution of LC FA outweighs that of PT FA, thalamus FA, and demographic variables.

Finally, we investigated the relationships between various neurological properties of LC (i.e., MD, AD, RD, CBF, GMV, and WMV) and sleep‐deprived PVT performances. No significant correlation was observed between these neurobiological properties and sleep‐deprived PVT performance (all p > .1).

4 DISCUSSION

In this study, we observed significant variations in vigilance vulnerability to sleep deprivation among individuals. The PVT performance during the sleep‐deprived state differs significantly between the high and low LC FA groups. Our findings revealed an association between LC integrity and vigilance vulnerability to sleep deprivation. Previous studies have established the critical role of the LC in regulating the transition between sleep and wakefulness. Therefore, it is plausible to posit that the microstructural features of LC neurons, represented by LC FA, could potentially serve as a biomarker to differentiate phenotypic variations in vigilance vulnerability to sleep deprivation.

Ensuring the reproducibility and reliability of LC FA values should be given top priority. The impact of sleep deprivation on the white matter integrity has yielded inconclusive findings. One study reported a significant increase in morning FA in the bilateral frontal and parietal lobes compared with the evening FA (Jiang et al., 2014). Another study reported a substantial and widespread increase in FA after a day of physical activity, followed by a notable decrease after sleep deprivation (Elvsåshagen et al., 2015). In contrast, a different study conducted within a similar timeframe found no significant changes in FA (Thomas et al., 2018). Comparing these results is challenging due to differences in scanning protocols and the diffusion tensor approaches employed in these studies. Nonetheless, the present findings demonstrate minimal alterations in LC FA across three scans, reinforcing our confidence in the reproducibility and reliability of LC FA as a metric, at least within the context of this study.

As anticipated, our findings have found that there is an association between LC integrity and vigilance vulnerability to sleep deprivation. LC is essential for the sleep–wake transition and arousal state maintenance (Osorio‐Forero et al., 2022). We believed that LC integrity serves as an indicator of these ability. Previous research has demonstrated that the LC‐NE system contributes to the sleep rebound following sleep deprivation (González et al., 1996). Further, aged humans often experience a significant decrease in LC neurons, which typically leads to an increase in sleep problems (Aston‐Jones et al., 2007). While the precise contribution of LC to sleep–wake transition and arousal state maintenance remains to be fully elucidated, there is no doubt about its central role in these processes. For instance, the activation of LC neurons promotes arousal and facilitates rapid transitions from sleep to wakefulness, whereas the inhibition of LC neurons diminishes wakefulness (Carter et al., 2010).

Diffusion MRI has demonstrated potentials to assess the microstructural integrity (Lin et al., 2024). Although the biological interpretation of FA needs further investigation, reduced FA values often reflect a disruption in tissue microstructure, particularly the disruption of axonal fibers and alterations of functional connectivity (Videtta et al., 2023). More specifically, reduction of FA probably accounts for the impairment of connectivity, suggesting a decrease in the amount of functional information transmitted, ultimately reducing the LC's capacity to regulate sleep–wake transition. However, due to the complex fiber architecture in the LC, MD may be not sensitive enough to detect its microstructural integrity (Lin et al., 2024). This might explain the absence of findings in LC MD.

The association between LC integrity and vigilance vulnerability to sleep deprivation may also stem from the unique role of the NE neurotransmitter in the central nervous system. Animals with highly excitable LC neurons exhibit significantly elevated NE concentrations in multiple brain regions (Kilbourn et al., 1998), potentially acting as a countermeasure against the adverse effects of sleep deprivation on psychomotor vigilance, although the exact mechanism is still unclear. For instance, in rats, sleep deprivation results in an increased level of NE transporter mRNA in the LC (Basheer et al., 1998). Similarly, in healthy men, sleep deprivation increased nocturnal and daytime circulating concentrations of NE (Benedict et al., 2011). Accumulating evidence suggests that sleep deprivation increases NE concentrations, potentially acting as a defense mechanism against its adverse effects. It's noteworthy that the present study did not specifically examine the roles of NE in sleep deprivation. Additionally, various neurotransmitters beyond NE are also associated with sleep deprivation, including 5‐hydroxytryptamine (5‐HT), orexin, glutamate, acetylcholine, gamma‐aminobutyric acid (GABA), and adenosine (Longordo et al., 2009). The involvement of multiple neurotransmitters in sleep deprivation represents a complex and multifaceted aspect that undoubtedly warrants further investigation.

Although employing multimodal imaging techniques, we noticed that the predictive ability on vigilance vulnerability was specifically evident in LC FA. FA, according to the theoretical diffusion model, is conventionally interpreted in relation to microstructural tissue elements, such as neural fiber density and membrane integrity (Beaulieu, 2002). Due to the complex fiber architecture surrounding the LC, other DTI metrics (AD, RD, MD) may lack the sensitivity of FA in detecting these microstructural tissue elements (Lin et al., 2024). CBF has a lower signal‐to‐noise ratio than DTI imaging, which may reduce its effectiveness in characterizing tissue microstructure. While GMV and WMV can analyze macrostructural properties well, they cannot capture microstructural neuronal connections (Pareek et al., 2018). Furthermore, FA measures the relative difference between the largest eigenvalue as compared to the others (Feldman et al., 2010). Unlike other MRI metrics, FA is sensitive to directionality, making it suitable for analyzing the complex fiber architecture surrounding the LC.

Our study possesses several notable strengths. First, we categorized participants into two groups according to their neurological properties before sleep deprivation and then compared their PVT performances during sleep deprivation. Unlike some previous studies that categorized participants into resilient and vulnerable groups solely based on performance during sleep deprivation (Rocklage et al., 2009; Yeo et al., 2015; Zhu et al., 2017), our study avoids circular reasoning driven by predefined notions of vulnerability. Second, our study stands as a well‐controlled laboratory investigation. By integrating gold standard PVT, multiple neuroimaging modalities and employing standardized imaging analysis procedures, we enhanced the interpretability of our findings. Third, the association of sleep‐deprived PVT performances with LC FA is anatomically specific. Specially, we found no significant relationship with the LC‐ adjacent PT FA and thalamus FA. This anatomically specific finding reduces the chances of spurious associations.

Our study has several potential limitations that require attention. First, we utilized a binary template to define the LC. Accurately defining the LC using a binary template with 3T MRI data poses challenges. However, it is important to note that this binary template has been successfully validated in several prior studies (Ciampa et al., 2022; Doppler et al., 2021; Murphy et al., 2014). In our study, we also assessed LC FA values from the binary template, which demonstrated good reproducibility and reliability. Second, it is important to underscore that the PVT only captures certain aspects of the multiple dimensional consequences of sleep deprivation. Specifically, although stepwise regression analyses revealed that LC FA significantly impacted the number of lapses, its influence on response speed was not statistically significant. This may be attributed to the notion that LC integrity may be more relevant to the deterioration of wake‐state stability, as measured by the number of lapses, rather than a diminution in the capacity to quickly execute neurobehavioral tasks, as assessed by response speed (Doran et al., 2001; Tkachenko & Dinges, 2018). The integration of supplementary cognitive assessments in future investigations is recommended to enrich the comprehensiveness of the study. In addition, the determination of whether the observed association between LC integrity and vigilance vulnerability to sleep deprivation reflects a causal relationship remains uncertain. It is plausible that LC integrity serves as a potential marker for an underlying, more general activation of the sleep–wake regulation system (Eban‐Rothschild et al., 2017). Finally, although our findings suggest that LC integrity serve as a potential indicator for assessing resilience to sleep loss, future studies are necessary to replicate our findings and explore the clinical significance and real‐world applications.

5 CONCLUSION

Our findings unveil a significant association between LC integrity and vigilance vulnerability to sleep deprivation. The explanatory power of LC FA is anatomically specific, suggesting it may originate from the unique axonal projection pattern of the LC‐NE system. Evaluating LC integrity may serve as a valuable tool in assessing individual resilience to sleep loss in future research, potentially mitigating the risk of accidents and injuries in demanding professions.

FUNDING INFORMATION

This research was supported in part by the grants from the National Institutes of Health (R01‐HL102119, R01‐MH107571, R21‐AG051981, CTRC UL1RR024134, and P30‐NS045839), National Natural Science Foundation of China (71942003 and 32200889), Shanghai International Studies University Research Projects (2021114002 and 2021KFKT012), Guangdong Scientific Research Platform and Projects for the Higher‐educational Institution (2023WTSCX034), and Guangdong Philosophy and Social Science Planning (GD23XXL07). The funders had no role in the study design, data collection and analysis, data interpretation, writing of the article, or the decision to submit the article for publication.

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Supporting information

FIGURE S1. The time course of PVT performance of the TSD/control group.

FIGURE S2. PVT performances of the TSD/control group before (Day 2) and after (Day 3) sleep deprivation/normal sleep.

ACKNOWLEDGMENTS

The authors would like to thank all the participants in this study.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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REFERENCES

Aston‐Jones, G. , Gonzalez, M. , & Doran, S. (2007). Role of the locus coeruleus‐norepinephrine system in arousal and circadian regulation of the sleep‐wake cycle. In Brain norepinephrine: Neurobiology and therapeutics (pp. 157–195). Cambridge University Press. 10.1017/CBO9780511544156.007
Basheer, R. , Magner, M. , McCarley, R. W. , & Shiromani, P. J. (1998). REM sleep deprivation increases the levels of tyrosine hydroxylase and norepinephrine transporter mRNA in the locus coeruleus. Molecular Brain Research, 57 (2 ), 235–240. 10.1016/S0169-328X(98)00088-6 9675421
Basner, M. , & Dinges, D. F. (2011). Maximizing sensitivity of the psychomotor vigilance test (PVT) to sleep loss. Sleep, 34 (5 ), 581–591. 10.1093/sleep/34.5.581 21532951
Beas, B. S. , Wright, B. J. , Skirzewski, M. , Leng, Y. , Hyun, J. H. , Koita, O. , Ringelberg, N. , Kwon, H.‐B. , Buonanno, A. , & Penzo, M. A. (2018). The locus coeruleus drives disinhibition in the midline thalamus via a dopaminergic mechanism. Nature Neuroscience, 21 (7 ), 963–973. 10.1038/s41593-018-0167-4 29915192
Beaulieu, C. (2002). The basis of anisotropic water diffusion in the nervous system – A technical review. NMR in Biomedicine, 15 (7–8 ), 435–455. 10.1002/nbm.782 12489094
Bell, T. R. , Franz, C. E. , Eyler, L. T. , Fennema‐Notestine, C. , Puckett, O. K. , Dorros, S. M. , Panizzon, M. S. , Pearce, R. C. , Hagler, D. J. , Lyons, M. J. , Beck, A. , Elman, J. A. , & Kremen, W. S. (2024). Probable chronic pain, brain structure, and Alzheimer's plasma biomarkers in older men. The Journal of Pain, 25 (6 ), 104463. 10.1016/j.jpain.2024.01.006 38199594
Benedict, C. , Hallschmid, M. , Lassen, A. , Mahnke, C. , Schultes, B. , Schiöth, H. B. , Born, J. , & Lange, T. (2011). Acute sleep deprivation reduces energy expenditure in healthy men. The American Journal of Clinical Nutrition, 93 (6 ), 1229–1236. 10.3945/ajcn.110.006460 21471283
Borbély, A. A. , Daan, S. , Wirz‐Justice, A. , & Deboer, T. (2016). The two‐process model of sleep regulation: A reappraisal. Journal of Sleep Research, 25 (2 ), 131–143. 10.1111/jsr.12371 26762182
Calarco, N. , Cassidy, C. M. , Selby, B. , Hawco, C. , Voineskos, A. N. , Diniz, B. S. , & Nikolova, Y. S. (2022). Associations between locus coeruleus integrity and diagnosis, age, and cognitive performance in older adults with and without late‐life depression: An exploratory study. NeuroImage: Clinical, 36 , 103182. 10.1016/j.nicl.2022.103182 36088841
Carandini, T. , Mancini, M. , Bogdan, I. , Rae, C. L. , Barritt, A. W. , Sethi, A. , Harrison, N. , Rashid, W. , Scarpini, E. , Galimberti, D. , Bozzali, M. , & Cercignani, M. (2021). Disruption of brainstem monoaminergic fibre tracts in multiple sclerosis as a putative mechanism for cognitive fatigue: A fixel‐based analysis. NeuroImage: Clinical, 30 , 102587. 10.1016/j.nicl.2021.102587 33610097
Carter, M. E. , Yizhar, O. , Chikahisa, S. , Nguyen, H. , Adamantidis, A. , Nishino, S. , Deisseroth, K. , & de Lecea, L. (2010). Tuning arousal with optogenetic modulation of locus coeruleus neurons. Nature Neuroscience, 13 (12 ), 1526–1533. 10.1038/nn.2682 21037585
Chen, W.‐H. , Chen, J. , Lin, X. , Li, P. , Shi, L. , Liu, J.‐J. , Sun, H.‐Q. , Lu, L. , & Shi, J. (2018). Dissociable effects of sleep deprivation on functional connectivity in the dorsal and ventral default mode networks. Sleep Medicine, 50 , 137–144. 10.1016/j.sleep.2018.05.040 30055480
Ciampa, C. J. , Parent, J. H. , Harrison, T. M. , Fain, R. M. , Betts, M. J. , Maass, A. , Winer, J. R. , Baker, S. L. , Janabi, M. , Furman, D. J. , D'Esposito, M. , Jagust, W. J. , & Berry, A. S. (2022). Associations among locus coeruleus catecholamines, tau pathology, and memory in aging. Neuropsychopharmacology, 47 (5 ), 1106–1113. 10.1038/s41386-022-01269-6 35034099
Clewett, D. V. , Lee, T.‐H. , Greening, S. , Ponzio, A. , Margalit, E. , & Mather, M. (2016). Neuromelanin marks the spot: Identifying a locus coeruleus biomarker of cognitive reserve in healthy aging. Neurobiology of Aging, 37 , 117–126.26521135
Cui, J. , Tkachenko, O. , Gogel, H. , Kipman, M. , Preer, L. A. , Weber, M. , Divatia, S. C. , Demers, L. A. , Olson, E. A. , Buchholz, J. L. , Bark, J. S. , Rosso, I. M. , Rauch, S. L. , & Killgore, W. D. S. (2015). Microstructure of frontoparietal connections predicts individual resistance to sleep deprivation. NeuroImage, 106 , 123–133. 10.1016/j.neuroimage.2014.11.035 25463450
Doppler, C. E. J. , Kinnerup, M. B. , Brune, C. , Farrher, E. , Betts, M. , Fedorova, T. D. , Schaldemose, J. L. , Knudsen, K. , Ismail, R. , Seger, A. D. , Hansen, A. K. , Stær, K. , Fink, G. R. , Brooks, D. J. , Nahimi, A. , Borghammer, P. , & Sommerauer, M. (2021). Regional locus coeruleus degeneration is uncoupled from noradrenergic terminal loss in Parkinson's disease. Brain, 144 (9 ), 2732–2744. 10.1093/brain/awab236 34196700
Doran, S. M. , Van Dongen, H. P. , & Dinges, D. F. (2001). Sustained attention performance during sleep deprivation: Evidence of state instability. Archives Italiennes de Biologie, 139 (3 ), 253–267. 10.1016/j.sleep.2017.11.442 11330205
Dormann, C. F. (2013). Parametrische Statistik. Springer.
Eban‐Rothschild, A. , Appelbaum, L. , & de Lecea, L. (2017). Neuronal mechanisms for sleep/wake regulation and modulatory drive. Neuropsychopharmacology, 43 (5 ), 937–952. 10.1038/npp.2017.294 29206811
Egle, M. , Hilal, S. , Tuladhar, A. M. , Pirpamer, L. , Bell, S. , Hofer, E. , Duering, M. , Wason, J. , Morris, R. G. , Dichgans, M. , Schmidt, R. , Tozer, D. J. , Barrick, T. R. , Chen, C. , de Leeuw, F.‐E. , & Markus, H. S. (2022). Determining the OPTIMAL DTI analysis method for application in cerebral small vessel disease. NeuroImage: Clinical, 35 , 103114. 10.1016/j.nicl.2022.103114 35908307
Elman, J. A. , Puckett, O. K. , Beck, A. , Fennema‐Notestine, C. , Cross, L. K. , Dale, A. M. , Eglit, G. M. L. , Eyler, L. T. , Gillespie, N. A. , Granholm, E. L. , Gustavson, D. E. , Hagler, D. J. , Hatton, S. N. , Hauger, R. , Jak, A. J. , Logue, M. W. , McEvoy, L. K. , McKenzie, R. E. , Neale, M. C. , … Kremen, W. S. (2021). MRI‐assessed locus coeruleus integrity is heritable and associated with multiple cognitive domains, mild cognitive impairment, and daytime dysfunction. Alzheimer's & Dementia, 17 (6 ), 1017–1025. 10.1002/alz.12261
Elman, J. A. , Puckett, O. K. , Beck, A. , Panizzon, M. S. , Sanderson‐Cimino, M. E. , Gustavson, D. E. , Lyons, M. J. , Franz, C. E. , & Kremen, W. S. (2020). MRI‐assessed locus coeruleus integrity is heritable and associated with cognition, Alzheimer's risk, and sleep‐wake disturbance. Alzheimer's & Dementia, 16 (S5 ), e044862. 10.1002/alz.044862
Elvsåshagen, T. , Norbom, L. B. , Pedersen, P. Ø. , Quraishi, S. H. , Bjørnerud, A. , Malt, U. F. , Groote, I. R. , & Westlye, L. T. (2015). Widespread changes in white matter microstructure after a day of waking and sleep deprivation. PLoS One, 10 (5 ), e0127351. 10.1371/journal.pone.0127351 26020651
Faul, F. , Erdfelder, E. , Buchner, A. , & Lang, A.‐G. (2009). Statistical power analyses using G*power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41 (4 ), 1149–1160. 10.3758/BRM.41.4.1149 19897823
Feldman, H. M. , Yeatman, J. D. , Lee, E. S. , Barde, L. H. F. , & Gaman‐Bean, S. (2010). Diffusion tensor imaging: A review for pediatric researchers and clinicians. Journal of Developmental & Behavioral Pediatrics, 31 (4 ), 346–356. 10.1097/DBP.0b013e3181dcaa8b 20453582
Floros, O. , Axelsson, J. , Almeida, R. , Tigerström, L. , Lekander, M. , Sundelin, T. , & Petrovic, P. (2021). Vulnerability in executive functions to sleep deprivation is predicted by subclinical attention‐deficit/hyperactivity disorder symptoms. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6 (3 ), 290–298. 10.1016/j.bpsc.2020.09.019 33341402
Goel, N. , Basner, M. , & Dinges, D. F. (2015). Chapter thirteen – phenotyping of neurobehavioral vulnerability to circadian phase during sleep loss. In A. Sehgal (Ed.), Methods in enzymology (Vol. 552 , pp. 285–308). Academic Press. 10.1016/bs.mie.2014.10.024 25707282
Goel, N. , & Dinges, D. F. (2012). Predicting risk in space: Genetic markers for differential vulnerability to sleep restriction. Acta Astronautica, 77 , 207–213. 10.1016/j.actaastro.2012.04.002 23524958
González, M. M. , Valatx, J. L. , & Debilly, G. (1996). Role of the locus coeruleus in the sleep rebound following two different sleep deprivation methods in the rat. Brain Research, 740 (1 ), 215–226. 10.1016/S0006-8993(96)00871-2 8973817
Hudson, A. N. , Van Dongen, H. P. A. , & Honn, K. A. (2019). Sleep deprivation, vigilant attention, and brain function: A review. Neuropsychopharmacology, 45 (1 ), 21–30. 10.1038/s41386-019-0432-6 31176308
Jiang, C. , Zhang, L. , Zou, C. , Long, X. , Liu, X. , Zheng, H. , Liao, W. , & Diao, Y. (2014). Diurnal microstructural variations in healthy adult brain revealed by diffusion tensor imaging. PLoS One, 9 (1 ), e84822. 10.1371/journal.pone.0084822 24400118
Keren, N. I. , Lozar, C. T. , Harris, K. C. , Morgan, P. S. , & Eckert, M. A. (2009). In vivo mapping of the human locus coeruleus. NeuroImage, 47 (4 ), 1261–1267. 10.1016/j.neuroimage.2009.06.012 19524044
Keren, N. I. , Taheri, S. , Vazey, E. M. , Morgan, P. S. , Granholm, A.‐C. E. , Aston‐Jones, G. S. , & Eckert, M. A. (2015). Histologic validation of locus coeruleus MRI contrast in post‐mortem tissue. NeuroImage, 113 , 235–245. 10.1016/j.neuroimage.2015.03.020 25791783
Kilbourn, M. R. , Sherman, P. , & Abbott, L. C. (1998). Reduced MPTP neurotoxicity in striatum of the mutant mouse tottering. Synapse, 30 (2 ), 205–210. 10.1002/(SICI)1098-2396(199810)30:2<205::AID-SYN10>3.0.CO;2-0 9723790
Killgore, W. D. S. (2010). Effects of sleep deprivation on cognition. In Progress in brain research (Vol. 185 , pp. 105–129). Elsevier. 10.1016/B978-0-444-53702-7.00007-5 21075236
Kochunov, P. , Jahanshad, N. , Marcus, D. , Winkler, A. , Sprooten, E. , Nichols, T. E. , Wright, S. N. , Hong, L. E. , Patel, B. , Behrens, T. , Jbabdi, S. , Andersson, J. , Lenglet, C. , Yacoub, E. , Moeller, S. , Auerbach, E. , Ugurbil, K. , Sotiropoulos, S. N. , Brouwer, R. M. , … Van Essen, D. C. (2015). Heritability of fractional anisotropy in human white matter: A comparison of human connectome project and ENIGMA‐DTI data. NeuroImage, 111 , 300–311. 10.1016/j.neuroimage.2015.02.050 25747917
Koo, D. L. , Cabeen, R. P. , Yook, S. H. , Cen, S. Y. , Joo, E. Y. , & Kim, H. (2023). More extensive white matter disruptions present in untreated obstructive sleep apnea than we thought: A large sample diffusion imaging study. Human Brain Mapping, 44 (8 ), 3045–3056. 10.1002/hbm.26261 36896706
Koo, T. K. , & Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15 (2 ), 155–163. 10.1016/j.jcm.2016.02.012 27330520
Landis, J. R. , & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33 (1 ), 159–174. 10.2307/2529310 843571
Lieberman, H. R. , Agarwal, S. , Caldwell, J. A. , & Fulgoni, V. L., III . (2019). Demographics, sleep, and daily patterns of caffeine intake of shift workers in a nationally representative sample of the US adult population. Sleep, 43 (3 ), zsz240. 10.1093/sleep/zsz240
Lim, J. , & Dinges, D. (2008). Sleep deprivation and vigilant attention. Annals of the New York Academy of Sciences, 1129 (1 ), 305. 10.1196/annals.1417.002 18591490
Lim, J. , & Dinges, D. F. (2010). A meta‐analysis of the impact of short‐term sleep deprivation on cognitive variables. Psychological Bulletin, 136 (3 ), 375–389. 10.1037/a0018883 20438143
Lin, C.‐P. , Frigerio, I. , Bol, J. G. J. M. , Bouwman, M. M. A. , Wesseling, A. J. , Dahl, M. J. , Rozemuller, A. J. M. , van der Werf, Y. D. , Pouwels, P. J. W. , van de Berg, W. D. J. , & Jonkman, L. E. (2024). Microstructural integrity of the locus coeruleus and its tracts reflect noradrenergic degeneration in Alzheimer's disease and Parkinson's disease. Translational Neurodegeneration, 13 (1 ), 9. 10.1186/s40035-024-00400-5 38336865
Liu, K. Y. , Acosta‐Cabronero, J. , Cardenas‐Blanco, A. , Loane, C. , Berry, A. J. , Betts, M. J. , Kievit, R. A. , Henson, R. N. , Düzel, E. , Howard, R. , & Hämmerer, D. (2019). In vivo visualization of age‐related differences in the locus coeruleus. Neurobiology of Aging, 74 , 101–111. 10.1016/j.neurobiolaging.2018.10.014 30447418
Longordo, F. , Kopp, C. , & Lüthi, A. (2009). Consequences of sleep deprivation on neurotransmitter receptor expression and function. European Journal of Neuroscience, 29 (9 ), 1810–1819. 10.1111/j.1460-9568.2009.06719.x 19492440
Mäki‐Marttunen, V. , & Espeseth, T. (2021). Uncovering the locus coeruleus: Comparison of localization methods for functional analysis. NeuroImage, 224 , 117409. 10.1016/j.neuroimage.2020.117409 33011416
Murphy, P. R. , O'Connell, R. G. , O'Sullivan, M. , Robertson, I. H. , & Balsters, J. H. (2014). Pupil diameter covaries with BOLD activity in human locus coeruleus. Human Brain Mapping, 35 (8 ), 4140–4154. 10.1002/hbm.22466 24510607
Ohm, T. , Busch, C. , & Bohl, J. (1997). Unbiased estimation of neuronal numbers in the human nucleus coeruleus during aging. Neurobiology of Aging, 18 (4 ), 393–399. 10.1016/S0197-4580(97)00034-1 9330970
Osorio‐Forero, A. , Cherrad, N. , Banterle, L. , Fernandez, L. M. J. , & Lüthi, A. (2022). When the locus coeruleus speaks up in sleep: Recent insights, emerging perspectives. International Journal of Molecular Sciences, 23 (9 ), 5028. 10.3390/ijms23095028 35563419
Pareek, V. , Rallabandi, V. S. , & Roy, P. K. (2018). A correlational study between microstructural white matter properties and macrostructural gray matter volume across Normal ageing: Conjoint DTI and VBM analysis. Magnetic Resonance Insights, 11 , 1178623X18799926. 10.1177/1178623x18799926
Patanaik, A. , Kwoh, C. K. , Chua, E. C. P. , Gooley, J. J. , & Chee, M. W. L. (2015). Classifying vulnerability to sleep deprivation using baseline measures of psychomotor vigilance. Sleep, 38 (5 ), 723–734. 10.5665/sleep.4664 25325482
Petersen, S. E. , & Posner, M. I. (2012). The attention system of the human brain: 20 years after. Annual Review of Neuroscience, 35 (1 ), 73–89. 10.1146/annurev-neuro-062111-150525
Rocklage, M. , Williams, V. , Pacheco, J. , & Schnyer, D. M. (2009). White matter differences predict cognitive vulnerability to sleep deprivation. Sleep, 32 (8 ), 1100–1103. 10.1093/sleep/32.8 19725262
Rolls, E. T. , Huang, C.‐C. , Lin, C.‐P. , Feng, J. , & Joliot, M. (2020). Automated anatomical labelling atlas 3. NeuroImage, 206 , 116189. 10.1016/j.neuroimage.2019.116189 31521825
Saksvik, I. B. , Bjorvatn, B. , Hetland, H. , Sandal, G. M. , & Pallesen, S. (2011). Individual differences in tolerance to shift work–a systematic review. Sleep Medicine Reviews, 15 (4 ), 221–235. 10.1016/j.smrv.2010.07.002 20851006
Saper, C. B. , Fuller, P. M. , Pedersen, N. P. , Lu, J. , & Scammell, T. E. (2010). Sleep state switching. Neuron, 68 (6 ), 1023–1042. 10.1016/j.neuron.2010.11.032 21172606
Smith, S. M. , Jenkinson, M. , Johansen‐Berg, H. , Rueckert, D. , Nichols, T. E. , Mackay, C. E. , Watkins, K. E. , Ciccarelli, O. , Cader, M. Z. , Matthews, P. M. , & Behrens, T. E. J. (2006). Tract‐based spatial statistics: Voxelwise analysis of multi‐subject diffusion data. NeuroImage, 31 (4 ), 1487–1505. 10.1016/j.neuroimage.2006.02.024 16624579
Smith, S. M. , Jenkinson, M. , Woolrich, M. W. , Beckmann, C. F. , Behrens, T. E. , Johansen‐Berg, H. , Bannister, P. R. , De Luca, M. , Drobnjak, I. , & Flitney, D. E. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23 , S208–S219. 10.1016/j.neuroimage.2004.07.051 15501092
Sweeten, B. L. W. , Sutton, A. M. , Wellman, L. L. , & Sanford, L. D. (2019). Predicting stress resilience and vulnerability: Brain‐derived neurotrophic factor and rapid eye movement sleep as potential biomarkers of individual stress responses. Sleep, 43 (1 ), zsz199. 10.1093/sleep/zsz199
Takahashi, K. , Kayama, Y. , Lin, J. S. , & Sakai, K. (2010). Locus coeruleus neuronal activity during the sleep‐waking cycle in mice. Neuroscience, 169 (3 ), 1115–1126. 10.1016/j.neuroscience.2010.06.009 20542093
Thomas, C. , Sadeghi, N. , Nayak, A. , Trefler, A. , Sarlls, J. , Baker, C. I. , & Pierpaoli, C. (2018). Impact of time‐of‐day on diffusivity measures of brain tissue derived from diffusion tensor imaging. NeuroImage, 173 , 25–34. 10.1016/j.neuroimage.2018.02.026 29458189
Tkachenko, O. , & Dinges, D. F. (2018). Interindividual variability in neurobehavioral response to sleep loss: A comprehensive review. Neuroscience & Biobehavioral Reviews, 89 , 29–48. 10.1016/j.neubiorev.2018.03.017 29563066
Van Egroo, M. , Koshmanova, E. , Vandewalle, G. , & Jacobs, H. I. L. (2022). Importance of the locus coeruleus‐norepinephrine system in sleep‐wake regulation: Implications for aging and Alzheimer's disease. Sleep Medicine Reviews, 62 , 101592. 10.1016/j.smrv.2022.101592 35124476
Van Egroo, M. , van Hooren, R. W. E. , & Jacobs, H. I. L. (2021). Associations between locus coeruleus integrity and nocturnal awakenings in the context of Alzheimer's disease plasma biomarkers: A 7T MRI study. Alzheimer's Research & Therapy, 13 (1 ), 159. 10.1186/s13195-021-00902-8
Videtta, G. , Squarcina, L. , Rossetti, M. G. , Brambilla, P. , Delvecchio, G. , & Bellani, M. (2023). White matter modifications of corpus callosum in bipolar disorder: A DTI tractography review. Journal of Affective Disorders, 338 , 220–227. 10.1016/j.jad.2023.06.012 37301293
Yeo, B. T. T. , Tandi, J. , & Chee, M. W. L. (2015). Functional connectivity during rested wakefulness predicts vulnerability to sleep deprivation. NeuroImage, 111 , 147–158. 10.1016/j.neuroimage.2015.02.018 25700949
Zhu, Y. , Wang, L. , Xi, Y. , Dai, T. , Fei, N. , Liu, L. , Xu, Z. , Yang, X. , Fu, C. , Sun, J. , Xu, J. , Shi, D. , Tian, J. , Yin, H. , & Qin, W. (2017). White matter microstructural properties are related to inter‐individual differences in cognitive instability after sleep deprivation. Neuroscience, 365 , 206–216. 10.1016/j.neuroscience.2017.09.047 28987509
