Alter spontaneous activity in amygdala and vmPFC during fear consolidation following 24 h sleep deprivation

Accepted Manuscript Alter spontaneous activity in amygdala and vmPFC during fear consolidation following 24�h sleep deprivation Pan Feng, Benjamin Becker, Tingyong Feng, Yong Zheng PII:

S1053-8119(18)30057-0

DOI:

10.1016/j.neuroimage.2018.01.057

Reference:

YNIMG 14670

To appear in:

NeuroImage

Received Date: 3 July 2017 Revised Date:

15 January 2018

Accepted Date: 21 January 2018

Please cite this article as: Feng, P., Becker, B., Feng, T., Zheng, Y., Alter spontaneous activity in amygdala and vmPFC during fear consolidation following 24�h sleep deprivation, NeuroImage (2018), doi: 10.1016/j.neuroimage.2018.01.057. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Abbreviated title: AMYGDALA AND VMPFC AND SLEEP DEPRIVATION

Alter spontaneous activity in amygdala and vmPFC during fear consolidation following 24 hours sleep deprivation

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Pan Feng1,2, Benjamin Becker3, Tingyong Feng1,2*, Yong Zheng1,2* School of Psychology, Southwest University, Chongqing, 400715, China

Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, 400715, China

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School of Life Science and Technology, University of Electronic Science and Technology of

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*Please address correspondence to: Tingyong Feng, Yong Zheng

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China, Chengdu, Sichuan, China, 611731

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Faculty of Psychology, Southwest University,

No. 1, Tian Sheng RD., Beibei, ChongQing 400715, China TEL: +86 23 68367572; 13667636167

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Fax: +86 23 68253629

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E-mail: [email protected]; [email protected]

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ACCEPTED MANUSCRIPT Abstract: Sleep deprivation (SD) has been associated with cognitive and emotional disruptions, however its impact on the acquisition of fear and subsequent fear memory consolidation remain unknown. To address this question, we measured human brain

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activity before and after fear acquisition under conditions of 24 hours sleep deprivation versus normal sleep using resting-state functional magnetic resonance

imaging (rs-fMRI). Additionally, we explored whether the fear acquisition-induced

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change of brain activity during the fear memory consolidation window can be

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predicted by subjective fear ratings and autonomic fear response, assessed by skin conductance responses (SCR) during acquisition. Behaviorally, the SD group demonstrated increased subjective and autonomic fear responses compared to controls at the stage of fear acquisition. During the stage of fear consolidation, the SD group

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displayed decreased ventromedial prefrontal cortex (vmPFC) activity and concomitantly increased amygdala activity. Moreover, in the SD group fear acquisition-induced brain activity changes in amygdala were positively correlated

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with both, subjective and autonomic fear indices during acquisition, whereas in

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controls changes vmPFC activity were positively correlated with fear indices during acquisition. Together, the present findings suggested that SD may weaken the top-down ability of the vmPFC to regulate amygdala activity during fear memory consolidation. Moreover, subjective and objective fear at fear acquisition stage can predict the change of brain activity in amygdala in fear memory consolidation following SD. Key words: Resting-state fMRI; sleep deprivation; fear consolidation; amygdala; 2

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prefrontal cortex.

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ACCEPTED MANUSCRIPT Introduction Exaggerated and persistent fear responses to reminders of the traumatic event, hyperarousal and sleep disturbances (Thordardottir et al., 2016; van Wyk, Thomas, Solms, & Lipinska, 2016) represent a key clinical symptom of posttraumatic stress

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disorder (PTSD) (Milad et al., 2009). Neurobiological models suggest that

mechanisms of fear learning and memory consolidation, and associated dysfunctions

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in salience processing and emotion regulation are critically involved in the development and maintenance of PTSD (Liberzon & Abelson, 2016).

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With respect to memory consolidation, non-adaptive over-consolidation of traumatic experiences and disruptions of memory consolidation processes during sleep have been associated with PTSD symptoms, including intrusive memories (Kleim, Wysokowsky, Schmid, Seifritz, & Rasch, 2016). Following acquisition,

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several studies suggest that emotional memories are preferentially reactivated and subsequent consolidated during sleep, suggesting that integrative memory processes

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during sleep might be of critical relevance for the development of trauma-induced psychiatric disorders (Pace-Schott, Germain, & Milad, 2015; Yoo, Gujar, Hu, Jolesz,

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& Walker, 2007).

Extensive previous research in laboratory animals and neuroimaging studies in

humans have mapped the neural basis of fear acquisition and fear memory consolidation. Animal models suggest a crucial contribution of the amygdala and prelimbic cortex during the acquisition and expression of fear (Asede, Bosch, Lüthi, Ferraguti, & Ehrlich, 2015; Corcoran & Quirk, 2007; Laurent & Westbrook, 2009; Sierra-Mercado, Padilla-Coreano, & Quirk, 2011; Vidal-Gonzalez, Vidal-Gonzalez, 4

ACCEPTED MANUSCRIPT Rauch, & Quirk, 2006). Specifically via its interactions with the amygdala the rodent PL promotes fear expression and opposes extinction (Burgos-Robles, Vidal-Gonzalez, & Quirk, 2009). Extensive imaging research in humans suggests that the ‘fear

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network’ serves a pivotal role in fear acquisition, including the amygdala, hippocampus, insula, dorsal anterior cingulate (dACC), the dorsolateral prefrontal cortex (dlPFC), orbitofrontal cortex(OFC) and temporal cortex (Etkin, Egner, &

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Kalisch, 2011; Etkin & Wager, 2007; Fullana et al., 2016; Greco & Liberzon, 2016;

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Hartley, Fischl, & Phelps, 2011; LaBar & Cabeza, 2006), and that amygdala, insula and dACC activation and thickness correlates with SCR during fear acquisition (Cacciaglia, Pohlack, Flor, & Nees, 2015; Linnman, Zeidan, Pitman, & Milad, 2013; Milad & Quirk, 2012; Milad, Quirk, et al., 2007). With respect to fear memory

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consolidation, animal models suggest a pivotal role of the amygdala in emotional memory consolidation (McINTYRE, Power, Roozendaal, & McGAUGH, 2003; Paré, 2003). During the consolidation stage the amygdala regulates synaptic in other brain

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regions including the hippocampus and prelimbic cortex (McGaugh, 2000;

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Roozendaal, McEwen, & Chattarji, 2009). The important role of the amygdala in fear memory consolidation is further emphasized by findings on increased theta synchronization between the amygdala and hippocampus during fear memory consolidation(Popa, Duvarci, Popescu, Léna, & Paré, 2010; Seidenbecher, Laxmi, Stork, & Pape, 2003), which could predict fear retention one day later (Popa et al., 2010). Previous neuroimaging studies examining fear consolidation memory in humans have indicated that spontaneous activity of amygdala, ventromedial prefrontal 5

ACCEPTED MANUSCRIPT cortex (vmPFC) and amygdala-dorsomedial prefrontal cortex(dmPFC), amygdala-dorsal anterior cingulated cortex(dACC), and amygdala-medial prefrontal cortex(mPFC) functional connectivity crucially contribute to fear memory

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consolidation (P. Feng, Feng, Chen, & Lei, 2014; T. Feng, Feng, & Chen, 2013; Schultz, Balderston, & Helmstetter, 2012; Van Marle, Hermans, Qin, & Fernández, 2010). Specifically, the functional connectivity of amygdala-insula and

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amygdala-dACC was increased following experimentally induced, moderate

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psychological stress (Van Marle et al., 2010). Studies examining the effects of fear acquisition further emphasize the role of the limbic-prefrontal circuitry in fear consolidation, with enhanced amygdala-dmPFC (Schultz et al., 2012), amygdala-dACC, hippocampus-insula functional connectivity(P. Feng et al., 2014)

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being demonstrated during the fear consolidation window. Notably, the change of functional connectivity between amygdala and superior frontal gyrus was positively correlated with subjective fear ratings, whereas the change in amygdala-ACC

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connectivity was positively correlated with objective fear indices (Schultz et al.,

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2012). Moreover, Feng et al. found that the change of amplitude of low-frequency fluctuation (ALFF) in vmPFC following fear acquisition was positively correlated with the subjective fear ratings and the amygdala-mPFC functional connectivity was negatively correlated with the subjective fear ratings (P. Feng et al., 2014; T. Feng et al., 2013), suggesting that changes in these regions vary as a function of experienced fear at the stage of fear acquisition.

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ACCEPTED MANUSCRIPT With regard to sleep deprivation (SD), previous findings suggest that SD can impair neurocognitive processes, such as learning and memory (Durmer & Dinges, 2005; Goel, Rao, Durmer, & Dinges, 2009; Walker & Stickgold, 2006) as well as emotional

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processes (Daniela et al., 2010; Kerkhof & Van Dongen, 2010; Vandekerckhove & Cluydts, 2010), including enhanced emotional reactivity towards threatening stimuli (Goldstein-Piekarski, Greer, Saletin, & Walker, 2015; Menz et al., 2013; Yoo et al.,

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2007) and reduced coping and regulation of negative emotions (W. Killgore, 2013; W.

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D. Killgore et al., 2008). Neuroimaging researches suggest that sleep deficits and SD are associated with altered neural processing in key regions involved in fear consolidation, emotional reactivity and emotion regulation, including increased amygdala reactivity to negative emotional stimuli (Van Der Helm, Gujar, & Walker,

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2010; Yoo et al., 2007; Zald, 2003), previously encountered emotional experiences (Van Der Helm et al., 2010) and concomitantly decreased connectivity of the medial prefrontal cortex and amygdala (Van Der Helm et al., 2010) and between the

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amygdala and the vmPFC (Yoo et al., 2007). Moreover, an early study found reduced

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global cerebral energy metabolism following SD, with reductions being particularly pronounced in the vmPFC (Thomas et al., 2000), a region that plays a key role in fear learning, particularly fear memory consolidation (Feng et al., 2013) and automatic regulation of negative affect(Etkin et al., 2011), including the inhibition of conditioned fear responses (Milad, Wright, et al., 2007; Sokol-Hessner, Camerer, & Phelps, 2012; Wagner & Heatherton, 2013). Together, these findings indicate that the amygdala and vmPFC might be particularly vulnerable to SD-induced changes and 7

ACCEPTED MANUSCRIPT may play a crucial role in emotion changes following sleep deprivation. However, the effects of sleep deprivation on the neural correlates fear memory consolidation and physiological and psychological parameters of fear acquisition has not been

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experimentally examined. Resting-state functional magnetic resonance imaging (rs-fMRI) allows to examine spontaneous brain activity (B. B. Biswal, 2012; Buckner & Vincent, 2007; Fox &

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Raichle, 2007). ALFF measures the absolute strength or intensity of low-frequency

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oscillations of BOLD fluctuations, and ReHo evaluates the temporal homogeneity of the time series of a given voxel compared to that of its nearest neighbors (Zang, Jiang, Lu, He, & Tian, 2004). Additionally, previous studies have indicated ALFF has the highest retest reliability (Yu-Feng et al., 2007; Zuo & Xing, 2014) and can be used to

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examine the neural substrates of memory consolidation following fear conditioning (T. Feng et al., 2013). Individual variations in ReHo have been associated with individual differences in behavioral performance (Tian, Ren, & Zang, 2012; L. Wang, Song,

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Jiang, Zhang, & Yu, 2011; T. Wang et al., 2014) and thus might allow to determine

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associations between individual variations in fear acquisition and subsequent fear memory consolidation. Based on this background, the present study employed a rs-fMRI approach to examine the impact of SD on the neural correlates fear memory consolidation and physiological and psychological parameters of fear acquisition. Furthermore, the study aimed to examine whether SD differentially affects associations between subjective and objective fear indices at the stage of acquisition

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ACCEPTED MANUSCRIPT and fear acquisition induced changes in spontaneous brain activity during the stage of consolidation. Based on previous findings, we expected that SD would increase subjective

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experience and autonomic reactivity (assessed by skin conductance) at the stage of fear acquisition and affect subsequent neural activity during fear consolidation.

Additionally, we expected a close association between the change of spontaneous

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brain activity associated with fear memory consolidation and individual differences in

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subjective and objective fear (fear ratings and SCR) indices at the stage of fear acquisition, and that SD would change these associations. Materials & Methods Participants

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Seventy right-handed, non-smoking college students from the Southwest University in China were enrolled in the study and received monetary compensation for their participation. Individuals with a history of psychiatric or neurological disorders

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(including head injury) were excluded. All participants had normal or

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corrected-to-normal vision. All participants were required to abstain from caffeine, alcohol, physical activities, intense mental or challenging activities during the 72 h before, and during the entire period of the experiment. None of the subjects had experienced a jet lag during the month before the experiment. Participants were randomly assigned to either the control group or the sleep-deprivation (SD) group. In the SD group, subjects remained awake during Day 1, and the night between Day 1 and Day 2 leading to approximately 24h of sleep deprivation before the experiment 9

ACCEPTED MANUSCRIPT (scheduled at 8:00 AM on Day 2). During the 24h of SD, subjects in the SD group were monitored by the experimenter and were allowed to engage in standardized activities (reading and watching movie etc.). In the control group, subjects slept

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normally at home across Night 1 prior to the experiment on Day 2 (8:00 AM) (see Figure 1A). All control participants confirmed that they slept before 00:00 AM during night 1, had at least 8h of sleep, and reported good sleep quality. The control group (n

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= 35; Mage = 20.93, SD = 1.59 years, 16 females), and the SD group (n = 35 ; Mage =

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21.89, SD = 1.97, 11 females) did not differ in sleep quality as assessed by the Pittsburgh Sleep Quality Index (PSQI) (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989). All participants had good sleep habits (>8 h of sleep/night; sleep time no later than 00:00 AM; wake time no later than 8:00 AM) as assessed by a two week sleep

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diary. Trait anxiety and state anxiety as assessed by the State Trait Anxiety Inventory (STAI) (Spielberger, 1983), depression as assessed by self-Rating Depression Scale (SDS) (Zung, 1965) and mood as assessed by the Positive and Negative Affect Scale

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(PANAS) (Watson, Clark, & Tellegen, 1988) did not differ between the experimental

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groups (see also table 1 for assessment of potential confounders). On the day of the experiment (Day 2) participants arrived at the laboratory at 8:00 AM and underwent resting state scan 1 (Rest 1, before fear acquisition), the fear acquisition paradigm and resting state scan 2 immediately after the fear acquisition paradigm (Rest 2). Before enrollment, written informed consent was obtained from all participants. The study was approved by the Institutional Review Board of the Southwest University and was in accordance with the latest revision of the Declaration of Helsinki. 10

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---------------------------------------------------------------------Figure 1A and Table 1 was inserted here

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Design and procedure

The experiment began with a baseline resting state scan (Rest1, 8min), followed by the fear acquisition paradigm and a second resting state scan (Rest2, 8min) that was acquired during the fear memory consolidation window (P. Feng et al., 2014; T. Feng

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et al., 2013) (see Figure 1B). The fear acquisition paradigm consisted of a validated discrimination, partial reinforcement (43.75%) paradigm. A blue, green and yellow square served as stimuli and where presented for 4s. Two colors (CS+) were paired

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with a mild electric shock to the wrist (200ms). The third square (CS-) was never

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paired with the shock. Acquisition included eighteen non-reinforced presentations of the two CS+s and the CS-, intermixed with an additional fourteen presentations of each CS+ with the shock, presented in a pseudorandom order with a jittered inter-trial-interval (ITI) of 6-10s and divided into two sessions. Participants were instructed to pay attention to the stimuli and try to determine the relationship between the colored squares and the shock. The designation of the colors as CS+ and CS- was counterbalanced between groups. During the resting state acquisition, participants 11

ACCEPTED MANUSCRIPT were instructed to keep their eyes opened and look at a fixation cross, relax and let their mind wander, without falling asleep or moving. Each resting state scan lasted for

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480s, and a total of 240 functional volumes were acquired during each session.

---------------------------------------------------------------------Figure 1B was inserted here

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Psychophysiological setup and assessment

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Appropriate shocks were delivered through a stimulating bar electrode attached with a Velcro strap to the right wrist. Prior to the experiment, participants received a mild shock (200ms duration, 50 pulses/s), which was gradually increased. Using this

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procedure, individual levels of the shock intensity for the fear acquisition stage were determined that were experienced as uncomfortable, but not painful. Shocks were delivered with duration of 200ms, and a current of 50 pulse per second. With respect

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to the shock level (pain level, mA), there was no difference between the SD group and

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the control group (SD group: M=0.88, SD=0.31; control group: M=0.90, SD=0.57; t=0.21, p =0. 83), arguing against confounding effects of SD on subjective pain sensitivity.

Skin conductance responses were acquired using shielded Ag-AgCl electrodes,

attached to the second and the third finger of the left hand. The skin conductance signal was amplified and recorded with a BIOPAC System skin conductance module connected to a Macintosh computer. Data was continuously recorded at a rate of 200 12

ACCEPTED MANUSCRIPT samples per second. In line with previous studies (Schiller, Kanen, LeDoux, Monfils, & Phelps, 2013; Schiller et al., 2010), event-related SCR responses were evaluated by assessing the base-to-peak difference in skin conductance of the largest deflections (in

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microsiemens, µs) in the 0.5-4.5s window after stimulus onset using a minimal response criterion of 0.02 µs. The raw SCR scores were square-root transformed to normalize distributions. These normalized scores were scaled according to each

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subject’s unconditioned response by dividing each response by the mean square-root

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transformed unconditioned stimulus response. In addition to the SCR response that served as objective fear measures, subjective fear experience was assessed by means of fear ratings of the stimuli (CS+ and CS–). The rating was acquired immediately after the acquisition stage by means of a 1-7 fearfulness rating scale (1: mildly; 4:

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moderately; and 7: extremely). Subjects rated each stimulus four times. Image acquisition and analysis fMRI acquisition

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Data were acquired using a Siemens 3T MRI-system (Siemens Magnetom Trio TIM,

Erlangen, Germany). Head movement was restricted using foam cushions (>2mm,

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2degree). T1-weighted images were acquired to improve normalization of the

functional data, with a total of 176 slices at a thickness of 1 mm and in-plane resolution of 0.98 ×0.98 mm (TR = 1900 ms; TE = 2.52 ms; flip angle = 9°; FOV = 250 × 250 mm2). During the resting state blood oxygen level dependent (BOLD) imaging was performed using a single shot EPI sequence with the following parameters: TR = 2000ms; TE = 25ms; flip angle= 85°; FoV =220 ×220mm2; matrix size = 64 × 64; voxel size = 3.4 × 3.4 × 4 mm3; inter-slice gap = 0mm; Slices = 32.

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ACCEPTED MANUSCRIPT fMRI data analysis SPM12, DPABI2.1 and REST1.8 software were used for the resting-state analysis (Chao-Gan & Yu-Feng, 2010; Friston et al., 1994; Song et al., 2011). After discarding the first five images to allow for scanner equilibration, the remaining T2*-weighted

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images, were slice time corrected and realigned to estimate the six head motion

parameters. The T1-weighted images were co-registered to the EPI mean image and segmented into white matter, grey matter, and Cerebrospinal fluid (CSF). The

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functional data was then normalized to MNI space in 3 × 3 × 3 mm3 voxel resolution, and spatially smoothed with a Gaussian kernel (full width at half maximum, FWHM,

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6 × 6 × 6 mm3). The linear drift was removed and data was filtered with a band pass filter (0.01–0.08 Hz) to reduce low frequency drift and high-frequency respiratory and cardiac noise (B. Biswal, Zerrin Yetkin, Haughton, & Hyde, 1995). We calculated the ALFF for each participant and converted it to normal distribution with Fisher’s z

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transformation (zALFF). However, ReHo was calculated before the smooth step. For each participant, ReHo analysis was performed on a voxel-wise basis by calculating Kendall’s coefficient of concordance (KCC) of the time series of a given voxel with

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those of its nearest neighbors (26 voxels). Then, the KCC value was then attributed to

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the center voxel and individual KCC maps were obtained representing the ReHo. Individual ReHo maps converting it to normal distribution with Fisher’s z transformation. Finally, zReHo maps were spatially smoothed with FHWM of 6mm. After achieving the zALFF and zReHo images of the control group and SD group,

paired sample t-test were employed to determine the resting state fear memory consolidation networks by comparing Rest1 and Rest2 of each group with a threshold of p<0.05, Voxel-wise FDR-corrected, Voxels≥10. Effects of SD on fear consolidation-associated brain activity was assessed by 14

ACCEPTED MANUSCRIPT directly comparing the regional signals (zALFF and zReHo) in amygdala and vmPFC between the groups. The regions of interest (ROIs) were selected on the basis of prior fear learning research (vmPFC, 4, 32, -5) with the radius of 6 mm (Agren et al., 2012; Milad, Wright, et al., 2007; Phelps, Delgado, Nearing, & LeDoux, 2004) and a

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standard anatomical atlas (bilateral amygdala from the Wake Forest PickAtlas)

To examine whether subjective (fear ratings) or objective (SCR for CS+ vs. CS-) fear indices at stage of fear acquisition predict neural indices of fear

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acquisition-induced neural changes during the consolidation window (changes in zALFF and zReHo between Rest 2 vs. Rest 1) in the vmPFC and amygdala, we

spontaneous brain activity changes. Results

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Data quality assessment

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conducted the correlations analysis between fear indices during acquisition and

Sixteen subjects were eliminated from the statistical analysis (6 from the control group, 10 from the SD group) because they failed to acquire fear conditioning as

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assessed by SCR as objective fear index. Based on quality assessment of the fMRI

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data eight participants were excluded from the fMRI analysis due to excessive head motion (controls, n = 3; SD, n = 5). In addition, four participants in SD group were removed from the rs-fMRI analysis because falling asleep during the resting state acquisition. Thus a total of n = 54 (n =29, controls; n = 25, SD) subjects were included in the behavioral analysis, a total of n = 42 subjects (n = 26, controls; n =16, SD) were included in the neural analysis and the correlational analysis. Behavioral results-subjective and objective fear indices

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ACCEPTED MANUSCRIPT Subjective fear ratings following the acquisition procedure were examined using a two-way mixed ANOVA analysis with group as between-subject factor (control group vs.SD group) and CS type as within subject factor (CSa+ , CSb+ and CS-). The

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analysis revealed a significant group×CS interaction effect (F(2,51)=3.27, p < 0.05). There was a significant main effect of CS type (F(2, 102)=435.4, p < 0.001) and a

significant main effect of group (F(1, 51)=7.39, p < 0.01). After that we performed a

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simple effect analysis to examine acquisition of fear. The following results were

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obtained: in the control group, the subjective fear ratings of CSa+ , CSb+ and CSwere as follows: the CSa+：M=4.27, SD=1.02, the CSb+：M=4.25, SD=1.19, the CS-： M=1.31, SD=0.58. Participants showed significantly stronger subjective fear ratings to CSa+ than to CS- (p <0.001), as well as to CSb+ compared to CS- (p<0.001).

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Moreover, the fear ratings of CSa+ and CSb+ was equivalent (p=0.93, not significant). In the SD group, the subjective fear ratings of CSa+ , CSb+ and CS- were as follows: the CSa+：M=4.84, SD=0.77, the CSb+：M=4.80, SD=0.77, the CS-：M=1.31, SD=0.57.

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Participants showed significantly stronger subjective fear ratings to CSa+ than to CS-

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(p<0.001), as well as to CSb+ compared to CS- (p<0.001). Moreover, the fear ratings of CSa+ and CSb+ was equivalent (p=0.79, not significant). Together these findings indicate that both groups acquired the subjective fear response, confirming successful conditioning. Targeting the effects of SD on subjective fear experience, the results revealed that participants in the SD group had greater fear ratings of both CSa+ (p < 0.01), as well as CSb+ (p < 0.05) compared to control group, but there was no significant between-group difference in subjective fear reported for the CS- (p=0.97, 16

ACCEPTED MANUSCRIPT not significant), indicating fear acquisition-specific effects rather than unspecific effects of SD on fear experience (See Figure 2A). With respect to the objective fear as assessed via SCR, two-way mixed ANOVA

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with the factors group (control group vs. SD group) and CS type (CSa+ , CSb+ and CS-) revealed a significant group×CS interaction effects (F(2, 51)=3.29, p < 0.05).

Moreover, there was a significant main effect of CS type (F(2, 102)=169.7, p < 0.001)

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and a significant main effect of group (F(1, 51)=7.81, p < 0.01).Simple effects

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analysis revealed that controls showed significantly stronger responses to CSa+ (M=0.51, SD=0.07) as well as CSb+ (M=0.50, SD=0.08) relative to the CS- (M=0.26, SD=0.08) (p<0.001). Moreover, both CSa+ and CSb+ elicited comparable SCR response (p=0.72, not significant). In the SD group, both CSa+ (M=0.68, SD=0.22), as

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well as CSb+ (M=0.64, SD=0.24), elicited significantly stronger SCRs relative to the CS- (M=0.34, SD=0.21) (p<0.001). Again, SCRs between the CS+ stimuli were comparable (p=0.17, not significant). Targeting the SD effects on objective fear

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responses, the results revealed participants in the SD group had greater fear responses

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compared to the controls to both, the CSa+ (p < 0.005), as well as to CSb+( p < 0.01). There were no significant between-group difference with respect to the fear response towards the CS- (p=0.12, not significant) (see Figure 2B), arguing against unspecific effects of SD on the SCR. The behavioral results suggested that the participants of two groups (the control group and SD group) acquired the conditioned fear, but the SD group showed stronger fear reactivity to the conditioned stimulus than controls. ---------------------------------------------------------------------17

ACCEPTED MANUSCRIPT Figure 2A and Figure 2B was inserted here ---------------------------------------------------------------------The different brain activity between two groups in fear memory consolidation

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Effects of SD on fear memory consolidation were examined by means of a two-way mixed ANOVA with group (SD group vs. control group) and time (before fear acquisition, Rest1 vs. after fear acquisition, Rest2) that revealed a significant

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group× time interaction effect for both neural indices (zALFF, F=3.96, p<0.05,

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Voxel-wise FDR-corrected p<0.05; zReHo F=3.96, Voxel-wise FDR-corrected p<0.05). There was a significant main effect of group (zALFF, F=23.51, Voxel-wise FDR-corrected p<0.05; zReHo F=27.05, Voxel-wise FDR-corrected p<0.05). Moreover, there was a significant main effect of time (zALFF, F=27.90, Voxel-wise

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FDR-corrected p<0.05; zReHo F=31.57, Voxel-wise FDR-corrected p<0.05). Next, we performed simple effect analysis. The following results obtained: in the SD group, the amygdala showed increased in zALFF (Left amygdala: peak voxel coordinates,

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(-21, -3, -12), t(15) = 6.40, Voxel-wise FDR-corrected p<0.05; Right amygdala: peak

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voxel coordinates, (21, 0, -12), t(15) =5.06, Voxel-wise FDR-corrected p<0.05) and zReHo (Left amygdala: peak voxel coordinates, (-21, -3, -18), t(15) = 5.24, Voxel-wise FDR-corrected p<0.05; Right amygdala: peak voxel coordinates, (21, -3, -15), t(15) =8.16, Voxel-wise FDR-corrected p<0.05) during the fear memory consolidation window compared to baseline rest (See Figure 3A and 3C and Table2). In contrast, in the control group, the vmPFC showed greater activation in both neural indices, including zALFF (vmPFC: peak voxel coordinates, (0, 39, 0), t(25) =7.26, 18

ACCEPTED MANUSCRIPT Voxel-wise FDR-corrected p<0.05) and zReHo(vmPFC: peak voxel coordinates, (-6, 24, -9), t(25) =10.54, Voxel-wise FDR-corrected p<0.05) in the fear memory consolidation window relative to the baseline assessment (See Figure 3B and 3D and

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Table 3). Additionally, to verify whether the change spontaneous brain activity difference between two groups were due to sleep deprivation, we performed two

sample t-test of Rest1 and Rest2 in two groups. The result revealed that there were no

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differences of zALFF and zReHo between two groups at Rest1 (correlated to brain

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activity of fear memory) (Voxel-wise FDR-corrected p<0.05), arguing against unspecific effects of SD on the baseline neural activity. However, the amygdala showed greater activation (zALFF and zReHo) in the SD group than that in the control group, while the vmPFC was higher active (zALFF and zReHo) in the control

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group than that in the SD group at rest2 (Voxel-wise FDR-corrected p<0.05). The results suggest that SD was associated with increased amygdala activity and reduced vmPFC activity during fear consolidation.

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Figure 3 and Table 2 and Table 3 was inserted here

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The ROI analysis employed two sample t-test on the differences of zALFF and

zReHo (the change score of the extracted values for Rest2 vs. Rest1) values between the control and SD group. A two-way mixed ANOVA with the factors group (control group vs. SD group) and brain activity pathway (amygdala vs. vmPFC) revealed a significant interaction effect (ZALFF, F (1, 40)=131.73, p < 0.001;ZReHo, F (1, 19

ACCEPTED MANUSCRIPT 40)=180.29, p < 0.001). In line with the whole brain approach, a simple effect analysis revealed that the SD group had a significantly greater change in spontaneous brain activity of the amygdala than the control group [zALFF (p<0.001); zReHo

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(p<0.001)], whereas the control group demonstrated a significantly greater change in the vmPFC relative to the control group[zALFF (p<0.001); zReHo (p<0.001)] (see

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Figure 4).

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---------------------------------------------------------------------Figure 4 was inserted here

---------------------------------------------------------------------Further, the functional connectivity analysis showed that the functional

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connectivity of amygdala-insula was increased in the SD group [peak voxel coordinate for left insula, (-36, 3, -12), t = 5.64, cluster FWE-corrected, p = 0.012, K=242; peak voxel coordinate for right insula, (30, 15, -18), t = 5.45, cluster

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FWE-corrected, p = 0.004, K=340], whereas the RSFC between amygdala and

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vmPFC was increased in the control group [peak voxel coordinate, (-9, 54, 6), t = 4.77, cluster FWE-corrected, p = 0.005, K=394] (a small volume using amygdala, insula and vmPFC as mask correction for the threshold) In line with previous findings (T. Feng et al., 2013; Schultz et al., 2012; Van Marle

et al., 2010), the present results emphasize the key role of the amygdala and vmPFC in fear memory consolidation. Importantly, the present findings indicate for the first time that SD might negatively interfere with fear-consolidation associated activity in 20

ACCEPTED MANUSCRIPT these core nodes, specifically SD appears to weaken top-down control of the vmPFC and associated hyperactive amygdala functioning during the consolidation window. Brain-behavior correlation results

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To examine subjective (self-reported) and objective (SCR) fear responses during fear acquisition would be predictive of the fear-consolidation induced changes in regional neural activity (zALFF and zReHo) of the amygdala and vmPFC,

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corresponding correlations were computed within each group. We mainly focused on

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the amygdala and vmPFC, given their key role in fear expression and top-down regulation, respectively. In the SD group, greater changes (Rest2 vs. Rest1) in the amygdala were positively related to higher subjective fear ratings (zALFF, r=0.50, zReHo, r=0.51, p<0.05), as well as higher objective fear ratings (SCR, zALFF, r=0.49,

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zReHo, r=0.50, p<0.05) during fear acquisition (See Figure 5A). Contrariwise, in the control group, neural changes in the vmPFC were positively related to both, subjective (zALFF, r=0.54, p<0.005; zReHo, r=0.41, p<0.05) and

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objective (zALFF, r=0.49, p<0.01; zReHo, r=0.43, p<0.05) fear indices during the

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acquisition stage (See Figure 5B). However, There was no correlation between vmPFC activity change (zALFF and zReHo) and fear ratings/SCR for the SD group(See Figure S1). Moreover, There was no correlation between amygdala activity change (zALFF and zReHo) and fear ratings/SCR for the control group (all p>0.05) (See Figure S2). Together, these findings indicate a direct relationship of the fear acquisition induced neural changes (zALFF and zReHo) which can be specifically observed in the amygdala for the SD and the vmPFC for the controls, suggesting that 21

ACCEPTED MANUSCRIPT they can predict subjective fear and objective fear for SD group and control group

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respectively.

---------------------------------------------------------------------Figure 5A and 5B was inserted here

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----------------------------------------------------------------------

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Discussion

We investigated the effects of 24 hours sleep deprivation on fear acquisition assessed with SCR and fear ratings and changes in spontaneous neural activity (zALFF and zReHo) associated with fear consolidation by comparing changes in

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rs-fMRI before and after fear acquisition. During fear acquisition, the SD group demonstrated CS+ specific increased objective (SCR) and subjective (fear experience) fear indices compared to the control group, suggesting that SD enhanced the

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acquisition of the conditioned fear response. On the neural level, this effect was

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accompanied by increased amygdala and decreased vmPFC activity (zALFF and zReHo) in the SD group relative to the controls during the stage of fear memory consolidation. Importantly, the groups did not differ with respect to neural activity (zALFF and zReHo) before fear acquisition or CS- associated fear indices, suggesting highly specific effects of SD on fear-acquisition and fear-consolidation. Moreover, in line with a previous study (Feng et al., 2013), higher objective and subjective fear indices during acquisition in the controls were associated with higher vmPFC changes, 22

ACCEPTED MANUSCRIPT whereas following SD both indices were associated with greater changes in the amygdala. Together, the present findings thus provide the first evidence that sleep deprivation increases fear acquisition and disrupts associated neural consolidation of

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fear memory in the amygdala and vmPFC. In line with prior research, suggesting that sleep deprivation is accompanied by enhanced fear responses, our behavioral data revealed higher subjective and

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autonomic fear responses in the SD group relative to controls during fear acquisition.

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In accordance with our observations, previous studies reported sleep deprivation induced emotional processing deficits, including a processing bias for threatening stimuli (van der Helm et al., 2011), as well as increased amygdala responses and reduced amygdala-prefrontal connectivity towards threatening stimuli (Yoo et al.,

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2007). Together with findings Anderson et al. reporting that sleep deprivation decreased inhibition (increased hit rate) and enhances impulsivity (decreased mean response time) to negative stimuli (Anderson & Platten, 2011), this suggests that sleep

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deprivation is associated with enhanced amygdala emotional reactivity and decreased

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emotional control of this response. Previous studies have shown that higher subjective and objective fear indices during fear acquisition associate with increased amygdala responses(Petrovic, Kalisch, Pessiglione, Singer, & Dolan, 2008), while threat-associated responses in this region are critically regulated by the vmPFC (Etkin et al., 2011; Etkin & Wager, 2007; Milad, Wright, et al., 2007; Motzkin, Philippi, Wolf, Baskaya, & Koenigs, 2015; Pace-Schott et al., 2017). Together, this might suggest that enhanced subjective and objective fear reactivity following SD might reflect a higher 23

ACCEPTED MANUSCRIPT amygdala-associated emotional reactivity as well as a decreased prefrontal emotional regulation of the fear response. In the present study, examining the neural data using whole brain and regionally

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focused approaches consistently revealed that during fear consolidation sleep deprivation increased amygdala activity and disrupted increased vmPFC activity (zALFF and zReHo) as shown by the non-sleep deprived control group. A study

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suggested that persistence of functional connectivity between amygdala and

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hippocampus and multi-voxel correlation structures during awake rest following fear acquisition predicted long-term expression of fear. Specifically, the functional connectivity between amygdala and hippocampus was increased successively during post-acquisition and post-extinction rest, as well as reinstatement of multi-voxel

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patterns within amygdala and hippocampus, furthermore persisted following fear acquisition. Importantly, both the findings were stronger in participants who exhibited spontaneous recovery 24 h later(Hermans et al., 2016).In line with the present

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findings, previous research consistently reported that acute sleep deprivation increases

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amygdala activity in response to negative emotional stimuli (Chuah et al., 2010; Motomura et al., 2013; Yoo et al., 2007), as well as significantly decreased global cerebral energy metabolism, which were particularly pronounced in the vmPFC (Thomas et al., 2000). In line with the fine-grained interplay of the amygdala-vmPFC circuit in emotion processing, the sleep-deprivation induced regional activity changes were accompanied by a disrupted interplay of the regions, such as decreased amygdala-vmPFC functional connectivity in response to negative stimuli (Yoo et al., 24

ACCEPTED MANUSCRIPT 2007) and previously encountered emotional experiences (van der Helm et al., 2011). In the prior study, Shao et al. found that the SD altered amygdala RSFC. Specifically, SD reduced functional connectivity between the basolateral amygdala (BLA) and

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executive control regions (dorsolateral prefrontal cortex, dlPFC, ACC, right inferior frontal gyrus, rIFG), but increased functional connectivity between the BLA and posterior cingulate cortex (PCC), precuneus and right parahippocampus.

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Concomitantly, the authors observed increased functional connectivity between the

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centromedial amygdala (CMA) and rACC following SD (Shao et al., 2014). Furthermore, Lei also found that SD was associated with decreasing functional connectivity between the superficial amygdala (SFA) and executive control regions, including the dACC and PCC (Lei et al., 2015). Together, the previous results

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suggests that sleep deprivation may lead to a failure to engage the vmPFC during fear consolidation in the context of decreased vmPFC control over the amygdala (Rosales-Lagarde et al., 2012; Shao et al., 2014) reflected in enhanced amygdala

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activity during the consolidation window. A similar pattern of increased amygdala

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reactivity in the context of decreased prefrontal control has been consistently reported in PTSD (Kim et al., 2011), and smaller volume and hypoactivation in the vmPFC associated with impaired fear inhibition has been reported in these patients (Bremner, Elzinga, Schmahl, & Vermetten, 2007; Jovanovic, Kazama, Bachevalier, & Davis, 2012). Further, there may be a key reason why our findings (Rest1) were inconsistent with prior study. Specifically, we mainly focused on the spontaneous brain activity using zALFF and zReHo (a suggestive index of regional spontaneous neuronal 25

ACCEPTED MANUSCRIPT activity) in the present study, while most other study focused on brain Network(such as Default Mode Network and dorsal attention Network et al.)(Bosch et al., 2013; De Havas, Parimal, Soon, & Chee, 2012; Kaufmann et al., 2016). The present findings

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suggest that sleep deprivation associates with similar emotional dysfunctions, possibly reflecting a weakened vmPFC regulatory influence on the amygdala during fear

memory consolidation which might promote to the development and maintenance of

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concomitant impaired prefrontal regulation.

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pathological anxiety via exaggerated amygdala fear learning and threat detection and

Interestingly, whereas in controls higher objective and subjective fear indices at the stage of acquisition associated with higher vmPFC activity changes (zALFF and zReHo) during consolidation, possibly reflecting the need for stronger subsequent

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emotion regulation and suppression of the fear response, a higher fear reactivity during SD was associated with higher activity changes in the amygdala (zALFF and zReHo), possibly reflecting a failure of vmPFC down-regulation of the amygdala

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during consolidation. Moreover, stronger responses of the vmPFC to a safety stimulus

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that previously predicted danger relative to responses to naive CS- (Schiller, Levy, Niv, LeDoux, & Phelps, 2008) have been demonstrated, suggesting an important role of the vmPFC in the flexible control of fear. Based on findings from human imaging studies and animals models of fear-associated learning, impaired emotional regulation/executive functioning has been suggested was key factors for the development of PTSD (Liberzon & Abelson, 2016). Deficits in cognitive control and associated top-down inhibitory modulation of autonomic and emotional activation 26

ACCEPTED MANUSCRIPT have been observed in PTSD patients (Powers, Etkin, Gyurak, Bradley, & Jovanovic, 2015). Specifically, decreased vmPFC top-down inhibitory control of the amygdala during fear suppression has been observed PTSD patients (Etkin & Wager, 2007;

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Jovanovic et al., 2013; Rougemont‐Bücking et al., 2011; Yehuda & LeDoux, 2007). The present findings suggest that sleep deprivation produces increased amygdala

activity and weakened vmPFC activity during the fear memory consolidation window,

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possibly reflecting a lack of regulatory control over the acquired fear response

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following sleep deprivation. The present findings indicated that neural activity in the vmPFC and amygdala circuit may be particularly vulnerable to sleep deprivation, with the regulation of the vmPFC of the acquired amygdala fear response being particularly impaired (Chuah et al., 2010; W. Killgore, Balkin, & Wesensten, 2005;

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Libedinsky et al., 2011; Venkatraman, Huettel, Chuah, Payne, & Chee, 2011). Moreover, the present study suggests that SD might increase fear acquisition and disrupts the neural consolidation in the amygdala and vmPFC, which was play an

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PTSD.

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important role in the development and maintenance of anxiety disorders such as

Summarizing, we examined human brain activity (zALFF and zReHo) following

fear acquisition under conditions of 24 hours sleep deprivation using rs-fMRI, and investigated whether the subjective fear and objective fear during fear acquisition can be predicted by the change of brain activity (zALFF and zReHo) for SD group at fear memory consolidation window. Accordingly, the present study provided insight into the neural basis of fear memory consolidation after sleep deprivation. Moreover, sleep 27

ACCEPTED MANUSCRIPT disturbances may play a causal role in the development of possibly pathological fear or PTSD by increasing the susceptibility of the sympathetic nervous system to stressful experiences.

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Certain limitations of this study should be taken into account. First, the current study did not include electroencephalography (EEG) and polysomnography (PSG)

sleep recordings to monitor sleep, which are commonly used to evaluate indices of

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sleep. These indices would have allowed a better characterization of sleep quality

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before the experiment and of sleep deprivation effects during the experiment. Moreover, polysomnographic recordings were not acquired in the control group to evaluate sleep quality in the night before the experiment. Second, future research should target the follow-up test to examine whether the observed the effect of

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overconsolidation persists and whether the spontaneous activity of amygdala and vmPFC recovers following longer time intervals. Finally, future research should also target the effects of SD on subsequent fear memory reconsolidation and on

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subsequent fear extinction.

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In summary, these findings suggest that the amygdala and vmPFC may serve a key role in disrupted fear memory consolidation following sleep deprivation. Importantly, fear ratings and SCR at fear acquisition stage for SD group and control group can predict the change spontaneous brain activity (zALFF and zReHo) in amygdala and vmPFC in fear consolidation respectively. It was important to note here that spontaneous brain activity (zALFF and zReHo) in amygdala and vmPFC following sleep deprivation may be of particular interest for PTSD treatment, providing further 28

ACCEPTED MANUSCRIPT evidence for the contribution of sleep disturbances in the development and

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maintenance of this disorder.

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Funding

This study was supported by the National Natural Science Foundation of China (31571128), the Fundamental Research Funds for the Central Universities (SWU1509392, SWU1709376), China Postdoctoral Science Foundation

(Xm2017161).

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(2017M612880). Chongqing Postdoctoral Science Foundation Special Funded Project

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Conflict of interest. None declared.

29

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ACCEPTED MANUSCRIPT

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Legends

Figure 1. Schematic illustration of the paradigm for the experiment. The zALFF and

group respectively.

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zReHo was compared between Rest1 and Rest2 for the SD group and the control

Figure 2. (A) Mean differential fear ratings (CSa+, CSb+ and CS-) at stage of fear

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acquisition for the control group and SD group. (B) Mean SCR (CSa+, CSb+ and CS-)

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at stage of fear acquisition for the control group and SD group. Figure 3. For zALFF and zReHo, the activation clusters display the difference between Rest1 and Rest2 for the SD group (A&C) and the control group (B&D), respectively.

Figure 4. For ROI analysis, the result showed that the amygdala increased zALFF, and zReHo in the SD group than that in the control group (p<0.001), whereas the vmPFC increased zALFF and zReHo in the control group than that in the SD group 42

ACCEPTED MANUSCRIPT (p<0.001). Figure 5 (A) The correlation analysis showed that the change of zALFF and zReHo in amygdala (Rest2 vs. Rest1) was positively correlated with the subjective fear

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ratings and SCR in SD group. (B) However, the change of zALFF and zReHo in vmPFC was positively correlated with the subjective fear ratings and SCR in control

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group.

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Sleep deprivation group

Control group

Scales

p SD

M

SD

Sleep quality

4.07

1.78

4.35

1.73

0.47

Trait anxiety

40.33

9.43

42.05

7.97

0.90

State anxiety

36.65

7.32

35.05

Depression

44.35

7.64

44.45

Positive emotion

27.07

7.02

27.70

Negative emotion

13.11

2.74

14.13

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0.28

7.37

0.96

5.54

0.65

3.44

0.19

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Table 2 The change of spontaneous brain activity for the Rest2-Rest1 conditions in SD group.

34 34

zReHo L.Amygdala R.Amygdala

34 34

Peak

Voxels

t-value

30 25

24 20

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No.

x

y

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BA

6.40 5.06

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Region

5.24 8.16

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-21 21

z

-3 0

-12 -12

-3 -3

-18 -15

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Table 3 The change of spontaneous brain activity for the Rest2-Rest1 conditions in control group. BA

No.

Peak

Voxels

t-value

zALFF

Ventromedial prefrontal cortex

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438

Ventromedial prefrontal cortex

138

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7.26

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x

y

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Region

10.54

z

0

39

0

-6

24

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