Cortico-autonomic local arousals and heightened somatosensory arousability during NREM sleep of mice in neuropathic pain

Chronic pain patients frequently suffer from sleep disturbances. Improvement of sleep quality alleviates pain, but neurophysiological mechanisms underlying sleep disturbances require clarification to advance therapeutic strategies. Chronic pain causes high-frequency electrical activity in pain-processing cortical areas that could disrupt the normal process of low-frequency sleep rhythm generation. We found that the spared-nerve-injury (SNI) mouse model, mimicking human neuropathic pain, had preserved sleep-wake behavior. However, when we probed spontaneous arousability based on infraslow continuity-fragility dynamics of non-rapid-eye-movement sleep (NREMS), we found more numerous local cortical arousals accompanied by heart rate increases in hindlimb primary somatosensory, but not in prelimbic, cortices of SNI mice. Closed-loop mechanovibrational stimulation revealed higher sensory arousability in SNI. Sleep in chronic pain thus looked preserved in conventional measures but showed elevated spontaneous and evoked arousability. Our findings develop a novel moment-to-moment probing of NREMS fragility and propose that chronic pain-induced sleep complaints arise from perturbed arousability.

comparative studies between sleep in chronic pain patients and in primary insomniacs (Bjurstrom & Irwin, 88 2016). Therefore, it is currently unclear whether chronic pain is accompanied by high-frequency cortical 89 activity during NREMS and whether this affects spontaneous and evoked arousability (Mathias et al., 2018;90 Kuner & Kuner, 2020). 91 This study pursues this question through implementing a real-time tracking method for 92 spontaneous and evoked arousability in the mouse spared-nerve-injury (SNI) model of neuropathic pain 93 (Bourquin et al., 2006). We start from previously described fragility-continuity dynamics of NREMS in mice 94 and humans that indicate variable arousability on the ~50-sec time scale (Lecci et al., 2017). The fragility-95 continuity dynamics are present while NREMS remains polysomnographically continuous and manifest in 96 fluctuating activity of several brain and peripheral parameters, notably in the power of sleep spindles 97 (10-15 Hz) in the global EEG and the local field potential (LFP) signals. On this close-to-minute timescale, 98 we show here that we are capable of tracking spontaneous and evoked arousability across NREMS in the 99 resting (light) phase. We find that the sleep disruptions in SNI animals concern both, altered spontaneous 100 and evoked arousability. In particular, we identify a novel, previously undescribed cortico-autonomic 101 We specifically evaluated power in the high-gamma frequency (60-80 Hz) range, a frequency band 132 linked to pain sensations when optogenetically induced in mouse (Tan et al., 2019). We found that relative 133 gamma power was increased in SNI at D20+ compared to baseline both in wake and in NREMS ( Figure 1F, 134 1-sample t-test for wake and NREMS in SNI, p = 0.011 and p = 0.0092, respectively). The heart rate was 135 also higher in NREMS of SNI animals at D20+ compared to baseline (Figure 1G, mixed-model ANOVA with 136 factors 'treatment' x 'state' x 'day' with interaction, p = 0.02, post-hoc paired t-test for SNI in NREMS, p = 137 0.002, with Bonferroni-corrected  = 0.0125). A tendency was also evident in REMS, during which heart 138 rate was already elevated (Figure 1G These data indicate that SNI animals do not suffer from major alterations in sleep-wake behaviors. 144 Still, pain-related pathological changes in brain and periphery continued to be present in sleep. This is 145 consistent with a state of "hyperarousal" whereby high-frequency power components are 146 disproportionately elevated during sleep that is normally dominated by low-frequency rhythms (van 147 Someren, 2020;Vargas et al., 2020), and where heart rate also remains elevated. As such alterations could 148 affect arousability, we asked when and where in the brain this abnormal activity appeared. Furthermore, 149 we developed an approach to systematically quantify alterations in both, spontaneous and evoked, types 150 of arousability from NREMS during the resting phase.   (Franken et al., 1999). For NREMS in early phases of the resting phase, we described a 0.02 Hz-185 fluctuation during NREMS that provides a minute-by-minute time raster to measure arousability driven 186 by sensory stimuli. This fluctuation subdivides NREMS bouts into ~25 s-long periods of continuity and 187 fragility that show low and high sensory-evoked arousability, respectively (Lecci et al. light phase, we first tested whether MAs associated with muscular activity (Figure 2A), well-established 190 correlates for spontaneous arousability, were phase-locked to the 0.02 Hz-fluctuation in healthy mice (n 191 = 30 mice with 9,476 MAs). The onset of MAs coincided with declining or low sigma power levels that 192 followed a pronounced sigma power peak (Figure 2B,C), which is characteristic for a fragility period (Lecci 193 et al., 2017;Fernandez & Lüthi, 2020). A spectral band typical for NREMS, such as delta (1-4 Hz) power, 194 showed a rapid decline preceding the MAs, indicating the momentary interruption of NREMS. The phase 195 values of the 0.02 Hz-fluctuation, calculated via a Hilbert transform (Figure 2 -figure supplement 1), 196 showed that MA onset times clustered around a mean preferred phase of 151.6° ± 1.1°, with 180° 197 representing the sigma power trough (Rayleigh test, p < 1x10 -16 ). The majority of MAs (89 %) was clustered 198 between 90-270°, which narrows the fragility period to the low values of sigma power around the trough 199 (Lecci et al., 2017). The phase-locking was also observed when time points at 4, 8 and 12 s before the 200 onset of a MA were quantified ( Figure 2D). This shows that the onset of the fragility period preceded the 201 MA. Fragility periods thus constitute moments during which MAs preferentially occur. 202 These phase relations persisted for all 1-h intervals across time-of-day (Figure 2E), although the 203 density of MAs showed a characteristic increase towards the end of the light phase and was higher during 204 the dark phase ( Figure 2F). The peak frequency of the 0.02 Hz-fluctuation also remained relatively 205 constant, with a minor decrease in power during the dark phase ( Figure 2G). Across the 24-h cycle, a 206 median of 33.6 % of all fragility periods were accompanied by a MA (Figure 2H). In sum, fragility periods 207 are permissive windows for MAs. This means that MAs appeared predominantly during fragility periods, 208 while a majority of fragility periods occurred with NREMS remaining consolidated. 209  across the light phase. Thus, neither its amplitude nor frequency (Figure 3A-C), or, equivalently, the 231 number of its cycles per h of NREMS, were different between the groups ( Figure 3D). The phase-coupling 232 to MAs was also unaltered ( Figure 3E, mean angle ± 95% CI: 152.3 ± 1.4 for Sham and 150.4 ± 1.3 for SNI) 233 and the distribution of fragility periods containing transitions to MAs, to REMS, or with continuation into 234 NREMS was indistinguishable ( Figure 3F). 235 It has been shown that sleep loss exacerbates pain (Alexandre et al., 2017). Sleep could thus be 236 relatively more disrupted in SNI animals after a period of sleep loss. We therefore carried out a 6 h-sleep 237 deprivation (SD) at the beginning of the light phase as done previously in the lab (n = 12 for Sham and SNI  showed a higher peak in S1HL than in PrL (Figure 4A,B). The cycles of successive continuity and fragility 286 periods were extracted (Figure 2, figure supplement 1) and their spectral dynamics plotted separately for 287 the relative contribution of power in the low-frequency delta (1-4 Hz) and the beta (16-25 Hz), low-288 (26-40 Hz) and high-(60-80 Hz) gamma bands ( Figure 4E-H for S1HL, Figure 4K-N for PrL). Average 289 values for the infraslow phase angles between 90-270°, corresponding to the fragility period enriched in 290 MAs (see Figure 2), and for the continuity period (from 270-90°), were calculated. Such analysis revealed 291 SNI-and region-specific alterations in the contributions of these bands to total power that were clearly 292 present in S1HL, but not detectable in PrL. In S1HL, delta power levels were decreased compared to Sham 293 ( Figure 4E) whereas high-frequency components in the beta and the low-gamma range were elevated 294 ( Figure 4F-G). Remarkably, delta power differences between Sham and SNI varied between fragility and 295 continuity periods ( Figure 4E, mixed-model ANOVA with factors 'treatment' and 'period', p = 0.001 for 296 the interaction). In Sham, there was a distinct rapid upstroke of power in this frequency band that reached 297 a peak during the fragility periods ( Figure 4E, post-hoc t-test for delta power in fragility vs continuity 298 period in Sham, p = 0.001). Fragility periods continuing into NREMS were thus clearly distinct from the 299 ones associated with MAs during which there is muscular activity and a decrease in EEG delta power (see 300 Figure 2C). In SNI animals, in contrast to Sham, there was no detectable elevation in delta power during 301 fragility periods continuing into NREMS ( Figure 4E, post-hoc t-test in SNI, p = 0.44). The high-frequency 302 bands in the beta and low-gamma range instead showed a tonic increase in SNI that was present 303 throughout continuity and fragility periods ( Figure 4F-G, mixed-model ANOVA with factors 'treatment' 304 and 'period', for beta, p = 0.01, p = 1.3x10 -9 and for low gamma, p = 0.0053, p = 1.4x10 -10 ) and that was 305 also present, although to a milder extent, in the high-gamma range ( Figure 4H). 306 We calculated an "activation index" (AI), defined by the ratio between the summed spectral power 342 in the beta and low-gamma bands and the delta band power, to quantify alterations in spectral balance 343 between high-and low-frequency power components, similarly to what has been done previously in 344 studies on insomnia disorders (Lecci et al., 2020). The AI is a measure for the degree of EEG 345 desynchronization and increases when NREMS moves closer to wakefulness. In the fragility periods 346 continuing into NREMS and devoid of EMG activity, the AI decreased, consistent with NREMS remaining 347 consolidated ( Figure 5A-C). In SNI animals, however, the AI was higher compared to Sham specifically in 348 the fragility periods ( Figure 5B, mixed-model ANOVA with factors 'treatment' and 'period', p = 0.039 for 349 interaction, post-hoc t-tests Sham vs SNI in fragility period, p = 0.005, in continuity period, p = 0.027, not 350 significant with  = 0.0125). Fragility periods during uninterrupted NREMS are thus specific moments 351 during which the AI in SNI conditions was significantly higher compared to continuity periods. 352 Can such mean differences in cortical activation profiles during NREMS qualify as differences in 353 cortical arousals? To address this, we compared the AI in fragility periods with a MA (associated with EMG 354 increase). As expected, the AI showed an intermittent phasic peak (Figure 5D-F) in most cases (75.2 ± 4.1 355 %), which is explained by the strong decline in delta power (see Figure 2C) and the appearance of higher 356 frequencies associated with MAs. Therefore, we inspected individual fragility periods continuing into 357 NREMS (without a MA) for the presence of similar phasic increases in AI. Indeed, we noticed that a subset 358 of these did indeed contain an intermittent peak resembling the one found during MAs ( Figure 5G) and 359 not evident in the mean AI in Figure 5B. The amplitudes of these peaks were higher in SNI, in accordance 360 with the tonically higher AI in these animals, but in size comparable to the ones of MAs (Figure 5H, mixed-361 model ANOVA with factors 'treatment' and 'MA', p = 0.002 for 'treatment', p = 2.1x10 -7 for 'MA', no 362 interaction). Moreover, the half-widths of these peaks were only moderately smaller than the ones of 363 MAs ( Figure 5I, mixed-model ANOVA with factors 'treatment' and 'MA', p = 0.62 for 'treatment', p = 364 3.28x10 -7 for 'MA', no interaction). These events could thus qualify as a local cortical arousal based on 365 phasic spectral properties reminiscent of a MA. To further support our assumption that these AI peaks 366 constituted arousals, we looked at heart rate increases known to accompany cortical arousals in human 367 (Sforza et al., 2000;Azarbarzin et al., 2014). The heart rate was distinctly higher during the fragility period 368 for cycles containing an AI peak as opposed to the ones without such peak ( Figure 5J, mixed-model ANOVA 369 with factors 'treatment', 'period' and 'peak', p = 0.007 for the 'peak' x 'period' interaction). These events 370 were more frequent in SNI animals and followed a similar time-of-day dependence as the classical MAs 371 ( Figure 5K,L, t-test Sham vs SNI, p = 0.02). Moreover, their increased occurrence was specific for S1HL 372 while absent in PrL and in the contralateral EEG ( Figure 5 -figure supplement 2). The presence of a 373 subgroup of fragility periods continuing into NREMS, yet showing a cortical arousal, is noteworthy for 374 several reasons. First, it demonstrates that rodent NREMS shows local cortical intrusion of wake-related 375 activity in the absence of muscular activity. Second, these local cortical arousals in SNI showed 376 intermittent peaks in AI that were close the ones of MAs, indicating comparable cortical 377 desynchronization at the local level. Third, they were accompanied by heart rate increases that are 378 sensitive hallmarks of arousal in human (Azarbarzin et al., 2014). Fourth, neuropathic pain goes along with 379 a specific increase in the relative occurrence of fragility periods with such AI peaks specifically in the S1HL 380 area. The systematic classification of fragility periods helped unravel these novel cortico-autonomic 381 arousals and their similarity to MAs. Still, other arousal-like events outside fragility periods could exist. 382 We finally examined sensory arousability in SNI conditions, focusing on the somatosensory 425 modality. To anticipate fragility and continuity periods in real-time in the sleeping animal, we trained a 426 machine learning software to predict online periods of continuity and fragility based on EEG/EMG 427 recordings (Figure 6A-E). For the training, we used online-calculated 0.02 Hz-fluctuation estimates onto 428 which fragility and continuity periods were labelled using peak-and-trough detection of sigma power 429 dynamics (Figure 6 -figure supplement 3). To control for the accuracy of the online prediction, we visually 430 scored MAs in 12 C57Bl/6J animals implanted only for polysomnography and verified their position in 431 either online detected peak-to-trough ('online fragility') or trough-to-peak phases ('online continuity') 432 ( Figure 6F). We compared the online prediction to that generated by chance through randomly shuffling 433 both online fragility and continuity point positions in the recordings. This showed that the MA proportions 434 obtained with the real detection exceeded those obtained by chance prediction (Figure 6G Evoked arousability was probed through applying sensory stimuli either during online detected 460 fragility or continuity periods. To deliver somatosensory stimuli remotely while the animals were asleep, 461 we attached vibrational motors to their head implant that could be triggered to briefly vibrate (for 3 s) to 462 test the chance for wake-up ( Figure 7A). These motors were calibrated to vibrate with the same low 463 intensity (~ 30% of full power) across animals (Figure 7 -figure supplement 4). Vibrations were applied 464 randomly with 25% probability during either online detected fragility or continuity periods, for at least 465 two complete light phases per condition ( Figure 7B). Intensity was chosen such that Sham animals showed 466 approximately equal chances for wake-up or sleep-through in online continuity periods ( Figure 7C). concomitant heart rate increases, appeared more frequently in SNI conditions. Fourth, we also 500 demonstrate that fine mechanovibrational stimuli triggered brief wake-ups from NREMS more easily in 501 SNI animals. In summary, chronic pain impacts on NREMS in terms of arousability, more specifically in the 502 probability that NREMS transits towards diverse levels of wakefulness, either spontaneously, or with 503 external stimuli. Chronic pain additionally instates a tonic regional elevation of high-frequency electrical 504 activity. Both these consequences are not detectable in conventional measures of sleep. However, they 505 bear resemblance to pathophysiological markers of insomnia disorders and, as we show here, produce 506 elevated responsiveness to fine vibrational stimuli. The study further suggests that, amongst the many 507 for more polysomnographic assessments in well-controlled patient populations has been highlighted 521 (Bjurstrom & Irwin, 2016). In rodents, only few studies have described cortical desynchronization events 522 without EMG activity, and these were not characterized with respect to autonomic correlates (Bergmann 523 et al., 1987;Franken, 2002;Léna et al., 2004;Fulda, 2011). This relative lack of arousal characterization in 524 mouse NREMS could have hampered the identification of models to replicate sleep disorders, as we found 525 here to be the case for chronic pain models. To the best of our knowledge, our analysis of the SNI mouse 526 is the first that qualifies as a rodent model replicating physiological correlates of insomnia disorders that 527 are hidden behind a comparatively normal sleep and that could raise awareness for refined analysis of the clustering at phases > 90° and < 270°, allowed us to allocate the fragility periods to the phases with low 540 sigma power. We consider this basic finding on MA timing in rodent NREMS significant for several reasons. 541 First, it suggests that the infraslow 0.02 Hz-fluctuation is part of an overarching process that periodically 542 sets a fragility of NREMS towards wake-promoting inputs, whether they arise from internal processes or 543 external stimuli. Mechanistically, this points to an involvement of widely projecting neuromodulatory 544 brain areas such as the locus coeruleus that remains active during NREMS (Aston-Jones & Bloom, 1981; 545 Kjaerby et al., 2020) and regulates sensory arousability (Hayat et al., 2020). Second, the finding contributes 546 to a long-standing uncertainty about the origin and the stochastic time-scales on which MAs are thought 547 to occur (Lo et al., 2004;Dvir et al., 2018). We now calculate that MAs in consolidated NREMS occur with 548 a mean ~35% probability on ~50-sec intervals, thus providing a temporal raster on which these can be 549 anticipated with a measurable degree of certainty. MAs depend on activity in wake-promoting brain areas 550 (Dvir et al., 2018), specifically on the histaminergic hypothalamus in mice (Huang et al., 2006) and on 551 cholinergic nicotinic receptors (Léna et al., 2004). This mechanistic origin of MAs is consistent with our 552 observation that they occur at moments of NREMS during which sensory wake-ups occur preferentially. 553 We therefore consider the identification of fragility periods as time raster for MAs as key to reinforce 554 mechanistic investigations into the origins of spontaneous arousals. This not only concerns MAs, but 555 opens the opportunity to search for other arousal-like events that could be relevant to model pathological 556 conditions of human patients. 557 The majority of this study was dedicated to providing a proof-of-concept for the usefulness of the 558 infraslow fragility periods to scrutinize arousability. The chosen SNI model appeared particularly 559 appropriate for this purpose because it produced a sensory deficit that could be exploited to specifically 560 test somatosensory arousability. The separation of NREMS into fragility and continuity periods throughout 561 the resting phase allowed us to sample and scrutinize the many fragility periods continuing apparently 562 uninterrupted into NREMS. The fragility periods were also critical to determine when AI became most 563 disparate between SNI and Sham and to identify previously undescribed cortico-autonomic arousals. 564 During these, the activation index increased because of a phasic decrease in low-frequency delta power 565 and an increase in high-frequency power. These events were more pronounced and more frequent in SNI 566 because both these phasic power alterations were disrupted, with the deficits in delta power most 567 pronounced. Without the raster provided by the fragility periods, the phasic differences amidst the 568 tonically elevated high-frequency power would easily have gone undetected. To further ascertain that 569 these detected events nevertheless constituted true arousals rather than accidental spectral fluctuations, 570 we sought for independent physiological correlates. Inspired by the human literature (Sforza et al., 2000;571 Azarbarzin et al., 2014), we found that heart rate increases were consistently higher when calculated for 572 cortical arousals with AI peak than for the ones without AI peak. Moreover, their distribution across the 573 resting phase was similar to the one found for MAs and they were present in both S1HL and PrL. This 574 result supports our interpretation that we have identified here a novel cortical-autonomic arousal subject 575 to similar time-of-day-dependent regulatory mechanisms. Still, we cannot exclude that arousal subtypes 576 outside the fragility periods went undetected that would require further characterization. We also remark 577 here clearly that, aside from more frequent cortico-autonomic arousals, SNI animals suffered from a 578 tonically elevated high-frequency power in S1HL that likely underlay the more elevated sensory 579 arousability throughout continuity and fragility periods. 580 The lack of major sleep disruptions in the SNI mouse model was initially unexpected but seemed in 581 line with other studies. We analyzed these animals at a time point when pain from the wound and 582 associated inflammations are largely over (Guida et al., 2020), both of which can strongly disrupt sleep 583 (Landis et al., 1989;Andersen & Tufik, 2003;Silva et al., 2008). Moreover, the animals showed a preserved 584 time spent in REMS, suggesting that they did not suffer from chronic mild stress-inflicted sleep disruptions 585 (Nollet et al., 2019). Other studies on chronic pain also report diverse moderate effects on sleep (Kontinen 586 et al., 2003;Leys et al., 2013). One study on rats at 2 and 10 days after SNI surgery suggested that brain 587 states intermediate between NREMS and wakefulness during the resting phase exist (Cardoso-Cruz et al., 588 2011), which could in part reflect our observations. We also found no alterations in theta power or for 589 shifts in theta peaks in wakefulness, as reported for other animal models of chronic pain (LeBlanc et al.,  590   2014) or for humans with severe neurogenic pain or arthritis (Sarnthein et al., 2006). Analysis of sleep 591 disruptions at later stages in the disease will help decide whether distinct phases of sleep disruptions mark 592 distinct phases of pain chronicity when anxiety-and depression-related behaviors appear more strongly 593 (Guida et al., 2020). 594 The spectral dynamics in two cortical regions we present here delineate possible areas of 595 pathological neuronal activity that underlie the cortical arousals. The continued presence of high-596 frequency activity in S1HL is reminiscent of the cortical oscillatory activity evoked with acute painful 597 stimuli, suggesting that nociceptive input continues to arrive in cortex during NREMS to generate 598 excessive excitation. Indeed, it has been suggested that the SNI model does show spontaneous ectopic 599 electrical activity in peripheral sensory neurons as a result of nerve injury (Wall & Devor, 1983;Devor, 600 2009). NREMS is thought to protect relatively weakly from nociceptive inputs (Claude et al., 2015), 601 therefore possibly allowing continued processing of spontaneous nociceptive activity that could explain 602 the cortical spectral changes we detected. It has also been shown that optogenetic stimulation of the 603 thalamic reticular nucleus, known to be implied in the balanced occurrence of delta and spindle waves 604 Patients with insomnia show such imbalances over widespread brain regions that include sensorimotor 614 areas (Lecci et al., 2020). Furthermore, higher power in the beta frequencies has been related to the 615 patients remaining hypervigilant or excessively ruminating at sleep onset (Perlis et al., 2001a), preventing 616 the deactivation of cortical processes required for the loss of consciousness. Although insomnia also needs 617 subjective assessments that are not possible in animals, this phenomenological comparison suggests that 618 SNI might suffer from similar experiences due to the tonically enhanced high-frequency oscillations. This 619 interpretation is supported by the elevated wake-up rates in response to mild vibrational stimuli 620 throughout the infraslow cycles, suggesting hyperalertness to environmental disturbance. On top of these 621 tonic changes, there were more frequent cortico-autonomic arousals. Although these do not seem to 622 elevate daytime sleepiness based on the mostly unchanged delta power dynamics across time-of-day, 623 frequent increases in heart rate during the night could augment cardiovascular risk in the long-term 624 testing performance in such tasks during wakefulness. 631 We provide here novel approaches to classify arousals in mouse NREMS that will help in the 632 examination and validation of future candidates for rodent models of sleep disorders. We noted a 633 remarkable stability of the 0.02 Hz-fluctuation across the resting phase that provided us with a temporal 634 raster to screen the characteristics of fragility periods. These led us to identify a previously undescribed 635 cortico-autonomic arousal in mouse NREMS that we also found more frequently in a chronic pain model. 636 Together, this study presents NREMS as a state that is interwoven with arousals showing diverse 637 combinations of physiological parameters with different graduations in intensity that can be the target of 638 pathophysiological changes. Recognizing NREMS as a fluctuating state between fragility and continuity 639 will thus further heighten awareness to arousability as a core component of sleep quality. In this study, 640 we unraveled a so far undescribed sleep disruption in chronic pain that we hope will facilitate further 641 research into the treatment of this devastating condition. Mice from the C57BL/6J line were singly housed in a temperature-and humidity-controlled environment 646 with a 12-h/12 h light-dark cycle (lights on at 9:00 am, corresponding to ZT0), with access to food and 647 water ad libitum. We first used 36 mice, 10-14 weeks-old and bred in our colonies in a conventional-clean 648 animal house, for polysomnography (combined EEG (ECoG)/EMG electrodes), followed by SNI or Sham 649 surgery (18 animals per Sham or SNI group). Mice were transferred from the animal house into the 650 recording room 2-3 d before surgery for polysomnography recording. We recorded a 48 h-long baseline 651 before SNI or Sham surgeries, followed by recording at 22-23 d after surgery (D20+). These data were used 652 for Figures 1 and 3. Total sleep deprivation in Figure 3 was done on 24 of these 36 animals (12 SNI, 12 653 Sham) within one day following the recording at D20+. The baseline data for Figure 2 were obtained from 654 the baseline recordings of 23 randomly selected animals from the previous 36, completed with 7 more 655 animals from previous baseline recording in the lab. For EEG (ECoG)/EMG/LFP recordings, 33 C57BL/6J 656 male mice of the same age were first operated for SNI or Sham (17 and 14, respectively) and 5 d later, 657 implanted for recordings from S1HL (4 Sham, 6 SNI) or PrL The Sham and SNI surgeries were performed as previously described (Decosterd & Woolf, 2000). Briefly, 668 mice were kept under gas anesthesia (1-2 % isofluorane, mixed with O 2 ). The left hindleg was shaved 669 and the skin incised. The muscles were minimally cut until the sciatic nerve was exposed. Just below the 670 trifurcation between common peroneal, tibial and sural branches of the nerve, the common peroneal and 671 tibial branches were ligated and transected. The Sham animals, as controls, went through the same 672 surgery without the transection. The muscle and the skin were then stitched closed and the animals were 673 monitored via a score sheet established with the Veterinarian Authorities. 674

Surgery for polysomnographic and LFP recordings in mice 675
Surgeries were performed as recently described (Lecci et al., 2017;. Animals were 676 maintained under gas anesthesia (1-2 % isofluorane, mixed with O 2 ). Small craniotomies were performed 677 in frontal and parietal areas over the right hemisphere and 2 gold-plated screws (1.1 mm diameter at their 678 base) (Mang & Franken, 2012) were gently inserted to serve as EEG electrodes. Careful scratching of the 679 skull surface with a blade strengthened the attachment of the implant by the glue, so that additional 680 stabilization screws were no longer necessary. Two gold wires were inserted into the neck muscle to serve 681 as EMG electrodes. In the case of LFP recordings, small craniotomies (0.2-0.3 mm) were performed to 682 implant high-impedance tungsten LFP microelectrodes (10-12 MΩ, 75-μm shaft diameter, FHC, Bowdoin, 683 ME) at the following stereotaxic coordinates relative to Bregma in mm, for S1HL: anteroposterior -0.7, 684 lateral -1.8, depth from surface -0.45; for PrL : +1.8, -0.3, -1.45). For the neutral reference for the LFP 685 recordings, a silver wire (Harvard Apparatus, Holliston, MA) was placed in contact with the bone within a 686 small grove drilled above the cerebellum. The electrodes were then soldered to a female connector and 687 the whole implant was covered with glue and dental cement. The animals were allowed 5 d of recovery, 688 while being monitored via a score sheet established with the Veterinarian Authorities, with access to 689 paracetamol (2mg/mL, drinking water). The paracetamol was removed when the animals were tethered 690 to the recording cable for another 5 d of habituation prior to the recording. 691

Polysomnographic recording 692
For sleep recordings, recording cables were connected to amplifier boards that were in turn connected to 693 a RHD USB interface board (C3100) using SPI cables (RHD recording system, Intan Technologies, Los 694 Angeles, CA). For EEG/EMG and/or LFP electrodes, signals were recorded through homemade adapters 695 connected to RHD2216 16-channel amplifier chips with bipolar input or RHD2132 32-channel amplifier 696 chips with unipolar inputs and common reference, respectively. Data were acquired at 1000 Hz via a 697 homemade Matlab recording software using the Intan Matlab toolbox. Each recording was then visually 698 scored in 4-s epochs into wake, NREMS, REMS, as described (Lecci et al., 2017) using a homemade Matlab 699 scoring software. 700

Total sleep deprivation protocol 701
Total SD was carried out from ZT0-ZT6 using the gentle handling method used previously in the lab (Kopp 702 et al., 2006), while animals remained tethered in their home cage. At ZT3, the cages were changed and, 703 from ZT5 to ZT6, new bedding material was provided. At ZT6, the animals were left undisturbed. The 704 recordings carried out during SD were visually scored to assure the absence of NREMS from ZT0 to ZT6. 705 There were no detected NREMS epochs during SD in the mice included in the analysis. 706

Probing sensory arousability with vibration motors and automatic wake-up classification 707
An online detection of continuity and fragility period (described below) was used in a closed-loop manner 708 to time vibration stimuli during NREMS such that sensory arousability could be probed. Small vibrating 709 motors (DC 3-4.2 V Button Type Vibration Motor, diameter 11 mm, thickness 3 mm) were fixed using 710 double-sided tape, at the end of the recording cables, close to the animals' heads. The motors were driven 711 using a Raspberry Pi 3B+ through a 3.3 V pulse-width modulation (PWM) signal. Each motor was calibrated 712 to find the necessary PWM duty-cycle to output the same amount of mild vibration using a homemade 713 vibration measurer equipped with a piezo sensor. A Python script was running on the Raspberry Pi to 714 detect the voltage change sent by the digital-out channels on the Intan RHD USB interface board. Upon 715 detecting a change from low to high, the Python script waited for an additional 4 s, and assessed the 716 voltage again. In case the voltage was still high, it launched a 3 s vibration with 25% probability. To close 717 the loop, the PWM signals from the Raspberry Pi driving the motors were as well fed into the analog-in 718 channels of the Intan RHD USB interface board to detect the stimuli time-locked to the EEG/EMG signals. 719 In the experiments, the voltage values were set to high during either continuity or fragility, using online 720 detection as described in Data analysis. Four animals could be tested in parallel for their sensory 721 arousability. 722

Histological verification of recording sites 723
After the in vivo LFP recordings, the animals were deeply anesthetized with pentobarbital (80 mg/kg) and Spectral power was computed on the raw EEG signal using a FFT on scored 4-s windows after offset 738 correction through subtraction of the mean value of each epoch. The median power spectrum for each 739 state was obtained for epochs non-adjacent to state transitions. The normalization was done through 740 dividing by the average of mean power levels (from 0.75-47 Hz) for each vigilance state, ensuring that 741 each state had the same weight in the averaging (Vassalli & Franken, 2017). This normalization was done 742 separately for Baseline and D20+ recordings. Gamma power at D20+ was extracted through calculating 743 mean power levels between 60-80 Hz. Data were normalized to corresponding baseline values. 744 The heart rate was extracted from the EMG signal as described previously (Lecci et al., 2017). Briefly, the 745 EMG signal was highpass-filtered (>25 Hz) and squared. The R peaks of the heartbeats were detected using 746 the Matlab 'Findpeaks' function. Only animals with clearly visible R peaks present in the EMG in NREMS 747 were included in this analysis (Fernandez et al., 2017). 748 For delta power time course, raw delta power (mean power between 1-4 Hz from FFT on mean-centered 749 epochs) was extracted for each NREMS epoch non-adjacent to a state transition. Total NREMS time was 750 divided into periods of equal amounts of NREMS (12 in light phases, 6 in dark phases) from which mean 751 values for delta power were computed. The position in time of these periods was not different between 752 groups. Normalization was done via mean values between ZT9-12, when sleep pressure is the lowest. 753 Wake-up and sleep-through events after vibration were scored automatically as follows. For each trial, 754 the EEG and EMG signals were analyzed within time intervals from 5 s prior to 5 s after the vibrations. To 755 distinguish wake-up and sleep-through events, three values were calculated: 1) The ratio theta (5-10 Hz)/ 756 delta (1-4 Hz) for the 5 s before stimulation, 2) the difference in the low-/ high-frequency ratios (1-4 Hz/ 757 100-500 Hz), before and after the stimulation, 3) the squared EMG amplitude ratio after/ before 758 stimulation. 759 A trial was rejected when the ratio theta/delta was > 1 before stimulation or the EMG amplitude was 760 larger before than after stimulation. In this way, trials starting in REMS or wakefulness were excluded. 761 Wake-up events were scored when the difference in low/high ratios mentioned above decreased 762 markedly after stimulation together with EMG activity. Occasionally, some wake-ups were also scored 763 when EEG or EMG activity was very high while the other channel showed moderate changes. Appropriate 764 thresholds were set upon visual inspection blinded to the animal's condition. 765 Analyses related to the 0.02 Hz-fluctuation 766 Extraction -The 0.02 Hz-fluctuation in sigma power (10-15 Hz) was extracted from EEG or LFP signals 767 using a wavelet transform (Morlet wavelet, 4 cycles), calculated over 12 h recordings in 0.5-Hz bins. The 768 resulting signal was down-sampled to 10 Hz and smoothed using an attenuating FIR filter (cutoff frequency 769 0.0125 Hz, order of 100, the low order allowing for frequencies above the cutoff). The mean of the 770 datapoints within NREMS and MA epochs was used for normalization (Figure 2-figure supplement 1D). 771 The peak and frequency of the 0.02 Hz-fluctuation were calculated through a FFT on continuous NREMS 772 bouts as described (Lecci et al., 2017). FFTs from individual bouts at frequency bins from 0 to 0.5 Hz were 773 interpolated to 201 points before averaging across bouts to obtain a single measure per mouse. The angles 774 of the phase of the 0.02 Hz-fluctuation were obtained through the Hilbert transform (Matlab signal 775 processing toolbox). We set the troughs of the 0.02 Hz-fluctuation at 180°, the peaks at 0° (Figure 2-figure  776 supplement 1G). 777 In several instances (Figures 3D, 4, 5), instead of calculating FFTs in the infraslow frequency range, we 778 needed to detect individual cycles of the 0.02 Hz-fluctuation. To do this, we applied the Matlab 779 "Findpeaks" function, with the conditions that the peak values were > mean and the trough values < mean, 780 each separated by > 20 s. With such parameters, the sequence trough-peak-trough appears only in NREMS 781 and allows to count individual cycles. Analysis of activation index -AI was computed by the natural logarithm (ln) of the ratio between beta (16-788 25 Hz) + low gamma (26-40 Hz) over delta power (1-4 Hz), extracted as described above. Individual cycles 789 from peak-to-peak were classified whether a MA was present in the fragility period or whether it 790 continued into NREMS. To assess the presence of peaks in activation indices, the "findpeaks" function was 791 used at phase values of 90-270°, with mean values used as a threshold. 792 Online detection of continuity and fragility periods -For the online detection of fragility and continuity 793 periods during closed-loop sensory stimulation, a homemade software was generated with two layers of 794 decision. The first one determined the likely current state of vigilance (wake, NREMS or REMS), whereas 795 the second one made a machine learning-based decision between a continuity or a fragility period. 796 1 -Determination of vigilance state. This assessment was based on power band ratios characteristic for 797 wake, NREMS and REMS using appropriate thresholds (Figure 6B,C). Every s, a FFT was calculated on the 798 mean-centered last 4 s of EEG values and the power ratio between the delta (1-4 Hz) and the theta (5-799 10 Hz) was calculated. 800 Transitions out of wake: 1) Switch to NREMS if the last 3 s of EMG were below a high threshold and at 801 least 2 out of the 3 last s of ratio were above a high threshold. 2) Switch to REMS if the full 5 s of EMG 802 were below a low threshold and the full 5 s of ratio were above a high threshold. 803 Transitions out of NREMS: 1) Switch to wake if the last s of EMG was above a high threshold. 2) Switch to 804 REMS if the last five s of EMG were below a low threshold and if among the last 5 s of ratio, at least 4 were 805 below a low threshold and all 5 were below a high threshold. 806 Transitions out of REMS: 1) Switch to wake if the last s of EMG were above a high threshold. 2) Switch to 807 NREMS if the ratio was above a high threshold for at least 4 out of 5 s. 808 2 -Continuity and fragility detection. From the previous step, the value of sigma (10-15 Hz) was kept 809 every second. The mean sigma value in NREMS was dynamically updated if the likely state was determined 810 as NREMS and used to normalize the incoming sigma power values. The last 200 s of sigma power 811 regardless of the likely state were kept in memory. We heuristically found that a 9 th -order polynomial fit 812 (Matlab 'polyfit' and 'polyval' functions) best approximated the 0.02 Hz-fluctuation. To train the network, 813 we first generated online-estimated 0.02 Hz-fluctuation at 1 Hz for the 12 h of the light phase. We next 814 applied offline cycle detection in NREMS periods. For simplicity, and in agreement with previous measures 815 of sensory arousability (Lecci et al., 2017), we set the continuity periods from trough to peak and fragility 816 from peak to trough. Then, we subdivided these recordings in chunks of 200 s (moving window of 1 s, as 817 they would appear online) and keeping the label continuity, fragility or none for each of them. We could 818 thus obtain 43,000 labelled chunks per 12 h of recording. We used 642,000 of these chunks from 13 819 animals to train a neural network (pattern recognition 'nprtool' from Matlab Statistics and Machine 820 Learning Toolbox) 70 % of the for training, 15 % for validation and 15 % for testing. The network was 821 composed of one hidden layer with 10 neurons and one output layer with the 3 different output. We then 822 used the generated neural network online to take the decision between continuity, fragility or none. 823

Statistics 824
The statistics were done using Matlab R2018a and the R statistical language version 3.6.1. The normality 825 and homogeneity of the variances (homoscedasticity) were assessed using the Shapiro-Wilk and the 826 Bartlett tests, respectively to decide for parametric statistics. In the cases where normality or 827 homoscedasticity were violated, a log transformation was assessed at first and finally, non-parametric 828 post-hoc tests were used (Wilcoxon rank sum test for unpaired and signed-rank test for paired data). The 829 degrees of freedom and residuals for the F values are reported according to the R output. Post-hoc 830 analyses were done only when the interaction between factors were significant (p < 0.05). Bonferroni's 831 correction for multiple comparisons was applied routinely, and the corrected α values are given in the 832 legends. The factors used in the ANOVAs are depicted with pictograms once the corresponding effects 833 were significant. The factors used in the analysis were: 'treatment' with two levels: Sham and SNI; 'day' 834 with two repeated levels: baseline and D20+; 'size' with three repeated levels: small, intermediate or long 835 bouts; 'period' with two repeated levels: continuity or fragility; 'SD' with two repeated levels: control or 836 recovery after sleep deprivation; 'state' with three repeated levels: wake, NREMS or REMS; 'MAs' with 837 two levels: with or without MA in the fragility period; 'peak' with two repeated levels: cycles with or 838 without a peak in AI during fragility periods. The circular statistics were done using the CircStat for Matlab 839 toolbox (Berens, 2009).