Beyond Circadian: A Yearlong Electroencephalography Study Reveals Hidden Ultralong-term Sleep Cycles

Beyond Circadian: A Yearlong Electroencephalography Study Reveals Hidden Ultralong-term Sleep Cycles | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Beyond Circadian: A Yearlong Electroencephalography Study Reveals Hidden Ultralong-term Sleep Cycles Tamir Avigdor , Jonas Duun-Henriksen , Esben Ahrens , Alyssa Ho , Matthew Moye , Birgit Frauscher , Sándor Beniczky doi: https://doi.org/10.1101/2025.09.14.676090 Tamir Avigdor 1 Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University , Montreal, Quebec, Canada 4 Analytical Neurophysiology Lab, Department of Neurology, Duke University Medical Center , Durham, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jonas Duun-Henriksen 2 UNEEG medical A/S , Lillerød, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Esben Ahrens 3 T&W Engineering A/S , Lillerød, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alyssa Ho 4 Analytical Neurophysiology Lab, Department of Neurology, Duke University Medical Center , Durham, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Matthew Moye 4 Analytical Neurophysiology Lab, Department of Neurology, Duke University Medical Center , Durham, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Birgit Frauscher 1 Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University , Montreal, Quebec, Canada 4 Analytical Neurophysiology Lab, Department of Neurology, Duke University Medical Center , Durham, NC, USA 5 Department of Biomedical Engineering, Duke Pratt School of Engineering , Durham, North Carolina, U.S.A. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sándor Beniczky 6 Department of Neurophysiology, Danish Epilepsy Center , Dianalund, Denmark 7 Department of Clinical Medicine, Aarhus University and Department of Clinical Neurophysiology, Aarhus University Hospital , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: sbz{at}filadelfia.dk Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Sleep is essential for brain function and overall health. While circadian rhythms and sleep stages across the night have been well-characterized, long-term variations in sleep remain poorly understood. We used a novel technology, subcutaneous electroencephalography, to collect yearlong sleep recordings from 20 healthy individuals. We investigated intrinsic and extrinsic drivers of sleep variability and identified recurring multi-day cycles of 8-60 days in sleep duration, latency, architecture, and stability. Additionally, sleep was modulated by external factors including season, weather, weekends and holidays. This reveals that while sleep processes are governed by internal dynamics, they remain sensitive to environmental influences. These results uncover a previously unrecognized temporal variation in human sleep and suggest new directions for understanding sleep patterns and their relevance to health and disease. Download figure Open in new tab Introduction The notion of cyclicity has recently emerged in studies of various physiological processes, as illustrated by the characterization of circadian rhythms ( 1 ). However, investigation of longer-term cycles in human sleep has only just begun. Classic investigation of the neurophysiological underpinnings of sleep started with the invention of the electroencephalogram (EEG) ( 2 ), which revealed the macrostructure of sleep ( 3 - 5 ). However, the difficulty of obtaining long-term EEG recordings under natural conditions has constrained our knowledge of how internal and external factors shape sleep architecture and its long-term cyclicity. Most studies on sleep macrostructure have been restricted to sleep laboratories, where recordings are typically short-term of one or two nights and performed under controlled conditions in unfamiliar environments ( 6 - 8 ). These artificial settings can alter sleep patterns, limiting the generalizability of findings to typical sleep in natural settings. The first month-long study of internal sleep rhythmic outside the sleep lab dates back to 1938, and established the ∼24h sleep cycle ( 9 ). However, such long-term studies were difficult to conduct and thus remained rare. The recent emergence of wearable devices that can approximate sleep patterns from modalities other than EEG has enabled sleep monitoring over several months ( 10 , 11 ). While this marks a significant step beyond traditional laboratory-based approaches, these devices lack the precision for in-depth assessment of the sleep architecture, and studies spanning a full calendar year are still uncommon. When long-term studies were conducted, they found seasonal cycles of some sleep features ( 12 - 14 ), influenced by geographic location ( 15 , 16 ), lifestyle factors ( 17 , 18 ), and even environmental conditions within special indoors settings ( 19 - 22 ). Although non-EEG wearables have advanced the ability to monitor sleep in naturalistic settings, they lack the precision of EEG recordings, which remain the gold standard for assessing sleep macrostructure. These devices often show limited reliability ( 23 , 24 ) with low agreement rates and high variability compared to polysomnography in features such as rapid eye movement sleep (0.13-0.37) and wakefulness during the night (0.41-0.72), depending on the device used ( 25 ). This is typically attributed to their low sensitivity ( 23 ). The investigation of multi-day endogenous sleep cycles has remained largely unexplored, except in studies focused on hormonal rhythms ( 26 ), which themselves were rarely conducted long-term in home-settings. The recent development of ultralong-term subcutaneous EEG (sqEEG) has the potential to bridge the gap between wearable sleep monitoring and the gold standard of scalp EEG ( 27 ). Implanted beneath the skin near the ear, sqEEG enables high-quality, long-term recordings, compatible with naturalistic, everyday sleep environments ( 28 , 29 ). In this study, we leveraged the first ultralong-term sqEEG dataset collected from healthy subjects to examine human sleep outside the laboratory. We analyzed a full year of nocturnal sleep recordings to investigate how internal and external factors influence sleep macrostructure. Our findings reveal that several aspects of sleep architecture exhibit intrinsic rhythmicity and are modulated by seasonal variation, weather conditions, calendar events, and other contextual influences. Results We leveraged a unique dataset of year-long sqEEG recordings ( 29 ) from 20 healthy subjects (11 females, median age 28 years [range: 19-62]) to investigate ultralong-term trends in human sleep. These recordings provide, for the first time, high-quality EEG data enabling accurate estimation of sleep across many nights of sleep. Using a previously validated deep-learning-based sleep scoring algorithm that is non-inferior to human readers ( 28 , 30 ), sleep was analyzed for each night throughout the year. Subject adherence was high, with a median adherence of 92.4% [range: 76.4-100.0]. The median total sleep time (TST) per night was 7h and 23m [443 minutes, IQR= 394.5-485.0]. We investigated first how external cues in daily life modulated sleep as well as internal long term sleep rhythms. We examined fluctuations in sleep macrostructure parameters, including TST, the duration and proportion of sleep stages, sleep onset and offset times, sleep latency and efficiency, and sleep disruption measured by wake after sleep onset (WASO), in relation to intrinsic cyclicity and external influences ( Figure 1 ). Download figure Open in new tab Figure 1. Methodological workflow. Each participant’s nightly EEG was pre-processed, automatically sleep-scored, and converted into a matrix of sleep-parameter features; the analysis was then split into two complementary tracks. Intrinsic rhythmicity was assessed by testing every sleep parameter for subject-specific cycles and, when significant, quantifying their strength with the phase-locking value. External cues modulation was assessed using permutation tests for each sleep parameter, considering season, day of the week, time of year, menstrual phase. Daily weather conditions, and physical activity recorded during the preceding day were correlated with sleep parameters accounting for the month of the year. Seasonal effects on sleep We observed a significant year-long pattern in sleep architecture associated with seasonal changes ( Figure 2 ). Using a permutation test, we confirmed that participants experienced longer TST during the winter and shorter TST during the summer - with differences of up to 30 minutes (p<0.05, d = 0.29 [IQR=0.15]). This seasonal variation was largely driven by shifts in sleep timing: during summer, sleep onset was delayed up to 10 minutes, and wake times were up to 20 minutes earlier compared to other months (p<0.05). Notably, the proportion of rapid eye movement sleep (REM) sleep decreased, while the proportion of N3 sleep increased during the summer (p < 0.05). Additionally, WASO decreased by up to 10 minutes during the summer months (p<0.05). Download figure Open in new tab Figure 2. Seasonal trend in sleep. Data from 20 subjects collected over one year are presented for multiple sleep metrics, with the mean and standard error plotted for each parameter. Periods showing a significant increase relative to the overall annual average are highlighted in green, while significant decreases are marked in red. Statistical significance was determined using a permutation test with 10,000 iterations at an alpha level of 0.05. A black trend line was fitted to capture the seasonal variation across the year. Analyzed sleep parameters include total sleep time (TST) in minutes, sleep onset hour, sleep offset hour, the proportions of N2, N3, and rapid eye movement sleep (REM) in percentage sleep relative to TST, and wake after sleep onset (WASO) in minutes. The effect of weather on sleep We further explored how daily fluctuations in sleep parameters were affected by various weather conditions from the prior day (Figure S1). We observed that daily maximum temperature negatively correlated with TST (r=00.31±0.07), with REM duration (r=00.37±0.07) and proportion (r=00.21±0.07) being negatively affected. Sleep offset was also negatively correlated (r=-0.68±0.05) with temperature, possibly explaining the lower REM duration and proportion. Duration of bright sunlight was negatively correlated with WASO (r=-0.24±0.06) as well as TST (r=-0.29±0.07). The latter was conversely positively correlated with cloud coverage (r=0.21±0.06). On nights when temperatures exceeded 20°C during night time, the effect was even more pronounced (Figure S2). TST significantly decreased (p<0.01), primarily due to later sleep onset time (p<0.05). Moreover, the proportion of N2 decreased while the proportion of N3 increased (p<0.05). The effect of special days: weekends, holidays, lunar phases, and birthdays We next examined how special days affected sleep macrostructure features ( Figure 3 ). As expected, subjects tended to fall asleep later and wake up as much as 1.5 hours later on weekends (p<0.001). Although TST varied, no significant differences were observed. However, sleep architecture changed with a gradual decrease in stage N2 sleep from Sunday through Friday (p < 0.05). Sleep latency was reduced by up to 15 min on weekend days (p<0.01), as determined by a permutation test comparing these days to a baseline distribution from Mondays and Tuesdays. Finally, WASO tended to be higher on Saturday night (p<0.01). Download figure Open in new tab Figure 3. The weekend effect on sleep parameters. Panels show changes in sleep onset/offset, sleep latency, sleep efficiency, N2, N3 and REM proportion, and wake after sleep onset (WASO) across the week. Significance was assessed with a permutation test using all Monday and Tuesday nights as baseline; p<0.05 values are indicated by asterisks *<0.05, **<0.01, *** <0.001. The weekend in Denmark is on Saturday and Sunday, thus starting on Friday night and ending on Sunday night. Analyzed sleep parameters include sleep onset/offset hour, sleep offset hour, the proportions of N2, N3, and rapid eye movement sleep (R) in percentage (sleep relative to total sleep time), and WASO and sleep latency, sleep efficiency in minutes. We next investigated the effect of the holiday season on sleep, focusing on late December, including Christmas and New Year’s Eve (Figure S3). We observed that sleep onset and offset times were significantly delayed on both Christmas and New Year’s Eve (p<0.05). TST was significantly reduced on New Year’s Eve (p<0.05) accompanied by changes in REM and N2 sleep proportions a day prior (p<0.05). Additionally, WASO slightly increased the day after New Year’s Eve. However, overall sleep efficiency was unaffected. During half-moons WASO increased (p<0.01), while sleep onset and offset shifted to later time-points around the middle of the moon cycle (full moon) as compared to the start of the moon cycle (p<0.05) (Figure S4). Finally, we examined whether a subject’s birthday celebration (Figure S5) might affect their sleep patterns. We observed a significant reduction in TST on the birthday itself (p<0.05) and an earlier sleep onset time on the following night (p<0.05). Significance was determined using a permutation test compared to a baseline period of 14 days prior to the time presented. Sex differences in sleep With a nearly balanced dataset of 11 females out of 20 participants, we examined potential sex differences in sleep patterns. While overall trends were broadly similar between both sexes, we observed divergent patterns in sleep onset and offset times during the fall to early winter months. Specifically, females had a slight tendency to go to sleep later and wake up later, whereas males showed the opposite behavior, with earlier sleep and wake times (Figure S6). When examining sex difference in sleep during weekends, both males and females showed similar trends of later sleep onset and offset. However, some differences emerged. The increase in N2 and N3% proportion toward the weekend was more pronounced in males than in females. Additionally, males exhibited stronger decrease in REM latency over the weekend, whereas females had a longer REM latency during this period. We included five female subjects who consistently reported their menstrual cycles over the year, and two additional subjects who reported them over a six-months period. We investigated the effects of the menstrual cycle on various sleep parameters. At menstruation onset, NREM sleep proportion decreased by up to 3% (p<0.001), primarily driven by reductions in N2 rather than N3 sleep (Figure S7). Correspondingly, REM sleep proportion increased by up to 3% (p<0.001). TST showed a slight increase in the days following menstruation onset, and it decreased slightly around 10 days later, toward the end of the luteal phase (p0.05, Figure S8). Intrinsic ultralong-term multi-day cycles We observed that all subjects exhibited intrinsic ultralong-term multi-day sleep cycles that repeated regularly, independent of external factors. The significance of the cycle was tested using a Rayleigh test ( 31 ) compared to a uniform distribution and corrected for multiple comparisons for all sleep parameters and periods tested (8-60 days). The sharpness and consistency of these cycles were quantified using the clustering of the phase locking value (PLV) ( 32 ). For each sleep parameter in all subjects with significant multi-day cyclicity, we calculated the PLV and, to highlight the more robust changes, we required a PLV above 0.1 to be considered meaningful. Note that a PLV of 1 would indicate complete phase locking i.e., no variation in that sleep parameter on other days. The specific characteristics of these long-term cycles varied across the subjects with different subject exhibiting different cycle strength at different periods depending on the sleep parameter (Figure S9). When analyzing data from all subjects, we found that TST, WASO, sleep latency, and sleep efficiency showed significant multi-day cyclicity in ≥80% of patients which co-oscillated for most sleep parameters at 8 to 11, 21, 31 and 55 to 58 days ( Figure 4 , Figure S10). The durations of N2, N3 and REM sleep also followed multi-day cycles, primarily driven by the cyclicity of TST. We found no significant differences between males and females in the presence or characteristics of these intrinsic multi-day cycles. Download figure Open in new tab Figure 4. Intrinsic multi-day cycles in sleep parameters. (A) (i) Phase-locking values (PLV) corresponding to the period with the highest PLV are shown for each subject and each sleep parameter. (ii) Intrinsic sleep duration cycles for all participants are displayed as the peak periods identified within the 8–60-day range. The x-axis represents individual subjects, the y-axis is the sleep parameter, and the number inside each box marks the period with the strongest cyclicity, determined by the PLV after confirming significance with a Rayleigh test (<0.05, false discovery rate corrected). (B) Examples of significant intrinsic cycles at 9, 21, 31, and 57 days from different subjects (# subject number) are shown for total sleep time (TST), wake after sleep onset (WASO), sleep latency, and sleep efficiency. The circular plots illustrate, for a given period, the mean value of each sleep parameter across a full year for that subject. Both color and radial length encode time in minutes, and the plots are rotated so that the maximum value of the cycle is aligned at the origin (top position). The accompanying line plots display the raw data (blue) with a fitted sinusoidal curve (orange) overlaid for visual guidance, where the x-axis represents the actual dates and the y-axis shows the raw values of the sleep parameter for that subjec Discussion In this study, we characterized ultralong-term sleep rhythmicity over the course of one year using sqEEG recordings collected in a naturalistic setting. We examined how this rhythmicity is shaped by both internal and external factors. We discovered intrinsic long-term cycles in most subjects for various sleep macrostructure features. These cycles oscillate at subject-specific timescales for sleep duration, WASO, sleep latency, and sleep efficiency. In addition, we observed that external influences including season, daily weather, moon cycles and specific days of the week and year modulated various sleep features, such as total sleep duration, which was shorter during the summer months. Moreover, in female participants, the menstrual cycle was also identified as a significant modulator of sleep macrostructure. These findings provide the first EEG-based insight into ultralong-term human sleep in the natural environment. Cycles are a common feature of physiology, influencing everything from hormone ( 26 ) release to body temperature ( 33 ). Although the circadian clock is well established ( 34 ), recent work about other physiological functions hint at slower rhythms operating over weeks or months ( 33 ). For sleep research, these longer oscillations are challenging because it calls for continuous, high-quality sleep recordings in everyday settings. Most available datasets rely on wearable devices rather than full EEG set-ups, and studies lasting more than a few months are uncommon (Table. S1 summarizes the literature on this topic). This limitation has made it difficult to disentangle the brain’s own dynamics from seasonal, weather-related, and calendar-based influences. In this study, we analyze a year-long, nightly EEG dataset to explore how sleep patterns change over extended periods. Our results identify long-term patterns that appear to be intrinsic to human sleep and quantify how external factors may modify these rhythms, offering a clearer view of long-term sleep regulation. Internal rhythmicity of sleep It is increasingly recognized that human biology is governed not only by 24-hour circadian rhythms but also by ultralong-term (multi-day) cycles. Research in chronobiology has documented recurring ultralong-period cycles, in the order of days to weeks, across numerous physiological parameters, such are heart rate ( 35 ), temperature, metabolism ( 36 ), and the internal correlations between them ( 37 ). However, the extent to which human sleep, and specifically sleep architecture, is influenced by endogenous multi-day rhythms has been poorly studied. In our study, we observed that total sleep duration, WASO, sleep latency, and sleep efficiency exhibited subject-specific recurring intrinsic cycles with approximate periodicities co-occurring at 8 to 11, 21, 31 and 55 to 58 days ( Figure 4 and Supplemental Figure S10). This question has often been overlooked and, to date, has primarily been studied in the context of hormonal regulation ( 26 ), shift work ( 38 ) or sleep restriction ( 39 ) for relatively short durations, rather than in the naturalistic and ultralong-term settings. Even in the broader literature on animal models, this area remains underexplored, with most studies focusing on the effects of genetic mutations or circadian rhythms ( 34 , 40 ), rather than on multi-day cycles. Our observations suggest that human sleep is modulated by previously underrecognized endogenous rhythms. These findings have important implications for biology and medicine. In conditions affected by sleep, such as dementia ( 41 ) and epilepsy ( 42 ), recognizing and accounting for multi-day sleep rhythms could improve symptom monitoring and treatment timing. For example, cognitive fluctuations in dementia or seizure likelihood in epilepsy may partially align with these intrinsic cycles. Longitudinal sleep tracking could thus serve as a non-invasive biomarker for forecasting symptom exacerbation or optimizing interventions such as medication timing or neuromodulation. Understanding ultralong-term sleep rhythms opens new avenues for personalized, rhythm-aware approaches to managing neurologic and psychiatric disorders. Year-long sleep trends By analyzing the longest continuous sleep EEG dataset from healthy individuals to date, we demonstrated the presence of seasonal variation in human sleep. Total sleep duration was generally shorter during the summer months, primarily due to shifts in sleep onset and offset times. These observations are consistent with previous studies that used non-continuous data to investigate the seasonal effect on sleep ( 43 - 46 ). Similar patterns have also been observed with commercially available auxiliary devices, further supporting the seasonal modulation of human sleep duration and sleep onset/offset ( 11 , 13 , 14 ), with effects influenced by geographic location ( 16 ) and distance from the equator ( 15 ). However, the relative composition of sleep has rarely been examined, likely due to the limited ability of wearable devices to accurately assess specific stages such as REM sleep ( 47 ). We found significant seasonal changes in the distribution of NREM and REM sleep ( Figure 2 ), with an increase in the relative content of NREM and decrease in REM sleep during the summer months. These shifts are likely driven by the overall shortening of sleep duration and earlier wake times. WASO also decreased during the summer, likely to compensate for the overall reduced sleep duration. This aspect was rarely examined in studies using auxiliary devices, which did not report any differences in WASO ( 45 ). Additionally, we identified subtle sex-specific differences in the annual patterns of certain sleep features (Figure S6). These differences have not been previously described in the literature ( 48 ), although prior long-term nap studies have reported sex differences ( 49 ). Interpretation of these findings should be approached with caution, given the relatively small sample sizes in the male and female subgroups (9 and 11 subjects). External modulators of sleep in natural environment A key advantage of ultralong-term sleep monitoring in the home environment is the ability to reliably assess the impact of external factors, such as weather and calendar-based events, on sleep patterns, while simultaneously disentangling these effects from potential endogenous modulations of sleep over time. We found that specific weather parameters were significantly associated with variations in sleep macrostructure features (Figure S1). Higher daily maximum temperatures were associated with shorter total sleep duration and a reduced duration and proportion of REM sleep. This relationship seems to arise because nights with higher temperatures might lead to an earlier awakening, and an earlier awakening time would lead to loss of REM-rich sleep cycle. Duration of bright sunlight was negatively correlated with WASO, and correlated TST after controlling for the season. A reverse relationship was observed between TST and cloud coverage. Previous studies have reported a modest effect on sleep onset and offset ( 11 ) in some models, as well as a negative correlation between sleep duration and high temperatures ( 50 , 51 ), findings we also observed (Figure S2). Our analysis further revealed that this reduction in sleep duration primarily affected REM sleep, as well as the duration of N3. Our findings regarding late sleep onset and offset during the weekends are consistent with previous research ( 14 , 52 - 54 ). However, very few studies have examined this effect over an extended period in a natural environment and using EEG. We found that this weekend-related shift induced a relative increase in N2 and decrease in N3 sleep as well as a decrease in sleep latency and increase in WASO ( Figure 3 ) patterns that have not been previously reported ( 55 ). We observed an effect of the lunar cycle on various sleep metrics (Figure S4). Previous work within a hospital setting in non-continuous data showed an effect of the full moon ( 56 ) on various sleep metrics which we were unable to reproduce except a tendency in REM latency, this might be due to the different setting and the nature of non-continuous data. In healthy subjects with a longer test duration ( 57 ) the picture is more complex, with an association for TST, deep sleep and REM sleep only found on full moon with weak effect. Compared to the first days of the moon cycle, sleep onset and offset shifted to later time-points around the middle of the moon cycle (full-moon). We also observed fluctuations in sleep patterns during holidays, between Christmas and New Year’s Eve, with changes in sleep onset and offset, TST and sleep latency (Figure S3). Similar alterations were detected around the subjects’ birthdays (Figure S4), a phenomenon not been previously reported in the literature. These changes in sleep timing may negatively impact cognitive performance ( 58 ), and therefore represent an important behavioral pattern to consider. Finally, some evidence suggests that these effects may vary by country ( 16 ), highlighting the need for replication in populations outside of Denmark. We believe these findings contribute to a deeper understanding of human behavior in relation to sleep hygiene. When examining the menstrual cycle, we observed an inverse relationship between REM and NREM sleep across different phases of the cycle (Figure S7). NREM sleep increased during the luteal phase, while REM sleep was elevated during the late follicular phase. Other sleep parameters, including TST and WASO, showed minimal or no detectable changes across the menstrual cycle. Most prior studies in this area have been conducted under laboratory conditions, and the evidence remains inconsistent ( 26 ). Our findings contrast with a previous study that reported a non-significant trend in REM sleep and the menstrual cycle ( 59 ). However, we were unable to replicate their reported changes in REM latency. In conclusion, this year-long, EEG-based study provides the most comprehensive assessment to date of how human sleep is shaped by both internal and external influences in naturalistic settings. Our findings reveal that sleep architecture is not only seasonally and contextually modulated but also governed by intrinsic ultralong-term cycles spanning weeks to months. These multi-day rhythms, previously undocumented in EEG sleep studies, highlight a novel temporal dimension of sleep regulation with important implications for health and disease. As sleep quality plays a pivotal role in cognitive function and neurological stability, especially in the many neuro-psychiatric diseases, recognizing these rhythms may enable more personalized, time-sensitive approaches to monitoring and intervention. Together, our results underscore the need to consider both environmental and endogenous factors in the study of human sleep and lay the groundwork for rhythm-aware medicine informed by real-world, long-term physiological monitoring. Methods Subjects, recordings and sleep scoring We conducted year-long ( 29 ) nightly recordings from 20 healthy subjects (11 females; median age 28 years, range:19-62) using a sqEEG recording device (24/7 EEG SubQ, UNEEG Medical A/S, Lilllerød, Denmark). An electrode with three contacts (two channels) was subcutaneously implanted with electrode locations of approximately TP9/TP10, T9/T10 and FT9/FT10 according to the international 10/10 system dependent on implanted hemisphere. An external device was adhesively attached to the skin over the housing of the implant, which was connected through an inductive link for power transmission and data storage. Recordings were sampled at 207 Hz and bandpass filtered between 0.5 and 48 Hz. The device also has an in-device accelerometer to measure the displacement and velocity of movement. The participants wear the device for a full year in which 92.4% of nights were recorded (with subjects recording durations ranging from 279 to 365 days). In addition, participants wore an Apple® Watch 3 (Apple®, Cupertino, CA, USA) during the day to track their daily activity. Sleep was scored using a modified version of U-Sleep ( 28 , 30 ), a convolutional deep neural network originally trained on scalp EEG and validated for use with sqEEG. To ensure analysis of nights with adequate macrostructure, only nights with at least 3 hours of sleep were included in the analysis (6999 of 7122 nights; 98.2%). Sleep macrostructure We analyzed the sleep macrostructure as derived from the automated sleep scoring using the previously validated method ( 28 , 30 ). Sleep parameters were determined for each night. Sleep onset was defined as the first occurrence of either three consecutive N1 epochs or a single N2 epoch. Sleep offset was defined as the last epoch of non-wake sleep stage. Sleep latency was calculated as the time from the first quite period as determined by the device-internal accelerometer after the start of the recording, defined as 5 minutes from the start of recordings in which the amplitude did not exceed the 90 th percentile. In nights where the accelerometer failed (<2% of nights), the start of sqEEG recording time was used to define the sleep onset. REM sleep latency was defined as the interval from sleep onset to the first REM epoch. Sleep efficiency was defined from the total sleep time between sleep onset and sleep offset. We extracted the duration spent in each sleep stage: N1, N2, N3 and REM. The TST was calculated as the sum of the time spent in all stages excluding wake. The proportion of each stage was calculated relative to TST. WASO was defined as the total duration of wake epochs occurring between sleep onset and sleep offset. Wearable We extracted data from the Apple® Watch, including: (1) total steps taken, (2) total distance traveled (in meters), and (3) exercise duration (in minutes), along with the corresponding start and end times. These metrics were recorded from the day preceding each night of interest. Additionally, for female subjects, the reported start date of menstruation was used to align sleep parameters with the menstrual cycle. Meteorological data Meteorological data were obtained from the Danish Meteorological Institute, which provides publicly accessible, high-resolution environmental records. We extracted daily measurements corresponding to the study period. The curated weather variables included average daily temperature (°C), total duration of bright sunlight (hours), mean cloud cover (the percentage of the sky covered by clouds), and total daily precipitation (millimeters). All variables were inspected for completeness and consistency. The resulting dataset was temporally aligned with the nightly sleep recordings to enable day-by-day comparisons between environmental conditions and sleep macrostructure. Measurements organization and alignment Each subject’s data from the sqEEG was matched with their corresponding actigraphy recordings and weather observations, based on the exact dates of the nights measured. To assess seasonal effects, data from all subjects were pooled and aligned by day and month of the year, irrespective of the specific calendar year. Finally, a 3-order polynomial was fitted to each sleep parameter to better visualize the trend. Nights with missing data for a given subject were excluded from the calculations. When examining days of excessive heat (≥20 degrees Celsius), three days before and after were pulled from all subjects which had a night measurement on that day. For weekend effects, subjects’ data were aligned according to the day of the week and pooled together. To examine the impact of the holiday season, nights between December 15th and January 5th were aggregated across subjects. Birthday analyses were performed by aligning data from three days before to three days after each subject’s birthday. For menstrual cycle effects, data were aligned by capturing the 14 days preceding and the 13 days following the onset of each menstrual cycle for every month where data were available. To examine ultra-long term internal cyclicity, we analyzed each subject’s data by segmenting it into between 8 to 60 days, with each window corresponding to a full 360° cycle. This was similar to the approach taken by other groups looking at seizure cycles in human EEG derived features ( 60 ). For example, in the 30-day cycle, day 1 and day 31 were both treated as the same phase (day 1) and overlaid to assess periodic patterns. Finally, all analyses were repeated by separating males and females. Statistical analysis When measuring the seasonal changes, for each sleep parameter of interest, a day-by-day permutation testing procedure ( 61 ) was conducted to assess deviations from an individual subject’s baseline across the annual cycle. First, daily measures were organized into subject-by-day matrices and temporally smoothed using a 10-day rolling average to reduce short-term fluctuations. Each subject’s baseline was defined as the median value across the year, and a deviation matrix was calculated by subtracting this baseline from the smoothed time series. At each time point, a two-tailed permutation test was performed to test whether the mean deviation across subjects significantly differed from zero. Specifically, the observed mean deviation was compared against a null distribution generated by randomly flipping the signs of the individual subject deviations across 10,000 permutations ( 62 ). The p-value at each time point was defined as the proportion of permuted means more extreme than the observed mean. A similar permutation approach was used to analyze the effects of weekends, holidays, birthdays, days of excessive heat, and the menstrual cycle, varying only the choice of baseline reference. For weekend analyses, Mondays and Tuesdays served as the baseline. For the holiday season, the baseline was defined as the period from December 1st to December 15th. For birthdays, the baseline included days 17 to 4 days prior to the birthday. For excessive heat days, the baseline consisted of the 14 to 3 days preceding the heat event. In the menstrual cycle analysis, the full 28-day cycle (14 days before and 14 days after the start of the cycle) was used, analogous to the approach applied for assessing seasonal trends. A significance threshold of α = 0.05 was used to identify time points showing statistically significant increases or decreases relative to the baseline. To examine how daily weather variables relate to sleep outcomes, we fitted linear mixed-effects models with month as a covariate and applied a false-discovery-rate (FDR) correction to control for multiple testing. We then performed post-hoc, subject-level Pearson correlations and reported the group-level mean ± standard error of the resulting correlation coefficients. In addition, the testing of the correlation between daily exercise and the following night microsleep features was done in a similar manner. 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Share Beyond Circadian: A Yearlong Electroencephalography Study Reveals Hidden Ultralong-term Sleep Cycles Tamir Avigdor , Jonas Duun-Henriksen , Esben Ahrens , Alyssa Ho , Matthew Moye , Birgit Frauscher , Sándor Beniczky bioRxiv 2025.09.14.676090; doi: https://doi.org/10.1101/2025.09.14.676090 Share This Article: Copy Citation Tools Beyond Circadian: A Yearlong Electroencephalography Study Reveals Hidden Ultralong-term Sleep Cycles Tamir Avigdor , Jonas Duun-Henriksen , Esben Ahrens , Alyssa Ho , Matthew Moye , Birgit Frauscher , Sándor Beniczky bioRxiv 2025.09.14.676090; doi: https://doi.org/10.1101/2025.09.14.676090 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17691) Bioengineering (13892) Bioinformatics (41937) Biophysics (21452) Cancer Biology (18588) Cell Biology (25504) Clinical Trials (138) Developmental Biology (13378) Ecology (19899) Epidemiology (2067) Evolutionary Biology (24320) Genetics (15609) Genomics (22506) Immunology (17736) Microbiology (40394) Molecular Biology (17181) Neuroscience (88605) Paleontology (666) Pathology (2832) Pharmacology and Toxicology (4824) Physiology (7641) Plant Biology (15156) Scientific Communication and Education (2045) Synthetic Biology (4294) Systems Biology (9825) Zoology (2271)