High social jetlag in healthy youth: Differences in tryptophan levels, temporal pole-centered gray matter volume, and functional connectivity

High social jetlag in healthy youth: Differences in tryptophan levels, temporal pole-centered gray matter volume, and functional connectivity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article High social jetlag in healthy youth: Differences in tryptophan levels, temporal pole-centered gray matter volume, and functional connectivity Masatoshi Yamashita, Qiulu Shou, Masanori Fujieda, Hidehiko Okazawa, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8431510/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Social jetlag (SJL), defined as a mismatch in midsleep timing between school or workdays and free days, has been associated with adverse mental health, cognitive, and brain outcomes in adults. However, its effects in young individuals remain insufficiently studied. We investigated the effects of SJL on these factors and on tryptophan metabolites, an amino acid implicated in sleep, fatigue, and social function, in young individuals. Based on SJL assessed using the Munich Chronotype Questionnaire, 89 healthy youths aged 6–17 years were classified into a high-SJL group (≥ 1 h; n = 25) or a low-SJL group (< 1 h; n = 64). Fatigue, chronotype, Trail Making Test (TMT) performance, urinary tryptophan and 5-hydroxyindoleacetic acid (5-HIAA) levels, gray matter volume (GMV), and resting-state functional connectivity (FC) were evaluated. Compared with the low-SJL group, the high-SJL group exhibited greater fatigue, a later chronotype, and higher urinary tryptophan levels. Moreover, the high-SJL group showed smaller GMV in several regions, including the temporal pole. Notably, the left temporal pole, a region implicated in socioemotional processing, showed reduced FC with the right pars triangularis in the high-SJL group. However, TMT performance and urinary 5-HIAA levels did not differ between groups. An SJL of ≥ 1 h was associated with temporal pole-centered neural vulnerability and elevated tryptophan levels in young individuals. These findings suggest that higher SJL may contribute to neural and molecular disadvantages related to socioemotional functioning during childhood and adolescence. healthy youth serotonin social jetlag temporal pole tryptophan Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The biological clock is an internal timing system that generates 24-h cycles and aligns with environmental signals on a daily basis (Zou et al. 2022 ). Conflicts of biological time with socially constrained schedules lead to social–circadian misalignment, known as social jetlag (SJL) (Roenneberg et al. 2012 ). SJL has also been measured as the discrepancy in midsleep time between weekdays and weekends (Roenneberg et al. 2012 ). Roenneberg et al. ( 2013 ) reported that nearly two-thirds of European adults experience SJL of ≥ 1 h. In addition, previous studies have indicated associations of SJL with adverse physiological outcomes (i.e., menstrual abnormalities) and with an increased risk of psychiatric disorders (i.e., depression) in young adults and workers (Islam et al. 2020 ; Komada et al. 2019 ; Meram et al. 2025 ; Min et al. 2023 ). Notably, a recent study of 6,890 adolescents (mean age 11.95 years) revealed that greater SJL predicted worse cognitive and academic performance (Li et al. 2024 ). These findings suggest that SJL plays an important role in maintaining mental health and cognitive function. Given the increasing concern about misaligned social and circadian schedules across all ages, identifying the neurobiological mechanisms (i.e., neurochemical and neural bases) underlying SJL at an early stage of development is important. Generally speaking, SJL is a form of circadian disruption, and thus, may also influence the neurochemical mechanisms regulating the sleep–wake cycle. One such mechanism involves tryptophan, an essential amino acid known for its role as a biomarker of sleep regulation and central fatigue (Yamashita 2020 ). For example, Yamashita and Yamamoto ( 2017 ) reported that, compared with control rats, rats with sleep–wake cycle abnormalities showed elevation of tryptophan levels and depletion of its metabolite, serotonin, in the brain. These changes were associated with central fatigue induction. Moreover, serotonin depletion disrupts the circadian timing by desynchronizing neuronal activity in sleep–wake regulatory centers (i.e., the suprachiasmatic nucleus) (Miyamoto et al. 2012 ). These findings indicate that tryptophan–serotonin pathways are sensitive to sleep and circadian rhythm disruption. Beyond its role as a biomarker of sleep–wake regulation and central fatigue, the tryptophan–serotonin pathway has also been implicated in socioemotional and cognitive functions, such as prosocial behavior, psychomotor activity, and executive function (Hogenelst et al. 2015 ; Williams et al. 2007 ; Yamashita and Yamamoto 2014 ; 2017 ). However, whether higher SJL is accompanied by changes in the endogenous tryptophan–serotonin metabolism in young individuals remains unclear. SJL has also been found to affect brain activity. A previous study reported that young adults with SJL of ≥ 1 h show alterations in spontaneous low-frequency neural activity in several brain regions compared with those without SJL during resting-state functional magnetic resonance imaging (fMRI) (Nechifor et al. 2020 ). In adolescence (ages approximately 11–13 years), Yang et al. ( 2023 ), using the Adolescent Brain Cognitive Development Study data, found that a greater SJL was associated with alterations in resting-state functional connectivity (FC) between widespread cortical networks and subcortical regions. Given the broad, population-oriented design of the Adolescent Brain Cognitive Development Study, the cohort necessarily included diagnostically heterogeneous participants (i.e., those with neurodevelopmental and psychiatric disorders), which may dilute associations that are SJL-specific. However, the structural brain underpinnings of SJL in healthy young individuals have not been examined directly, and the relationship between any regionally specific gray matter volume (GMV) differences and resting-state FC alterations remains unclear. Accordingly, if structural loci differ according to SJL status, investigating FC using these loci as seed regions may help to clarify whether such differences extend to their functional coupling. Collectively, evidence to date has indicated that SJL can alter mental health, cognitive function, and brain activity. However, most previous studies on this topic have focused on adults and working individuals, whereas the neurobiological mechanisms in healthy young individuals have received scant attention. Given that childhood and adolescence are sensitive periods for circadian consolidation and brain network reorganization (Hagmann et al. 2010 ; Roenneberg et al. 2012 ; Uhlhaas et al. 2009 ), clarifying whether SJL is accompanied by molecular, structural, and functional differences during this developmental period is important. This cross-sectional study addressed two primary research questions in healthy young individuals (aged 6–17 years). First, do individuals with high SJL (≥ 1 h) differ from those with low SJL (< 1 h) in urinary tryptophan and 5-hydroxyindoleacetic acid (5-HIAA), an index of serotonergic turnover? Second, do individuals with high SJL show alterations in brain structure compared with those with low SJL, and, if so, do these loci also exhibit region-specific resting-state FC differences when used as seed regions? To address these questions, we compared urinary tryptophan and 5-HIAA levels, brain structure, and seed-based resting-state FC between individuals with high and low SJL. In addition, we compared behavioral measures, including fatigue, chronotype, and Trail Making Test (TMT) performance. We hypothesized that young individuals with high SJL would show higher tryptophan and lower 5-HIAA levels, consistent with findings from a rat model of sleep–wake cycle anomalies. We further surmised that high SJL would be associated with reduced GMV in one or more regions and with reduced seed-based resting-state FC in those regions. Given growing concerns about sleep-related social and psychiatric outcomes, clarifying the impact of SJL in young individuals may inform developmental models of brain and mental health and support the development of child-focused sleep and circadian guidelines.​ Methods Participants The Research Ethics Committee of the University of Fukui approved the study protocol (Assurance No. FU-20220061) and the procedures of the study complied with the principles of the Declaration of Helsinki. All parents provided written informed consent, and all children provided assent. The study involved 92 healthy young individuals, aged 6–17 years. Such individuals who did not receive special-support education were recruited from the local community. None of the participants had any history of neurological, cardiovascular, or psychiatric illness and none had contraindications for MRI. Data from 3 individuals were excluded from the analysis because of claustrophobia that impeded the MRI scan. Thus, data from 89 healthy young individuals were used in the final analysis. Psychological questionnaires The Munich Chronotype Questionnaire (Roenneberg et al. 2003) was used to assess the participants’ SJL and chronotype. This standardized self-rating scale assesses an individual’s habitual sleep–wake timing on work/school days and on free days. The variables consisted of (1) sleep start (bedtime and sleep onset latency), (2) sleep end (wake-up time), (3) alarm clock usage, and (4) sleep duration (total amount of time between sleep start and sleep end). Additionally, the midpoint of sleep on free days was calculated as the midpoint between sleep onset and wake-up time. However, most individuals accumulate sleep debt during the school day and extend their sleep time on free days. Therefore, the corrected midpoint of sleep on free days, known as the chronotype index, was calculated as the midpoint of sleep on free days minus a correction for sleep debt equal to half the difference between sleep duration on free days and average sleep duration over the week. In addition, SJL was defined as the absolute difference between the midpoint of sleep on school days and the midpoint of sleep on free days. Larger values indicated greater misalignment between biological and social sleep timing. For group comparisons, we defined high-SJL (≥ 1 h) and low-SJL (< 1 h) groups, as previously reported (Hunt et al. 2025; Roenneberg et al. 2012). The Japanese version of the Chalder Fatigue Scale (Tanaka et al. 2008) was used to assess individuals’ fatigue. This scale consists of 11 items that assess symptoms of mental and physical fatigue. Participants were asked to respond to questions about tiredness, rest, feeling sleepy, motivation, energy, muscular strength, weakness, concentration, and speech by using a 4-point scale: “less than usual,” “much more than usual,” “better than usual,” and “much worse than usual.” Cognitive measurement The Japanese version of parts A and B of the TMT (Reitan and Wolfson 1985) were applied to evaluate executive function. In the TMT-A, participants were asked to draw lines, sequentially connecting 25 numbers in ascending order. In the TMT-B, participants were asked to draw lines alternately between numbers and letters (1, あ, 2, い, etc.). The time to complete each task was measured. Furthermore, we assessed TMT-Δ, calculated as the time to complete part B minus that to complete part A, which is considered an index of cognitive flexibility and set-shifting (Bowie and Harvey 2006). Determination of tryptophan metabolite On the day before testing, the participants were instructed to refrain from intense physical activity and from consuming certain foods and beverages that can be difficult to digest (i.e., alcohol, coffee, high-fat fish, red beef, and blue cheese) for 24 h (Yamashita and Yamamoto 2021). On the test day, fresh urine samples were collected after a 30-min rest. Urine samples were diluted with 6.7 mM hydrochloric acid and 2.5% perchloric acid to separate albumin, according to a previously reported method (Yamashita and Yamamoto 2021). The obtained supernatants were stored at -80°C until analysis using high-performance liquid chromatography (HPLC). Tryptophan and 5-HIAA content were measured using HPLC with an electrochemical detector (Nanospace SI-2 3005, Osaka Soda Corporation, Osaka, Japan) and a chromate recorder (C-R8A; Shimadzu Corporation, Kyoto, Japan). Standards for these compounds were obtained from Sigma-Aldrich Inc. (St Louis, MO, USA). The mobile phase consisted of 15% methanol in a solution (pH 4.13) containing 30 mM citric acid, 10 mM disodium hydrogen phosphate, 0.5 mM sodium octyl sulfate, 50 mM sodium chloride, and 0.05 mM ethylenediaminetetraacetic acid, as previously reported (Yamashita and Yamamoto 2014; 2017; 2021). The solution was passed through a 5-μM C 18 column (150 mm × 4.6 mm; TSK gel, ODS-80TM, Tosoh, Tokyo, Japan) at a flow rate of 0.7 mL/min, using a single pump (Nanospace SI-2 3101; Osaka soda Corporation, Osaka, Japan) at a column oven (Nanospace SI-I 2004; Osaka soda Corporation, Osaka, Japan) temperature of 25°C. Tryptophan was detected electrochemically at 800 mV, with a retention time of approximately 16 min. 5-HIAA was detected electrochemically at 700 mV, with a retention time of approximately 12 min. Statistical analysis of demographic, psychological, cognitive, and tryptophan metabolite data Statistical analyses of demographic, psychological, cognitive, and tryptophan metabolite data were conducted using R (version 4.3.0; The R Foundation for Statistical Computing, Vienna, Austria). All data were initially assessed for normality and homogeneity of variance. Student’s t -tests, Welch’s t -tests, Chi-square tests, and Fisher’s exact test were applied as appropriate. The group effect on chronotype, fatigue, TMT-A and B performance, and urinary tryptophan metabolite levels were analyzed using a linear mixed-effects model (R packages “lmerTest,” “MuMIn,” and “jtools”). The categorical variable of group (high-SJL and low-SJL) was modeled as fixed effects. To account for non-independence of observations within-families (i.e., among siblings), family ID was modeled as a random intercept. Covariates included demographic variables that showed significant differences between the high- and low-SJL groups, in addition to weekly sleep duration as previously reported (Tamura et al. 2022; Yamashita et al. 2024). For all analyses, the statistical threshold was set at p < 0.05, false discovery rate (FDR)-corrected by using the Benjamini–Hochberg method. Image acquisition Brain scanning was performed using a 3T GE signa PET/MR scanner (General Electric Healthcare, Chicago, IL, USA). Participants’ heads were immobilized using a head-coil scanner (eight channels). High-resolution structural images were acquired using an axial T1-weighted magnetization-prepared rapid gradient-echo pulse sequence (repetition time = 8.5 ms; echo time = 3.2 ms; field-of-view = 256 × 256; matrix size = 256 × 256; voxel size = 1 × 1 × 1 mm; 176 slices). Moreover, a gradient echo-planar imaging sequence was used to obtain resting-state functional blood oxygen level-dependent (BOLD) images under the following conditions: repetition time, 2300 ms; echo time, 30 ms; flip angle, 81; number of slices, 40; slice thickness, 3.5 mm; acquisition matrix, 64 × 64; and voxel size, 3 × 3 × 3.5. The scan lasted approximately 8 min. All participants were instructed to focus continuously on the crosshairs projected onto the screen during resting-state fMRI. Image preprocessing and statistical analysis of structural data To pre-process T1-weighted images and calculate the GMV across the whole brain, voxel-based morphometry was performed using Statistical Parametric Mapping 12 software (SPM; Wellcome Department of Cognitive Neurology, London, UK), implemented in MATLAB R2021a. Structural T1-weighted images were first segmented to separate different types of tissues: gray matter, white matter, cerebrospinal fluid, soft tissue, and the skull. The gray matter and white matter images were then spatially normalized using diffeomorphic anatomical registration with exponentiated Lie algebra. To preserve the absolute GMV, modulation was performed on normalized gray matter images by multiplying the Jacobian determinants derived from spatial normalization. Finally, the modulated gray matter images were smoothed with an 8-mm full-width at half-maximum (FWHM) Gaussian kernel. Nonparametric two-sample t -tests were used to compare the high-SJL and low-SJL groups, using the threshold-free cluster enhancement option for SPM with 5,000 permutations and applying the Freedman-Lane method (Freedman and Lane 1983; Winkler et al. 2014). Covariates included the same variables used for comparing the psychological and tryptophan metabolite data between the groups, in addition to the total intracranial volume and handedness. The search space was explicitly restricted by using an additional mask defined as the intersection of a binarized gray matter probability mask (threshold: TPM > 0.2) and the SPM model-estimated analysis mask (0/1 valid-voxel mask), to confine the inference to gray matter voxels valid across all participants and to provide an appropriate multiplicity space for family-wise error (FWE) control. Statistical inference was based on permutation-derived FWE-corrected p -values within this gray matter mask, and voxels were considered significant at p -FWE < 0.05. Image preprocessing and statistical analysis of resting-state FC The CONN toolbox version 18b (https://www.conn-toolbox.org) was used to analyze FC during resting-state fMRI. By using default preprocessing, the possible confounding effects of head motion artifacts, as well as cerebrospinal fluid and BOLD signals, were defined and addressed. For denoising the data, signals from the white matter, cerebrospinal fluid, and motion parameter time series were regressed from the functional data. Data were spatially smoothed with an 8-mm FWHM Gaussian kernel and were band-pass filtered (0.008–0.09 Hz). For motion quality control, two participants in the low-SJL group were excluded from resting-state FC analysis because their mean framewise displacement exceeded 0.3 mm. Resting-state FC analysis was conducted by using seed-to-voxel analysis. Thereafter, the seeds were set with the regions-of-interest (ROIs) obtained from the results of the structural analysis. FC strength was represented by Fisher-transformed bivariate correlation coefficients from a weighted general linear model, estimated separately for each seed area and target voxel, and modeling the association between their BOLD signal time series. Second-level analyses were conducted for the differences in connectivity between low-SJL and high-SJL groups by using the two-sample t -test. Covariates included the same variables employed to examine structural data, except for the total intracranial volume. Statistical thresholds were set at p < 0.001, uncorrected for voxel-wise comparisons, and p < 0.05 FWE-corrected for multiple comparisons at the cluster level. Furthermore, in exploratory mediation analyses, we evaluated whether urinary tryptophan metabolites (i.e., the neurochemical measure showing a group difference) mediated associations between SJL and brain measures selected from the primary group comparisons for GMV and resting-state FC. Mediation for GMV was restricted to the ROI that both showed a significant group difference in GMV and for which seed-based FC exhibited a significant group difference, ensuring a shared anatomical locus across modalities. For statistical analysis details, see Online Resource 1 . Results Distribution of social jetlag and participant characteristics The distribution of SJL across participants is shown in Figure 1 . Using the prespecified cutoff of 1 h, 25 of 89 healthy young individuals (28.1%) were classified as high-SJL (≥ 1 hour) and 64 (71.9%) as low-SJL (< 1 h). Participant characteristics are summarized in Table 1 . Age and education, but not sex ratio and annual household income, differed statistically significantly between groups. Psychological and cognitive measures The psychological and cognitive data are shown in Figures 2 and 3 . For chronotype, the main effect of group in the linear mixed-effects model indicated that the high-SJL group had significantly later midsleep on free days than did the low-SJL group ( Figure 2a : β = 0.82, 95% confidence interval [CI: 0.45, 1.19], R 2 = 0.40, t = 4.37, df = 74.17, p -FDR = 0.00007; Cohen’s d = 1.45, 95%CI [0.78, 2.11]). For fatigue, the main effect of group in the linear mixed-effects model revealed that fatigue scores were significantly higher in the high-SJL than in the low-SJL group ( Figure 2b : β = 0.45, 95%CI [0.05, 0.84], R 2 = 0.38, t = 2.22, df = 83.37, p -FDR = 0.028; Cohen’s d = 0.67, 95%CI [0.07, 1.28]). For TMT performance, the main effect of group was not significant (TMT-A in Figure 3a : β = 0.03, 95%CI [-0.37, 0.45], R 2 = 0.36, t = 0.17, df = 83.60, p -FDR = 0.858; Cohen’s d = 0.05, 95%CI [-0.51, 0.61]; TMT-B in Figure 3b : β = 0.31, 95%CI [-0.07, 0.69], R 2 = 0.43, t = 1.56, df = 82.41, p -FDR = 0.183; Cohen’s d = 0.43, 95%CI [-0.11, 0.98]; TMT-Δ in Figure 3c : β = 0.35, 95%CI [-0.07, 0.78], R 2 = 0.31, t = 1.62, df = 82.48, p -FDR = 0.183; Cohen’s d = 0.44, 95%CI [-0.10, 0.99]). Tryptophan metabolite levels The tryptophan metabolite data are shown in Figure 4 . For tryptophan, the main effect of group in the linear mixed-effects model showed that the high-SJL group had significantly higher levels of tryptophan than did the low-SJL group ( Figure 4a : β = 0.59, 95%CI [0.16, 1.02], R 2 = 0.05, t = 2.72, df = 45.55, p -FDR = 0.018; Cohen’s d = 1.09, 95%CI [0.28, 1.90]). For 5-HIAA, the main effect of group was not significant ( Figure 4b : β = 0.34, 95%CI [-0.15, 0.84], R 2 = 0.05, t = 1.34, df = 83.00, p -FDR = 0.183; Cohen’s d = 0.34, 95%CI [-0.16, 0.86]). Brain measures Brain measures are shown in Figure 5 . Whole-brain voxel-based gray matter analyses revealed significantly smaller GMVs in the left fusiform gyrus, left temporal pole, left superior parietal lobule, right angular gyrus, and right inferior temporal gyrus of the high-SJL group than those in the low-SJL group ( Figure 5a and Online Resource 2 ). We then used ROIs in these brain regions with GMV reductions as the seeds for the FC analyses of resting-state fMRI data. Compared with the low-SJL group, the high-SJL group demonstrated reduced FC between the left temporal pole and right pars triangularis ( Figure 5b and Online Resource 3 ). In contrast, seed-based FC analyses using the other four regions showing GMV reductions as seeds did not reveal any between-group differences. Mediation analyses testing whether tryptophan mediated associations between SJL and temporal pole GMV or temporal pole–pars triangularis FC are reported in Online Resource 4 . Discussion The present study primarily investigated differences in urinary tryptophan and 5-HIAA levels, regional GMV, and seed-based resting-state FC between young individuals with high SJL and those with low SJL. We also examined behavioral outcomes as secondary measures. The main findings revealed that, compared with the low-SJL group, the high-SJL group (i) showed higher urinary tryptophan levels; (ii) had smaller GMV in several brain regions, such as the left fusiform gyrus and left temporal pole; and (iii) exhibited lower resting-state FC between the left temporal pole and right pars triangularis. In contrast, urinary 5-HIAA levels did not differ between the groups. Behaviorally, the high-SJL group also demonstrated later midsleep on free days and greater fatigue than did the low-SJL group, whereas TMT performance did not differ between the groups. Exploratory mediation analyses did not identify significant indirect effects of tryptophan on the temporal pole GMV or its resting-state FC with the pars triangularis (see Online Resource 4 for interpretation). These findings may suggest temporal pole-centered neural alterations associated with higher SJL along with an increase in tryptophan levels in young individuals.​ The behavioral comparisons revealed that the high-SJL group showed a later chronotype (later midsleep on free days) than did the low-SJL group. Consistent with the findings of (Roenneberg et al. 2019 ), later chronotypes tend to accrue more weekday sleep debt and compensate by delaying and extending weekend sleep, which amplifies their SJL. Moreover, we observed greater fatigue in the high-SJL group, in line with a previous study reporting an association between SJL and fatigue in Japanese adolescents, even after adjusting for sleep duration (Tamura et al. 2022 ). Considering that later weekend wake times can delay the circadian phase and increase SJL (Taylor et al. 2008 ), young individuals with greater SJL may experience more persistent fatigue, potentially via accumulated weekday sleep debt that is associated with such a phase delay. Furthermore, consistent with the later chronotype and greater fatigue, the high-SJL group showed elevated urinary tryptophan levels compared with the low-SJL group. Tryptophan, along with serotonin, has been implicated in sleep–circadian regulation and central fatigue (Miyamoto et al. 2012 ; Yamashita 2020 ). A previous study reported that, compared with young adults who received an oral placebo, those who received oral tryptophan had higher plasma levels of free tryptophan and increased sleepiness and lethargy (Morgan et al. 2007 ), suggesting that greater tryptophan availability may influence arousal regulation and sleep–wake timing. Consistently, in rats with sleep–wake cycle anomalies, tryptophan elevation and serotonin depletion in the brain were associated with induction of central fatigue-like states, accompanied by reduced social interaction and inattention (Yamashita and Yamamoto 2014 ; 2017 ). Accordingly, the present findings provide the novel evidence connecting such tryptophan-focused mechanistic research on sleep and circadian disruption with naturally occurring social misalignment during development. In addition, a previous study showed that individuals who took tryptophan capsules displayed more quarrelsome and less agreeable behavior at home than did those who took placebo capsules (Hogenelst et al. 2015 ), suggesting that greater tryptophan availability may contribute to altered interpersonal behavior with close others. Moreover, Williams et al. ( 2007 ) reported that experimental manipulation of tryptophan availability altered neural activity in socioemotional processing-related regions, such as the temporal pole, superior temporal sulcus, and amygdala, during facial emotion processing tasks. Against this background, the present findings suggest the possibility that in youth, SJL of ≥ 1 h may be accompanied by altered tryptophan metabolism, which affects socioemotional functioning in addition to sleep and circadian regulation. Despite a clear elevation in urinary tryptophan in the high-SJL group, urinary 5-HIAA levels were comparable between the groups, suggesting precursor elevation without detectable changes in downstream serotonergic turnover. This discrepancy might be explained by the involvement of another metabolic pathway: approximately 95% of tryptophan is metabolized via the kynurenine pathway (Yamashita 2020 ). Among the various metabolites synthesized in this pathway, kynurenic acid, an antagonist of N-methyl-D-aspartate (NMDA) and α7-nicotinic acetylcholine (nACh) receptors, was elevated in the hypothalamus of rats with sleep–wake cycle anomalies (Yamashita and Yamamoto 2014 ; 2017 ). NMDA and nACh receptors, which modulate glutamate and acetylcholine release at synapses, respectively, have been implicated in sleep–wake regulation (Brown et al. 2012 ; Dash et al. 2009 ). Furthermore, in rats given intraperitoneal kynurenine, an increase in kynurenic acid levels in the brain was associated with a reduced duration of rapid eye movement sleep and increased wake time as measured using polysomnography (Pocivavsek et al. 2017 ). This suggests a crucial role for brain kynurenic acid alterations in sleep–wake architecture. We propose that young individuals with a greater SJL may show a tendency toward increased routing of tryptophan to the kynurenine pathway, which might influence glutamatergic and cholinergic signaling, which, in turn, may help explain neural alterations associated with social–circadian misalignment. Beyond these molecular findings suggesting involvement of both sleep–circadian regulation and socioemotional functioning, several brain regions showed smaller GMV in the high-SJL than in the low-SJL group. Among these regions, the temporal pole is noteworthy because it also showed reduced resting-state FC with the right pars triangularis. These findings suggest that SJL possibly induces neural alterations in two key ways. First, we demonstrated reduced temporal pole GMV in young individuals with higher SJL, indicating a potential structural disadvantage relative to those with lower SJL. This was consistent with previous findings of a smaller temporal pole GMV in adults with sleep abnormalities (i.e., poor sleep quality, sleep–wake cycle disruption, and idiopathic rapid eye movement sleep behavior disorder) (Amorim et al. 2018 ; Sun et al. 2020 ; Wang et al. 2025 ). Second, childhood and adolescence are periods during which large-scale networks are sensitive to sleep–circadian factors (Anastasiades et al. 2022 ), suggesting that frontotemporal connectivity may be sensitive to sleep–circadian pressures. This supports the hypothesis that social–circadian misalignment could contribute to reduced temporal pole–pars triangularis connectivity. Our findings suggest that SJL of ≥ 1 h is associated with temporal pole-centered structural and functional vulnerabilities in youth. The temporal pole is a paralimbic hub for socioemotional processing, including person-specific knowledge, affective meaning, and mental state attribution (Herlin et al. 2021 ; Olson et al. 2007 ; Tsukiura et al. 2002 ; Völlm et al. 2006 ). Benetti et al. ( 2010 ) reported an association between attachment-related anxiety and reduced temporal pole GMV in healthy adults. This finding suggests that structural reduction in this region may lead to diminished emotional and social engagement. Moreover, childhood and adolescence are marked by heightened emotional instability (Bailen et al. 2018 ), suggesting that this instability may be related to abnormalities in the temporal pole. Accordingly, reduced temporal pole GMV in young individuals with higher SJL may reflect greater vulnerability in terms of socioemotional processing during this stage. In addition to its socioemotional roles, the temporal pole is connected to the inferior frontal gyrus (i.e., pars triangularis) via long frontotemporal association fibers (i.e., the uncinate fasciculus and extreme capsule fiber system) (Schmahmann and Pandya 2006 ), which support language processing (Chen et al. 2024 ; Murphy et al. 2022 ; Rolls et al. 2022 ). Rolls et al. ( 2022 ) reported that, among brain networks involved in language, coupling between the pars triangularis and temporal pole is associated with speech production and syntactic comprehension. Moreover, a previous study reported that, during a verbal fluency task, individuals with insomnia showed increased FC between the pars triangularis and superior temporal gyrus (i.e., the temporal pole) (Chen et al. 2024 ), suggesting that sleep problems may alter phonological processing speed. Taken together, these findings support the possibility that SJL of ≥ 1 h may worsen speech production and phonological processing by reducing FC between the temporal pole and pars triangularis in young individuals. This neural diminishment within the frontotemporal language network may contribute to worsened language ability over time. Outside the temporal pole, the high-SJL group also showed smaller GMV in the left fusiform gyrus, left superior parietal lobule, right angular gyrus, and right inferior temporal gyrus than that in the low-SJL group. The fusiform and inferior temporal gyri support visual processing (i.e., perception of faces and color) and visuospatial imagery (Spagna et al. 2024 ). The superior parietal lobule is implicated in voluntary attention and top-down processes (i.e., retrieval search and monitoring), whereas the angular gyrus is involved in recollection-related orienting and the mnemonic content recovery (Cabeza et al. 2008 ; Vilberg and Rugg 2008 ). These findings may point to localized vulnerability within the inferior temporal and parietal circuits subserving high-order cognitive processes, beyond the socioemotional and language domains. Finally, although a greater SJL affected tryptophan levels and brain function, it did not affect TMT performance. The failure to detect a significant effect of SJL on the TMT may be explained by the neural profile in our participants with higher SJL. While our participants exhibited temporal pole-centered structural and functional vulnerabilities associated with socioemotional and language processing, TMT performance primarily depends on frontoparietal executive networks that support attention and processing speed (Oswald et al. 2022 ; Shirdel et al. 2025 ). Thus, SJL-related neural alterations in temporal pole-centered structure and function may have had only a limited effect on TMT performance. Another possible reason is the developmental trajectory of TMT performance in school-age children. Vakil et al. ( 2008 ) reported that TMT performance improves markedly with age, suggesting that age-related developmental change has a strong influence on individual differences in TMT performance. In our cross-sectional sample, which had a wide age range, this developmental gradient may have attenuated observable SJL-related differences in TMT performance, even though age and educational level were statistically controlled. Our study had some limitations. First, the cross-sectional design precluded causal inference: the disadvantages observed in healthy young individuals with higher SJL cannot be attributed to prolonged SJL exposure. Second, cognitive assessment was limited to the TMT. Future research should investigate the cognitive effects of high SJL by using tasks other than the TMT, such as a verbal fluency task. Third, socioemotional behavior was not measured (i.e., facial emotion evaluation tasks). Future research should evaluate whether the observed neural changes translate into socioemotional behavioral outcomes. Conclusion This study revealed evidence of neurobiological alterations in healthy young individuals with greater SJL. Young individuals with higher SJL exhibited increased urinary tryptophan levels, decreased left temporal pole GMV, and reduced resting-state FC between the left temporal pole and the right pars triangularis than did those with lower SJL, suggesting that the former group had diminished intrinsic regional GMV and network integration related to socioemotional and language processes. In contrast, urinary 5-HIAA levels did not differ between the groups, indicating that they had no detectable increase in downstream serotonergic turnover. This pattern suggests that increased tryptophan levels may be preferentially metabolized into the kynurenine pathway under conditions of greater SJL. Taken together, SJL of ≥ 1 h is related to structural and functional vulnerabilities in the neural system involving the temporal pole, along with tryptophan elevation. These findings provide new insights into the mechanisms by which SJL may contribute to socioemotional and language-related neural vulnerability during childhood and adolescence. Declarations Compliance with Ethical Standard Conflict of Interest The authors have no relevant financial or non-financial interests to disclose. Ethics Approval The Research Ethics Committee of the University of Fukui approved the study protocol (Assurance No. FU-20220061). The procedures complied with the principles of the Declaration of Helsinki. Consent to Participate All parents provided written informed consent, and all children provided assent. Data Availability The data will be made available via the Child Developmental MRI (CDM) project database, which is currently under construction. Additionally, data will be provided upon signing a data sharing agreement and after receiving a brief research proposal along with evidence of approval from the requester’s institutional review board. Authors’ Contributions Masatoshi Yamashita: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. Qiulu Shou: Investigation, Writing – review & editing. Masanori Fujieda: Investigation, Writing – review & editing. Hidehiko Okazawa: Resources, Writing – review & editing. Yoshiyuki Hirano: Resources, Writing – review & editing. Kuriko Kagitani-Shimono: Resources, Writing – review & editing. Yoshifumi Mizuno: Funding acquisition, Resources, Supervision, Writing – review & editing. Acknowledgments We are grateful to Tomoe Morita and Manami Onogi for supporting us with the experiments. This study was conducted using the PET/MRI scanner and related facilities of the Biomedical Imaging Research Center, University of Fukui. This work was supported by a Grant-in-Aid for Scientific Research (KAKENHI) from the Japan Society for the Promotion of Science (grant number 23K12814 to Masatoshi Yamashita), a grant from Taiju Life Social Welfare Foundation (award years 2023 and 2025 to Masatoshi Yamashita), Life Science Innovation Center (grant number LSI24101 to Masatoshi Yamashita), the Kawano Masanori Memorial Public Interest Incorporated Foundation for Promotion of Pediatrics (award year 2022 to Yoshifumi Mizuno), the Mother and Child Health Foundation (award year 2024 to Yoshifumi Mizuno), and Research Grants from the University of Fukui (academic year 2024 to Yoshifumi Mizuno). 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Demographics of the high-SJL group and low-SJL group High-SJL (n = 25) Low-SJL (n = 64) p value Sex (n) Male Female 11 (44.00) 14 (56.00) 31 (48.43) 33 (51.56) 0.888 Age (years) 12.56 (3.13) 10.25 (2.97) 0.0006 Education (years) 7.76 (3.06) 5.75 (2.96) 0.002 Income (JPY, n) < 3 000 000 3 000 000—5 000 000 5 000 000—7 000 000 ≥7 000 000 1 (4.00) 5 (20.00) 9 (36.00) 10 (40.00) 5 (7.81) 4 (6.25) 14 (21.85) 41 (64.06) 0.163 Weekly sleep duration (h) 7.96 (1.04) 8.24 (1.11) 0.276 Parameters are indicated as the mean ( SD ) or n (%). P -values for age, education, and income are from t -tests for the comparison of high-SJL group and low-SJL group. P -values for sex ratio is from chi-square tests for the comparison of high-SJL group with low-SJL group. JPY, Japanese yen; SJL, social jetlag. Additional Declarations No competing interests reported. Supplementary Files ESM2.docx ESM1.docx ESM3.docx ESM4.docx floatimage1.png Graphical Abstract Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 27 Dec, 2025 Editor assigned by journal 26 Dec, 2025 Submission checks completed at journal 24 Dec, 2025 First submitted to journal 23 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8431510","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593823069,"identity":"d1eba901-48ad-44cf-8226-165807e23eab","order_by":0,"name":"Masatoshi Yamashita","email":"data:image/png;base64,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","orcid":"","institution":"University of Fukui","correspondingAuthor":true,"prefix":"","firstName":"Masatoshi","middleName":"","lastName":"Yamashita","suffix":""},{"id":593823072,"identity":"2554ffcf-bfda-4e20-b5c4-af32fe2b4d65","order_by":1,"name":"Qiulu Shou","email":"","orcid":"","institution":"University of Fukui","correspondingAuthor":false,"prefix":"","firstName":"Qiulu","middleName":"","lastName":"Shou","suffix":""},{"id":593823073,"identity":"b0f0d979-9515-4037-9574-fe54bfd09e05","order_by":2,"name":"Masanori Fujieda","email":"","orcid":"","institution":"University of Fukui","correspondingAuthor":false,"prefix":"","firstName":"Masanori","middleName":"","lastName":"Fujieda","suffix":""},{"id":593823074,"identity":"1544167c-07cc-4dc9-a081-7906680f1af4","order_by":3,"name":"Hidehiko Okazawa","email":"","orcid":"","institution":"University of Fukui","correspondingAuthor":false,"prefix":"","firstName":"Hidehiko","middleName":"","lastName":"Okazawa","suffix":""},{"id":593823075,"identity":"30695410-8aef-49c7-aedf-b761060e03bb","order_by":4,"name":"Yoshiyuki Hirano","email":"","orcid":"","institution":"Chiba University","correspondingAuthor":false,"prefix":"","firstName":"Yoshiyuki","middleName":"","lastName":"Hirano","suffix":""},{"id":593823077,"identity":"a0c43c85-0efe-47ec-9e27-e9895e6b6de4","order_by":5,"name":"Kuriko Kagitani-Shimono","email":"","orcid":"","institution":"United Graduate School of Child Development, The University of Osaka, Kanazawa University, Hamamatsu University of Medicine, Chiba University, and University of Fukui","correspondingAuthor":false,"prefix":"","firstName":"Kuriko","middleName":"","lastName":"Kagitani-Shimono","suffix":""},{"id":593823080,"identity":"c6b3e616-b9c4-42c4-bb2f-f518cabac658","order_by":6,"name":"Yoshifumi Mizuno","email":"","orcid":"","institution":"University of Fukui","correspondingAuthor":false,"prefix":"","firstName":"Yoshifumi","middleName":"","lastName":"Mizuno","suffix":""}],"badges":[],"createdAt":"2025-12-23 07:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8431510/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8431510/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103073816,"identity":"a986c517-7fa5-4693-9d35-f9905c68e033","added_by":"auto","created_at":"2026-02-20 12:56:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44340,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of social jetlag.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8431510/v1/f82e4e5304db88c439ca9b99.png"},{"id":103073792,"identity":"57e10e1e-8a74-47af-9362-563deb6e2766","added_by":"auto","created_at":"2026-02-20 12:56:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79451,"visible":true,"origin":"","legend":"\u003cp\u003eChronotype and fatigue results. (a) The high-SJL group showed a later midpoint of sleep than that of the low-SJL group. (b) The high-SJL group exhibited higher fatigue scores than did the low-SJL group. * \u003cem\u003ep\u003c/em\u003e-FDR \u0026lt; 0.05, *** \u003cem\u003ep\u003c/em\u003e-FDR \u0026lt; 0.001. SJL, social jetlag.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8431510/v1/51c374ed2eea78837e406f62.png"},{"id":103073794,"identity":"4f31142a-c30b-4bc1-a8c7-f0799629e16e","added_by":"auto","created_at":"2026-02-20 12:56:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122775,"visible":true,"origin":"","legend":"\u003cp\u003eTMT results. (a, b, and c) No significant group differences were observed in TMT performances. TMT, Trail Making Test.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8431510/v1/6b411b775787ff75c62123c0.png"},{"id":103073804,"identity":"e3d9e1d1-ab1e-4376-adee-f89f952c898d","added_by":"auto","created_at":"2026-02-20 12:56:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":81601,"visible":true,"origin":"","legend":"\u003cp\u003eTryptophan metabolite results. (a) Tryptophan levels were higher in the high-SJL than in the low-SJL group. (b) No significant group difference was observed in urinary 5-HIAA levels. * \u003cem\u003ep\u003c/em\u003e-FDR \u0026lt; 0.05. 5-HIAA, 5-hydroxyindoleacetic acid; SJL, social jetlag.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8431510/v1/256ab081497591100e7d3a18.png"},{"id":103073828,"identity":"38b6290c-0c3e-4e86-a344-b2a4270f2a13","added_by":"auto","created_at":"2026-02-20 12:56:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":621433,"visible":true,"origin":"","legend":"\u003cp\u003eRegional gray matter volume and resting-state functional connectivity results. (a) The high-SJL group showed smaller GMVs in the left fusiform gyrus, left temporal pole, left superior parietal lobule, right angular gyrus, and right inferior temporal gyrus than those in the low-SJL group. (b) Using ROIs in the left temporal pole as the seed region (where the GMV was smaller in the high-SJL than in the low-SJL group), the high-SJL group exhibited reduced FC between the left temporal pole and right parts triangularis during the resting-state task compared with the low-SJL group. AnG, angular gyrus; Fug, fusiform gyrus; FC, functional connectivity; GMV, gray matter volume; ITG, inferior temporal gyrus; ROI, region-of-interest; SJL, social jetlag; SPL, superior parietal lobule. TP, temporal pole; TrIFG, triangular part of the inferior frontal gyrus (i.e., the pars triangularis).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8431510/v1/6dd83759df5bea9300cbdcb0.png"},{"id":103073869,"identity":"344bac69-83e7-440d-91cf-873771848bd5","added_by":"auto","created_at":"2026-02-20 12:56:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1951790,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8431510/v1/ce55783f-83b1-4ecc-aefb-dde4ff7ae328.pdf"},{"id":103073829,"identity":"ddbc23c9-34a1-4782-8d09-d1d39faf360c","added_by":"auto","created_at":"2026-02-20 12:56:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16814,"visible":true,"origin":"","legend":"","description":"","filename":"ESM2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8431510/v1/2bc0a7526e538ea718082a85.docx"},{"id":103073785,"identity":"9cb5789c-1601-4c9b-a851-677b44b1ef22","added_by":"auto","created_at":"2026-02-20 12:56:15","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25001,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8431510/v1/4ee3d61ea301e69bbd480433.docx"},{"id":103073793,"identity":"0bf9febd-c29f-49a6-b913-6fa1616e604d","added_by":"auto","created_at":"2026-02-20 12:56:16","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":25639,"visible":true,"origin":"","legend":"","description":"","filename":"ESM3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8431510/v1/8e04c0cac445e249da8f4984.docx"},{"id":103073776,"identity":"181180e4-974e-49ef-b5a8-ee89548e4f24","added_by":"auto","created_at":"2026-02-20 12:56:13","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":134180,"visible":true,"origin":"","legend":"","description":"","filename":"ESM4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8431510/v1/847da9acc2540cbc76aaf7c1.docx"},{"id":103073795,"identity":"e8c0a434-f6d6-498c-a134-241a10d9aa71","added_by":"auto","created_at":"2026-02-20 12:56:17","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":396881,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8431510/v1/05171bfc0c6644aeb56913f0.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"High social jetlag in healthy youth: Differences in tryptophan levels, temporal pole-centered gray matter volume, and functional connectivity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe biological clock is an internal timing system that generates 24-h cycles and aligns with environmental signals on a daily basis (Zou et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Conflicts of biological time with socially constrained schedules lead to social\u0026ndash;circadian misalignment, known as social jetlag (SJL) (Roenneberg et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). SJL has also been measured as the discrepancy in midsleep time between weekdays and weekends (Roenneberg et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Roenneberg et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) reported that nearly two-thirds of European adults experience SJL of \u0026ge;\u0026thinsp;1 h. In addition, previous studies have indicated associations of SJL with adverse physiological outcomes (i.e., menstrual abnormalities) and with an increased risk of psychiatric disorders (i.e., depression) in young adults and workers (Islam et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Komada et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Meram et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Min et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, a recent study of 6,890 adolescents (mean age 11.95 years) revealed that greater SJL predicted worse cognitive and academic performance (Li et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings suggest that SJL plays an important role in maintaining mental health and cognitive function. Given the increasing concern about misaligned social and circadian schedules across all ages, identifying the neurobiological mechanisms (i.e., neurochemical and neural bases) underlying SJL at an early stage of development is important.\u003c/p\u003e \u003cp\u003eGenerally speaking, SJL is a form of circadian disruption, and thus, may also influence the neurochemical mechanisms regulating the sleep\u0026ndash;wake cycle. One such mechanism involves tryptophan, an essential amino acid known for its role as a biomarker of sleep regulation and central fatigue (Yamashita \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For example, Yamashita and Yamamoto (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported that, compared with control rats, rats with sleep\u0026ndash;wake cycle abnormalities showed elevation of tryptophan levels and depletion of its metabolite, serotonin, in the brain. These changes were associated with central fatigue induction. Moreover, serotonin depletion disrupts the circadian timing by desynchronizing neuronal activity in sleep\u0026ndash;wake regulatory centers (i.e., the suprachiasmatic nucleus) (Miyamoto et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These findings indicate that tryptophan\u0026ndash;serotonin pathways are sensitive to sleep and circadian rhythm disruption. Beyond its role as a biomarker of sleep\u0026ndash;wake regulation and central fatigue, the tryptophan\u0026ndash;serotonin pathway has also been implicated in socioemotional and cognitive functions, such as prosocial behavior, psychomotor activity, and executive function (Hogenelst et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Williams et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Yamashita and Yamamoto \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, whether higher SJL is accompanied by changes in the endogenous tryptophan\u0026ndash;serotonin metabolism in young individuals remains unclear.\u003c/p\u003e \u003cp\u003eSJL has also been found to affect brain activity. A previous study reported that young adults with SJL of \u0026ge;\u0026thinsp;1 h show alterations in spontaneous low-frequency neural activity in several brain regions compared with those without SJL during resting-state functional magnetic resonance imaging (fMRI) (Nechifor et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In adolescence (ages approximately 11\u0026ndash;13 years), Yang et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), using the Adolescent Brain Cognitive Development Study data, found that a greater SJL was associated with alterations in resting-state functional connectivity (FC) between widespread cortical networks and subcortical regions. Given the broad, population-oriented design of the Adolescent Brain Cognitive Development Study, the cohort necessarily included diagnostically heterogeneous participants (i.e., those with neurodevelopmental and psychiatric disorders), which may dilute associations that are SJL-specific. However, the structural brain underpinnings of SJL in healthy young individuals have not been examined directly, and the relationship between any regionally specific gray matter volume (GMV) differences and resting-state FC alterations remains unclear. Accordingly, if structural loci differ according to SJL status, investigating FC using these loci as seed regions may help to clarify whether such differences extend to their functional coupling.\u003c/p\u003e \u003cp\u003eCollectively, evidence to date has indicated that SJL can alter mental health, cognitive function, and brain activity. However, most previous studies on this topic have focused on adults and working individuals, whereas the neurobiological mechanisms in healthy young individuals have received scant attention. Given that childhood and adolescence are sensitive periods for circadian consolidation and brain network reorganization (Hagmann et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Roenneberg et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Uhlhaas et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), clarifying whether SJL is accompanied by molecular, structural, and functional differences during this developmental period is important.\u003c/p\u003e \u003cp\u003eThis cross-sectional study addressed two primary research questions in healthy young individuals (aged 6\u0026ndash;17 years). First, do individuals with high SJL (\u0026ge;\u0026thinsp;1 h) differ from those with low SJL (\u0026lt;\u0026thinsp;1 h) in urinary tryptophan and 5-hydroxyindoleacetic acid (5-HIAA), an index of serotonergic turnover? Second, do individuals with high SJL show alterations in brain structure compared with those with low SJL, and, if so, do these loci also exhibit region-specific resting-state FC differences when used as seed regions? To address these questions, we compared urinary tryptophan and 5-HIAA levels, brain structure, and seed-based resting-state FC between individuals with high and low SJL. In addition, we compared behavioral measures, including fatigue, chronotype, and Trail Making Test (TMT) performance. We hypothesized that young individuals with high SJL would show higher tryptophan and lower 5-HIAA levels, consistent with findings from a rat model of sleep\u0026ndash;wake cycle anomalies. We further surmised that high SJL would be associated with reduced GMV in one or more regions and with reduced seed-based resting-state FC in those regions. Given growing concerns about sleep-related social and psychiatric outcomes, clarifying the impact of SJL in young individuals may inform developmental models of brain and mental health and support the development of child-focused sleep and circadian guidelines.​\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Research Ethics Committee of the University of Fukui approved the study protocol (Assurance No. FU-20220061) and the procedures of the study complied with the principles of the Declaration of Helsinki. All parents provided written informed consent, and all children provided assent. The study involved 92 healthy young individuals, aged 6\u0026ndash;17 years. Such individuals who did not receive special-support education were recruited from the local community. None of the participants had any history of neurological, cardiovascular, or psychiatric illness and none had contraindications for MRI. Data from 3 individuals were excluded from the analysis because of claustrophobia that impeded the MRI scan. Thus, data from 89 healthy young individuals were used in the final analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePsychological questionnaires\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Munich Chronotype Questionnaire (Roenneberg et al. 2003) was used to assess the participants\u0026rsquo; SJL and chronotype. This standardized self-rating scale assesses an individual\u0026rsquo;s habitual sleep\u0026ndash;wake timing on work/school days and on free days. The variables consisted of (1) sleep start (bedtime and sleep onset latency), (2) sleep end (wake-up time), (3) alarm clock usage, and (4) sleep duration (total amount of time between sleep start and sleep end). Additionally, the midpoint of sleep on free days was calculated as the midpoint between sleep onset and wake-up time. However, most individuals accumulate sleep debt during the school day and extend their sleep time on free days. Therefore, the corrected midpoint of sleep on free days, known as the chronotype index, was calculated as the midpoint of sleep on free days minus a correction for sleep debt equal to half the difference between sleep duration on free days and average sleep duration over the week. In addition, SJL was defined as the absolute difference between the midpoint of sleep on school days and the midpoint of sleep on free days. Larger values indicated greater misalignment between biological and social sleep timing. For group comparisons, we defined high-SJL (\u0026ge; 1 h) and low-SJL (\u0026lt; 1 h) groups, as previously reported (Hunt et al. 2025; Roenneberg et al. 2012).\u003c/p\u003e\n\u003cp\u003eThe Japanese version of the Chalder Fatigue Scale (Tanaka et al. 2008) was used to assess individuals\u0026rsquo; fatigue. This scale consists of 11 items that assess symptoms of mental and physical fatigue. Participants were asked to respond to questions about tiredness, rest, feeling sleepy, motivation, energy, muscular strength, weakness, concentration, and speech by using a 4-point scale: \u0026ldquo;less than usual,\u0026rdquo; \u0026ldquo;much more than usual,\u0026rdquo; \u0026ldquo;better than usual,\u0026rdquo; and \u0026ldquo;much worse than usual.\u0026rdquo;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCognitive measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Japanese version of parts A and B of the TMT (Reitan and Wolfson 1985) were applied to evaluate executive function. In the TMT-A, participants were asked to draw lines, sequentially connecting 25 numbers in ascending order. In the TMT-B, participants were asked to draw lines alternately between numbers and letters (1, あ, 2, い, etc.). The time to complete each task was measured. Furthermore, we assessed TMT-\u0026Delta;, calculated as the time to complete part B minus that to complete part A, which is considered an index of cognitive flexibility and set-shifting (Bowie and Harvey 2006).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of tryptophan metabolite\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn the day before testing, the participants were instructed to refrain from intense physical activity and from consuming certain foods and beverages that can be difficult to digest (i.e., alcohol, coffee, high-fat fish, red beef, and blue cheese) for 24 h (Yamashita and Yamamoto 2021). On the test day, fresh urine samples were collected after a 30-min rest. Urine samples were diluted with 6.7 mM hydrochloric acid and 2.5% perchloric acid to separate albumin, according to a previously reported method (Yamashita and Yamamoto 2021). The obtained supernatants were stored at -80\u0026deg;C until analysis using high-performance liquid chromatography (HPLC). Tryptophan and 5-HIAA content were measured using HPLC with an electrochemical detector (Nanospace SI-2 3005, Osaka Soda Corporation, Osaka, Japan) and a chromate recorder (C-R8A; Shimadzu Corporation, Kyoto, Japan). Standards for these compounds were obtained from Sigma-Aldrich Inc. (St Louis, MO, USA). The mobile phase consisted of 15% methanol in a solution (pH 4.13) containing 30 mM citric acid, 10 mM disodium hydrogen phosphate, 0.5 mM sodium octyl sulfate, 50 mM sodium chloride, and 0.05 mM ethylenediaminetetraacetic acid, as previously reported (Yamashita and Yamamoto 2014; 2017; 2021). The solution was passed through a 5-\u0026mu;M C\u003csub\u003e18\u003c/sub\u003e column (150 mm \u0026times; 4.6 mm; TSK gel, ODS-80TM, Tosoh, Tokyo, Japan) at a flow rate of 0.7 mL/min, using a single pump (Nanospace SI-2 3101; Osaka soda Corporation, Osaka, Japan) at a column oven (Nanospace SI-I 2004; Osaka soda Corporation, Osaka, Japan) temperature of 25\u0026deg;C. Tryptophan was detected electrochemically at 800 mV, with a retention time of approximately 16 min. 5-HIAA was detected electrochemically at 700 mV, with a retention time of approximately 12 min.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis of demographic, psychological, cognitive, and tryptophan metabolite data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses of demographic, psychological, cognitive, and tryptophan metabolite data were conducted using R (version 4.3.0; The R Foundation for Statistical Computing, Vienna, Austria). All data were initially assessed for normality and homogeneity of variance. Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-tests, Welch\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-tests, Chi-square tests, and Fisher\u0026rsquo;s exact test were applied as appropriate.\u003c/p\u003e\n\u003cp\u003eThe group effect on chronotype, fatigue, TMT-A and B performance, and urinary tryptophan metabolite levels were analyzed using a linear mixed-effects model (R packages \u0026ldquo;lmerTest,\u0026rdquo; \u0026ldquo;MuMIn,\u0026rdquo; and \u0026ldquo;jtools\u0026rdquo;). The categorical variable of group (high-SJL and low-SJL) was modeled as fixed effects. To account for non-independence of observations within-families (i.e., among siblings), family ID was modeled as a random intercept. Covariates included demographic variables that showed significant differences between the high- and low-SJL groups, in addition to weekly sleep duration as previously reported (Tamura et al. 2022; Yamashita et al. 2024). For all analyses, the statistical threshold was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, false discovery rate (FDR)-corrected by using the Benjamini\u0026ndash;Hochberg method.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBrain scanning was performed using a 3T GE signa PET/MR scanner (General Electric Healthcare, Chicago, IL, USA). Participants\u0026rsquo; heads were immobilized using a head-coil scanner (eight channels). High-resolution structural images were acquired using an axial T1-weighted magnetization-prepared rapid gradient-echo pulse sequence (repetition time = 8.5 ms; echo time = 3.2 ms; field-of-view = 256 \u0026times; 256; matrix size = 256 \u0026times; 256; voxel size = 1 \u0026times; 1 \u0026times; 1 mm; 176 slices). Moreover, a gradient echo-planar imaging sequence was used to obtain resting-state functional blood oxygen level-dependent (BOLD) images under the following conditions: repetition time, 2300 ms; echo time, 30 ms; flip angle, 81; number of slices, 40; slice thickness, 3.5 mm; acquisition matrix, 64 \u0026times; 64; and voxel size, 3 \u0026times; 3 \u0026times; 3.5. The scan lasted approximately 8 min. All participants were instructed to focus continuously on the crosshairs projected onto the screen during resting-state fMRI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage preprocessing and statistical analysis of structural data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo pre-process T1-weighted images and calculate the GMV across the whole brain, voxel-based morphometry was performed using Statistical Parametric Mapping 12 software (SPM; Wellcome Department of Cognitive Neurology, London, UK), implemented in MATLAB R2021a. Structural T1-weighted images were first segmented to separate different types of tissues: gray matter, white matter, cerebrospinal fluid, soft tissue, and the skull. The gray matter and white matter images were then spatially normalized using diffeomorphic anatomical registration with exponentiated Lie algebra. To preserve the absolute GMV, modulation was performed on normalized gray matter images by multiplying the Jacobian determinants derived from spatial normalization. Finally, the modulated gray matter images were smoothed with an 8-mm full-width at half-maximum (FWHM) Gaussian kernel.\u003c/p\u003e\n\u003cp\u003eNonparametric two-sample \u003cem\u003et\u003c/em\u003e-tests were used to compare the high-SJL and low-SJL groups, using the threshold-free cluster enhancement option for SPM with 5,000 permutations and applying the Freedman-Lane method (Freedman and Lane 1983; Winkler et al. 2014). Covariates included the same variables used for comparing the psychological and tryptophan metabolite data between the groups, in addition to the total intracranial volume and handedness. The search space was explicitly restricted by using an additional mask defined as the intersection of a binarized gray matter probability mask (threshold: TPM \u0026gt; 0.2) and the SPM model-estimated analysis mask (0/1 valid-voxel mask), to confine the inference to gray matter voxels valid across all participants and to provide an appropriate multiplicity space for family-wise error (FWE) control. Statistical inference was based on permutation-derived FWE-corrected \u003cem\u003ep\u003c/em\u003e-values within this gray matter mask, and voxels were considered significant at \u003cem\u003ep\u003c/em\u003e-FWE \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage preprocessing and statistical analysis of resting-state FC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CONN toolbox version 18b (https://www.conn-toolbox.org) was used to analyze FC during resting-state fMRI. By using default preprocessing, the possible confounding effects of head motion artifacts, as well as cerebrospinal fluid and BOLD signals, were defined and addressed. For denoising the data, signals from the white matter, cerebrospinal fluid, and motion parameter time series were regressed from the functional data. Data were spatially smoothed with an 8-mm FWHM Gaussian kernel and were band-pass filtered (0.008\u0026ndash;0.09 Hz). For motion quality control, two participants in the low-SJL group were excluded from resting-state FC analysis because their mean framewise displacement exceeded 0.3 mm.\u003c/p\u003e\n\u003cp\u003eResting-state FC analysis was conducted by using seed-to-voxel analysis. Thereafter, the seeds were set with the regions-of-interest (ROIs) obtained from the results of the structural analysis. FC strength was represented by Fisher-transformed bivariate correlation coefficients from a weighted general linear model, estimated separately for each seed area and target voxel, and modeling the association between their BOLD signal time series. Second-level analyses were conducted for the differences in connectivity between low-SJL and high-SJL groups by using the two-sample \u003cem\u003et\u003c/em\u003e-test. Covariates included the same variables employed to examine structural data, except for the total intracranial volume. Statistical thresholds were set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, uncorrected for voxel-wise comparisons, and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 FWE-corrected for multiple comparisons at the cluster level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, in exploratory mediation analyses, we evaluated whether urinary tryptophan metabolites (i.e., the neurochemical measure showing a group difference) mediated associations between SJL and brain measures selected from the primary group comparisons for GMV and resting-state FC. Mediation for GMV was restricted to the ROI that both showed a significant group difference in GMV and for which seed-based FC exhibited a significant group difference, ensuring a shared anatomical locus across modalities. For statistical analysis details, see \u003cstrong\u003eOnline Resource 1\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDistribution of social jetlag and participant characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distribution of SJL across participants is shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e. Using the prespecified cutoff of 1 h, 25 of 89 healthy young individuals (28.1%) were classified as high-SJL (\u0026ge; 1 hour) and 64 (71.9%) as low-SJL (\u0026lt; 1 h). Participant characteristics are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e. Age and education, but not sex ratio and annual household income, differed statistically significantly between groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePsychological and cognitive measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe psychological and cognitive data are shown in \u003cstrong\u003eFigures 2\u003c/strong\u003e and \u003cstrong\u003e3\u003c/strong\u003e. For chronotype, the main effect of group in the linear mixed-effects model indicated that the high-SJL group had significantly later midsleep on free days than did the low-SJL group (\u003cstrong\u003eFigure 2a\u003c/strong\u003e: \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.82, 95% confidence interval [CI: 0.45, 1.19], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.40, \u003cem\u003et\u003c/em\u003e = 4.37, \u003cem\u003edf\u003c/em\u003e = 74.17, \u003cem\u003ep\u003c/em\u003e-FDR = 0.00007; Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 1.45, 95%CI [0.78, 2.11]). For fatigue, the main effect of group in the linear mixed-effects model revealed that fatigue scores were significantly higher in the high-SJL than in the low-SJL group (\u003cstrong\u003eFigure 2b\u003c/strong\u003e: \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.45, 95%CI [0.05, 0.84], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.38, \u003cem\u003et\u003c/em\u003e = 2.22, \u003cem\u003edf\u003c/em\u003e = 83.37, \u003cem\u003ep\u003c/em\u003e-FDR = 0.028; Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.67, 95%CI [0.07, 1.28]). For TMT performance, the main effect of group was not significant (TMT-A in \u003cstrong\u003eFigure 3a\u003c/strong\u003e: \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.03, 95%CI [-0.37, 0.45], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.36, \u003cem\u003et\u003c/em\u003e = 0.17, \u003cem\u003edf\u003c/em\u003e = 83.60, \u003cem\u003ep\u003c/em\u003e-FDR = 0.858; Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.05, 95%CI [-0.51, 0.61]; TMT-B in \u003cstrong\u003eFigure 3b\u003c/strong\u003e: \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.31, 95%CI [-0.07, 0.69], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.43, \u003cem\u003et\u003c/em\u003e = 1.56, \u003cem\u003edf\u003c/em\u003e = 82.41, \u003cem\u003ep\u003c/em\u003e-FDR = 0.183; Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.43, 95%CI [-0.11, 0.98]; TMT-\u0026Delta; in \u003cstrong\u003eFigure 3c\u003c/strong\u003e: \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.35, 95%CI [-0.07, 0.78], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.31, \u003cem\u003et\u003c/em\u003e = 1.62, \u003cem\u003edf\u003c/em\u003e = 82.48, \u003cem\u003ep\u003c/em\u003e-FDR = 0.183; Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.44, 95%CI [-0.10, 0.99]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTryptophan metabolite levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe tryptophan metabolite data are shown in \u003cstrong\u003eFigure 4\u003c/strong\u003e. For tryptophan, the main effect of group in the linear mixed-effects model showed that the high-SJL group had significantly higher levels of tryptophan than did the low-SJL group (\u003cstrong\u003eFigure 4a\u003c/strong\u003e: \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.59, 95%CI [0.16, 1.02], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.05, \u003cem\u003et\u003c/em\u003e = 2.72, \u003cem\u003edf\u003c/em\u003e = 45.55, \u003cem\u003ep\u003c/em\u003e-FDR = 0.018; Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 1.09, 95%CI [0.28, 1.90]). For 5-HIAA, the main effect of group was not significant (\u003cstrong\u003eFigure 4b\u003c/strong\u003e: \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.34, 95%CI [-0.15, 0.84], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.05, \u003cem\u003et\u003c/em\u003e = 1.34, \u003cem\u003edf\u003c/em\u003e = 83.00, \u003cem\u003ep\u003c/em\u003e-FDR = 0.183; Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.34, 95%CI [-0.16, 0.86]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBrain measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBrain measures are shown in \u003cstrong\u003eFigure 5\u003c/strong\u003e. Whole-brain voxel-based gray matter analyses revealed significantly smaller GMVs in the left fusiform gyrus, left temporal pole, left superior parietal lobule, right angular gyrus, and right inferior temporal gyrus of the high-SJL group than those in the low-SJL group (\u003cstrong\u003eFigure 5a\u003c/strong\u003e and \u003cstrong\u003eOnline Resource 2\u003c/strong\u003e). We then used ROIs in these brain regions with GMV reductions as the seeds for the FC analyses of resting-state fMRI data. Compared with the low-SJL group, the high-SJL group demonstrated reduced FC between the left temporal pole and right pars triangularis (\u003cstrong\u003eFigure 5b\u003c/strong\u003e and \u003cstrong\u003eOnline Resource 3\u003c/strong\u003e). In contrast, seed-based FC analyses using the other four regions showing GMV reductions as seeds did not reveal any between-group differences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMediation analyses testing whether tryptophan mediated associations between SJL and temporal pole GMV or temporal pole\u0026ndash;pars triangularis FC are reported in \u003cstrong\u003eOnline Resource 4\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study primarily investigated differences in urinary tryptophan and 5-HIAA levels, regional GMV, and seed-based resting-state FC between young individuals with high SJL and those with low SJL. We also examined behavioral outcomes as secondary measures. The main findings revealed that, compared with the low-SJL group, the high-SJL group (i) showed higher urinary tryptophan levels; (ii) had smaller GMV in several brain regions, such as the left fusiform gyrus and left temporal pole; and (iii) exhibited lower resting-state FC between the left temporal pole and right pars triangularis. In contrast, urinary 5-HIAA levels did not differ between the groups. Behaviorally, the high-SJL group also demonstrated later midsleep on free days and greater fatigue than did the low-SJL group, whereas TMT performance did not differ between the groups. Exploratory mediation analyses did not identify significant indirect effects of tryptophan on the temporal pole GMV or its resting-state FC with the pars triangularis (see \u003cb\u003eOnline Resource 4\u003c/b\u003e for interpretation). These findings may suggest temporal pole-centered neural alterations associated with higher SJL along with an increase in tryptophan levels in young individuals.​\u003c/p\u003e \u003cp\u003eThe behavioral comparisons revealed that the high-SJL group showed a later chronotype (later midsleep on free days) than did the low-SJL group. Consistent with the findings of (Roenneberg et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), later chronotypes tend to accrue more weekday sleep debt and compensate by delaying and extending weekend sleep, which amplifies their SJL. Moreover, we observed greater fatigue in the high-SJL group, in line with a previous study reporting an association between SJL and fatigue in Japanese adolescents, even after adjusting for sleep duration (Tamura et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Considering that later weekend wake times can delay the circadian phase and increase SJL (Taylor et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), young individuals with greater SJL may experience more persistent fatigue, potentially via accumulated weekday sleep debt that is associated with such a phase delay.\u003c/p\u003e \u003cp\u003eFurthermore, consistent with the later chronotype and greater fatigue, the high-SJL group showed elevated urinary tryptophan levels compared with the low-SJL group. Tryptophan, along with serotonin, has been implicated in sleep\u0026ndash;circadian regulation and central fatigue (Miyamoto et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yamashita \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A previous study reported that, compared with young adults who received an oral placebo, those who received oral tryptophan had higher plasma levels of free tryptophan and increased sleepiness and lethargy (Morgan et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), suggesting that greater tryptophan availability may influence arousal regulation and sleep\u0026ndash;wake timing. Consistently, in rats with sleep\u0026ndash;wake cycle anomalies, tryptophan elevation and serotonin depletion in the brain were associated with induction of central fatigue-like states, accompanied by reduced social interaction and inattention (Yamashita and Yamamoto \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Accordingly, the present findings provide the novel evidence connecting such tryptophan-focused mechanistic research on sleep and circadian disruption with naturally occurring social misalignment during development.\u003c/p\u003e \u003cp\u003eIn addition, a previous study showed that individuals who took tryptophan capsules displayed more quarrelsome and less agreeable behavior at home than did those who took placebo capsules (Hogenelst et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), suggesting that greater tryptophan availability may contribute to altered interpersonal behavior with close others. Moreover, Williams et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) reported that experimental manipulation of tryptophan availability altered neural activity in socioemotional processing-related regions, such as the temporal pole, superior temporal sulcus, and amygdala, during facial emotion processing tasks. Against this background, the present findings suggest the possibility that in youth, SJL of \u0026ge;\u0026thinsp;1 h may be accompanied by altered tryptophan metabolism, which affects socioemotional functioning in addition to sleep and circadian regulation.\u003c/p\u003e \u003cp\u003eDespite a clear elevation in urinary tryptophan in the high-SJL group, urinary 5-HIAA levels were comparable between the groups, suggesting precursor elevation without detectable changes in downstream serotonergic turnover. This discrepancy might be explained by the involvement of another metabolic pathway: approximately 95% of tryptophan is metabolized via the kynurenine pathway (Yamashita \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among the various metabolites synthesized in this pathway, kynurenic acid, an antagonist of N-methyl-D-aspartate (NMDA) and α7-nicotinic acetylcholine (nACh) receptors, was elevated in the hypothalamus of rats with sleep\u0026ndash;wake cycle anomalies (Yamashita and Yamamoto \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). NMDA and nACh receptors, which modulate glutamate and acetylcholine release at synapses, respectively, have been implicated in sleep\u0026ndash;wake regulation (Brown et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Dash et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Furthermore, in rats given intraperitoneal kynurenine, an increase in kynurenic acid levels in the brain was associated with a reduced duration of rapid eye movement sleep and increased wake time as measured using polysomnography (Pocivavsek et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This suggests a crucial role for brain kynurenic acid alterations in sleep\u0026ndash;wake architecture. We propose that young individuals with a greater SJL may show a tendency toward increased routing of tryptophan to the kynurenine pathway, which might influence glutamatergic and cholinergic signaling, which, in turn, may help explain neural alterations associated with social\u0026ndash;circadian misalignment.\u003c/p\u003e \u003cp\u003eBeyond these molecular findings suggesting involvement of both sleep\u0026ndash;circadian regulation and socioemotional functioning, several brain regions showed smaller GMV in the high-SJL than in the low-SJL group. Among these regions, the temporal pole is noteworthy because it also showed reduced resting-state FC with the right pars triangularis. These findings suggest that SJL possibly induces neural alterations in two key ways. First, we demonstrated reduced temporal pole GMV in young individuals with higher SJL, indicating a potential structural disadvantage relative to those with lower SJL. This was consistent with previous findings of a smaller temporal pole GMV in adults with sleep abnormalities (i.e., poor sleep quality, sleep\u0026ndash;wake cycle disruption, and idiopathic rapid eye movement sleep behavior disorder) (Amorim et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Second, childhood and adolescence are periods during which large-scale networks are sensitive to sleep\u0026ndash;circadian factors (Anastasiades et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), suggesting that frontotemporal connectivity may be sensitive to sleep\u0026ndash;circadian pressures. This supports the hypothesis that social\u0026ndash;circadian misalignment could contribute to reduced temporal pole\u0026ndash;pars triangularis connectivity. Our findings suggest that SJL of \u0026ge;\u0026thinsp;1 h is associated with temporal pole-centered structural and functional vulnerabilities in youth.\u003c/p\u003e \u003cp\u003eThe temporal pole is a paralimbic hub for socioemotional processing, including person-specific knowledge, affective meaning, and mental state attribution (Herlin et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Olson et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tsukiura et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; V\u0026ouml;llm et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Benetti et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) reported an association between attachment-related anxiety and reduced temporal pole GMV in healthy adults. This finding suggests that structural reduction in this region may lead to diminished emotional and social engagement. Moreover, childhood and adolescence are marked by heightened emotional instability (Bailen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), suggesting that this instability may be related to abnormalities in the temporal pole. Accordingly, reduced temporal pole GMV in young individuals with higher SJL may reflect greater vulnerability in terms of socioemotional processing during this stage.\u003c/p\u003e \u003cp\u003eIn addition to its socioemotional roles, the temporal pole is connected to the inferior frontal gyrus (i.e., pars triangularis) via long frontotemporal association fibers (i.e., the uncinate fasciculus and extreme capsule fiber system) (Schmahmann and Pandya \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), which support language processing (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Murphy et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rolls et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Rolls et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported that, among brain networks involved in language, coupling between the pars triangularis and temporal pole is associated with speech production and syntactic comprehension. Moreover, a previous study reported that, during a verbal fluency task, individuals with insomnia showed increased FC between the pars triangularis and superior temporal gyrus (i.e., the temporal pole) (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), suggesting that sleep problems may alter phonological processing speed. Taken together, these findings support the possibility that SJL of \u0026ge;\u0026thinsp;1 h may worsen speech production and phonological processing by reducing FC between the temporal pole and pars triangularis in young individuals. This neural diminishment within the frontotemporal language network may contribute to worsened language ability over time.\u003c/p\u003e \u003cp\u003eOutside the temporal pole, the high-SJL group also showed smaller GMV in the left fusiform gyrus, left superior parietal lobule, right angular gyrus, and right inferior temporal gyrus than that in the low-SJL group. The fusiform and inferior temporal gyri support visual processing (i.e., perception of faces and color) and visuospatial imagery (Spagna et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The superior parietal lobule is implicated in voluntary attention and top-down processes (i.e., retrieval search and monitoring), whereas the angular gyrus is involved in recollection-related orienting and the mnemonic content recovery (Cabeza et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Vilberg and Rugg \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These findings may point to localized vulnerability within the inferior temporal and parietal circuits subserving high-order cognitive processes, beyond the socioemotional and language domains.\u003c/p\u003e \u003cp\u003eFinally, although a greater SJL affected tryptophan levels and brain function, it did not affect TMT performance. The failure to detect a significant effect of SJL on the TMT may be explained by the neural profile in our participants with higher SJL. While our participants exhibited temporal pole-centered structural and functional vulnerabilities associated with socioemotional and language processing, TMT performance primarily depends on frontoparietal executive networks that support attention and processing speed (Oswald et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shirdel et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Thus, SJL-related neural alterations in temporal pole-centered structure and function may have had only a limited effect on TMT performance. Another possible reason is the developmental trajectory of TMT performance in school-age children. Vakil et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) reported that TMT performance improves markedly with age, suggesting that age-related developmental change has a strong influence on individual differences in TMT performance. In our cross-sectional sample, which had a wide age range, this developmental gradient may have attenuated observable SJL-related differences in TMT performance, even though age and educational level were statistically controlled.\u003c/p\u003e \u003cp\u003eOur study had some limitations. First, the cross-sectional design precluded causal inference: the disadvantages observed in healthy young individuals with higher SJL cannot be attributed to prolonged SJL exposure. Second, cognitive assessment was limited to the TMT. Future research should investigate the cognitive effects of high SJL by using tasks other than the TMT, such as a verbal fluency task. Third, socioemotional behavior was not measured (i.e., facial emotion evaluation tasks). Future research should evaluate whether the observed neural changes translate into socioemotional behavioral outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study revealed evidence of neurobiological alterations in healthy young individuals with greater SJL. Young individuals with higher SJL exhibited increased urinary tryptophan levels, decreased left temporal pole GMV, and reduced resting-state FC between the left temporal pole and the right pars triangularis than did those with lower SJL, suggesting that the former group had diminished intrinsic regional GMV and network integration related to socioemotional and language processes. In contrast, urinary 5-HIAA levels did not differ between the groups, indicating that they had no detectable increase in downstream serotonergic turnover. This pattern suggests that increased tryptophan levels may be preferentially metabolized into the kynurenine pathway under conditions of greater SJL. Taken together, SJL of \u0026ge;\u0026thinsp;1 h is related to structural and functional vulnerabilities in the neural system involving the temporal pole, along with tryptophan elevation. These findings provide new insights into the mechanisms by which SJL may contribute to socioemotional and language-related neural vulnerability during childhood and adolescence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standard\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of Interest\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics Approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Research Ethics Committee of the University of Fukui approved the study protocol (Assurance No. FU-20220061). The procedures complied with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to Participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll parents provided written informed consent, and all children provided assent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data will be made available via the Child Developmental MRI (CDM) project database, which is currently under construction. Additionally, data will be provided upon signing a data sharing agreement and after receiving a brief research proposal along with evidence of approval from the requester\u0026rsquo;s institutional review board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMasatoshi Yamashita: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. Qiulu Shou: Investigation, Writing \u0026ndash; review \u0026amp; editing. Masanori Fujieda: Investigation, Writing \u0026ndash; review \u0026amp; editing. Hidehiko Okazawa: Resources, Writing \u0026ndash; review \u0026amp; editing. Yoshiyuki Hirano: Resources, Writing \u0026ndash; review \u0026amp; editing. Kuriko Kagitani-Shimono: Resources, Writing \u0026ndash; review \u0026amp; editing. Yoshifumi Mizuno: Funding acquisition, Resources, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Tomoe Morita and Manami Onogi for supporting us with the experiments. This study was conducted using the PET/MRI scanner and related facilities of the Biomedical Imaging Research Center, University of Fukui. This work was supported by a Grant-in-Aid for Scientific Research (KAKENHI) from the Japan Society for the Promotion of Science (grant number 23K12814 to Masatoshi Yamashita), a grant from Taiju Life Social Welfare Foundation (award years 2023 and 2025 to Masatoshi Yamashita), Life Science Innovation Center (grant number LSI24101 to Masatoshi Yamashita), the Kawano Masanori Memorial Public Interest Incorporated Foundation for Promotion of Pediatrics (award year 2022 to Yoshifumi Mizuno), the Mother and Child Health Foundation (award year 2024 to Yoshifumi Mizuno), and Research Grants from the University of Fukui (academic year 2024 to Yoshifumi Mizuno). These funding sources were not involved in the study design or implementation; the collection, analysis, or interpretation of data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmorim L, Magalh\u0026atilde;es R, Coelho A, Moreira PS, Portugal-Nunes C, Castanho TC, Marques P, Sousa N, Santos NC (2018) Poor Sleep Quality Associates With Decreased Functional and Structural Brain Connectivity in Normative Aging: A MRI Multimodal Approach. Front Aging Neurosci 10: 375.\u003c/li\u003e\n\u003cli\u003eAnastasiades PG, de Vivo L, Bellesi M, Jones MW (2022) Adolescent sleep and the foundations of prefrontal cortical development and dysfunction. Prog Neurobiol 218: 102338.\u003c/li\u003e\n\u003cli\u003eBailen NH, Green LM, Thompson RJ (2018) Understanding Emotion in Adolescents: A Review of Emotional Frequency, Intensity, Instability, and Clarity. 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Int J Tryptophan Res 13: 1178646920936279.\u003c/li\u003e\n\u003cli\u003eYamashita M, Shou Q, Mizuno Y (2024) Association of chronotype with language and episodic memory processing in children: implications for brain structure. Front Integr Neurosci 18: 1437585.\u003c/li\u003e\n\u003cli\u003eYamashita M, Yamamoto T (2014) Tryptophan and kynurenic Acid may produce an amplified effect in central fatigue induced by chronic sleep disorder. Int J Tryptophan Res 7: 9-14.\u003c/li\u003e\n\u003cli\u003eYamashita M, Yamamoto T (2017) Tryptophan circuit in fatigue: From blood to brain and cognition. Brain Res 1675: 116-126.\u003c/li\u003e\n\u003cli\u003eYamashita M, Yamamoto T (2021) Impact of Long-Rope Jumping on Monoamine and Attention in Young Adults. Brain Sci 11.\u003c/li\u003e\n\u003cli\u003eYang FN, Picchioni D, Duyn JH (2023) Effects of sleep-corrected social jetlag on measures of mental health, cognitive ability, and brain functional connectivity in early adolescence. Sleep 46.\u003c/li\u003e\n\u003cli\u003eZou H, Zhou H, Yan R, Yao Z, Lu Q (2022) Chronotype, circadian rhythm, and psychiatric disorders: Recent evidence and potential mechanisms. Front Neurosci 16: 811771.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Demographics of the high-SJL group and low-SJL group\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.9451%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7887%;\"\u003e\n \u003cp\u003eHigh-SJL\u003c/p\u003e\n \u003cp\u003e(n = 25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7887%;\"\u003e\n \u003cp\u003eLow-SJL\u003c/p\u003e\n \u003cp\u003e(n = 64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4775%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.9451%;\"\u003e\n \u003cp\u003eSex (n)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Male\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11 (44.00)\u003c/p\u003e\n \u003cp\u003e14 (56.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31 (48.43)\u003c/p\u003e\n \u003cp\u003e33 (51.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4775%;\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.9451%;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7887%;\"\u003e\n \u003cp\u003e12.56 (3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7887%;\"\u003e\n \u003cp\u003e10.25 (2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4775%;\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.9451%;\"\u003e\n \u003cp\u003eEducation (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7887%;\"\u003e\n \u003cp\u003e7.76 (3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7887%;\"\u003e\n \u003cp\u003e5.75 (2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4775%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.9451%;\"\u003e\n \u003cp\u003eIncome (JPY, n)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt; 3 000 000\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;3 000 000\u0026mdash;5 000 000\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;5 000 000\u0026mdash;7 000 000\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u0026ge;7 000 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1 (4.00)\u003c/p\u003e\n \u003cp\u003e5 (20.00)\u003c/p\u003e\n \u003cp\u003e9 (36.00)\u003c/p\u003e\n \u003cp\u003e10 (40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5 (7.81)\u003c/p\u003e\n \u003cp\u003e4 (6.25)\u003c/p\u003e\n \u003cp\u003e14 (21.85)\u003c/p\u003e\n \u003cp\u003e41 (64.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4775%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.9451%;\"\u003e\n \u003cp\u003eWeekly sleep duration (h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7887%;\"\u003e\n \u003cp\u003e7.96 (1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7887%;\"\u003e\n \u003cp\u003e8.24 (1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4775%;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eParameters are indicated as the mean (\u003cem\u003eSD\u003c/em\u003e) or n (%). \u003cem\u003eP\u003c/em\u003e-values for age, education, and income are from \u003cem\u003et\u003c/em\u003e-tests for the comparison of high-SJL group and low-SJL group. \u003cem\u003eP\u003c/em\u003e-values for sex ratio is from chi-square tests for the comparison of high-SJL group with low-SJL group. JPY, Japanese yen; SJL, social jetlag.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"journal-of-neural-transmission","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Journal of Neural Transmission","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"healthy youth, serotonin, social jetlag, temporal pole, tryptophan","lastPublishedDoi":"10.21203/rs.3.rs-8431510/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8431510/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSocial jetlag (SJL), defined as a mismatch in midsleep timing between school or workdays and free days, has been associated with adverse mental health, cognitive, and brain outcomes in adults. However, its effects in young individuals remain insufficiently studied. We investigated the effects of SJL on these factors and on tryptophan metabolites, an amino acid implicated in sleep, fatigue, and social function, in young individuals. Based on SJL assessed using the Munich Chronotype Questionnaire, 89 healthy youths aged 6–17 years were classified into a high-SJL group (≥ 1 h; n = 25) or a low-SJL group (\u0026lt; 1 h; n = 64). Fatigue, chronotype, Trail Making Test (TMT) performance, urinary tryptophan and 5-hydroxyindoleacetic acid (5-HIAA) levels, gray matter volume (GMV), and resting-state functional connectivity (FC) were evaluated. Compared with the low-SJL group, the high-SJL group exhibited greater fatigue, a later chronotype, and higher urinary tryptophan levels. Moreover, the high-SJL group showed smaller GMV in several regions, including the temporal pole. Notably, the left temporal pole, a region implicated in socioemotional processing, showed reduced FC with the right pars triangularis in the high-SJL group. However, TMT performance and urinary 5-HIAA levels did not differ between groups. An SJL of ≥ 1 h was associated with temporal pole-centered neural vulnerability and elevated tryptophan levels in young individuals. These findings suggest that higher SJL may contribute to neural and molecular disadvantages related to socioemotional functioning during childhood and adolescence.\u003c/p\u003e","manuscriptTitle":"High social jetlag in healthy youth: Differences in tryptophan levels, temporal pole-centered gray matter volume, and functional connectivity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 12:55:19","doi":"10.21203/rs.3.rs-8431510/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-15T22:25:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-05T03:10:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99611750801129177791120008831236663778","date":"2026-02-09T16:58:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-27T08:58:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-26T12:36:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-24T12:17:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neural Transmission","date":"2025-12-23T07:30:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"journal-of-neural-transmission","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Journal of Neural Transmission","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3535ead2-81f2-4957-b728-c60c746ba9f9","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T18:23:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 12:55:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8431510","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8431510","identity":"rs-8431510","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}