Neuroimaging subtypes of adolescent sleep insufficiency stratify natural short sleepers from comorbidity or environment driven insufficiency

Domain: chemistry
Published: 2026-03-07
Editorial outcome: minor_revision

Abstract
Insufficient sleep (less than 8 hours for adolescents) has heterogeneous neurobiological underpinnings. Using the SuStaIn model on brain MRI from ABCD Study, we identified three distinct subtypes with reduced cortical thickness, starting from the postcentral, pericalcarine, and entorhinal cortices. The subtypes diverged in sleep-related determinants. The postcentral subtype mirrored the controls of sufficient sleep in sleep behavior and sleep environment and showed no psychiatric comorbidities, which aligned with natural short sleepers (phenotypically normal despite short sleep). Notably, this subtype displayed significantly advanced brain age, and polygenetic score analysis revealed a genetic predisposition for short sleep. The pericalcarine subtype was linked to environmental factors (e.g., light/noise pollution), with sleep duration mediating environmental effects on cortical thinning. The entorhinal subtype showed elevated psychiatric risk, younger brain age, and larger spatial correlations with psychosis-related neurotransmitter systems. This work deciphers the heterogeneous neurobiology of insufficient sleep and offers a framework for stratified interventions.

Introduction
Insufficient sleep is prevalent in modern society, affecting 1/3 of the population1. Particularly, sleep problems in adolescents have become a major concern, which are complicated by rapid neurobiological development2. According to previous studies, adolescent sleep of less than 8 hours is defined as insufficient sleep, affecting 16% of sixth graders3. A good night's sleep is hard to have, as it is affected by various factors4, including physiological changes of puberty5, academic stress load6, mental health7, and sleeping environments8. Insufficient sleep also causes major social and health consequences, including both physical9,10 and mental fatigue11, negatively influencing social life12 and school performance13,14. A recent study reported that sleep disturbances emerged as the most influential predictor of mental health risk15.

The multifaceted and heterogeneous determinants and outcomes hinder our understanding of insufficient sleep in children and adolescents16. For instance, some children may be biologically disposed to short sleep duration without compromised development17, while others sleep less because of external disturbance18, psychiatric comorbidity19, or other conditions that require entirely different interventions. To capture the different sleep profiles, previous studies focused on various objective and subjective sleep measures using latent profile analysis20,21. Previous studies used the social-ecological framework to understand the etiology of pediatric sleep problems. However, the socio-ecological measurements have raised concerns for low reproducibility22 and subjectivity23. Other studies of sleep genetics24 and proteomics25 found genes and proteins for sleep regulation. Moreover, as insufficient sleep has major links to brain development26, an in-depth understanding of neurobiology needs to be further addressed. To settle such a problem, a reliable, unbiased, and informative biomarker that reflects the underlying neurobiology becomes the key for stratification.

In recent years, MRI-based neuroimaging approaches have been used frequently to unravel the underlying neurobiological mechanisms of sleep problems. Cross-sectional studies found that shorter sleep time is correlated with lower cortical brain volume for adolescents27,28. A longitudinal study confirmed that shorter objective sleep time is correlated with a thinner cortex29. A review summarized that insufficient sleep strongly correlates with neuroanatomical structures and functions of adolescent brain development, but due to the heterogeneity of insufficient sleep, a precise conclusion cannot be drawn30. Studies with subjective31 or objective32 sleep measures also showed distinct brain phenotypes, indicating the high heterogeneity and presence of subtypes in the insufficient sleep population.

The rich anatomical and functional information and the unbiased nature of neuroimaging make it an ideal neurobiological marker for the stratification of sleep disorders. For instance, previous studies found different insomnia subtypes in adults with distinctive cortical topography and structural connectivity33. It is not yet known whether individuals with insufficient sleep (i.e., short sleep duration) exhibit subgroups with distinct brain patterns, especially in the adolescent population, and whether the imaging phenotypes possess explicit social-ecological characteristics that can help to inform effective interventional strategies.

In this study, we aim to decipher the social-ecological heterogeneity of insufficient sleep in adolescents based on brain MRI. We adopted the Subtype and Stage Inference (SuStaIn)34 method to identify insufficient sleep subtypes based on cortical thickness (CT) using the Adolescent Brain Cognitive Development (ABCD) study with MRI and Fitbit-based sleep measurements. An optimal number of three subtypes was found using the Bayesian model selection and was validated using the longitudinal data from the ABCD dataset and a house-collected independent dataset. To understand the socio-ecological determinants underlying these subtypes, we comprehensively examined pediatric sleep health, physical or mental conditions, and family and neighborhood factors among these subtypes. Lastly, we compared the brain ages, polygenic risk score (PRS), associations with psychiatric disorders, and neurotransmitter profiles among these subtypes. Figure 1 provides an overview of the present study.

Results
Brain morphology-based subtyping of insufficient sleep
We used objective sleep duration measured by Fitbit to characterize insufficient sleep. According to guidelines35,36, adolescents with objectively measured sleep of less than 8 hours were defined as insufficient sleepers. We also compared other objective sleep measurements between the insufficient and sufficient sleep groups. The insufficient subtype had significantly higher sleep efficiency (P < 1x10-5), fewer average wake counts per night (P < 1x10-5), and similar sleep latency (P = 0.71).

We first identified the brain patterns most susceptible to insufficient sleep by comparing the insufficient and sufficient sleep groups. Using the Desikan-Killiany (DK) Atlas as a template, we identified the region of interest (ROI) with significantly altered cortical thickness (CT) across the whole-brain after controlling for random factors of age, sex, ethnicity, BMI and site, using subjects from the ABCD second-year follow-up (N = 853, N = 2,413). Fig. 2a demonstrated that sleep had a profound effect on the developing adolescent brain. 28 of 68 brain regions showed reduced CT in the insufficient sleep group compared to the controls, with the left lingual cortex showing the most reduced CT with Cohen’s d of 0.200 (P = 7.61x10-6).

We then used the SuStaIn model to identify distinct longitudinal trajectories of CT reduction with three z-score cutoffs: 1, 1.5, and 2. A three-cluster model stood out with minimum cross-validation information criterion (Fig. S3a) and maximal log-likelihood (Fig. S3b) under ten-fold validation. This resulted in three subtypes with distinctive topography, i.e., subtype 1 showed CT reduction across the whole whole-brain (n=178, 20.86% of the insufficient sleep subjects), subtype 2 showed a lower CT in the occipital lobe (n=417, 48.89% of the insufficient sleep subjects), and subtype 3 showed a lower CT in the entorhinal cortex (n=258, 30.25% of the insufficient sleep subjects). To test the stability of the results, other z-score cutoffs (1, 2, and 3; 2, 3, and 4) for SuStaIn were also tested (Fig. S6).

Sleep deficit-induced brain changes also showed different progressive trajectories among the subtypes. Subtype 1 exhibited initial change in the postcentral cortex, which then progressed to the supramarginal precuneus and inferior parietal area, namely, the postcentral subtype. Subtype 2 showed initial changes in the pericalcarine area, which then progressed to the lingual area, namely, the pericalcarine subtype. Subtype 3 first demonstrated alteration in the entorhinal cortex, then progressed to the inferior temporal and precentral cortex, namely, the entorhinal subtype.

The SuStaIn model assigned each insufficient sleep adolescent with the most likely subtype and stage label. The subjects assigned to ‘stage 0’ of the progression stage were noted as in the earliest stage (number of ‘stage 0’ in Postcentral subtype = 59; number of ‘stage 0’ in Pericalcarine subtype = 219; number of ‘stage 0’ in Entorhinal subtype = 153), while stages larger than 0 were noted as ‘advanced-stage’. The number of subjects assigned to each stage of the three insufficient sleep subtypes is shown in Fig. S4. The probability-based distribution of advanced-stage individuals in the three subtypes was depicted in Fig. 2c. Fig. 2d illustrated the different stages in the three subtypes of sleep deficit, represented as the mean z-value images, i.e., reduction of CT in the advanced-stage phase compared to the controls.

Longitudinal change of brain morphology in the three subtypes
To measure the change of brain CT and compare it with pseudo-staging from SuStaIn, we obtained ΔCT by subtracting the CT values between two rounds of longitudinal MRI scans while regressing the age gap between scans and other covariates. We measured the variability of ΔCT by log-transformed Euclidean distances within groups, and found the insufficient sleep group had higher within-group variability under multiple atlases (DK and Destrieux atlas, Fig. 3a), supporting the higher heterogeneity and the necessity for subtyping. After subtyping, we found that the within-group variability of ΔCT for the postcentral subtype no longer showed a significant difference compared with the sufficient sleep group, indicating the similarity of this subgroup to the controls (Fig. 3b).

Fig. 3c demonstrated that the postcentral subtype showed much greater ΔCT in the parietal lobe, while the pericalcarine subtype showed greater ΔCT in the occipital lobe, and the entorhinal subtype showed greater ΔCT in the temporal cortex, which were in accordance with the “pseudo progression” obtained from SuStaIn model (Fig. 3d). We performed spatial correlations between the cortical change maps (Fig 3c, 4-year - 2-year) and SustaIn-based pseudo-progression maps (Fig 3d, stage III – stage I). All three subtypes showed significant correlations (Postcentral subtype, Rho = 0.550, p = 0.005; Pericalcarine subtype, Rho = 0.483, p = 0.016; Postcentral subtype, Rho = 0.798, p < 0.001; Fig. S9).

Characterization of subtypes by social-ecological profiles
Previous studies used socio-ecological and systems frameworks to understand the determinants of sleep patterns and problems in children and adolescents23,37,38. To take the layers of socio-ecological factors (Fig. 4a) into full account, we compared the insufficient sleep subtypes with the control group in terms of sleep factors, physical or mental conditions, and neighborhood environment (Fig. 4b), while controlling for confounders including age, sex, BMI, site, race, ethnicity, socioeconomic status. Interestingly, no significant differences were found between the postcentral subtype and the control for any of the socio-ecological factors. Therefore, we interpreted this subtype as natural short sleepers who although sleep relatively less, do not show compromised development, and thus may not require any intervention.

The pericalcarine subtype was found under adverse environmental conditions (brighter night light, louder noise at night, less neighborhood income, lower neighborhood education, and more serious neighborhood disadvantage), and worse physical activities, leading to sleep problems (initiating and maintaining sleep and weekly sleep loss). Therefore, we labeled this subtype as environment-induced sleep deficits. We further performed mediation analysis to investigate the relationship between night light, sleep duration, and pericalcarine in this group.

The entorhinal subtype was mostly affected in pediatric sleep domains, including initiating and maintaining sleep and weekly sleep loss and weekly sleep loss. By examing the linear correlations between SuStaIn stages and residualized CBCL score, we found higher staging in the entorhinal subtype were associated with higher internalizing problems (r = 0.263, P = 0.007; Fig. 5f), and overall problems (r = 0.225, P = 0.021; Fig. 5h), but not correlated with externalizing problems (r = 0.107, P = 0.276; Fig. 5g). Given that this subtype showed higher degree of psychiatric comorbidity compared to the other subtypes, we characterized this group as comorbidity-related. The overall socio-ecological profiles of the subtypes were summarized in Fig. 4d.

Brain age differences among subtypes
During adolescence, the need for sleep duration strongly declines with brain maturation39. As brain age derived from neuroimaging has been widely used as a biomarker for brain aging and maturation40, we tested whether the brain age differed among the subtypes, although the chronological age showed no significant difference (Fig. S11). Compared with the control, the postcentral subtype (aka. short sleepers) showed higher (P = 0.006, Bonferroni corrected; Fig. 5a) and the entorhinal (aka. comorbid related) showed lower (P = 0.021, Bonferroni corrected; Fig. 5a) brain age. Polygenetic risk score of short sleep among subtypes
Previous studies found a significant genetic basis for short sleepers17,41. Hassan et.al41 found 27 significant loci correlated with short sleepers in adults (subjective sleep < 7 hours), and were further validated with accelerometer-measured objective sleep. We calculated the short sleep duration polygenetic risk score (PRS, details see Method). The postcentral subtype had a significantly higher PRS value compared with the control (P = 0.0042; Fig. 5b), which indicated a genetic predisposition for short sleep duration. The other insufficient sleep subtypes showed no significant differences in PRS compared with the control.

Mediating effects of sleep on the environment and the brain
To further understand the potential environmental effect on the pericalcarine subtype, we used mediation analysis to determine whether the associations between night light pollution and CT changes in the pericalcarine cortex could be explained by sleep duration. In the pericalcarine subtype, sleep duration mediated the association between nightlight and pericalcarine CT (path C: -0.061, 95% CI [-0.112, -0.010], p = 0.022; path a*b: -0.021, 95% CI [-0.037, -0.009], p < 0.001; Fig. 5d). The mediation effects were not significant for the postcentral (path C: -0.056, 95% CI [-0.123, 0.010], p = 0.094; path a*b: -0.002, 95% CI [-0.008, 0.003], p=0.486; Fig. 5c) or entorhinal (path C: -0.027, 95% CI [-0.091, 0.037], p = 0.406; path a*b: 0.004, 95% CI [-0.002, 0.012], p=0.166; Fig. 5e) subtypes.

Correlations with mental conditions and SustaIn stages
To understand the potential connections between mental conditions and insufficient subtypes, we further performed correlations between the SuStaIn stage (advanced-stage subjects) and the CBCL scores (after controlling for age, sex, and ethnicity, Fig. 5f-h). Only the entorhinal subtype showed positive correlations between SuStaIn stages and CBCL scores for internalizing problems (Rho = 0.263, P = 0.007) and overall problems (Rho = 0.225, P = 0.021), but not for externalizing problems (Rho = 0.107, P = 0.276). The correlations were not significant for the postcentral subtype (internalizing problems, Rho = 0.112, P = 0.227; externalizing problems, Rho = 0.033, P = 0.723; overall problems, Rho = 0.064, P = 0.490) and the pericalcarine subtype (internalizing problems, Rho = -0.020, P = 0.776; externalizing problems, Rho = -0.026, P = 0.718; overall problems, Rho = 0.023, P = 0.744).

Neurotransmitter basis of insufficient sleep subtypes
Studies using PET and SPECT measured a range of receptors and transporters highly correlated with psychiatric disorders. This enabled us to correlate the spatial patterns of CT in each subtype and the density maps of neurotransmitter and transporter systems (5-hydroxytryptamine: 5HT1a, 5HT1b, 5HT2, and 5HT4; dopamine: D1, D2, and DAT; GABA: GABAa). With Bootstrapped Spearman correlations, we found the postcentral subtype (Fig. 5i) was significantly correlated with D2, DAT that involved in dopaminergic signaling (Rho = -0.375, 0.416, respectively, all P < 0.001). The pericalcarine subtype (Fig. 5j) was significantly correlated with a serotonin receptor 5HT1a (Rho = 0.33, P = 0.001). The entorhinal subtype (Fig. 5k) was negatively correlated with 5HT1a (Rho = -0.613, P < 0.001), and positively correlated with D2 and GABAa (Rho = 0.508, 0.511, respectively, all P < 0.001) (Fig. 5k).

Validation of the subtyping results
To test the robustness of subtyping results against different definitions of sleep insufficiency, we applied the SuStaIn model to other insufficient sleep cutoffs (7.5 and 8.5 hours) and compared the sequences of biomarker progression with the primary results using the 8-hour cutoff (Fig. S5). The SuStaIn trajectories of both replication cutoffs showed high consistency with the original cutoff, with the correlation coefficients (r) of 0.785, 0.811, and 0.689 for the three subtypes between 8 hours and 7.5 hours cutoffs, respectively (P < 0.001, FDR corrected, Fig. S5e) and r of 0.857, 0.921, and 0.811 for the three subtypes between 8 hours and 8.5 hours cutoffs (P < 0.001, FDR corrected; Fig. S5f). We further validated the results with a subjective sleep cutoff (< 8 hours of sleep), which also showed a high consistency (Fig. S8).

We then validated the results on our private dataset (Shanghai Sleep Birth Cohort, SSBC, n = 115) using the identical procedure. The SuStaIn model trained on ABCD was directly applied to SSBC. The subtypes found in the SSBC dataset showed high consistency with the discovery set of the ABCD dataset, e.g., subtypes showed starting epicenters in the postcentral cortex (Fig. 6a), pericalcarine cortex (Fig. 6b), and entorhinal cortex (Fig. 6c). For the SSBC dataset, the postcentral subtype was found with similar pediatric sleep presentations as the short sleeper found in ABCD; while the pericalcarine and entorhinal subtype were found of worse pediatric sleep presentations compared with the control group (Fig. 6d). The stage progression in the entorhinal subtype was found to be significantly correlated with internalizing problem and the overall problem scores, but not for the externalizing problems (Fig. 6e-g). These results were overall in accordance with results from the ABCD study.

Methods
Data preparation
In the second-year follow-up of the ABCD study, 4,134 adolescents with Fitbit worn on weekdays and weekends and with at least seven nights of recorded sleep were included71. Note that the baseline ABCD data do not have Fitbit measurements, and thus was not used for discovery. Subjects missing imaging data or covariates (age, sex, BMI, site, race, ethnicity, socioeconomic status) were excluded, leaving 3,266 individuals for formal analysis. The participant inclusion flowchart was provided in Supplementary Fig. S1. Sleep duration was measured as sleep onset – offset interval. To derive an average weekday-weekend sleep profile, we calculated the sleep hours weighted according to the number of days [(∑weekday sleep duration) + (∑weekend sleep duration)] / (weekday day counts + weekend day counts) and winsorized21. Participants were divided into sufficient or insufficient sleep groups based on a cutoff of 8 hours3,51 of average sleep based on Fitbit, resulting in 853 adolescents with insufficient sleep and 2,413 with sufficient sleep (age 12.03 ± 0.63 years, 56.5% male for insufficient sleep; age 11.87 ± 0.64 years, 50.6% male for sufficient sleep). We also tested other choices of 7.5 hours and 8.5 hours as cutoffs (Supplementary Fig. S5). The demographic characteristics are summarized in Supplementary Table S1. All study procedures from the ABCD dataset received approval from institutional review boards at individual sites, and written consent was obtained from parents, with verbal assent from the children.

A private dataset of the Shanghai Sleep Birth Cohort72 (SSBC) was used for the replication test. This dataset included 52 participants with insufficient sleep and 63 with sufficient sleep based on actigraphy (age 10.33 ± 0.27, 51.9% male for insufficient sleep; age 10.33 ± 0.25 years, 38.7% male for sufficient sleep). As subjects from the SSBC dataset were younger, 8.5 hours of sleep was used as the cutoff for insufficient sleep51. The study procedures received approval from institutional review boards, and written consent was obtained from parents, with verbal assent from the children.

Image pre-processing
We used the 3D T1-weighted MRI scans from each dataset. The ABCD data were preprocessed by a standard pipeline of cortical reconstruction and volumetric segmentation using FreeSurfer v7.1.173 (https://surfer.nmr.mgh.harvard.edu/) from the ABCD study Data Analysis, Informatics, and Resources Center (DAIRC) along with standard quality control measures, reported in previous studies74. Aligning with the ABCD study, similar image pre-processing processes were performed for the SSBC dataset. We focused on the cortical thickness (CT) features that were shown to be significantly altered in previous studies. We used the Desikan-Killiany Atlas75 template to parcellate the brain into 68 ROIs. Site-specific variations were estimated and regressed with Neuro-Combat76. Before comparing the CT measures between sufficient and insufficient sleep groups, propensity score matching was used to reduce bias caused by confounding variables. Sufficient and insufficient sleep samples were matched based on one-to-one matching without replacement within a predefined propensity score radius (ie, caliper=0·1). Consequently, a total of 1560 participants from the original control group were dropped. After propensity score matching, all covariates were balanced (Fig. S2). Specifically, covariates including age, sex, site, race, ethnicity, socioeconomic status, and BMI were controlled.

Pediatric sleep measurements
Sleep disturbance in ABCD participants is measured based on the Sleep Disturbance Scale for Children (SDSC)77 with 26 questions, assessing six sleep disturbance dimensions, including Disorders of initiating and maintaining sleep, Sleep breathing disorders, Disorders of arousal, Sleep-wake transition disorders, Disorders of excessive somnolence and Sleep hyperhydrosis. For the SSBC dataset, the Children’s Sleep Habits Questionnaire (CSHQ)78 was used to assess sleep behavior in eight aspects, including Bedtime resistance, Sleep onset delay, Sleep duration, Sleep anxiety, Night wakings, Parasomnia, Sleep breathing disorders, and Daytime sleepiness. As adolescents usually address weekday sleep debt in weekends3, we used weekly sleep loss ((weekly average sleep duration – workday average sleep duration) / workday count) measurement from the Munich Chronotype Questionnaire79 for evaluation. The higher the weekly sleep loss measure, the larger the workday sleep debt repaid during freedays.

Sleep environment measurements
In the ABCD study, the child’s primary residential address was geocoded, and variables of the American Community Survey (5-year estimates from 2011 to 2015) were linked according to the US census tract80. Potentially sleep-affecting neighborhood environmental measures were selected, including night light, income, education, noise, and neighborhood disadvantage18.

Mental and physical condition measurements
For the ABCD dataset, mental health conditions of internalizing, externalizing, and overall problems were assessed with Parent Child Behavior Checklist Scores (CBCL)81. For the SSBC dataset, subjects’ mental conditions of internalizing, externalizing, and overall problems were assessed via the Strengths and Difficulties Questionnaire (SDQS)82,83. Daily physical activity is measured with step counts with Fitbit.

Brain age
In order to evaluate the difference in brain age between the subtypes, we used the a three layer multi-layer perceptron (MLP) for estimating brain age based on cortical thickness. Cortical thickness from all 68 regions was standardized while regressing out sex and site. Univariate correlation analysis (Pearson’s r) identified regions significantly associated with chronological age (all ROIs have negative correlations with age, p < 0.05, adjusted with Bonferroni correction). We used the age-correlated ROIs to train a linear regression model.

The brain age predictor MLP consists of a sequential stack of fully-connected layers: an input layer accepting the 68-dimensional feature vector, followed by a hidden layer of 128 units with ReLU activation and a 30% dropout, a second hidden layer of 64 units with ReLU activation and a 20% dropout, a third hidden layer of 32 units with ReLU activation, and a final linear output layer that produces a single scalar value representing the predicted age. The dropout layers were incorporated during training to mitigate overfitting. The brain age predictor MLP model was trained with the 2,413 sufficient sleep control samples. The model accuracy was evaluated using Pearson’s correlation coefficient (r = 0.58) and mean absolute error (MAE = 6.39 months) between predicted and chronological age. The brain age predictor model was applied to the insufficient sleep group for brain age calculation.

Neurotransmitter profiles
Neurochemical density profiles across cortical regions were quantified through retrospective analysis of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) data obtained from a cohort of healthy controls84. Eight receptors across three different neurotransmitter systems (5-hydroxytryptamine: 5HT1a, 5HT1b, 5HT2a, and 5TH4; dopamine: D1, D2, and DAT; GABA: GABAa; details in Supplement Table S11) were investigated with JuSpace85. These density maps were registered to the Montreal Neurological Institute (MNI) stereotaxic coordinate system and segmented into 68 anatomically defined cortical regions using the Desikan-Killiany (DK) atlas75.

Polygenetic risk score
We compute polygenetic risk score (PRS) of short sleep duration using imputed ABCD study genotype data with quality control, based on the largest GWAS data of short sleep duration (n = 446,118) from UK BioBank41. For sleep duration measurement in this adult dataset, participants were asked about how many hours of sleep within 24 hours, and participants with less than 7 hours of sleep were deemed as short sleepers. We calculated the short sleep PRS for adolescents of both sufficient sleep (n = 2,413) and insufficient sleep (n = 853). Among ABCD children of European ancestry, we used PRSice-286 to calculate individual PRSs by adding SNP alleles with weights of the SNP allele effect size estimated in a previous GWAS (short sleep population GWAS). Parameters for polygenic scoring were a clumped variation of linkage disequilibrium r2 < .10 within a 300-kb window cutoff. We calculated PRSs for each trait based on six different p-value thresholds (pTs) with pT < 5e-08 (32 SNPs), pT < 0.001 (4,009 SNP), pT < 0.005 (12,675 SNPs), pT < 0.01 (22,140 SNPs), pT < 0.05 (73,429 SNPs), pT < 0.1 (129,416 SNPs), and pT < 0.5 (422,712 SNPs), as recommended86. An optimal p-value threshold (pT < 0.5) was found with the highest percentage of the variance (R2 = 0.016, p = 4.45e-05) in the outcome estimated by PRSice-2, and were further Z-score normalized. A higher PRS score indicates a genetic predisposition for short sleep duration. We compared polygenic risk scores between sleep-subtype advanced-stage subjects and controls to understand their genetic basis.

Subtyping of insufficient-sleep related brain morphology
We employed a widely used data-driven neuroimage morphological progression model named Subtype and Stage Inference (SuStaIn)34, which identifies progression subtypes with an event-based model. The SuStaIn model is designed to identify both the subtypes and progression of the abnormality pattern due to an event (with reference to controls) along a continuous severity gradient (stages) from cross-sectional data. SuStaIn operates by identifying a canonical sequence of events (e.g., regional cortical thinning) for each potential subtype. It then estimates for each individual both the probability of belonging to each subtype and their most likely stage along that subtype's progression sequence. For our study, the stages revealed the epicenters/origin (stage I) and the subsequent progression in each sleep insufficiency subtype. This is superior to traditional clustering models (e.g., k-means, hierarchy clustering), which only assign a cluster label without temporal heterogeneity within a cluster.

We first regressed out confounding factors (age, sex, site, race, ethnicity, socioeconomic status, and BMI) from regions of interest (ROIs) using linear regression. Scanner-induced effects were regressed with Neuro-Combat76. The adjusted ROIs of the insufficient sleep group were z-scored relative to the sufficient sleep group, with a higher z-value indicating greater deviations from sufficient sleep. As the regions with reduced CT in the insufficient sleep group didn’t show significant brain lateralization (Fig. 1a, Supplementary Table S8), we averaging the CT values of the left and right hemispheres to keep the number of input ROIs below 2034,87. Finally, we used twelve ROIs with significantly reduced CT after averaging as SuStaIn inputs (P <0.05, Fig. 1 d, FDR Supplementary Table S8). Based on the parameter settings outlined in previous research, we used an expectation-maximization algorithm for model initialization and used 25 random starting points to give the optimum solution. We applied 1,000,000 rounds of Markov Chain Monte Carlo (MCMC) to estimate the developmental trajectories of the identified subtypes. Three z-score cutoffs (1, 1.5, and 2) were employed to describe the amount of negative impact of sleep deficits on adolescents. We assessed subtyping solutions with 2-5 clusters, and an optimal choice of 3 was chosen according to its lower CVIC88 and higher log-likelihood through ten-fold cross-validation (Supplementary Fig. S3). To test the stability of the results, other z-score cutoffs for SuStaIn were also tested in Supplementary Fig. S6.

Statistical analysis
After identifying SuStaIn insufficient sleep subtypes, we compared different socio-ecological factors affecting sleep among the subtypes for their pediatric sleep health, physical or mental conditions, and family and neighborhood factors using the Mann-Whitney U test and computed the effect size using Cohen’s d test.

We further compared the brain age (in measurement for brain maturation) and short sleep PRS (in measurement for genetic backgrounds) among subtypes using the Mann-Whitney U test. As for the associations among night light pollution, sleep duration, and brain structure, we performed mediation analysis using sleep duration as the mediator. All variables were normalized before entering the model. Sex, age, parent education level, household income, ethnicity, BMI, and site were used as covariates. Total, direct, and indirect associations were quantified through 10,000 bootstrap iterations, with 95% bias-corrected and accelerated confidence intervals calculated. All statistical analyses were performed using the R mediation package (v4.0.0). The statistical significance threshold for these analyses was set at P < 0.05. To understand the subtype-specific neurochemical signatures, we performed a spatial correlation using bootstrapped Spearman correlation with 10,000 iterations between each neurochemical density matrix and cortical thickness T-statistic maps for each subtype.

Longitudinal validation
To test the reliability of the SuStaIn staging results, we utilized ABCD longitudinal data at the first and the second visits, which were two years apart. We calculated the actual change of cortical thickness of individuals in comparison with the pseudo-progression of SuStaIn. The change of cortical thickness (ΔCT) was obtained by subtracting the value of the baseline year from the second-year follow-up in each ROI while regressing the age gap between the scans, which were compared between the sufficient and insufficient sleep groups. We further compared the longitudinal morphological changes (ΔCT map) and the 'pseudo-progression' maps derived by subtracting the mean z-score brain map of Stage I from that of Stage III. This approach was chosen because Stage I marked the earliest alteration, while Stage III provided a stable and representative intermediate point in the SustaIn modeling sequence, thereby optimally illustrating the direction and pattern of change without the loss of sensitivity to subtype-specific features (all three subtypes showed significant changes for most ROIs in Stage V). Comparisons with Stage VI-I and Stage V-I were also tested for generalizability (Fig. S10).

Independent validation
We applied SustaIn model train via the ABCD sample on a private dataset of the Shanghai Sleep Birth Cohort. We first regressed out confounding factors (age, sex and BMI) and Z-score transformed cortical thickness in insufficient sleep group relative to the sufficient sleep group. As the number of subjects from the SSBC dataset was not sufficient to retrain the SuStaIn model, we directly applied the SuStaIn model trained from the ABCD dataset to the SSBC dataset. We compared the brain phenotype, pediatric sleep, and psychiatric comorbid between the identified subtypes and controls. The results showed consistency.

Furthermore, to test the robustness of subtyping results against different definitions of sleep insufficiency, we retrained the SustaIn on other insufficient sleep objective sleep cutoffs (7.5 and 8.5 hours). As sleep duration is also measured via structured scales, we used the Sleep Disturbance Scale for Children (SDSC) measured sleep duration as a cutoff (< 8 hours of sleep), and retrained the SustaIn model. We compared the consistency of results obtained by different cutoff criteria via linear correlation between the original and the retrained sequences of biomarker progression.

Data availability
The data repository houses all data generated by the Adolescent Brain Cognitive Development (ABCD) Study at https://nda.nih.gov/abcd/. Data from the Shanghai Sleep Birth Cohort will be made available upon reasonable request to the corresponding author, provided that institutional approval has been obtained and a signed data access agreement is in place, or with the permission of the Shanghai Children's Medical Center.

Code availability
Python of the SuStaIn algorithm is available on https://github.com/ucl-pond.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In the manuscript “Heterogeneous Neurophenotypes of Adolescent Sleep Insufficiency Stratify Natural Short Sleepers from Comorbidity or Environment Driven Insufficiency” Chen and colleagues employ a data-driven clustering model to describe three insufficient sleep subtypes based on cortical thickness measurements. Further assessing the three subtypes, the authors describe differences in socio-ecological factors, neuroimaging-derived brain age and polygenic risk score for short sleep. The ABCD data is well suited for the aim and is complemented by a second, smaller dataset. The approach provides an interesting opportunity to better understand potential brain mechanisms underlying different forms of insufficient sleep using a novel analytic approach to describe subtypes. The reported differences in brain age and PRS between subtypes are a strong contribution.\nHowever, in my opinion the manuscript in its current form lacks details in the results and requires more clarity in the reporting and reasoning. Below are some questions that remained and suggested improvements:\n- Throughout the manuscript the authors leave an important question unaddressed: Why search for subtypes based on neuroimaging if there was a clear expectation that subtypes would emerge from socio-ecological factors?\n- The authors mention several times that neuroimaging can be used as biomarker or to facilitate stratified interventions. With the previous comment in mind, the manuscript provides insufficient reasoning for these statements. MRI scans are expensive and it seems that the groups can be discerned using questionnaire data. Unless there are additional arguments, I believe the manuscript would benefit by focussing on better understanding the brain mechanisms which by itself is good reason to conduct a study.\n- Please provide more clarity regarding the SustaIn model and its interpretability, especially how it estimates subtypes and stages. What makes it better compared to other clustering models? It also remained unclear why the authors opted for 12 ROIs (method section) while 29 out of the 68 brain regions showed significantly reduced CT in the results section.\n- In line with the previous comment. The SustaIn model estimates “sleep-induced brain alteration trajectories”. Based on the provided demographics the age range seems limited which raises the question: what do the stages reflect?\n- Insufficient sleep was determined based on 7 days of Fitbit data. While this is better than a single night, a week of insufficient sleep does not necessarily reflect sustained insufficient sleep which is currently assumed in the manuscript. The authors should discuss their view of how representative the measured sleep insufficiency is.\n- Please provide more details regarding the results\n- In line with the previous comment, I would prefer tables complementary to (or replacing) Fig 4b-c and Fig 6d. Especially the current curved bar graphs make it very difficult for the reader to extract and compare results. Confidence intervals are missing.\n- The result section “Characterization of subtypes by social-ecological profiles” starts interpreting results, even prior to describing those results. Please move to the appropriate section.\nMinor\n- The method section mentions that propensity score matching was used to reduce bias. How many subjects were dropped in the matching process? And please provide some details on the reasoning for the ratio between sufficient and insufficient sleepers.\n- Please provide information on when questionnaire data was collected relative to the imaging data? How does this affect the interpretation?\n- The brain age analysis is an interesting and valuable addition to the manuscript. However, the current interpretation of the results, more advanced or delayed development, warrants more extensive analyses. E.g. Is there data on cognitive performance available in the ABCD study?\n- Please clarify the “% of the total” in the result section regarding CT reduction for the different subtypes. Does this mean that this percentage of the region is affected or do the authors observe a reduction of this percentage?\n- Results “Longitudinal change of brain morphology in the three subtypes”: To what extend do the cortical change maps (fig 3c) and SustaIn maps (fig 3d) correlate?\n- Discussion contains results on sleeping hours that are missing in the result section. Please report all relevant results in the result section. “In comparison with the control group, despite significantly lower sleeping hours (7.46 +/- 0.51 hours ), this postcentral subtype had similar presentations in all sleep-related socio-ecological factors and no negative psychiatric outcomes.”\n- The authors correctly report the limitation that their results are based on cross-sectional data. When interpreting the results, causality sometimes is introduced by using sentences such as “In summary, we used a data-driven method to disentangle the heterogeneity of the impact of insufficient sleep on the brain, identifying three subtypes with distinct and replicable neuroimaging patterns.”. Please refrain from interpreting the directionality of the associations.", "reviewer_2": "Heterogeneous Neurophenotypes of Adolescent Sleep Insufficiency Stratify Natural Short Sleepers from Comorbidity or Environment Driven Insufficiency\nSummary:\nThis study aims to (1) identify and categorize sleep insufficiency phenotypes through cortical thickness analysis, (2) explore the socio-ecological underpinnings of these neurobiological patterns, and (3) systematically compare brain maturation, polygenic predisposition, psychiatric morbidity and neurotransmitter characteristics among the identified subtypes.\nStrength of paper:\n1. This research paper addresses a highly interesting and important topic.\n2. It was conducted with a large, well-characterized dataset with objective sleep measurements (Fitbit) and comprehensive neuroimaging.\n3. It provides multi-stage validation, including comparison of progression patterns derived from SuStaIn with actual longitudinal cortical thickness changes (ΔCT) and independent replication in the Shanghai cohort.\n4. Areas where study limitations were unavoidable are systematically and well described in the Limitations section.\nGeneral conceptual or design problems:\nThe authors defined the group sleeping 8 hours or more as the sufficient sleep group, and the group sleeping less than 8 hours as the insufficient sleep group.\nHowever, insufficient sleep is generally defined as “a shorter sleep duration than required to maintain daytime wakefulness.”\nMoreover, the characteristics exhibited by the postcentral group do not suggest impaired daytime wakefulness. Therefore, it would be worthwhile to consider whether the definitions of the sufficient sleep group and insufficient sleep group are appropriate, and whether revisions to these definitions might be necessary.\nSpecific problems/Areas needing more or less detailed coverage:\n1. Abstract\n“The postcentral-originated subtype mirrored healthy controls in sleep behavior and sleep environment and showed no psychiatric comorbidities, which aligned phenotypically with natural short sleepers. “\n:In the above sentence, ‘controls’ is understandable, but can we call them ‘healthy controls’? While the main text refers to ‘the controls’ throughout, it never explicitly states ‘healthy controls’. They are simply a control group sleeping over 8 hours; we cannot determine if they are healthy.\n2. Results\n“The SuStaIn model assigned each insufficient sleep adolescent with the most likely subtype and stage label. The subjects assigned to ‘stage 0’ of the progression stage were noted as ‘pre-effect’, while others were noted as ‘post-effect’.”\n: In this context, the definitions of ‘pre-effect’ and ‘post-effect’ require consideration—whether these terms are appropriate or if alternative definitions are needed.", "reviewer_3": "The Wu team leveraged advanced imaging data and a data-driven model within the ABCD cohort to identify three distinct subtypes of insufficient sleep of adolescents: the parietal, pericalcarine, and entorhinal subtypes. Notably, these subtypes exhibited significant divergence in sleep-related social-ecological factors, corresponding phenotypically to natural short sleepers, environmentally driven sleep insufficiency, and elevated psychiatric risk, respectively. By integrating brain age modelling, polygenic risk score (PRS) analysis, and neurotransmitter mapping, the study provided deeper insights into the underlying mechanisms of these subtypes. This work is both intriguing and well-written, shedding light on the heterogeneous impacts of insufficient sleep on the brain in adolescents, and offering novel avenues for stratified prevention and intervention strategies. However, a few concerns still need to be addressed to further strengthen the study.\n1. It would be helpful to clarify the number of subjects assigned to ‘stage 0’ for each of the three insufficient sleep subtypes.\n2. The longitudinal changes in brain morphology across the three subtypes align with the pseudo-progression brain maps, which were derived by subtracting stage I z-score brain maps from stage III z-score brain maps (Figures 3c and 3d). However, the rationale for selecting stage I and stage III for comparison is not fully explained. A more detailed justification for these choices, along with a discussion of whether continuous comparisons across intermediate stages could offer a more comprehensive understanding, would strengthen the analysis.\n3. While the pericalcarine and entorhinal subtypes exhibit distinct differences in neighborhood environment and mental/physical conditions compared to the sufficient sleep group, it would be interesting to explore whether these two subtypes themselves differ in these dimensions. Given the potential interplay between environmental factors, sleep, and psychiatric risk, a comparative analysis between these subtypes could provide deeper insights into their unique etiologies.\n4. The correlation coefficient between predicted age using the brain age model and chronological age is relatively low (0.17), suggesting that the model may not adequately capture brain maturation. This could lead to misleading interpretations in the analysis. Additionally, the mean absolute error (MAE) between predicted and chronological age was not reported, which is a critical metric for evaluating model accuracy. Including this information would enhance the validity of the findings.\n5. In the “Neurotransmitter Basis of Insufficient Sleep Subtypes” section, there appears to be a mismatch in the labeling of Figures 5g and 5h, which may actually correspond to Figures 5j and 5i. Furthermore, Figures 5f-h illustrate the association between SuStaIn stage and residualized CBCL scores, but the corresponding results were not explicitly discussed in the Results section.\n6. While the SSBC dataset was used to validate the findings, the sample size (n=52) appears relatively small. Given that the SuStaIn model requires at least 48 subjects for reliable validation (if the same SuStaIn model producer was applied in the validation database), the accuracy of the model’s fit in the validation dataset may be questionable. A more detailed description of the methods and results related to the validation process would strengthen the robustness of the results."}, "author_response": "R1Q1. Throughout the manuscript the authors leave an important question unaddressed: Why search for subtypes based on neuroimaging if there was a clear expectation that subtypes would emerge from socio-ecological factors?\nThe authors mention several times that neuroimaging can be used as biomarker or to facilitate stratified interventions. With the previous comment in mind, the manuscript provides insufficient reasoning for these statements. MRI scans are expensive and it seems that the groups can be discerned using questionnaire data. Unless there are additional arguments, I believe the manuscript would benefit by focusing on better understanding the brain mechanisms which by itself is good reason to conduct a study.\nResponse: We appreciate this opportunity to clarify. The reviewer is concerned that the heterogeneity of insufficient sleep could emerge from socio-ecological factors. In fact, previous diagnostic criteria for insomnia (DSM-III-R, ICSD-R, DSM-IV-TR, RDC, ICSD-2) all used socio-ecological factors to capture the heterogeneity in insomnia patients (nosology with environmental factors, mental health conditions, medical conditions, etc.). However, due to low reliability and diagnostic agreement for the aforementioned insomnia phenotypes, the current diagnostic criteria (ICSD-3 and DSM-5-TR) used a “lumping” (general insomnia diagnostic criteria without subtypes) approach1. Thus, relying solely on the socio-ecological model is insufficient to capture the heterogeneity of sleep insufficiency in adolescents.\nTherefore, the primary and novel aim of our study was not to confirm these known relationships, but to discover data-driven neurobiological subtypes and characterize them comprehensively across multiple domains (including socio-ecological factors). The advantage of MRI lies in its objective, reproducible, and comprehensive quantification of the whole brain pattern.\nWe totally agree with the reviewer’s excellent point, “understanding the brain mechanisms which by itself is good reason to conduct a study”. Neuroimaging provides multimodal biological markers of the brain that may underlie the core pathophysiology of insufficient sleep. We have now clarified that the purpose of our study is to find the different neurobiological responses to insufficient sleep in adolescents. This could indicate personalized insufficient sleep treatments targeting brain development. We further used the socio-ecological factors to show the effectiveness and clinical value of the subtypes.\nThanks to the reviewer’s excellent point, we have now rephrased the Introduction section (Page 3, Line 59-70): “The multifaceted and heterogeneous determinants and outcomes hinder our understanding of insufficient sleep in children and adolescents. For instance, some children may be biologically disposed to short sleep duration without compromised development, while others sleep less because of external disturbance, psychiatric comorbidity, or other conditions that require entirely different interventions. To capture the different sleep profiles, previous studies focused on various objective and subjective sleep measures using latent profile analysis. Previous studies also used the social-ecological framework to understand the etiology of pediatric sleep problems. However, the socio-ecological measurements have raised concerns for low reproducibility and subjectivity. Other studies of sleep genetics and proteomics found genes and proteins for sleep regulation. Moreover, as insufficient sleep has major links on brain development, an in-depth understanding of brain mechanisms needs to be further addressed. To settle such a problem, a reliable, unbiased, and informative biomarker that reflects brain mechanism becomes the key for stratification. … In recent years, MRI-based neuroimaging approaches have been used frequently to unravel the underlying neurobiological mechanisms of sleep problems …”\nTo further address the reviewer’s concerns, we added socio-ecological factors along with MRI factors to find SustaIn subtypes. We used the Sleep loss measurement as the representative for sleep-related factors and the CBCL overall score as the representative for mental health conditions. The neighborhood disadvantage factor was included as the environmental factor, as it shows the most significant difference (P = 2.29e-05, Cohen’s FDR d = 0.28) among all environmental factors. Sleep loss, CBCL overall score, and neighborhood disadvantage were z-scored and transformed, and covariates were controlled.\nSubtype 1 exhibited initial changes in the MRI factors (the pericalcarine, the lingual, the cuneus, and the postcentral thickness). This subtype mirrored that of the postcentral subtype (the natural short sleepers), with no significant deficits in the socio-ecological factor changes. Subtype 2 exhibited initial changes in environmental-related factors (neighborhood disadvantage), the sleep loss factor, followed by the MRI factors (the pericalcarine and the lingual thickness). This subtype mirrored that of the pericalcarine subtype (the environmentally related subtype). Subtype 3 exhibited initial changes in sleep loss and the entorhinal ROI. This mirrored that of the entorhinal subtype (the mental health problems-related subtype).\nFig Response 1. Cortical thickness (CT) and socio-ecological factors developmental ‘trajectories’ in the three subgroups. The positional variance diagrams employ varying colors to depict the probability of each brain region attaining a specific z-score. Different colors correspond with varying degrees of reduced CT: mildly reduced regions are shown in red (z = 1), moderately reduced regions in magenta (z = 1.5), and severely reduced regions in blue (z = 2). Colors indicate the proportion of the posterior distribution where events occur in a particular position in the sequence.\n[1] Nyhuis C C, Fernandez‐Mendoza J. Insomnia nosology: a systematic review and critical appraisal of historical diagnostic categories and current phenotypes[J]. Journal of sleep research, 2023, 32(6): e13910.\n\nR1Q2. Please provide more clarity regarding the SustaIn model and its interpretability, especially how it estimates subtypes and stages. What makes it better compared to other clustering models? It also remained unclear why the authors opted for 12 ROIs (method section) while 29 out of the 68 brain regions showed significantly reduced CT in the results section.\nIn line with the previous comment. The SustaIn model estimates “sleep-induced brain alteration trajectories”. Based on the provided demographics the age range seems limited which raises the question: what do the stages reflect?\nResponse: We apologize for the lack of clarity in our original manuscript. The reviewer is right about the age range of the study. The SuStaIn (Subtype and Stage Inference) model is designed to identify both the subtypes and progression of the abnormality pattern due to an event (with reference to controls) along a continuous severity gradient (stages) from cross-sectional data. SuStaIn operates by identifying a canonical sequence of events (e.g., regional cortical thinning) for each potential subtype. It then estimates for each individual both the probability of belonging to each subtype and their most likely stage along that subtype's progression sequence. For our study, the stages revealed the epicenters/origin (stage I) and the subsequent progression in each sleep insufficiency subtype. This is superior to traditional clustering models (e.g., k-means, hierarchy clustering), which only assign a cluster label without temporal heterogeneity within a cluster. We found that the origin of the brain alterations between different subtypes is one defining feature to reveal the different mechanisms of insufficient sleep, e.g., the entorhinal subtypes (originated in the entorhinal cortex) show substantial comorbidity that commonly attacks the entorhinal cortex, and the pericalcarine (occipital visual cortex) subtype is significantly related to sleep night light. We have added these explanations on Page 24, Line 532.\nAs for the question “It also remained unclear why the authors opted for 12 ROIs (method section) while 29 out of the 68 brain regions showed significantly reduced CT in the results section.”, we first compared the CT differences between sufficient and insufficient sleep using the Desikan-Killiany (DK) Atlas (68 ROIs). Results showed that 28 of 68 brain regions showed reduced CT in the insufficient sleep group compared to the controls (Table S8). We miscounted the number of significant ROIs from 28 to 29. The manuscript is corrected accordingly (Page 6, Line 123). As shown in Fig. 1a and Table S8, the reduced CT didn’t show significant brain lateralization. Due to the heavy computing resource demands of the SuStaIn model1,2, we restricted the number of input ROIs below 20 by averaging the CT values of the left and right hemispheres, and keeping the twelve averaged ROIs with significantly reduced CT (P <0.05, Table S8). All ROIs showing FDR significance in both hemispheres were kept. The entorhinal was also kept for its strong correlation in the left hemisphere (P = 0.003) and marginal correlations in the right hemisphere (P = 0.071). Thank you for pointing out this issue. We clarified this in the method section (Page 25, Line 544).\nSupplementary Table S8 Statistical comparison of cortical thickness between sufficient and insufficient sleep groups.\nROIs P ROIs P\nFDR\nBanks of Superior 0.292 LH 0.432\nTemporal Sulcus\nRH 0.207\nCaudal anterior cingulate 0.554 LH 0.140\nRH 0.663\nCaudal middle frontal 0.259 LH 0.437\nRH 0.101\nCuneus 0.002 LH 4.35e-4\nRH 0.001\nEntorhinal 0.025 LH 0.003\nRH 0.071\nFusiform 0.052 LH 0.052\nRH 0.027\nInferior parietal 0.033 LH 0.015\nRH 0.024\nInferior temporal 0.011 LH 0.003\nRH 0.008\nIsthmus cingulate 0.222 LH 0.453\nRH 0.075\nLateral occipital < 0.001 LH 1.21e-4\nRH 1.96e-5\nLateral orbitofrontal 0.207 LH 0.053\nRH 0.412\nLingual < 0.001 LH 1.12e-7\nRH 9.72e-5\nMedial orbitofrontal 0.176 LH 0.362\nRH 0.049\nMiddle temporal 0.098 LH 0.265\nRH 0.016\nParahippocampal 0.096 LH 0.073\nRH 0.075\nParacentral 0.096 LH 0.180\nRH 0.023\nPars opercularis 0.953 LH 0.815\nRH 0.900\nPars orbitalis 0.783 LH 0.615\nRH 0.300\nPars triangularis 0.433 LH 0.807\nRH 0.164\nPericalcarine < 0.001 LH 4.74e-5\nRH 7.35e-5\nPostcentral 0.004 LH 2.38e-4\nRH 0.009\nPosterior cingulate 0.323 LH 0.303\nRH 0.353\nprecentral 0.033 LH 0.048\nRH 0.007\nprecuneus 0.019 LH 0.004\nRH 0.014\nRostral anterior cingulate 0.433 LH 0.660\nRH 0.329\nRostral middle frontal 0.096 LH 0.028\nRH 0.113\nSuperior frontal 0.433 LH 0.505\nRH 0.303\nSuperior parietal 0.033 LH 0.018\nRH 0.014\nSuperior temporal 0.433 LH 0.571\nRH 0.272\nSupramarginal 0.019 LH 0.036\nRH 0.002\nFrontal pole 0.783 LH 0.799\nRH 0.436\nTemporal pole 0.062 LH 0.051\nRH 0.057\nTransverse temporal 0.097 LH 0.084\nRH 0.103\nInsula 0.258 LH 0.135\nRH 0.368\n* Significant numbers of P < 0.05 were shown in bold. P was Benjamini-Hochberg FDR corrected.. RH stands for right hemisphere. LH stands for left hemisphere.\n[1] Young A L, Marinescu R V, Oxtoby N P, et al. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference[J]. Nature communications, 2018, 9(1): 4273.\n[2] Chen D, Wang X, Voon V, et al. Neurophysiological stratification of major depressive disorder by distinct trajectories[J]. Nature Mental Health, 2023, 1(11): 863-875.\n\nR1Q3. Insufficient sleep was determined based on 7 days of Fitbit data. While this is better than a single night, a week of insufficient sleep does not necessarily reflect sustained insufficient sleep which is currently assumed in the manuscript. The authors should discuss their view of how representative the measured sleep insufficiency is.\nResponse: We fully agree with the reviewer that 7 days of actigraphy monitoring, while providing a more reliable measure than subjective reports or a single night monitoring, may not fully capture an individual's long-term, sustained sleep patterns. It is indeed possible that our sample includes participants experiencing acute or short-term sleep insufficiency rather than a chronic condition. Nevertheless, a 7-day measurement period is a widely accepted and practical standard in sleep research1-3, as it captures a full week including both workdays and weekends, providing a robust estimate of habitual sleep for most individuals. According to Briscoe et al2., to optimize both the efficiency and accuracy of objective sleep measurement, one week of wrist actigraphy is widely recommended, and is regarded sufficient to reflect long-term sleep conditions2. Nevertheless, we discussed that an extended monitoring period in future research will be ideal to validate the current findings (Page 20, Line 415).\n[1] Aili K, Åström-Paulsson S, Stoetzer U, et al. Reliability of actigraphy and subjective sleep measurements in adults: the design of sleep assessments[J]. Journal of Clinical Sleep Medicine, 2017, 13(1): 39-47.\n[2] Briscoe S, Hardy E, Pengo M F, et al. Comparison of 7 versus 14 days wrist actigraphy monitoring in a sleep disorders clinic population[J]. Chronobiology international, 2014, 31(3): 356-362.\n[3] Zhang L, Sasser J, Doane L D, et al. Latent profiles of sleep patterns in early adolescence: associations with behavioral health risk[J]. Journal of Adolescent Health, 2024, 74(1): 177-185.\n\nR1Q4. Group sizes of the three subtypes and a description of relevant characteristics (age, sex, bmi etc.)\nResponse: Thank you for bringing this issue to our attention. We added a detailed description of relevant characteristics, including sleep duration, age, sex, body mass index (BMI), ethnics, race, parental characteristics, and income in Supplementary Table S3.\nSupplementary Table S3. ABCD study second-year follow-up covariate characteristics\nPostcentral Pericalcarine Entorhinal\nsubtype subtype subtype\nN = 179 N = 417 N = 257\nDemographic Characteristics\nAge (years), mean (SD) 12.03 ± 0.65 12.05 ± 0.62 12.02 ± 0.65\nSex (male%) 58.6 54.2 57.4\nRace (White%/ Black%/ 69.0/18.4/3.4/9.2 68.1/23.2/2.7/6.0 74.4/17.8/1.2/6.6\nAsian% / Other%)\nEthnic (Hispanic/ Latino%) 21.3 20.8 26.0\nBMI, mean (SD) 18.8 ± 4.2 19.1 ± 4.4 18.5 ± 4.3\nParental Characteristics\nMaternal Birth age (years) 28.7 ± 7.0 29.3 ± 6.4 30.1 ± 5.8\nParental Education 86.2 86.6 86.4\n(college or more%)\nHousehold income\n< $50 000 (%) 35.8 32.4 35.6\n$50 000 – $99999 (%) 32.1 34.1 31.4\n>$100 000 (%) 32.1 33.5 33.0\nContinuous variables are shown as mean (standard deviation; SD) and categorical variables as percentage (%). BMI stands for body mass index.\n\nR1Q5. Number of subjects assigned to each stage within subtypes.\nResponse: Thank you for this critically important comment. We added a figure showing the number of subjects assigned to each stage within the subtypes in Fig. S4. We added this information in the Results section accordingly. “The subjects assigned to ‘stage 0’ of the progression stage were noted as in the earliest stage (number of ‘stage 0’ in Postcentral subtype = 57; number of ‘stage 0’ in Pericalcarine subtype = 207; number of ‘stage 0’ in Entorhinal subtype = 144), while stages larger than 0 were noted as ‘advanced-stage’. The number of subjects assigned to each stage of the three insufficient sleep subtypes were shown in Fig. S4.” (Page 7 Line 141-146)\nFig. S4 The number of subjects in each stage of the three insufficient sleep subtypes.\n\nR1Q6. Actual mean group values for the variables of interest are not reported. Some measure of confidence is also missing (CI95 or SD). The reported Cohen’s d values are important, but insufficient for the reader to interpret the results.\nIn line with the previous comment, I would prefer tables complementary to (or replacing) Fig 4b-c and Fig 6d. Especially the current curved bar graphs make it very difficult for the reader to extract and compare results. Confidence intervals are missing.\nResponse: Thank you for bringing this issue to our attention. We added the measure of confidence (95% CI) as the reviewer suggested in Supplementary Table S5 (complementary to Fig 4b-c) and Table S7 (complementary to Fig 6d). We further added the actual mean and SD values for the insufficient and sufficient sleep groups (Supplementary Table S9, S10).\nSupplementary Table S5 Statistical comparison of three layers of socio-ecological factors (pediatric sleep, individual child factors, and neighborhood environments) between the neuroimaging-based subtypes from the ABCD dataset. Data were presented as P / Cohen’s d (Rho) [95% CI]).\nFDR\nPostcentral vs Pericalcarine vs Entorhinal vs\nsufficient sleep sufficient sleep sufficient sleep\nSleep disturbance (P / Cohen’s d [95% CI])\nFDR\n0.256 / 0.08 0.009 / 0.20 <0.001 / 0.33\nDIMS\n[-0.04, 0.92] [0.17, 0.97] [0.81,1.72]\n0.820 / -0.06 0.208 / 0.11 0.704 / 0.05\nSBD\n[-0.08, 0.07] [-0.00, 0.12] [-0.05,0.09]\n0.704 / -0.03 0.256 / -0.07 0.704 / -0.05\nDA\n[-0.02, 0.01] [-0.014, 0.00] [-0.01,0.01]\n0.099 / -0.13 0.009 / -0.16 0.704 / -0.05\nSWTD\n[-0.38, 0.02] [-0.35, -0.02] [-0.27,0.03]\n0.878 / -0.02 0.339 / 0.08 0.647 / 0.05\nDOES\n[-0.25, 0.20] [-0.04, 0.31] [-0.10, 0.32]\n0.767/ 0.07 0.704 / -0.04 0.704 / -0.02\nSHY\n[-0.03, 0.02] [-0.02, 0.01] [-0.03, 0.01]\n0.397 / 0.12 <0.001 / 0.31 0.018 / 0.19\nWeekly sleep loss\n[-0.12, 0.35] [0.24, 0.71] [0.01,0.48]\nPhysical (P / Cohen’s d [95% CI]) and mental factors (P / Rho)\nFDR FDR\n0.984 / -0.002 0.041 / -0.17 0.015 / -0.26\nPhysical activities\n[-540, 610] [-945, -91] [-1270, -144]\nExternalizing disorders 0.227 / 0.112 0.776 / -0.020 0.007 / 0.263\nInternalizing disorders 0.723/ 0.033 0.718 / -0.026 0.276 / 0.107\nOverall disorders 0.490 / 0.064 0.744 / 0.023 0.021/ 0.225\nNeighborhood environment (P / Cohen’s d [95% CI])\nFDR\n0.758 / 0.01 0.002 / 0.21 0.311 / 0.07\nNight light\n[-11.42, 8.18] [4.03, 19.71] [-4.01, 13.34]\n0.717 / 0.08 <0.001 / 0.21 0.068 / 0.14\nIncome\n[-0.03, 0.04] [0.02, 0.07] [-0.002, 0.06]\n0.478 / -0.01 <0.001 / 0.19 0.246 / 0.06\nEducation\n[-0.02, 0.04] [0.03, 0.08] [-0.01, 0.05]\n0.281 / 0.06 <0.001 / 0.26 0.068 / 0.17\nNH disadvantage\n[-0.00, 0.02] [0.01, 0.02] [0.00, 0.02]\n0.776 / <0.01 0.002 / 0.21 0.112 / 0.16\nNight noise\n[-0.69, 0.52] [0.27, 1.16] [0.09, 1.01]\n* Significant numbers of P < 0.05 were shown in bold, Bonferroni corrected. NH abbreviates for neighborhood; DIMS abbreviates for disorders of initiating and maintaining sleep; SBD abbreviates for sleep breathing disorders; DA abbreviates for disorder of arousal nightmares; SWTD abbreviates for sleep wake transition disorders; DOES abbreviates for disorder of excessive somnolence; SHY abbreviates for sleep hyperhidrosis. All environmental variables were modified such that higher values indicate a poorer sleep environment (e.g., more nighttime light exposure, more adults with insufficient sleep, lower socioeconomic status, lower education level, more neighborhood disadvantage, and greater noise levels). Covariates, including age, sex, BMI, site, race, ethnicity, and socioeconomic status, were excluded.\nSupplementary Table S7 Statistical comparison of pediatric sleep between the neuroimaging-based subtypes from the Shanghai dataset. Data were presented as P / Cohen’s d [95% CI].\nPostcentral vs Pericalcarine vs Entorhinal vs\nsufficient sleep sufficient sleep sufficient sleep\nPediatric sleep (P / Cohen’s d [95% CI])\nBedtime resistance 0.906 / 0.04 0.639 / 0.12 0.567 / 0.17\n[-2.00, 2.25] [-1.00, 1.61] [-1.21, 2.18]\nSleep onset delay 0.416 / 0.30 0.760 / 0.08 0.413 / 0.25\n[-0.31, 0.76] [-0.29, 0.40] [-0.25, 0.61]\nSleep duration 0.509 / 0.25 0.022 / 0.59 0.009 / 0.81\n[-0.67, 1.34] [0.12, 1.45] [0.28, 1.84]\nSleep anxiety 0.842 / 0.07 0.061 / 0.47 0.049 / 0.60\n[-1.21, 1.49] [-0.05, 1.99] [<0.001, 2.28]\nNight wakings 0.872 / -0.06 0.268 / 0.28 0.167 / 0.42\n[-0.28, 0.23] [-0.09, 0.33] [-0.08, 0.44]\nParasomnias 0.210 / -0.47 0.742 / -0.08 0.990 / > -0.01\n[-0.96, 0.21] [-0.46, 0.33] [-0.52, 0.51]\nSleep disordered 0.238 / 0.44 0.183 / 0.33 0.808 / 0.07\nbreathing [-0.16, 0.63] [-0.09, 0.25] [-0.28, 0.36]\nDaytime sleepiness 0.493 / -0.26 0.285 / 0.27 0.960 / -0.01\n[-1.81, 0.87] [-0.26, 0.11] [-1.07, 1.03]\n* Significant numbers of P < 0.05 were shown in bold, Bonferroni corrected. Covariates, including age and sex, were excluded.\nSupplementary Table S9 Statistics of socio-ecological factors (pediatric sleep, individual child factors, and neighborhood environments) for insufficient and sufficient sleep subjects.\nPostcentral Pericalcarine Entorhinal Sufficient sleep\nSleep disturbance (mean / SD)\nDIMS 12.13 / 3.41 12.57 / 4.01 13.08 / 3.99 11.82 / 3.72\nSBD 3.61 / 1.01 3.81 / 1.32 3.74 / 1.27 3.67 / 1.16\nDA 3.38 / 0.73 3.34 / 0.75 3.56 / 0.61 3.39 / 0.71\nSWTD 7.55 / 2.39 7.49 / 2.20 7.75 / 2.37 7.87 / 2.55\nDOES 3.73 / 2.57 4.02 / 2.74 3.91 / 2.63 3.80 / 2.65\nSHY 2.84 / 1.25 2.72 / 0.99 2.74 / 0.99 2.76 / 1.05\nWeekly sleep loss 2.55 / 2.91 3.04 / 3.25 2.68 / 3.02 2.21 / 2.31\nPhysical (mean / SD)\nPhysical activities 8399 / 3083 7933 / 2843 7674 / 2593 8405 / 2819\nNeighborhood environment (mean / SD)\nNight light 92.73 / 84.26 108.86 / 84.38 96.90 / 83.01 91.36 / 80.52\nIncome 0.37/ 0.21 0.33 / 0.21 0.35 / 0.20 0.38 / 0.22\nEducation 0.37 / 0.19 0.32 / 0.19 0.35 / 0.18 0.39 / 0.21\nNH disadvantage 0.43 / 0.18 0.39 / 0.18 0.41 / 0.17 0.44 / 0.19\nNight noise 50.88 / 3.43 51.68 / 3.57 51.52 / 3.52 50.88 / 3.85\nNH abbreviates for neighborhood; DIMS abbreviates for disorders of initiating and maintaining sleep; SBD abbreviates for sleep breathing disorders; DA abbreviates for disorder of arousal nightmares; SWTD abbreviates for sleep wake transition disorders; DOES abbreviates for disorder of excessive somnolence; SHY abbreviates for sleep hyperhidrosis.\nSupplementary Table S10 Pediatric sleep statistics of insufficient sleep and sufficient sleep subjects.\nPostcentral Pericalcarine Entorhinal Sufficient sleep\nPediatric sleep (mean / SD)\nBedtime 8.9 / 3.4 9.1 / 2.52 9.3 / 3.15 8.8 / 2.70\nresistance\nSleep onset delay 1.7 / 0.87 1.5 / 0.69 1.6 / 0.81 1.4 / 0.69\nSleep duration 4.3 / 1.73 4.8 / 1.45 5.1 / 1.44 4.0 / 1.25\nSleep anxiety 5.6 / 1.87 6.4 / 2.35 6.6 / 2.16 5.4 / 1.80\nNight wakings 3.1 / 0.33 3.3 / 0.52 3.3 / 0.60 3.1 / 0.34\nParasomnias 7.4 / 0.73 7.8 / 0.80 7.8 / 0.98 7.8 / 0.80\nSleep disordered 3.4 / 0.73 3.4 / 0.63 3.3 / 0.68 3.2 / 0.47\nbreathing\nDaytime 9.5 / 1.24 10.6 / 2.48 10.0 / 1.36 10.0 / 1.90\nsleepiness\n\nR1Q7. The result section “Characterization of subtypes by social-ecological profiles” starts interpreting results, even prior to describing those results. Please move to the appropriate section.\nResponse: Thank you for mentioning this issue. We removed the results interpretation of brain age and polygenetic risk score analyses from the “Characterization of subtypes by social-ecological profiles” Results section.\n“We further revealed this group exhibited advanced brain age that may explain the lower CT and increased ΔCT in this group. Comparing the polygenetic risk score (PRS) based on GWAS data of short sleep duration, this group also showed a short sleep duration genetic predisposition.” (Page 11).\n\nMinor points:\nR1Q8. The method section mentions that propensity score matching was used to reduce bias. How many subjects were dropped in the matching process? And please provide some details on the reasoning for the ratio between sufficient and insufficient sleepers.\nResponse: Thank you for mentioning this issue. Our initial sample comprised 853 participants with insufficient sleep and 2413 participants with sufficient sleep (the original control group), for a total of 3266 participants. Sufficient and insufficient sleep samples were matched based on one-to-one matching without replacement within a predefined propensity score radius (caliper = 0.1). Consequently, a total of 1560 participants from the original control group were dropped. After propensity score matching, all covariates were balanced (Fig. S2). This information was updated in the method section (Page 22, Line 459).\nThe choice of a 1:1 matching ratio was used based on the following considerations: (1) While a higher matching ratio (e.g., 1:3) can increase statistical power and precision by retaining a larger sample, it often does so at the potential cost of increasing bias. The 1:1 matching is widely recognized in the methodological literature as the optimal balance for bias reduction. It provides the closest and most precise match for each treated individual, which is our primary objective for causal inference. We prioritized minimizing bias over maximizing sample size. (2) A 1:1 matching protocol is a canonical and universally understood approach in clinical research1. It provides a clear, paired-case structure that is straightforward to interpret and present.\nFig. S2 Covariates propensity score matching for controls. The standardized mean differences of covariates between insufficient sleep and sufficient sleep were reduced below 0.05.\nTable. S2 Dropped sample test\nMatched sample Unmatched sample P value\nMean (std) Mean (std)\nAge 144.33 (7.72) 141.58 (7.52) 0.000\nSex 0.57 (0.50) 0.474 (0.50) 0.000\nRace 1.16 (0.57) 0.98 (0.30) 0.000\nEthnic 1.77 (0.42) 1.85 (0.35) 0.000\nBMI 18.87 (4.79) 18.65 (4.15) 0.855\nMaternal birth age 29.37 (5.99) 31.13 (5.4) 0.000\nParental education 3.02 (0.75) 3.28 (0.62) 0.000\nHousehold income 3.75 (1.24) 4.46 (0.77) 0.000\nP value shows the unpaired T test in all covariate domains between matched sample controls and unmatched sample controls.\n[1] Yang F N, Xie W, Wang Z. Effects of sleep duration on neurocognitive development in early adolescents in the USA: a propensity score matched, longitudinal, observational study[J]. The Lancet Child & Adolescent Health, 2022, 6(10): 705-712.\n\nR1Q9. Please provide information on when questionnaire data was collected relative to the imaging data? How does this affect the interpretation?\nResponse: Thank you for bringing this issue up. During the ABCD study, adolescents participated in an in-person assessment session once a year (questionnaire data) and a magnetic resonance imaging (MRI) session every 2 years (see figure below). The questionnaire data used in this study were from the same visit as the MRI collecting session.\n[1] “ABCD study design”, ABCD study official site. October 20, 2025.\nhttps://abcdstudy.org/wp-content/uploads/2019/02/Timelines_Stud-Par-Time_english_R.jpg\n\nR1Q10. The brain age analysis is an interesting and valuable addition to the manuscript. However, the current interpretation of the results, more advanced or delayed development, warrants more extensive analyses. E.g. Is there data on cognitive performance available in the ABCD study?\nResponse: Thank you for your suggestion. We further measured the cognitive performance by picture vocabulary, flanker inhibitory control and attention, pattern comparison processing speed, picture sequence memory, and oral reading recognition using the NIH toolbox, and found no significant differences between the postcentral subtype and the control. Previous studies have shown that cortical thickness was not significantly correlated with cognitive performance1. Thus, a significantly bigger brain age measure with cortical thickness is not relevant to better cognitive performance. Furthermore, as the age range in our study is quite narrow (within two years), there remains a possibility that the cortical thickness measured larger brain age has not yet been reflected in the NIH toolbox scores. We agree with the reviewer that the statement for advanced for delayed development was too strong, as the brain age was only obtained from cortical thickness measurement. We have rephrased “advanced development” to “advanced brain age” throughout the manuscript. (Page 2, Line 39; Page 18, Line 346; Page 19, Line 366)\n[1] Qiu B, Qian R, Gu B, et al. Neural correlates differ between crystallized and fluid intelligence in adolescents[J]. Translational Psychiatry, 2025, 15(1): 246.\n\nR1Q11. Please clarify the “% of the total” in the result section regarding CT reduction for the different subtypes. Does this mean that this percentage of the region is affected or do the authors observe a reduction of this percentage?\nResponse: Thank you for raising this important point for clarification. The term \"% of the total\" refers specifically to the proportion of participants belonging to each subtype within the total insufficient sleep population. It is a descriptor of the population distribution across the identified subtypes (e.g., Subtype 1 comprised 20.98% of all insufficient sleep population) and is not a measure of the extent of cortical thinning or the percentage of a brain region affected. We have now revised the relevant section in the Results (Page 6, Line 128):\n“This resulted in three subtypes with distinctive topography, i.e., subtype 1 showed CT reduction across the whole whole-brain (n=179, 20.98% of the insufficient sleep subjects), subtype 2 showed a lower CT in the occipital lobe (n=417, 48.89% of the insufficient sleep subjects), and subtype 3 showed a lower CT in the entorhinal cortex (n=257, 30.13% of the insufficient sleep subjects).\"\n\nR1Q12. Results “Longitudinal change of brain morphology in the three subtypes”: To what extent do the cortical change maps (fig 3c) and SustaIn maps (fig 3d) correlate?\nResponse: Thank you for your suggestion. We did spatial correlations between the cortical change maps (fig 3c, 4-year follow-up - 2-year follow-up) and SustaIn-based pseudo-progression maps (fig 3d, stage III – stage I). All three subtypes showed significant correlations (Postcentral subtype, Rho = 0.550, p = 0.005; Pericalcarine subtype, Rho = 0.483, p = 0.016; Postcentral subtype, Rho = 0.798, p < 0.001; Fig. S8). We added this results in the Results sections (Page 9, Line 175)\nFig. S8. Spatial correlations between subtype longitudinal progression and SustaIn modeling. Subtype longitudinal progression is measured as T value differences of ΔCT between insufficient sleep subtypes and sufficient sleep group. “Pseudo progression” modelled by SustaIn modelling was obtained by subtracting subtypes’ stage I z-score brain maps from stage III z-score brain maps.\n\nR1Q13. Discussion contains results on sleeping hours that are missing in the result section. Please report all relevant results in the result section. “In comparison with the control group, despite significantly lower sleeping hours (7.46 +/- 0.51 hours ), this postcentral subtype had similar presentations in all sleep-related socio-ecological factors and no negative psychiatric outcomes.”\nResponse: Thank you for your suggestion. We added objective sleep duration measurements for all three insufficient sleep subtypes in the Supplementary Table S4.\nSupplementary Table S4. ABCD study second-year follow-up sample characteristics\nPostcentral Pericalcarine Entorhinal Sufficient\nsleep\nSleep characteristics\nSleep duration (hours), 7.46 ± 0.51 7.39 ± 0.53 7.50 ± 0.46 8.68 ± 0.46\nmean (SD)\nSleep latency (minutes), 6.18 ± 4.20 6.79 ± 5.32 6.87 ± 4.83 6.79 ± 5.02\nmean (SD)\nWake (minutes), 49.44 ± 8.42 47.83 ± 8.34 48.95 ± 9.26 60.22 ± 9.91\nmean (SD)\nWake (numbers) , 25.41 ± 4.27 25.82 ± 4.16 25.16 ± 3.91 31.28 ± 4.85\nmean (SD)\nSleep duration is measured as sleep onset – offset interval. Sleep latency is measured as first minute participant was asleep – first minute in bed. Wake counts are measured as the number of awakenings during the sleep period.\n\nR1Q14. The authors correctly report the limitation that their results are based on cross-sectional data. When interpreting the results, causality sometimes is introduced by using sentences such as “In summary, we used a data-driven method to disentangle the heterogeneity of the impact of insufficient sleep on the brain, identifying three subtypes with distinct and replicable neuroimaging patterns.”. Please refrain from interpreting the directionality of the associations.\nResponse: Thank you for this critically important comment. You are right that causality is introduced in some of the discussion sentences, which cannot be inferred from the cross-sectional nature of the data. We have now revised the entire manuscript to ensure our language is precise.\nSpecifically, we revised the highlighted sentence in the summary and all other instances of causal language (e.g., \"impact,\" \"influence\") throughout the discussion and conclusion sections and replaced these terms with strictly accurate terminology such as \"association,\" \"link,\" \"relationship,\" and \"correlation.\"\nYour comment has significantly improved the scientific rigor of our manuscript. We are grateful for your expert review.\n\nResponse to Reviewer 2:\nThank you for your positive evaluation and encouraging comments on our manuscript. We are truly encouraged that you found our topic both interesting and important, and that you recognized the strengths of our methodology, particularly the use of a large dataset with objective sleep measurements and comprehensive neuroimaging.\nYour supportive comments are a significant motivation for our continued work in this area.\n\nR2Q1. The authors defined the group sleeping 8 hours or more as the sufficient sleep group, and the group sleeping less than 8 hours as the insufficient sleep group. However, insufficient sleep is generally defined as “a shorter sleep duration than required to maintain daytime wakefulness.”\nMoreover, the characteristics exhibited by the postcentral group do not suggest impaired daytime wakefulness. Therefore, it would be worthwhile to consider whether the definitions of the sufficient sleep group and insufficient sleep group are appropriate, and whether revisions to these definitions might be necessary.\nResponse: We sincerely thank you for this insightful and critical comment. You have raised a fundamental point regarding the definition of sleep insufficiency, which is defined as “a shorter sleep duration than required to maintain daytime wakefulness”. We fully acknowledge that the physiologically relevant definition of insufficient sleep is indeed functional, based on an individual's requirement to maintain daytime wakefulness, rather than a fixed duration. However, in epidemiology, insufficient sleep is generally defined as sleeping less than recommended by public health authorities for health benefits1, 2. Sleep duration is frequently used as the indicator for insufficient sleep in both children3 and adolescents4,5, and adult populations6. Previous pediatric consensus suggested that more than 8 hours of sleep is optimal for adolescent sleep health7. Short sleep duration acts as an effective indicator for daytime sleepiness8, low performance9, 10, and health consequences11, despite that the subject may feel normal for the time being.\nFurthermore, objective sleep duration acts as an important subtyping biomarker for sleep disorders, including insomnia12 and obstructive sleep apnea13, 14. In an adult study, insomnia with objective sleep duration shorter than 6 hours was found with higher biological vulnerability, higher stress, and more impaired neurocognitive functioning12. Patients suffering from obstructive sleep apnea with less than 7 hours of objective sleep duration showed significantly higher risks in all-cause mortality, independent of the apnea-hypopnea index14.\nTherefore, although individual sleep durations may vary, we believe the definition of insufficient sleep (< 8 hours of sleep) is a clinically accepted definition of teenage insufficient sleep. We hope this response satisfies you. Also, we are open to change, replacing \"insufficient sleep or sleep insufficiency\" with \"short sleep duration.\"\n[1] Chaput J P, McHill A W, Cox R C, et al. The role of insufficient sleep and circadian misalignment in obesity[J]. Nature Reviews Endocrinology, 2023, 19(2): 82-97.\n[2] Shochat T, Cohen-Zion M, Tzischinsky O. Functional consequences of inadequate sleep in adolescents: a systematic review[J]. Sleep medicine reviews, 2014, 18(1): 75-87.\n[3] Yang F N, Xie W, Wang Z. Effects of sleep duration on neurocognitive development in early adolescents in the USA: a propensity score matched, longitudinal, observational study[J]. The Lancet Child & Adolescent Health, 2022, 6(10): 705-712.\n[4] Baiden P, Tadeo S K, Tonui B C, et al. Association between insufficient sleep and suicidal ideation among adolescents[J]. Psychiatry research, 2020, 287: 112579.\n[5] Wang Z, Tang Y, Wang G, et al. Insufficient sleep is Associated with increasing trends in adolescent suicidal behaviors[J]. Journal of Adolescent Health, 2024, 74(6): 1198-1207.\n[6] Stolicyn A, Lyall L M, Lyall D M, et al. Comprehensive assessment of sleep duration, insomnia, and brain structure within the UK Biobank cohort[J]. Sleep, 2024, 47(2): zsad274.\n[7] Paruthi S, Brooks L J, D'Ambrosio C, et al. Consensus statement of the American Academy of Sleep Medicine on the recommended amount of sleep for healthy children: methodology and discussion[J]. Journal of clinical sleep medicine, 2016, 12(11): 1549-1561.\n[8] Alves F R, de Souza E A, de França Ferreira L G, et al. Sleep duration and daytime sleepiness in a large sample of Brazilian high school adolescents[J]. Sleep medicine, 2020, 66: 207-215.\n[9] Ming X, Koransky R, Kang V, et al. Sleep insufficiency, sleep health problems and performance in high school students[J]. Clinical Medicine Insights: Circulatory, Respiratory and Pulmonary Medicine, 2011, 5: CCRPM. S7955.\n[10] Dewald J F, Meijer A M, Oort F J, et al. The influence of sleep quality, sleep duration and sleepiness on school performance in children and adolescents: A meta-analytic review[J]. Sleep medicine reviews, 2010, 14(3): 179-189.\n[11] Steptoe A, Peacey V, Wardle J. Sleep duration and health in young adults[J]. Archives of internal medicine, 2006, 166(16): 1689-1692.\n[12] Vgontzas A N, Fernandez-Mendoza J. Insomnia with short sleep duration: nosological, diagnostic, and treatment implications[J]. Sleep Medicine Clinics, 2013, 8(3): 309.\n[13] Pejovic S, Vgontzas A N, Fernandez-Mendoza J, et al. Obstructive sleep apnea comorbid with insomnia symptoms and objective short sleep duration is associated with clinical and preclinical cardiometabolic risk factors: Clinical implications[J]. Sleep Medicine, 2024, 124: 115-119.\n[14] Lin Y, Wu Y, Lin Q, et al. Objective sleep duration and all-cause mortality among people with obstructive sleep apnea[J]. JAMA network open, 2023, 6(12): e2346085-e2346085.\n\nR2Q2. “The postcentral-originated subtype mirrored healthy controls in sleep behavior and sleep environment and showed no psychiatric comorbidities, which aligned phenotypically with natural short sleepers. “\nIn the above sentence, ‘controls’ is understandable, but can we call them ‘healthy controls’? While the main text refers to ‘the controls’ throughout, it never explicitly states ‘healthy controls’. They are simply a control group sleeping over 8 hours; we cannot determine if they are healthy.\nResponse: Thank you for this excellent point. You are absolutely correct. Our use of the term \"healthy controls\" was inaccurate and not supported by our study design, as we did not explicitly assess or define the overall health status of the control group beyond their sleep duration. We have revised the manuscript throughout by referring to them as \"the control of sufficient sleep\" or \"controls\", without the \"healthy\" descriptor. The specific sentence you highlighted has been changed to: \"The postcentral-originated subtype mirrored controls in sleep behavior and sleep environment and showed no psychiatric comorbidities, which aligned phenotypically with natural short sleepers.\" (Page 2, Line 37)\n\nR2Q3. “The SuStaIn model assigned each insufficient sleep adolescent with the most likely subtype and stage label. The subjects assigned to ‘stage 0’ of the progression stage were noted as ‘pre-effect’, while others were noted as ‘post-effect’.”\nIn this context, the definitions of ‘pre-effect’ and ‘post-effect’ require consideration—whether these terms are appropriate or if alternative definitions are needed.\nResponse: Thank you for this important comment. We realize that the term \"pre-effect\" is inaccurate because it implies that individuals in Stage 0 exhibit no impact from insufficient sleep. In fact, Stage 0 likely represents the earliest detectable signature of change within the inferred progression pattern. We have comprehensively revised the following statement by changing \"pre-effect\" into the “earliest stage” and \"post-effect” into “advanced-stage”.\nThe specific sentence you highlighted has been changed to: “The subjects assigned to ‘stage 0’ of the progression stage were noted as in the earliest stage (number of ‘stage 0’ in Postcentral subtype = 57; number of ‘stage 0’ in Pericalcarine subtype = 207; number of ‘stage 0’ in Entorhinal subtype = 144), while others were noted as advanced-stage. The number of subjects assigned to each stage of the three insufficient sleep subtypes is shown in Fig. S4. The probability-based distribution of advanced-stage individuals in the three subtypes was depicted in Fig. 2c. Fig. 2d illustrated the different stages in the three subtypes of sleep deficit, represented as the mean z-value images, i.e., reduction of CT in the advanced-stage phase compared to the controls. ” (Page 7, Line 141-149)\n\nResponse to Reviewer 3:\nR3Q1. It would be helpful to clarify the number of subjects assigned to ‘stage 0’ for each of the three insufficient sleep subtypes.\nResponse: Thank you for your suggestion. We added the number of subjects assigned to ‘stage 0’ for each of the three subtypes on Page 7, Line 141-146. “The subjects assigned to ‘stage 0’ of the progression stage were noted as in the earliest stage (number of ‘stage 0’ in Postcentral subtype = 57; number of ‘stage 0’ in Pericalcarine subtype = 207; number of ‘stage 0’ in Entorhinal subtype = 144), while stages larger than 0 were noted as ‘advanced-stage’.” The proportion of subjects assigned to the “stage 0” is comparable to previous SustaIn model application on Alzheimer’s disease1, schizophrenia2 and major depressive disorders3. Furthermore, the number of subjects assigned to other stages were shown in Fig. S4.\nFig. S4 The number of subjects in each stage of the three insufficient sleep subtypes.\n[1] Young A L, Marinescu R V, Oxtoby N P, et al. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference[J]. Nature communications, 2018, 9(1): 4273.\n[2] Jiang Y, Luo C, Wang J, et al. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm[J]. Nature communications, 2024, 15(1): 5996.\n[3] Chen D, Wang X, Voon V, et al. Neurophysiological stratification of major depressive disorder by distinct trajectories[J]. Nature Mental Health, 2023, 1(11): 863-875.\n\nR3Q2. The longitudinal changes in brain morphology across the three subtypes align with the pseudo-progression brain maps, which were derived by subtracting stage I z-score brain maps from stage III z-score brain maps (Figures 3c and 3d). However, the rationale for selecting stage I and stage III for comparison is not fully explained. A more detailed justification for these choices, along with a discussion of whether continuous comparisons across intermediate stages could offer a more comprehensive understanding, would strengthen the analysis.\nResponse: We sincerely thank you for this critical and insightful comment. As the reviewer correctly mentioned, “the rationale for selecting stage I and stage III for comparison is not fully explained”, We have now added a justification in the Methods section (Page 26, Line 578) explaining that Stage I and III were chosen as Stage I indicated the baseline time point and Stage III was an intermediate stage that corresponded to the follow-up time point. As the two-year follow-up was not too far from the baseline, we feel an intermediate stage would be more appropriate than the end stages.\nFurthermore, as suggested, we computed the “pseudo progression” obtained from SuStaIn model (across multiple stages) by subtracting subtypes’ stage I z-score brain maps from stage III, stage IV, or stage V z-score brain maps (shown in Fig. S9). The SustaIn “pseudo progression” of the insufficient sleep subtypes matched their actual longitudinal progression (shown in Fig. 3c). Finally, to quantitatively strengthen our claim that the pseudo-progression matches longitudinal change, we computed spatial correlations between the maps, which were highly significant (Postcentral subtype, Rho = 0.550, p = 0.005; Pericalcarine subtype, Rho = 0.483, p = 0.016; Postcentral subtype, Rho = 0.798, p < 0.001; Fig. S8). This new analysis has been added to the Results section (Page 9, Line 175).\nFig. S9 Subtype brain morphology pseudo-progression pattern found with the SuStaIn model. “Pseudo progression” modelled by SustaIn modelling was obtained by subtracting subtypes’ stage I z-score brain maps from stage III, stage IV, or stage V z-score brain maps.\nFig. S8. Spatial correlations between longitudinal progression and “pseudo progression” from SustaIn modeling. Longitudinal progression is measured as T value differences of ΔCT between the insufficient sleep subtypes and the sufficient sleep group. “Pseudo progression” modelled by SustaIn modelling was obtained by subtracting stage I z-score brain maps from stage III z-score brain maps in each subtype.\n\nR3Q3. While the pericalcarine and entorhinal subtypes exhibit distinct differences in neighborhood environment and mental/physical conditions compared to the sufficient sleep group, it would be interesting to explore whether these two subtypes themselves differ in these dimensions. Given the potential interplay between environmental factors, sleep, and psychiatric risk, a comparative analysis between these subtypes could provide deeper insights into their unique etiologies.\nResponse: We sincerely thank you for your kind suggestion. We agree that exploring the differences between these two subtypes could provide additional layers of understanding regarding their unique etiologies. The interplay between environmental factors and psychiatric risk is indeed a complex and important area of inquiry.\nWe have added the comparison between the pericalcarine and entorhinal subtypes in terms of pediatric sleep, neighborhood environment, and mental/physical health variables. The results are briefly summarized below and have been added to the Supplementary Results section (Table S6). As shown in the table, the pericalcarine subtype was significantly different than the entorhinal subtype in night light (p = 0.015), and marginally significantly higher in weekly sleep loss (p = 0.071). This result confirms the entorhinal subtype as an environmentally related subtype. But to fully understand the characteristics of the subtypes, we believe the comparison with the control is more straightforward.\nSupplementary Table S6 Statistical comparison of three layers of socio-ecological factors (pediatric sleep, individual child factors, and neighborhood environments) between the neuroimaging-based subtypes from the ABCD dataset. Data were presented as P / Cohen’s d (Rho) [95% CI].\nFDR\nPericalcarine vs Entorhinal\nSleep disturbance (P / Cohen’s d [95% CI])\nFDR\nDIMS 0.185 / -0.13 [-1.07, -0.03]\nSBD 0.694 / 0.05 [-0.03, 0.12]\nDA 0.794 / -0.02 [-0.01, 0.01]\nSWTD 0.208 / -0.12 [-0.33, 0.01]\nDOES 0.794 / 0.04 [-0.20, 0.27]\nSHY 0.794 / -0.02 [-0.02, 0.03]\nWeekly sleep loss 0.071 / 0.11 [-0.01, 0.51]\nPhysical (P / Cohen’s d [95% CI])\nFDR\nPhysical activities 0.592 / 0.09 [-458, 819]\nNeighborhood environment (P / Cohen’s d [95% CI])\nFDR\nNight light 0.015 / 0.14 [2.27, 17.72]\nIncome 0.383 / 0.07 [-0.02, 0.04]\nEducation 0.101 / 0.09 [-0.007, 0.049]\nNH disadvantage 0.216 / 0.08 [-0.01, 0.04]\nNight noise 0.685 / 0.04 [-0.43, 0.65]\n*Significant numbers of P < 0.05 were shown in bold, Bonferroni corrected. NH FDR abbreviates for neighborhood. DIMS abbreviates for disorders of initiating and maintaining sleep; SBD abbreviates for sleep breathing disorders; DA abbreviates for disorder of arousal nightmares; SWTD abbreviates for sleep wake transition disorders; DOES abbreviates for disorder of excessive somnolence; SHY abbreviates for sleep hyperhidrosis.\n\nR3Q4. The correlation coefficient between predicted age using the brain age model and chronological age is relatively low (0.17), suggesting that the model may not adequately capture brain maturation. This could lead to misleading interpretations in the analysis. Additionally, the mean absolute error (MAE) between predicted and chronological age was not reported, which is a critical metric for evaluating model accuracy. Including this information would enhance the validity of the findings.\nResponse: Thank you for this critical and insightful feedback. We agree entirely that the low correlation coefficient and the absence of the MAE metric were significant limitations that affected the interpretation of our primary findings.\nIn direct response to your comments, we have retrained our brain age prediction model with a refined approach. We changed the brain age model from the Connectome based predictive modelling (CPM) framework to a three-layer multi-layer perceptron (MLP). This has led to a substantial improvement in model performance. The correlation coefficient (r) between predicted and chronological age is now 0.58, and the mean absolute error (MAE) is 6.39 months. We have revised the manuscript according to the adjustments in the method (Page 23, Line 494) and results sections (Page 13, Line 240). Despite the change of analysis, our primary findings remained the same that the postcentral subtype displayed larger brain age and the entorhinal subtype displayed lower brain age compared with control.\nMethod section is updated as follows. “The brain age predictor MLP consists of a sequential stack of fully-connected layers: an input layer accepting the 68-dimensional feature vector, followed by a hidden layer of 128 units with ReLU activation and a 30% dropout, a second hidden layer of 64 units with ReLU activation and a 20% dropout, a third hidden layer of 32 units with ReLU activation, and a final linear output layer that produces a single scalar value representing the predicted age. The dropout layers were incorporated during training to mitigate overfitting. The brain age predictor MLP model was trained with the 2,413 sufficient sleep control samples. The model accuracy was evaluated using Pearson’s correlation coefficient (r = 0.58) and mean absolute error (MAE = 6.39 months) between predicted and chronological age. The brain age predictor model was applied to the insufficient sleep group for brain age calculation.” (Page 23, Line 494)\nResults section is updated as follows. “Compared with the control, the postcentral subtype (aka. short sleepers) showed higher (P = 0.006, Bonferroni corrected; Fig. Bonferroni 5a) and the entorhinal (aka. comorbid related) showed lower (P = 0.021, Bonferroni Bonferroni corrected; Fig. 5a) brain age.” (Page 13, Line 240)\n\nR3Q5. In the “Neurotransmitter Basis of Insufficient Sleep Subtypes” section, there appears to be a mismatch in the labeling of Figures 5g and 5h, which may actually correspond to Figures 5j and 5i. Furthermore, Figures 5f-h illustrate the association between SuStaIn stage and residualized CBCL scores, but the corresponding results were not explicitly discussed in the Results section.\nResponse: We sincerely thank you for your careful review and for pointing out our mistakes. You are absolutely correct. There was an error in the Results describing section of Figures 5g, 5h, 5i, and 5j in the original version of the manuscript. The labels have now been corrected throughout the text to match the figure. (Page 14, Line 274)\nRegarding the undiscussed results for Figures 5f-h, we apologize for this omission. We have added the “Correlations with mental conditions and SustaIn stages” section in the Results to explicitly discuss these findings (Page 14, Line 260): “To understand the potential connections between mental conditions and insufficient subtypes, we further performed correlations between the SuStaIn stage (advanced-stage subjects) and the CBCL scores (after controlling for age, sex, and ethnicity, Fig. 5f-h). Only the entorhinal subtype showed positive correlations between SuStaIn stages and CBCL scores for internalizing problems (Rho = 0.263, P = 0.007) and overall problems (Rho = 0.225, P = 0.021), but not for externalizing problems (Rho = 0.107, P = 0.276). The correlations were not significant for the postcentral subtype (internalizing problems, Rho = 0.112, P = 0.227; externalizing problems, Rho = 0.033, P = 0.723; overall problems, Rho = 0.064, P = 0.490) and the pericalcarine subtype (internalizing problems, Rho = -0.020, P = 0.776; externalizing problems, Rho = -0.026, P = 0.718; overall problems, Rho = 0.023, P = 0.744).”\n\nR3Q6. While the SSBC dataset was used to validate the findings, the sample size (n=52) appears relatively small. Given that the SuStaIn model requires at least 48 subjects for reliable validation (if the same SuStaIn model producer was applied in the validation database), the accuracy of the model’s fit in the validation dataset may be questionable. A more detailed description of the methods and results related to the validation process would strengthen the robustness of the results.\nResponse: We sincerely thank you for this critical and insightful comment. You were right about the number needed to train SustaIn model. In practice, we didn’t retrain the SustaIn model in the SSBC dataset but directly applied the model trained in the ABCD dataset to SSBC. We are sincerely sorry for the lack of this information in the manuscript. We added the following in the method and results section for further clarification (Page 26, Line 587; Page 16, Line 310). “We first regressed out confounding factors (age, sex, and BMI) and Z-score transformed the cortical thickness in insufficient sleep group relative to the sufficient sleep group. As the number of subjects from the SSBC dataset was not sufficient to retain the SuStaIn model, we applied the SuStaIn model with identical parameters from the ABCD dataset to the SSBC dataset.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I would like to thank the authors for their time and effort addressing the concerns raised by me and the other reviewers. The revised manuscript is more clear and together with the supplemental material provides important details about the findings. This allows readers to properly weight and interpret the results. The findings provide novel insights regarding the link between sleep patterns and the brain in the adolescent population.\nThe only minor comment that I still have is readability of Figure 4b. The curved bar graph with repetitive x-axis makes it difficult to compare subtypes. However, this might be a matter of personal preference.", "reviewer_2": "The authors have adequately addressed all of my previous concerns. I recommend acceptance.", "reviewer_3": "The authors have addressed all my concerns. congratulations"}, "author_response": "Response to Reviewer 1:\nThank you for your thoughtful and positive feedback. It is greatly appreciated.\nI am pleased to hear that the revised manuscript and supplemental material have successfully addressed the concerns raised during the review process. The insights into the link between sleep patterns and the adolescent brain are indeed a key contribution of this study, and I am glad that this comes across clearly in the revised version.\nThank you once again for your constructive input throughout the review process. It has undoubtedly strengthened the manuscript.\nMinor points:\nThe only minor comment that I still have is readability of Figure 4b. The curved bar graph with repetitive x-axis makes it difficult to compare subtypes. However, this might be a matter of personal preference.\nResponse:\nThank you for this additional comment. We appreciate you highlighting the potential readability challenge in Figure 4b.\nUpon careful consideration, we have decided to retain the current design for the following reasons: it maintains visual consistency with related figures in the manuscript, and it effectively displays the subtypes’ characteristics in the presented categories, which was a primary goal.\nHowever, to address your valid concern and aid reader interpretation, we have added a guiding sentence to the figure legend (e.g., \"For optimal interpretation, readers are advised to compare the three colored bars within each individual sub-factor sector (e.g., 'Noise') to assess subtype differences.\").\nWe also remain open to revisiting this should the Editor feel a change is necessary.\nThank you again for your meticulous review.\n\nResponse to Reviewer 2:\nThank you for your positive evaluation of our revisions. We are very pleased that our responses and modifications have adequately addressed your previous concerns.\nWe appreciate the time and insightful feedback provided throughout the review process, which has significantly strengthened our work.\n\nResponse to Reviewer 3:\nThank you for your final positive assessment and for your congratulations. We are very grateful that our revisions have addressed your concerns. We sincerely appreciate the time and expertise you invested in reviewing our manuscript, as your feedback was instrumental in refining this work."}]

Decision letter