Wireless ear EEG to monitor drowsiness.

Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.

Differentiation Across Bipolar Disorder and Major Depressive Disorder by Whole-Night Polysomnographic Findings.

Abstract.Background: There is growing evidence that understanding the role of sleep disturbance in bipolar disorder (BD) and major depressive disorder (MDD) is helpful when studying the high heterogeneity of patients across psychiatric disorders.Objective: The present study was designed to investigate the transdiagnostic role of sleep disturbance measured by polysomnography (PSG) in differentiating from MDD with BD.Methods: A total of 256 patients with MDD and 107 first-episode and never medicated patients with BD using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria were recruited. All patients completed 1 night of PSG recording, and the changes in objective sleep structure parameters were determined by PSG analysis.Results: We showed that patients with MDD had statistically longer rapid eye movement (REM) latency, a higher percentage of stage N2 sleep, and lower percentages of stage N3 sleep and REM sleep than those with BD after controlling for confounding factors (all P < .05). Moreover, using the logistic regression analysis, we identified that REM latency was associated with BD diagnosis among the PSG sleep features. The cutoff value for PSG characteristics to differentiate BD from MDD was 261 in REM latency (sensitivity: 41.4% and specificity: 84.1%).Conclusions: Our findings suggest that PSG-measured sleep abnormalities, such as reduced REM latency, may be a diagnostic differentiating factor between MDD and BD, indicating their roles in identifying homogeneous transdiagnostic subtypes across psychiatric disorders.

GRU-powered sleep stage classification with permutation-based EEG channel selection.

We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU's ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels.

Common sleep data pipeline for combined data sets.

Over the past few years, sleep research has shown impressive performance of deep neural networks in the area of automatic sleep-staging. Recent studies have demonstrated the necessity of combining multiple data sets to obtain sufficiently generalizing results. However, working with large amounts of sleep data can be challenging, both from a hardware perspective and because of the different preprocessing steps necessary for distinct data sources. Here we review the possible obstacles and present an open-source pipeline for automatic data loading. Our solution includes both a standardized data store as well as a 'data serving' portion which can be used to train neural networks on the standardized data, allowing for different configuration options for different studies and machine learning designs. The pipeline, including implementation, is made public to ensure better and more reproducible sleep research.

The effect of central sleep apnea on sleep architecture in children with obstructive sleep apnea.

OBJECTIVE: This study aimed to investigate how central sleep apnea (CSA) impacts sleep patterns in children with obstructive sleep apnea (OSA). METHODS: Children undergoing polysomnography (PSG) were enrolled and sorted into two groups: those with OSA alone (Group A) and those with both OSA and CSA (CAI <1 nd: children with 10 % CSA or more and less than 50 %, Group B). Statistical analysis was conducted to compare sleep structure and clinical features between Group A and Group B. RESULTS: Group B exhibited significantly higher respiratory events, apnea hypoventilation index, apnea index and oxygen desaturation index (ODI) compared to Group A (p < 0.05). Group B also showed higher total sleep time and arousal index than Group A (P < 0.05). The proportion of time spent in stage N3 was lower in Group B than in Group A (P < 0.05). Moreover, mean heart rate and minimum heart rate were higher in Group B compared to Group A (P < 0.05).Minimum oxygenation levels (including non-rapid eye movement (NREM) stages) were lowe in Group B than in Group A (P < 0.05). Additionally, the prevalence of positional obstructive sleep apnea (P-OSA) was greater in Group B than in Group A (P < 0.05). CONCLUSION: In comparison to those with OSA alone, children with OSA and concurrent CSA exhibited distinct sleep patterns, including reduced N3uration, higher arousal index, longer respiratory events, higher ODI, and lower oxygen saturation, higher heart rate.

Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers.

Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed, and in particular, deep-learning based algorithms have achieved performance on par with human annotation. Despite improved performance, a limitation of most deep-learning based algorithms is their black-box behavior, which have limited their use in clinical settings. Here, we propose a cross-modal transformer, which is a transformer-based method for sleep stage classification. The proposed cross-modal transformer consists of a cross-modal transformer encoder architecture along with a multi-scale one-dimensional convolutional neural network for automatic representation learning. The performance of our method is on-par with the state-of-the-art methods and eliminates the black-box behavior of deep-learning models by utilizing the interpretability aspect of the attention modules. Furthermore, our method provides considerable reductions in the number of parameters and training time compared to the state-of-the-art methods. Our code is available at https://github.com/Jathurshan0330/Cross-Modal-Transformer. A demo of our work can be found at https://bit.ly/Cross_modal_transformer_demo.

Do all sedatives promote biological sleep electroencephalogram patterns? A machine learning framework to identify biological sleep promoting sedatives using electroencephalogram.

BACKGROUND: Sedatives are commonly used to promote sleep in intensive care unit patients. However, it is not clear whether sedation-induced states are similar to the biological sleep. We explored if sedative-induced states resemble biological sleep using multichannel electroencephalogram (EEG) recordings. METHODS: Multichannel EEG datasets from two different sources were used in this study: (1) sedation dataset consisting of 102 healthy volunteers receiving propofol (N = 36), sevoflurane (N = 36), or dexmedetomidine (N = 30), and (2) publicly available sleep EEG dataset (N = 994). Forty-four quantitative time, frequency and entropy features were extracted from EEG recordings and were used to train the machine learning algorithms on sleep dataset to predict sleep stages in the sedation dataset. The predicted sleep states were then compared with the Modified Observer's Assessment of Alertness/ Sedation (MOAA/S) scores. RESULTS: The performance of the model was poor (AUC = 0.55-0.58) in differentiating sleep stages during propofol and sevoflurane sedation. In the case of dexmedetomidine, the AUC of the model increased in a sedation-dependent manner with NREM stages 2 and 3 highly correlating with deep sedation state reaching an AUC of 0.80. CONCLUSIONS: We addressed an important clinical question to identify biological sleep promoting sedatives using EEG signals. We demonstrate that propofol and sevoflurane do not promote EEG patterns resembling natural sleep while dexmedetomidine promotes states resembling NREM stages 2 and 3 sleep, based on current sleep staging standards.

Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes.

Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an electroencephalography (EEG)-based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM), the K nearest neighbor (KNN), the Naive Bayes (NB), the Decision Tree (DT), and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake and drowsy). Segmentation into 10 s intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.

Unveil sleep spindles with concentration of frequency and time (ConceFT).

Objective.Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency (TF) analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated algorithm for sleep spindle annotation in EEG data and to measure spindle instantaneous frequencies (IFs).Approach.ConceFT effectively reduces stochastic EEG influence, enhancing spindle visibility in the TF representation. Our automated spindle detection algorithm, ConceFT-Spindle (ConceFT-S), is compared to A7 (non-deep learning) and SUMO (deep learning) using Dream and Montreal Archive of Sleep Studies (MASS) benchmark databases. We also quantify spindle IF dynamics.Main results.ConceFT-S achieves F1 scores of 0.765 in Dream and 0.791 in MASS, which surpass A7 and SUMO. We reveal that spindle IF is generally nonlinear.Significance.ConceFT offers an accurate, interpretable EEG-based sleep spindle detection algorithm and enables spindle IF quantification.

Spindle Detection Based on Elastic Time Window and Spatial Pyramid Pooling.

BACKGROUND: Sleep spindles have emerged as valuable biomarkers for assessing cognitive abilities and related disorders, underscoring the importance of their detection in clinical research. However, template matching-based algorithms using fixed templates may not be able to fully adapt to spindles of different durations. Moreover, inspired by the multiscale feature extraction of images, the use of multiscale feature extraction methods can be used to better adapt to spindles of different frequencies and durations. METHODS: Therefore, this study proposes a novel automatic spindle detection algorithm based on elastic time windows and spatial pyramid pooling (SPP) for extracting multiscale features. The algorithm utilizes elastic time windows to segment electroencephalogram (EEG) signals, enabling the extraction of features across multiple scales. This approach accommodates significant variations in spindle duration and polarization positioning during different EEG epochs. Additionally, spatial pyramid pooling is integrated into a depthwise separable convolutional (DSC) network to perform multiscale pooling on the segmented spindle signal features at different scales. RESULTS: Compared with existing template matching algorithms, this algorithm's spindle wave polarization positioning is more consistent with the real situation. Experimental results conducted on the public dataset DREAMS show that the average accuracy of this algorithm reaches 95.75%, with an average negative predictive value (NPV) of 96.55%, indicating its advanced performance. CONCLUSIONS: The effectiveness of each module was verified through thorough ablation experiments. More importantly, the algorithm shows strong robustness when faced with changes in different experimental subjects. This feature makes the algorithm more accurate at identifying sleep spindles and is expected to help experts automatically detect spindles in sleep EEG signals, reduce the workload and time of manual detection, and improve efficiency.

Normative longitudinal EEG recordings during sleep stage II in the first year of age.

The electroencephalogram (EEG) is a fundamental diagnostic procedure that explores brain function. This manuscript describes the characteristics of a sample of healthy at-term infants. One hundred and three (103) infants from Mexico between 15 days and 12.5 months of age were recorded during physiological sleep. Referential EEG recordings were obtained using linked ear lobes as reference. The amplifier gain was 10,000, the bandwidth was set between 0.3 and 30 Hz, and the sample rate was 200 Hz. Sample windows of 2.56 s were marked for later quantitative analysis. To our knowledge, this is the first dataset of normal infants during the first year of age.

Efferent pathways from the suprachiasmatic nucleus to the horizontal limbs of diagonal band promote NREM sleep during the dark phase in mice.

The regulation of circadian rhythms and the sleep-wake states involves in multiple neural circuits. The suprachiasmatic nucleus (SCN) is a circadian pacemaker that controls the rhythmic oscillation of mammalian behaviors. The basal forebrain (BF) is a critical brain region of sleep-wake regulation, which is the downstream of the SCN. Retrograde tracing of cholera toxin subunit B showed a direct projection from the SCN to the horizontal limbs of diagonal band (HDB), a subregion of the BF. However, the underlying function of the SCN-HDB pathway remains poorly understood. Herein, activation of this pathway significantly increased non-rapid eye movement (NREM) sleep during the dark phase by using optogenetic recordings. Moreover, activation of this pathway significantly induced NREM sleep during the dark phase for first 4 h by using chemogenetic methods. Taken together, these findings reveal that the SCN-HDB pathway participates in NREM sleep regulation and provides direct evidence of a novel SCN-related pathway involved in sleep-wake states regulation.

Sleep architecture in idiopathic hypersomnia: the influence of age, sex, and body mass index.

This study aimed to progress the understanding of idiopathic hypersomnia (IH) by assessing the moderating influence of individual characteristics, such as age, sex, and body mass index (BMI) on sleep architecture. In this retrospective study, 76 IH participants (38.1 ± 11.3 years; 40 women) underwent a clinical interview, an in-laboratory polysomnography with a maximal 9-h time in bed and a multiple sleep latency test (MSLT). They were compared to 106 healthy controls (38.1 ± 14.1 years; 60 women). Multiple regressions were used to assess moderating influence of age, sex, and BMI on sleep variables. We used correlations to assess whether sleep variables were associated with Epworth Sleepiness Scale scores and mean sleep onset latency on the MSLT in IH participants. Compared to controls, IH participants had shorter sleep latency (p = 0.002), longer total sleep time (p < 0.001), more time spent in N2 sleep (p = 0.008), and showed trends for a higher sleep efficiency (p = 0.023) and more time spent in rapid eye movement (REM) sleep (p = 0.022). No significant moderating influence of age, sex, or BMI was found. More severe self-reported sleepiness in IH patients was correlated with shorter REM sleep latency and less N1 sleep in terms of proportion and duration (ps < 0.01). This study shows that, when compared to healthy controls, patients with IH had no anomalies in their sleep architecture that can explain their excessive daytime sleepiness. Moreover, there is no moderating influence of age, sex, and BMI, suggesting that the absence of major group differences is relatively robust.

Comparison of automated deep neural network against manual sleep stage scoring in clinical data.

OBJECTIVE: To compare the accuracy and generalizability of an automated deep neural network and the Philip Sleepware G3™ Somnolyzer system (Somnolyzer) for sleep stage scoring using American Academy of Sleep Medicine (AASM) guidelines. METHODS: Sleep recordings from 104 participants were analyzed by a convolutional neural network (CNN), the Somnolyzer and skillful technicians. Evaluation metrics were derived for different combinations of sleep stages. A further comparison between the Somnolyzer and the CNN model using a single-channel signal as input was also performed. Sleep recordings from 263 participants with a lower prevalence of OSA served as a cross-validation dataset to validate the generalizability of the CNN model. RESULTS: The overall agreement between automated and manual scoring for sleep staging in 104 participants outperformed that of the Somnolyzer according to various metrics (accuracy: 81.81 % vs. 77.07 %; F1: 76.36 % vs. 73.80 %; Cohen's kappa: 0.7403 vs. 0.6848). The results showed that the left electrooculography (EOG) single-channel model had minor advantages over the Somnolyzer. In terms of consistency with manual sleep staging, the CNN model demonstrated superior performance in identifying more pronounced sleep transitions, particularly in the N2 stage and sleep latency metrics. Conversely, the Somnolyzer showed enhanced proficiency in the analysis of REM stages, notably in measuring REM latency. The accuracy in the cross-validation set of 263 participants was also above 80 %. CONCLUSIONS: The CNN-based automated deep neural network outperformed the Somnolyzer and is sufficiently accurate for sleep study analyses using the AASM classification criteria.

Improved sleep stage predictions by deep learning of photoplethysmogram and respiration patterns.

Sleep staging is a crucial tool for diagnosing and monitoring sleep disorders, but the standard clinical approach using polysomnography (PSG) in a sleep lab is time-consuming, expensive, uncomfortable, and limited to a single night. Advancements in sensor technology have enabled home sleep monitoring, but existing devices still lack sufficient accuracy to inform clinical decisions. To address this challenge, we propose a deep learning architecture that combines a convolutional neural network and bidirectional long short-term memory to accurately classify sleep stages. By supplementing photoplethysmography (PPG) signals with respiratory sensor inputs, we demonstrated significant improvements in prediction accuracy and Cohen's kappa (k) for 2- (92.7 %; k = 0.768), 3- (80.2 %; k = 0.714), 4- (76.8 %, k = 0.550), and 5-stage (76.7 %, k = 0.616) sleep classification using raw data. This relatively translatable approach, with a less intensive AI model and leveraging only a few, inexpensive sensors, shows promise in accurately staging sleep. This has potential for diagnosing and managing sleep disorders in a more accessible and practical manner, possibly even at home.

Permutation time irreversibility in sleep electroencephalograms: Dependence on sleep stage and the effect of equal values.

Time irreversibility (TIR) refers to the manifestation of nonequilibrium brain activity influenced by various physiological conditions; however, the influence of sleep on electroencephalogram (EEG) TIR has not been sufficiently investigated. In this paper, a comprehensive study on permutation TIR (pTIR) of EEG data under different sleep stages is conducted. Two basic ordinal patterns (i.e., the original and amplitude permutations) are distinguished to simplify sleep EEGs, and then the influences of equal values and forbidden permutation on pTIR are elucidated. To detect pTIR of brain electric signals, five groups of EEGs in the awake, stages I, II, III, and rapid eye movement (REM) stages are collected from the public Polysomnographic Database in PhysioNet. Test results suggested that the pTIR of sleep EEGs significantly decreases as the sleep stage increases (p<0.001), with the awake and REM EEGs demonstrating greater differences than others. Comparative analysis and numerical simulations support the importance of equal values. Distribution of equal states, a simple quantification of amplitude fluctuations, significantly increases with the sleep stage (p<0.001). If these equalities are ignored, incorrect probabilistic differences may arise in the forward-backward and symmetric permutations of TIR, leading to contradictory results; moreover, the ascending and descending orders for symmetric permutations also lead different outcomes in sleep EEGs. Overall, pTIR in sleep EEGs contributes to our understanding of quantitative TIR and classification of sleep EEGs.

Driver drowsiness is associated with altered facial thermal patterns: Machine learning insights from a thermal imaging approach.

Driver drowsiness is a significant factor in road accidents. Thermal imaging has emerged as an effective tool for detecting drowsiness by enabling the analysis of facial thermal patterns. However, it is not clear which facial areas are most affected and correlate most strongly with drowsiness. This study examines the variations and importance of various facial areas and proposes an approach for detecting driver drowsiness. Twenty participants underwent tests in a driving simulator, and temperature changes in various facial regions were measured. The random forest method was employed to evaluate the importance of each facial region. The results revealed that temperature changes in the nasal area exhibited the highest value, while the eyes had the most correlated changes with drowsiness. Furthermore, drowsiness was classified with an accuracy of 88 % utilizing thermal variations in the facial region identified as the most important regions by the random forest feature importance model. These findings provide a comprehensive overview of facial thermal imaging for detecting driver drowsiness and introduce eye temperature as a novel and effective measure for investigating cognitive activities.

The influence of wakefulness fluctuations on brain networks involved in centrotemporal spike occurrence.

OBJECTIVE: Drowsiness has been implicated in the modulation of centro-temporal spikes (CTS) in Self-limited epilepsy with Centro-Temporal Spikes (SeLECTS). Here, we explore this relationship and whether fluctuations in wakefulness influence the brain networks involved in CTS generation. METHODS: Functional MRI (fMRI) and electroencephalography (EEG) was simultaneously acquired in 25 SeLECTS. A multispectral EEG index quantified drowsiness ('EWI': EEG Wakefulness Index). EEG (Pearson Correlation, Cross Correlation, Trend Estimation, Granger Causality) and fMRI (PPI: psychophysiological interactions) analytic approaches were adopted to explore respectively: (a) the relationship between EWI and changes in CTS frequency and (b) the functional connectivity of the networks involved in CTS generation and wakefulness oscillations. EEG analyses were repeated on a sample of routine EEG from the same patient's cohort. RESULTS: No correlation was found between EWI fluctuations and CTS density during the EEG-fMRI recordings, while they showed an anticorrelated trend when drowsiness was followed by proper sleep in routine EEG traces. According to PPI findings, EWI fluctuations modulate the connectivity between the brain networks engaged by CTS and the left frontal operculum. CONCLUSIONS: While CTS frequency per se seems unrelated to drowsiness, wakefulness oscillations modulate the connectivity between CTS generators and key regions of the language circuitry, a cognitive function often impaired in SeLECTS. SIGNIFICANCE: This work advances our understanding of (a) interaction between CTS occurrence and vigilance fluctuations and (b) possible mechanisms responsible for language disruption in SeLECTS.

Automatic Sleep Stage Classification Using Nasal Pressure Decoding Based on a Multi-Kernel Convolutional BiLSTM Network.

Sleep quality is an essential parameter of a healthy human life, while sleep disorders such as sleep apnea are abundant. In the investigation of sleep and its malfunction, the gold-standard is polysomnography, which utilizes an extensive range of variables for sleep stage classification. However, undergoing full polysomnography, which requires many sensors that are directly connected to the heaviness of the setup and the discomfort of sleep, brings a significant burden. In this study, sleep stage classification was performed using the single dimension of nasal pressure, dramatically decreasing the complexity of the process. In turn, such improvements could increase the much needed clinical applicability. Specifically, we propose a deep learning structure consisting of multi-kernel convolutional neural networks and bidirectional long short-term memory for sleep stage classification. Sleep stages of 25 healthy subjects were classified into 3-class (wake, rapid eye movement (REM), and non-REM) and 4-class (wake, REM, light, and deep sleep) based on nasal pressure. Following a leave-one-subject-out cross-validation, in the 3-class the accuracy was 0.704, the F1-score was 0.490, and the kappa value was 0.283 for the overall metrics. In the 4-class, the accuracy was 0.604, the F1-score was 0.349, and the kappa value was 0.217 for the overall metrics. This was higher than the four comparative models, including the class-wise F1-score. This result demonstrates the possibility of a sleep stage classification model only using easily applicable and highly practical nasal pressure recordings. This is also likely to be used with interventions that could help treat sleep-related diseases.

NREM Slow-Wave Activity in Adolescents Is Differentially Associated With ADHD Levels and Normalized by Pharmacological Treatment.

BACKGROUND: A compelling hypothesis about attention-deficit/hyperactivity disorder (ADHD) etiopathogenesis is that the ADHD phenotype reflects a delay in cortical maturation. Slow-wave activity (SWA) of non-rapid eye movement (NREM) sleep electroencephalogram (EEG) is an electrophysiological index of sleep intensity reflecting cortical maturation. Available data on ADHD and SWA are conflicting, and developmental differences, or the effect of pharmacological treatment, are relatively unknown. METHODS: We examined, in samples (Mage = 16.4, SD = 1.2), of ever-medicated adolescents at risk for ADHD (n = 18; 72% boys), medication-naïve adolescents at risk for ADHD (n = 15, 67% boys), and adolescents not at risk for ADHD (n = 31, 61% boys) matched for chronological age and controlling for non-ADHD pharmacotherapy, whether ADHD pharmacotherapy modulates the association between NREM SWA and ADHD risk in home sleep. RESULTS: Findings indicated medication-naïve adolescents at risk for ADHD exhibited greater first sleep cycle and entire night NREM SWA than both ever-medicated adolescents at risk for ADHD and adolescents not at risk for ADHD and no difference between ever-medicated, at-risk adolescents, and not at-risk adolescents. CONCLUSIONS: Results support atypical cortical maturation in medication-naïve adolescents at risk for ADHD that appears to be normalized by ADHD pharmacotherapy in ever-medicated adolescents at risk for ADHD. Greater NREM SWA may reflect a compensatory mechanism in middle-later adolescents at risk for ADHD that normalizes an earlier occurring developmental delay.