Association between sleep and problematic behaviours in gifted children: a polysomnographic study.

Neurological uniqueness, maladaptive behaviours, as well as atypical sleep patterns are reported to be defining characteristics of giftedness, but this has received little empirical support. We studied the polysomnography recorded sleep of gifted and typically-developing children together with features of maladaptive behaviours. The association of sleep macrostructure and sleep instability with maladaptive behaviours was also investigated in gifted children. In all, 19 gifted children (74% boys) and 17 typically-developing children (76% boys) aged 6-12 years were studied. Giftedness was identified using Renzulli's three-factor definition. The microarousal index, number of awakenings, and number of Stage shifts between sleep stages throughout the night were computed as sleep instability parameters. Maladaptive behaviours were assessed using the Child Behaviour Checklist. We found significantly more Stage N1 and less Stage N3 in gifted children compared to typically-developing children. More Stage N1 sleep was correlated with more externalising problems and less Stage N3 sleep was correlated with more internalising problems. Gifted children also displayed more rapid eye movement (REM) sleep, but this was not significantly correlated with behavioural scales. Gifted children displayed two opposing trends of sleep instability: more instability involving N1 sleep and less instability involving N2, N3 and REM sleep. More total Stage shifts were correlated with more internalising and externalising problems. The results of this study provide initial evidence of polysomnography-based characteristics of giftedness. Further studies are needed to explore common pathways linking sleep alterations and maladaptive behaviours in children with giftedness.

A longitudinal study of polysomnographic variables in patients with mild cognitive impairment converting to Alzheimer's disease.

The main condition at increased risk of dementia is considered to be mild cognitive impairment. Mild cognitive impairment has been defined as a transitional state between normal aging and dementia, of which it may represent a prodrome. The aim of our study was to evaluate whether sleep variables (both conventional and microstructural ones) in subjects with mild cognitive impairment correlate with conversion to dementia. Nineteen subjects with amnestic mild cognitive impairment (mean age 68.5 ±â€…7.0 years) and 11 cognitively intact healthy elderly individuals (mean age 69.2 ±â€…12.6 years) underwent ambulatory polysomnography for the evaluation of nocturnal sleep architecture and cyclic alternating pattern parameters. Amnestic mild cognitive impairment subjects were clinically and cognitively re-evaluated after 2 years, during routine follow-up, and further classified as amnestic mild cognitive impairment converters (that is, patients developing Alzheimer's disease, N = 11) and amnestic mild cognitive impairment non-converters. Compared with healthy elderly individuals, amnestic mild cognitive impairment showed disrupted sleep with decreased rapid eye movement sleep, cyclic alternating pattern rate and cyclic alternating pattern slow-wave-related phases (A1 index). Standard sleep architecture analysis did not show significant differences between the two subgroups of amnestic mild cognitive impairment, whereas cyclic alternating pattern analysis showed that cyclic alternating pattern rate, A1 index and A3 index are significantly reduced in converters compared with non-converters. Our data confirm that in amnestic mild cognitive impairment subjects there is a sleep impairment, particularly when considering more refined sleep parameters and that sleep variables at baseline are different among converters versus non-converters at the 2-year follow-up. Specific sleep alterations might represent potential further biomarkers for the diagnosis and prognosis of early-phase cognitive impairment.

Differentiation of central disorders of hypersomnolence with manual and artificial-intelligence-derived polysomnographic measures.

Differentiation of central disorders of hypersomnolence (DOH) is challenging but important for patient care. This study aimed to investigate whether biomarkers derived from sleep structure evaluated both by manual scoring as well as with artificial intelligence (AI) algorithms allow distinction of patients with different DOH. We included video-polysomnography data of 40 narcolepsy type 1 (NT1), 26 narcolepsy type 2 (NT2), 23 patients with idiopathic hypersomnia (IH) and 54 participants with subjective excessive daytime sleepiness (sEDS). Sleep experts manually scored sleep stages. A previously validated AI algorithm was employed to obtain automatic hypnograms and hypnodensity graphs (where each epoch is represented as a mixture of sleep stage probabilities). One-thousand-three features describing sleep architecture and instability were extracted from manual/automatic hypnogram and hypnodensity graphs. After feature selection, random forest classifiers were trained and tested in a 5-fold-cross-validation scheme to distinguish groups pairwise (NT1-vs-NT2, NT1-vs-IH, …) and single groups from the pooled remaining ones (NT1-vs-rest, NT2-vs-rest,…). The accuracy/F1-score values obtained in the test sets were: 0.74 ± 0.04/0.79 ± 0.05 (NT1-vs-NT2), 0.89 ± 0.09/0.91 ± 0.08 (NT1-vs-IH), 0.93 ± 0.06/0.91 ± 0.07 (NT1-vs-sEDS), 0.88 ± 0.04/0.80 ± 0.07 (NT1-vs-rest), 0.65 ± 0.10/0.70 ± 0.09 (NT2-vs-IH), 0.72 ± 0.12/0.60 ± 0.10 (NT2-vs-sEDS), 0.54 ± 0.19/0.38 ± 0.13 (NT2-vs-rest), 0.57 ± 0.11/0.35 ± 0.18 (IH-vs-sEDS), 0.71 ± 0.08/0.35 ± 0.10 (IH-vs-rest) and 0.76 ± 0.08/0.71 ± 0.13 (sEDS-vs-rest). The results confirm previous findings on sleep instability in patients with NT1 and show that combining manual and automatic AI-based sleep analysis could be useful for better distinction of NT2 from IH, but no precise sleep biomarker of NT2 or IH could be identified. Validation in a larger and multi-centric cohort is needed to confirm these findings.

Sleep biomarkers for stress-induced vulnerability to depression.

Stress can push individuals close to the threshold to depression. An individual's intrinsic vulnerability before a stressful event determines how close they come to the threshold of depression. Identification of vulnerability biomarkers at early (before the stressful event) and late (close to the threshold after the stressful event) stages would allow for corrective actions. Social defeat is a stressful event that triggers vulnerability to depression in half of exposed rats. We analyzed the sleep properties of rats before (baseline) and after (recovery) social defeat by telemetry electroencephalogram recordings. Using Gaussian partitioning, we identified three non-rapid eye movement stages (N-S1, N-S2, and N-S3) in rats based on a sleep depth index (relative δ power) and a cortical activity index (fractal dimension). We found (1) that, at baseline, N-S3 lability and high-θ relative power in wake identified, with 82% accuracy, the population of rats that will become vulnerable to depression after social defeat, and (2) that, at recovery, N-S1 instability identified vulnerable rats with 83% accuracy. Thus, our study identified early and late sleep biomarkers of vulnerability to depression, opening the way to the development of treatments at a prodromal stage for high sensitivity to stress, and for stress-induced vulnerability to depression.

Disrupted nighttime sleep and sleep instability in narcolepsy.

STUDY OBJECTIVES: This review aimed to summarize current knowledge about disrupted nighttime sleep (DNS) and sleep instability in narcolepsy, including self-reported and objective assessments, potential causes of sleep instability, health consequences and functional burden, and management. METHODS: One hundred two peer-reviewed publications from a PubMed search were included. RESULTS: DNS is a key symptom of narcolepsy but has received less attention than excessive daytime sleepiness and cataplexy. There has been a lack of clarity regarding the definition of DNS, as many sleep-related symptoms and conditions disrupt sleep quality in narcolepsy (eg, hallucinations, sleep paralysis, rapid eye movement sleep behavior disorder, nightmares, restless legs syndrome/periodic leg movements, nocturnal eating, sleep apnea, depression, anxiety). In addition, the intrinsic sleep instability of narcolepsy results in frequent spontaneous wakings and sleep stage transitions, contributing to DNS. Sleep instability likely emerges in the setting of orexin insufficiency/deficiency, but its exact pathophysiology remains unknown. DNS impairs quality of life among people with narcolepsy, and more research is needed to determine its contributions to cardiovascular risk. Multimodal treatment is appropriate for DNS management, including behavioral therapies, counseling on sleep hygiene, and/or medication. There is strong evidence showing improvement in self-reported sleep quality and objective sleep stability measures with sodium oxybate, but rigorous clinical trials with other pharmacotherapies are needed. Treatment may be complicated by comorbidities, concomitant medications, and mood disorders. CONCLUSIONS: DNS is a common symptom of narcolepsy deserving consideration in clinical care and future research. CITATION: Maski K, Mignot E, Plazzi G, Dauvilliers Y. Disrupted nighttime sleep and sleep instability in narcolepsy. J Clin Sleep Med. 2022;18(1):289-304.

Non-rapid eye movement sleep instability in adults with epilepsy: a systematic review and meta-analysis of cyclic alternating pattern.

STUDY OBJECTIVES: Epilepsy is characterized by disrupted sleep architecture. Studies on sleep macro- and microstructure revealed that patients with epilepsy experience disturbed rapid eye movement (REM) sleep; however, no consensus has been reached on non-REM (NREM) sleep changes. Cyclic alternating pattern (CAP) is a marker of sleep instability that occurs only during NREM sleep. This meta-analysis investigated CAP differences between patients with epilepsy and healthy controls. METHODS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines in searching PubMed, Embase, and Cochrane Central database for studies comparing polysomnographic sleep microstructures between patients with epilepsy and healthy controls. A meta-analysis using a random-effects model was performed. We compared CAP rates, percentages of phase A1, A2, A3 subtypes, and phase B durations between patients with epilepsy and healthy controls. RESULTS: A total of 11 studies, including 209 patients with epilepsy and 197 healthy controls, fulfilled the eligibility criteria. Compared with healthy controls, patients with epilepsy had significantly increased CAP rates and decreased A1 subtype percentages, and patients with sleep-related epilepsy had increased A3 subtype percentages. Subgroup analyses revealed that antiseizure medications (ASMs) decreased CAP rates and increased phase B durations but did not affect the microstates of phase A in patients with sleep-related epilepsy. CONCLUSIONS: This meta-analysis detected statistically significant differences in CAP parameters between patients with epilepsy and healthy controls. Our findings suggest patients with epilepsy experience NREM sleep instability. ASMs treatment may decrease NREM instability but did not alter the microstates of phase A.

A systematic review and meta-analysis of the cyclic alternating pattern across the lifespan.

BACKGROUND: Cyclic alternating pattern (CAP) is the electroencephalogram (EEG) pattern described as a marker of sleep instability and assessed by NREM transient episodes in sleep EEG. It has been associated with brain maturation. The aim of this review was to evaluate the normative data of CAP parameters according to the aging process in healthy subjects through a systematic review and meta-analysis. METHODS: Two authors independently searched databases using PRISMA guidelines. Discrepancies were reconciled by a third reviewer. Subgroup analysis and tests for heterogeneity were conducted. RESULTS: Of 286 studies, 10 submitted a total of 168 healthy individuals to CAP analysis. Scoring of CAP can begin at 3 months of life, when K-complexes, delta bursts, or spindles can be recognized. Rate of CAP increased with age, mainly during the first 2 years of life, then decreased in adolescence, and increased in the elderly. The A1 CAP subtype and CAP rate were high in school-aged children during slow-wave sleep (SWS). A1 CAP subtypes were significantly more numerous in adolescents compared with other groups, while the elderly showed the highest amounts of A2 and A3 CAP subtypes. Our meta-analysis registered the lowest CAP rate in infants younger than 2 years old and the highest in the elderly. CONCLUSIONS: This review summarized the normative data of CAP in NREM sleep during the aging process. The CAP rate increased with age and sleep depth, especially during SWS. Parameters of CAP may reflect gender hormonal effects and neuroplasticity. More reports on CAP subtypes are needed for their reference values establishment.

Sleep Instability Correlates with Attentional Impairment in Boys with Attention Deficit Hyperactivity Disorder.

Theoretical models of sleep and attention deficit hyperactivity disorder (ADHD) suggest that symptoms of ADHD are associated with daytime sleepiness, but it has received little support. The present study aimed at testing an alternative model involving the association of attentional instability with sleep instability, i.e., sleep stage transitions and arousals. Twelve ADHD and 15 healthy control (HC) boys aged between 8 and 12 years old underwent polysomnography recording and attentional testing. The microarousal index, the number of awakenings, and the number of stage shifts between stages 1, 2, 3, 4 and REM sleep throughout the night were computed as sleep stability parameters. Attentional functioning was assessed using the Continuous Performance Test-II. We found significantly higher sleep instability in ADHD compared to HC. Sleep arousals and stage transitions (micro arousal index, stage 4/3 and 2/4 transitions) in ADHD significantly correlated with lower attentional scores. No association whatsoever was found between sleep instability and attentional functioning in HC. The results show that sleep instability is associated with lower attentional performance in boys with ADHD, but not in HC. This could be compatible with a model according to which attention and sleep stability share a common neural substrate in ADHD.

A data-driven system to identify REM sleep behavior disorder and to predict its progression from the prodromal stage in Parkinson's disease.

OBJECTIVES: To investigate electroencephalographic (EEG), electrooculographic (EOG) and micro-sleep abnormalities associated with rapid eye movement (REM) sleep behavior disorder (RBD) and REM behavioral events (RBEs) in Parkinson's disease (PD). METHODS: We developed an automated system using only EEG and EOG signals. First, automatic macro- (30-s epochs) and micro-sleep (5-s mini-epochs) staging was performed. Features describing micro-sleep structure, EEG spectral content, EEG coherence, EEG complexity, and EOG energy were derived. All features were input to an ensemble of random forests, giving as outputs the probabilities of having RBD or not (P (RBD) and P (nonRBD), respectively). A patient was classified as having RBD if P (RBD)≥P (nonRBD). The system was applied to 107 de novo PD patients: 54 had normal REM sleep (PDnonRBD), 26 had RBD (PD + RBD), and 27 had at least two RBEs without meeting electromyographic RBD cut-off (PD + RBE). Sleep diagnoses were made with video-polysomnography (v-PSG). RESULTS: Considering PDnonRBD and PD + RBD patients only, the system identified RBD with accuracy, sensitivity, and specificity over 80%. Among the features, micro-sleep instability had the highest importance for RBD identification. Considering PD + RBE patients, the ones who developed definite RBD after two years had significantly higher values of P (RBD) at baseline compared to the ones who did not. The former were distinguished from the latter with sensitivity and specificity over 75%. CONCLUSIONS: Our method identifies RBD in PD patients using only EEG and EOG signals. Micro-sleep instability could be a biomarker for RBD and for proximity of conversion from RBEs, as prodromal RBD, to definite RBD in PD patients.

Non-REM sleep instability in children with restless sleep disorder.

STUDY OBJECTIVES: Restless sleep disorder (RSD) is a newly recognized condition characterized by motor movements involving large muscle groups with frequent repositioning or bed sheets disruption. We analyzed cyclic alternating pattern (CAP) in these children, a marker of sleep instability that might be associated with the motor episodes of RSD and may play a role in their daytime symptoms. METHODS: Polysomnographic recordings from thirty-eight children who fulfilled RSD diagnostic criteria (23 boys and 15 girls), 23 children with restless legs syndrome (RLS, 18 boys and 5 girls) and 19 controls (10 boys and 9 girls) were included. For CAP analysis, a previously developed, highly precise automated system, based on a deep learning recurrent neural network, was used. RESULTS: Age and gender were not statistically different between groups. RSD patients showed a lower percentage of A3 CAP subtypes than controls (median 9.8 vs. 18.2, p = 0.0089), accompanied by shorter duration of the B phase of the CAP cycle (median 28.2 vs. 29.8 in controls, 30.2 in RLS, p = 0.005) and shorter CAP cycle duration than both controls and RLS subjects (median 33.8 vs. 35.0 in controls, 35.8 in RLS, p = 0.002). Finally, RSD children also showed a longer duration of CAP cycle sequences, when compared to controls (median 172.7 vs. 141.9, p = 0.0063). CONCLUSIONS: In conclusion, our study indicates that NREM sleep EEG shows an increased instability in RSD; these findings add to the current knowledge on the mechanisms of this newly recognized sleep disorder and suggest that sleep instability might be a favoring mechanism for the emergence of the motor episodes characterizing RSD.

NREM Sleep Instability in Pediatric Migraine Without Aura.

Children with migraine headaches appear to have a range of sleep disturbances. The aim of the present study was to assess the NREM sleep instability in a population of school-aged individuals affected by migraine without aura (MoA). Thirty-three children with MoA (20 males, 13 females, mean age 10.45 ± 2.06 years) underwent to overnight Polysomnographic (PSG) recordings and Cyclic Alternating Pattern (CAP) analyses accordingly with international criteria. MoA group showed a reduction in sleep duration parameters (TIB, SPT, TST; p ≤ 0.001 for all) and in arousal index during REM sleep and an increase in awakenings per hour (AWK/h) vs. Controls (C) (p = 0.008). In particular, MoA children showed a reduced CAP rate% (p ≤ 0.001), CAP rate% in S1 (p ≤ 0.001) and CAP rate% in SWS (p = 0.004) vs. C. Moreover, A phases distribution were characterized by a reduction in slow wave components (total number CAP A1%, CAP A1 index) (p ≤ 0.001) and an increase of fast components representation (total number of CAP A2% and CAP A3%) (p < 0.001) in MoA vs. C. Moreover, MoA children showed an increased A1 and A2 mean duration (p ≤ 0.001). Our findings show a reduction of arousability in MoA group and lower NREM lower sleep instability associated with MoA in children.

Correlating Interictal Spikes with Sigma and Delta Dynamics during Non-Rapid-Eye-Movement-Sleep.

Interictal spikes (IS) are one of the major hallmarks of epilepsy. Understanding the factors promoting or suppressing IS would increase our comprehension of epilepsy and possibly open new avenues for therapy. Sleep strongly influences epileptic activity, and the modulatory effects of the different sleep stages on IS have been studied for decades. However, several aspects are still disputed, in particular the role of sleep spindles and slow waves in the activation of IS during Non-REM sleep. Here, we correlate the rate of IS with quantitative measures derived from stereo-EEG during one Non-REM cycle in 10 patients suffering from drug-resistant epilepsy due to type 2 focal cortical dysplasia. We show that the IS rate (ISR) is positively correlated with sigma power (a surrogate for sleep-spindle density) but negatively correlated with delta power (surrogate for slow wave activity). In addition, we present two new indices for quantifying the spatial and temporal instability of sleep. We found that both instability indices are correlated with a high ISR. The main contribution of this study is to confirm the suppressive effect of stable deep sleep on IS. This result might influence future guidelines for therapy of patients suffering from epilepsy and sleep disorders.

Non-rapid eye movement sleep instability in mild cognitive impairment: a pilot study.

OBJECTIVE: Polysomnographic (PSG) studies in mild cognitive impairment (MCI) are not conclusive and are limited only to conventional sleep parameters. The aim of our study was to evaluate sleep architecture and cyclic alternating pattern (CAP) parameters in subjects with MCI, and to assess their eventual correlation with cognition. METHODS: Eleven subjects with MCI (mean age 68.5 ± 7.0 years), 11 patients with mild probable Alzheimer's disease (AD; mean age 72.7 ± 5.9 years), referred to the Outpatient Cognitive Disorders Clinic, and 11 cognitively intact healthy elderly individuals (mean age 69.2 ± 12.6 years) underwent ambulatory PSG for the evaluation of nocturnal sleep architecture and CAP parameters. RESULTS: Rapid eye movement sleep, CAP rate, and CAP slow components (A1 index) were decreased in MCI subjects and to a greater extent in AD patients, compared to cognitively intact controls. AD showed also decreased slow wave sleep (SWS) relative to healthy elderly individuals. MCI nappers showed decreased nocturnal SWS and A1 subtypes compared to non-nappers. Several correlations between sleep variables and neuropsychological tests were found. CONCLUSIONS: MCI and AD subjects showed a decreased sleep instability correlated with their cognitive decline. Such a decrease may be considered as a potential biomarker of underlying neurodegeneration.