Leila Tarokh

The human circadian metabolome

The circadian clock orchestrates many aspects of human physiology, and disruption of this clock has been implicated in various pathologies, ranging from cancer to metabolic syndrome and diabetes. Although there is evidence that metabolism and the circadian clockwork are intimately linked on a transcriptional level, whether these effects are directly under clock control or are mediated by the rest–activity cycle and the timing of food intake is unclear. To answer this question, we conducted an unbiased screen in human subjects of the metabolome of blood plasma and saliva at different times of day. To minimize indirect effects, subjects were kept in a 40-h constant routine of enforced posture, constant dim light, hourly isocaloric meals, and sleep deprivation. Under these conditions, we found that ∼15% of all identified metabolites in plasma and saliva were under circadian control, most notably fatty acids in plasma and amino acids in saliva. Our data suggest that there is a strong direct effect of the endogenous circadian clock on multiple human metabolic pathways that is independent of sleep or feeding. In addition, they identify multiple potential small-molecule biomarkers of human circadian phase and sleep pressure.

A Longitudinal Assessment of Sleep Timing, Circadian Phase, and Phase Angle of Entrainment across Human Adolescence

The aim of this descriptive analysis was to examine sleep timing, circadian phase, and phase angle of entrainment across adolescence in a longitudinal study design. Ninety-four adolescents participated; 38 (21 boys) were 9–10 years (“younger cohort”) and 56 (30 boys) were 15–16 years (“older cohort”) at the baseline assessment. Participants completed a baseline and then follow-up assessments approximately every six months for 2.5 years. At each assessment, participants wore a wrist actigraph for at least one week at home to measure self-selected sleep timing before salivary dim light melatonin onset (DLMO) phase – a marker of the circadian timing system – was measured in the laboratory. Weekday and weekend sleep onset and offset and weekend-weekday differences were derived from actigraphy. Phase angles were the time durations from DLMO to weekday sleep onset and offset times. Each cohort showed later sleep onset (weekend and weekday), later weekend sleep offset, and later DLMO with age. Weekday sleep offset shifted earlier with age in the younger cohort and later in the older cohort after age 17. Weekend-weekday sleep offset differences increased with age in the younger cohort and decreased in the older cohort after age 17. DLMO to sleep offset phase angle narrowed with age in the younger cohort and became broader in the older cohort. The older cohort had a wider sleep onset phase angle compared to the younger cohort; however, an age-related phase angle increase was seen in the younger cohort only. Individual differences were seen in these developmental trajectories. This descriptive study indicated that circadian phase and self-selected sleep delayed across adolescence, though school-day sleep offset advanced until no longer in high school, whereupon offset was later. Phase angle changes are described as an interaction of developmental changes in sleep regulation interacting with psychosocial factors (e.g., bedtime autonomy).

Developmental Changes in Brain Connectivity Assessed Using the Sleep EEG

Adolescence represents a time of significant cortical restructuring. Current theories posit that during this period connections between frequently utilized neural networks are strengthened while underutilized synaptic connections are discarded. The aim of the present study was to examine the developmental evolution of connectivity between brain regions using the sleep EEG. All-night sleep EEG recordings in two longitudinal cohorts (children and teens) followed at 1.5-3 year intervals and one cross-sectional cohort (adults) were analyzed. The children and teen cohorts were 9/10 and 15/16 years at the initial assessment; ages of the adults were 20 to 23 years. Intrahemispheric, interhemispheric, and diagonal coherence was measured between all six possible pairings of two central (C3/A2 and C4/A1) and two occipital (O2/A1 and O1/A2) derivations during slow wave, stage 2, and, REM sleep. Within-subjects analyses were performed for the children and teen cohorts, and a linear regression analysis was performed across every assessment of all cohorts. Within-subject analyses revealed a maturational increase in coherence for both age cohorts, though the frequencies, sleep states, and regions differed between cohorts. Regression analysis across all age cohorts showed an overall linear increase in left and right intrahemispheric coherence for all sleep states across frequencies. Furthermore, coherence between diagonal electrode pairs also increased in a linear manner for stage 2 and REM sleep. No age-related trend was found in interhemispheric coherence. Our results indicate that sleep EEG coherence increases with age and that these increases are confined to specific brain regions. This analysis highlights the utility of the sleep EEG to measure developmental changes in brain maturation.

Trait-Like Characteristics of the Sleep EEG across Adolescent Development

Waking and sleep data in adults show high heritability and trait-like characteristics in EEG spectra. This phenomenon has not been examined in children and adolescents where brain development influences the EEG. The present study examines whether a trait-like sleep EEG pattern is detectable across adolescent development. Two consecutive nights of standard sleep recordings were performed in 19 9–10-year-old children and 26 15–16-year-old teens, and were repeated 1.5–3 years later. EEG spectra averaged across the night for non-rapid eye movement and rapid eye movement sleep separately were classified using hierarchical cluster analysis, which showed that all 4 nights of a participant clustered together for a majority of participants. Intraclass correlation coefficients were also very high (>0.7) across nights separated by several years, indicating a trait-like feature of the sleep EEG. In summary, our results, using two measures of stability, indicate that a “trait-like” aspect can be detected in the sleep EEG across adolescent development despite considerable neurodevelopmental changes. This finding indicates that the brain oscillators responsible for generating the sleep EEG signal remain relatively stable across adolescent development.

Sleep EEG provides evidence that cortical changes persist into late adolescence.

STUDY OBJECTIVES To examine developmental changes in the human sleep electroencephalogram (EEG) during late adolescence. SETTING A 4-bed sleep laboratory. PARTICIPANTS Fourteen adolescents (5 boys) were studied at ages 15 or 16 (initial) and again at ages 17 to 19 (follow-up). INTERVENTIONS N/A. MEASUREMENTS AND RESULTS All-night polysomnography was recorded at each assessment and scored according to the criteria of Rechtschaffen and Kales. A 27% decline in duration of slow wave sleep, and a 22% increase of stage 2 sleep was observed from the initial to the follow-up session. All-night spectral analysis of 2 central and 2 occipital leads revealed a significant decline of NREM and REM sleep EEG power with increasing age across frequencies in both states. Time-frequency analysis revealed that the decline in power was consistent across the night for all bands except the delta band. The decreases in power were most pronounced over the left central (C3/A2) and right occipital (O2/A1) derivations. CONCLUSIONS Using longitudinal data, we show that the developmental changes to the sleeping EEG that begin in early adolescence continue into late adolescence. As with early adolescents, we observed hemispheric asymmetry in the decline of sleep EEG power. This decline was state and frequency nonspecific, suggesting that it may be due to the pruning of synapses known to occur during adolescence.

Sleep in adolescence: Physiology, cognition and mental health.

Sleep is a core behavior of adolescents, consuming up to a third or more of each day. As part of this special issue on the adolescent brain, we review changes to sleep behaviors and sleep physiology during adolescence with a particular focus on the sleeping brain. We posit that brain activity during sleep may provide a unique window onto adolescent cortical maturation and compliment waking measures. In addition, we review how sleep actively supports waking cognitive functioning in adolescence. Though this review is focused on sleep in healthy adolescents, the striking comorbidity of sleep disruption with nearly all psychiatric and developmental disorders (for reviews see ,) further highlights the importance of understanding the determinants and consequences of adolescent sleep for the developing brain. Figure 1 illustrates the overarching themes of our review, linking brain development, sleep development, and behavioral outcomes.

Dissipation of sleep pressure is stable across adolescence

The sleep electroencephalogram (EEG) undergoes many changes during adolescence. We assessed whether sleep homeostasis is altered across adolescent development using two measures: the dissipation of slow-wave activity (SWA, 0.6-4.6Hz) across the night and the rate of build-up of SWA in the first non-rapid eye movement (NREM) sleep episode. Furthermore, we examined the association between homeostatic and circadian measures, by correlating the build-up of SWA in the first non-rapid eye movement (NREM) sleep episode with circadian phase. Finally, we compared the dissipation of SWA in individuals with (PH+) and without (PH-) a parental history of alcohol abuse/dependence. Twenty children (8 PH+) and 25 teens (10 PH+) underwent two consecutive polysomnographic recordings at ages 9/10 and 15/16 years and again 1.5-3 years later. Thirteen young adults (ages 20-23 years; no PH+) were assessed one time. The decay of Process S was modeled for each individual at each assessment using data from both recordings. Four parameters of Process S were derived for EEG derivation C3/A2: time constant of the decay, lower asymptote (LA), the level of S at sleep onset (S(SO)), and S(SO) minus LA. We found no change in these parameters between assessments for the children and teen cohorts. Between-subject analysis of the follow-up assessment for children (ages 11-13 years) and the initial assessment for teens (ages 15/16 years) showed no difference in these parameters, nor did follow-up assessment of teens (ages 17-19 years) compared to the single assessment of young adults (ages 20-23 years). Similarly, we observed no developmental changes in the rate of the build-up of SWA in the first NREM sleep episode for our within- and between-subject analyses, or a correlation between this measure and circadian phase for either cohort. With regard to parental alcohol history, we found no difference in the dissipation of sleep pressure between PH+ and PH- children and teens. These results indicate that the dissipation of sleep pressure does not change across adolescent development, is not correlated with circadian phase, and does not differ between PH+ and PH- children and teens.

Sleep electroencephalogram in children with a parental history of alcohol abuse/dependence.

We examined the sleep electroencephalogram (EEG) in 9‐ and 10‐year‐old children with (PH+) and without (PH−) a parental history of alcohol abuse/dependence to determine whether sleep disturbances associated with alcohol precede the onset of alcohol use. Participants slept on a fixed sleep schedule that ensured at least a 10‐h time in bed for 1 week before an adaptation and baseline night. Data were collected in a four‐bed sleep research laboratory. Thirty healthy boys and girls aged 9 or 10 years were classified as either PH+ or PH− based on DSM‐IV criteria applied to structured parental interviews. All‐night polysomnography was performed, sleep data were scored visually in 30‐s epochs, and EEG power spectra were calculated for each epoch. All‐night EEG spectra were calculated for rapid eye movement (REM) and non‐REM (NREM) sleep, and cycle‐by‐cycle spectra were calculated for NREM sleep. The two groups did not differ on any sleep stage variable. All‐night analyses revealed normalized power in the delta band and spindle range were lower in PH+ children. Within NREM sleep cycles PH+ children exhibited less normalized power in the delta band and spindle range compared with PH− children. This effect occurred in the first four cycles and was most pronounced in the first sleep cycle of the night. We found no signs of sleep disruption in sleep stages for PH+ children. Sleep EEG spectral differences, however, suggest that certain circuits responsible for ‘protecting’ sleep may be impaired in PH+ children, which may lead to disrupted sleep later in life.

Adolescence and parental history of alcoholism: insights from the sleep EEG.

BACKGROUND Disrupted sleep is a common complaint of individuals with alcohol use disorder and in abstinent alcoholics. Furthermore, among recovering alcoholics, poor sleep predicts relapse to drinking. Whether disrupted sleep in these populations results from prolonged alcohol use or precedes the onset of drinking is not known. The aim of this study was to examine the sleep electroencephalogram (EEG) in alcohol-naïve, parental history positive (PH+), and negative (PH-) boys and girls. METHODS All-night sleep EEG recordings in 2 longitudinal cohorts (child and teen) followed at 1.5 to 3 year intervals were analyzed. The child and teen participants were 9/10 and 15/16 years old at the initial assessment, respectively. Parental history status was classified by Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria applied to structured interviews (DIS-IV) resulting in 14 PH- and 10 PH+ children and 14 PH- and 10 PH+ teens. Sleep data were visually scored in 30-second epochs using standard criteria. Power spectra were calculated for EEG derivations C3/A2, C4/A1, O2/A1, O1/A2 for nonrapid eye movement (NREM) and rapid eye movement (REM) sleep. RESULTS We found no difference between PH+ and PH- individuals in either cohort for any visually scored sleep stage variable. Spectral power declined in both cohorts across assessments for NREM and REM sleep in all derivations and across frequencies independent of parental history status. With regard to parental history, NREM sleep EEG power was lower for the delta band in PH+ teens at both assessments for the central derivations. Furthermore, power in the sigma band for the right occipital derivation in both NREM and REM sleep was lower in PH+ children only at the initial assessment. CONCLUSIONS We found no gross signs of sleep disruption as a function of parental history. Modest differences in spectral EEG power between PH+ and PH- children and teens indicate that a marker of parental alcohol history may be detectable in teens at risk for problem drinking.

The spectrum of the non-rapid eye movement sleep electroencephalogram following total sleep deprivation is trait-like

The sleep electroencephalogram (EEG) spectrum is unique to an individual and stable across multiple baseline recordings. The aim of this study was to examine whether the sleep EEG spectrum exhibits the same stable characteristics after acute total sleep deprivation. Polysomnography (PSG) was recorded in 20 healthy adults across consecutive sleep periods. Three nights of baseline sleep [12 h time in bed (TIB)] following 12 h of wakefulness were interleaved with three nights of recovery sleep (12 h TIB) following 36 h of sustained wakefulness. Spectral analysis of the non‐rapid eye movement (NREM) sleep EEG (C3LM derivation) was used to calculate power in 0.25 Hz frequency bins between 0.75 and 16.0 Hz. Intraclass correlation coefficients (ICCs) were calculated to assess stable individual differences for baseline and recovery night spectra separately and combined. ICCs were high across all frequencies for baseline and recovery and for baseline and recovery combined. These results show that the spectrum of the NREM sleep EEG is substantially different among individuals, highly stable within individuals and robust to an experimental challenge (i.e. sleep deprivation) known to have considerable impact on the NREM sleep EEG. These findings indicate that the NREM sleep EEG represents a trait.