The maturation of cortical sleep rhythms and networks over early development

Interhemispheric coherence of EEG rhythms in children: Maturation and differentiation in corpus callosum dysgenesis

OBJECTIVES

To evaluate the evolution of interhemispheric coherences (ICo) in background and spindle frequency bands during childhood and use it to identify individuals with corpus callosum dysgenesis (CCd).

METHODS

A monocentric cohort of children aged from 0.25 to 15 years old, consisting of 13 children with CCd and 164 without, was analyzed. The ICo of background activity (ICOBckgrdA), sleep spindles (ICOspindles), and their sum (sICO) were calculated. The impact of age, gender, and CC status on the ICo was evaluated, and the sICO was used to discriminate children with or without CCd.

RESULTS

ICOBckgrdA, ICOspindles and sICO increased significantly with age without any effect of gender (p < 10-4), in both groups. The regression equations of the different ICo were stronger, with adjusted R2 values of 0.54, 0.35, and 0.57, respectively. The ICo was lower in children with CCd compared to those without CCd (p < 10-4 for all comparisons). The area under the precision recall curves for predicting CCd using sICO was 0.992 with 98.9 % sensitivity and 87.5 % specificity.

DISCUSSION

ICo of spindles and background activity evolve in parallel to brain maturation and depends on the integrity of the corpus callosum. sICO could be an effective diagnostic biomarker for screening children with interhemispheric dysfunction.

A growth chart of brain function from infancy to adolescence based on EEG

BACKGROUND

In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function.

METHODS

We developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years. We analysed a 10- to 15-min segment of 18-channel EEG recorded during light sleep (N1 and N2 states).

FINDINGS

The FBA had a weighted mean absolute error (wMAE) of 0.85 years (95% CI: 0.69-1.02; n = 1056). A two-channel version of the FBA had a wMAE of 1.51 years (95% CI: 1.30-1.73; n = 1056) and was validated on an independent set of EEG recordings (wMAE = 2.27 years, 95% CI: 1.90-2.65; n = 723). Group-level maturational delays were also detected in a small cohort of children with Trisomy 21 (Cohen's d = 0.36, p = 0.028).

INTERPRETATION

A FBA, based on EEG, is an accurate, practical and scalable automated tool to track brain function maturation throughout childhood with accuracy comparable to widely used physical growth charts.

FUNDING

This research was supported by the National Health and Medical Research Council, Australia, Helsinki University Diagnostic Center Research Funds, Finnish Academy, Finnish Paediatric Foundation, and Sigrid Juselius Foundation.

Parent-led massage and sleep EEG for term-born infants: A randomized controlled parallel-group study

AIM

To examine the impact of parent-led massage on the sleep electroencephalogram (EEG) features of typically developing term-born infants at 4 months.

METHOD

Infants recruited at birth were randomized to intervention (routine parent-led massage) and control groups. Infants had a daytime sleep EEG at 4 months and were assessed using the Griffiths Scales of Child Development, Third Edition at 4 and 18 months. Comparative analysis between groups and subgroup analysis between regularly massaged and never-massaged infants were performed. Groups were compared for sleep stage, sleep spindles, quantitative EEG (primary analysis), and Griffiths using the Mann-Whitney U test.

RESULTS

In total, 179 out of 182 infants (intervention: 83 out of 84; control: 96 out of 98) had a normal sleep EEG. Median (interquartile range) sleep duration was 49.8 minutes (39.1-71.4) (n = 156). A complete first sleep cycle was seen in 67 out of 83 (81%) and 72 out of 96 (75%) in the intervention and control groups respectively. Groups did not differ in sleep stage durations, latencies to sleep and to rapid eye movement sleep. Sleep spindle spectral power was greater in the intervention group in main and subgroup analyses. The intervention group showed greater EEG magnitudes, and lower interhemispherical coherence on subgroup analyses. Griffiths assessments at 4 months (n = 179) and 18 months (n = 173) showed no group differences in the main and subgroup analyses.

INTERPRETATION

Routine massage is associated with distinct functional brain changes at 4 months.

WHAT THIS PAPER ADDS

Routine massage of infants is associated with differences in sleep electroencephalogram biomarkers at 4 months. Massaged infants had higher sleep spindle spectral power, greater sleep EEG magnitudes, and lower interhemispherical coherence. No differences between groups were observed in total nap duration or first cycle macrostructure.

The sleep and wake electroencephalogram over the lifespan

Both sleep and wake encephalograms (EEG) change over the lifespan. While prior studies have characterized age-related changes in the EEG, the datasets span a particular age group, or focused on sleep and wake macrostructure rather than the microstructure. Here, we present sex-stratified data from 3372 community-based or clinic-based otherwise neurologically and psychiatrically healthy participants ranging from 11 days to 80 years of age. We estimate age norms for key sleep and wake EEG parameters including absolute and relative powers in delta, theta, alpha, and sigma bands, as well as sleep spindle density, amplitude, duration, and frequency. To illustrate the potential use of the reference measures developed herein, we compare them to sleep EEG recordings from age-matched participants with Alzheimer's disease, severe sleep apnea, depression, osteoarthritis, and osteoporosis. Although the partially clinical nature of the datasets may bias the findings towards less normal and hence may underestimate pathology in practice, age-based EEG reference values enable objective screening of deviations from healthy aging among individuals with a variety of disorders that affect brain health.

Source EEG reveals that Rolandic epilepsy is a regional epileptic encephalopathy

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Children with RE have fewer spindles but they have typical time–frequency features.

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Spindle deficits extend to multiple cortical regions in Rolandic epilepsy.

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Cognitive deficits are predicted by spindle rate in Rolandic epilepsy.

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Regional spindle rate predicts motor deficits better than Rolandic spindle deficit.

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Spindle features in RE identify a regional thalamocortical epileptic encephalopathy.

Rolandic epilepsy is the most common form of epileptic encephalopathy, characterized by sleep-potentiated inferior Rolandic epileptiform spikes, seizures, and cognitive deficits in school-age children that spontaneously resolve by adolescence. We recently identified a paucity of sleep spindles, physiological thalamocortical rhythms associated with sleep-dependent learning, in the Rolandic cortex during the active phase of this disease. Because spindles are generated in the thalamus and amplified through regional thalamocortical circuits, we hypothesized that: 1) deficits in spindle rate would involve but extend beyond the inferior Rolandic cortex in active epilepsy and 2) regional spindle deficits would better predict cognitive function than inferior Rolandic spindle deficits alone. To test these hypotheses, we obtained high-resolution MRI, high-density EEG recordings, and focused neuropsychological assessments in children with Rolandic epilepsy during active (n = 8, age 9–14.7 years, 3F) and resolved (seizure free for > 1 year, n = 10, age 10.3–16.7 years, 1F) stages of disease and age-matched controls (n = 8, age 8.9–14.5 years, 5F). Using a validated spindle detector applied to estimates of electrical source activity in 31 cortical regions, including the inferior Rolandic cortex, during stages 2 and 3 of non-rapid eye movement sleep, we compared spindle rates in each cortical region across groups. Among detected spindles, we compared spindle features (power, duration, coherence, bilateral synchrony) between groups. We then used regression models to examine the relationship between spindle rate and cognitive function (fine motor dexterity, phonological processing, attention, and intelligence, and a global measure of all functions). We found that spindle rate was reduced in the inferior Rolandic cortices in active but not resolved disease (active P = 0.007; resolved P = 0.2) compared to controls. Spindles in this region were less synchronous between hemispheres in the active group (P = 0.005; resolved P = 0.1) compared to controls; but there were no differences in spindle power, duration, or coherence between groups. Compared to controls, spindle rate in the active group was also reduced in the prefrontal, insular, superior temporal, and posterior parietal regions (i.e., “regional spindle rate”, P < 0.039 for all). Independent of group, regional spindle rate positively correlated with fine motor dexterity (P < 1e-3), attention (P = 0.02), intelligence (P = 0.04), and global cognitive performance (P < 1e-4). Compared to the inferior Rolandic spindle rate alone, models including regional spindle rate trended to improve prediction of global cognitive performance (P = 0.052), and markedly improved prediction of fine motor dexterity (P = 0.006). These results identify a spindle disruption in Rolandic epilepsy that extends beyond the epileptic cortex and a potential mechanistic explanation for the broad cognitive deficits that can be observed in this epileptic encephalopathy.

Sleep Power Topography in Children with Attention Deficit Hyperactivity Disorder (ADHD)

OBJECTIVE

Recent years saw an increasing interest towards sleep microstructure abnormalities in attention-deficit/hyperactivity disorder (ADHD). However, the existing literature on sleep electroencephalographic (EEG) power in ADHD is still controversial, often based on single electrode recordings, and mainly focused on slow wave activity (SWA) during NREM sleep. This study aimed to systematically investigate sleep power topography in all traditional frequency bands, in all sleep stages and across sleep cycles using high-density EEG (HD-EEG).

METHOD

Thirty drug-naïve children with ADHD (10.5 ± 2.1 years, 21 male) and 23 typically developing (TD) control participants (mean age: 10.2 ± 1.6 years, 13 male) were included in the current analysis. Signal power topography was computed in classical frequency bands during sleep, contrasted between groups and sleep cycles, and correlated with measures of ADHD severity, cognitive functioning and estimated total sleep time.

RESULTS

Compared to TD subjects, patients with ADHD consistently displayed a widespread increase in low-frequency activity (between 3 and 10 Hz) during NREM sleep, but not during REM sleep and wake before sleep onset. Such a difference involved a wide centro-posterior cluster of channels in the upper SWA range, in Theta, and low-Alpha. Between-group difference was maximal in sleep stage N3 in the first sleep cycle, and positively correlated with average total sleep time.

CONCLUSIONS

These results support the concept that children with ADHD, compared to TD peers, have a higher sleep pressure and altered sleep homeostasis, which possibly interfere with (and delay) cortical maturation.

Brain Data in Pediatric Disorders of Consciousness: Special Considerations

SUMMARY

The diagnosis and management of disorders of consciousness in children continue to present a clinical, research, and ethical challenge. Though the practice guidelines for diagnosis and management of disorders of consciousness in adults are supported by decades of empirical and pragmatic evidence, similar guidelines for infants and children are lacking. The maturing conscious experience and the limited behavioral repertoire to report consciousness in this age group restrict extrapolation from the adult literature. Equally challenging is the process of heightened structural and functional neuroplasticity in the developing brain, which adds a layer of complexity to the investigation of the neural correlates of consciousness in infants and children. This review discusses the clinical assessment of pediatric disorders of consciousness and delineates the diagnostic and prognostic utility of neurophysiological and neuroimaging correlates of consciousness. The potential relevance of these correlates for the developing brain based on existing theoretical models of consciousness in adults is outlined.

OUP accepted manuscript

Angelman syndrome is a neurodevelopmental disorder caused by deficiency of the maternally inherited UBE3A gene in neurons. Antisense oligonucleotide therapies are under development to reinstate UBE3A protein production. Non-invasive biomarkers to detect target engagement and treatment response are needed to support clinical trials. Delta power measured in the scalp EEG is a reliable biomarker for Angelman syndrome but varies widely across individuals and throughout development, making detection of a treatment effect using single measurements challenging.

We utilized a longitudinal dataset of 204 EEG recordings from 56 subjects with Angelman syndrome to develop a natural history model of delta (2–4 Hz) power, with predictors of age, elapsed time, and relative delta power at an initial recording. Using this model, we computed the sample and effect sizes needed to detect a treatment effect in a human clinical trial with 80% power. We applied the same model structure to a mouse model of Angelman syndrome (n = 41) to detect antisense oligonucleotide-mediated treatment effects on absolute delta activity and Ube3a expression. In humans, delta power at a second time point can be reliably predicted using the natural history model. In mice, a treatment effect can be detected after antisense oligonucleotide treatment targeting the Ube3a-antisense transcript through at least 8 weeks post-treatment (P < 1e-15). Deviations in delta power from the expected natural history correlated with Ube3a expression in the mouse model (P < 0.001). Deviations in delta power from a human natural history model in Angelman syndrome can detect antisense oligonucleotide-mediated improvement in Ube3a expression in Angelman syndrome mice and may be relevant for human clinical trials.

Evolution of Cortical Functional Networks in Healthy Infants

During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0-2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21-24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.

Nonrapid eye movement sleep characteristics and relations with motor, memory, and cognitive ability from infancy to preadolescence

Sleep plays a critical role in neural neurodevelopment. Hallmarks of sleep reflected in the electroencephalogram during nonrapid eye movement (NREM) sleep are associated with learning processes, cognitive ability, memory, and motor functioning. Research in adults is well-established; however, the role of NREM sleep in childhood is less clear. Growing evidence suggests the importance of two NREM sleep features: slow-wave activity and sleep spindles. These features may be critical for understanding maturational change and the functional role of sleep during development. Here, we review the literature on NREM sleep from infancy to preadolescence to provide insight into the network dynamics of the developing brain. The reviewed findings show distinct relations between topographical and maturational aspects of slow waves and sleep spindles; however, the direction and consistency of these relationships vary, and associations with cognitive ability remain unclear. Future research investigating the role of NREM sleep and development would benefit from longitudinal approaches, increased control for circadian and homeostatic influences, and in early childhood, studies recording daytime naps and overnight sleep to yield increased precision for detecting age-related change. Such evidence could help explicate the role of NREM sleep and provide putative physiological markers of neurodevelopment.

Developmental features of sleep electrophysiology in family dogs

Age-related differences in dog sleep and the age at which dogs reach adulthood as indexed by sleep electrophysiology are unknown. We assessed, in (1) a Juvenile sample (n = 60) of 2-14-month-old dogs (weight range: 4-68 kg), associations between age, sleep macrostructure, and non-rapid eye movement (NREM) EEG power spectrum, whether weight moderates associations, and (2) an extended sample (n = 91) of 2-30-months-old dogs, when sleep parameters stabilise. In Juvenile dogs, age was positively associated with time in drowsiness between 2 and 8 months, and negatively with time in rapid eye movement (REM) sleep between 2 and 6 months. Age was negatively associated with delta and positively with theta and alpha power activity, between 8 and 14 months. Older dogs exhibited greater sigma and beta power activity. Larger, > 8-month-old dogs had less delta and more alpha and beta activity. In extended sample, descriptive data suggest age-related power spectrum differences do not stabilise by 14 months. Drowsiness, REM, and delta power findings are consistent with prior results. Sleep electrophysiology is a promising index of dog neurodevelopment; some parameters stabilise in adolescence and some later than one year. Determination of the effect of weight and timing of power spectrum stabilisation needs further inquiry. The dog central nervous system is not fully mature by 12 months of age.

Scalp EEG markers of normal infant development using visual and computational approaches

The infant brain is rapidly developing, and these changes are reflected in scalp electroencephalography (EEG) features, including power spectrum and sleep spindle characteristics. These biomarkers not only mirror infant development, but they are also altered by conditions such as epilepsy, autism, developmental delay, and trisomy 21. Prior studies of early development were generally limited by small cohort sizes, lack of a specific focus on infancy (0-2 years), and exclusive use of visual marking for sleep spindles. Therefore, we measured the EEG power spectrum and sleep spindles in 240 infants ranging from 0-24 months. To rigorously assess these metrics, we used both clinical visual assessment and computational techniques, including automated sleep spindle detection. We found that the peak frequency and power of the posterior dominant rhythm (PDR) increased with age, and a corresponding peak occurred in the EEG power spectra. Based on both clinical and computational measures, spindle duration decreased with age, and spindle synchrony increased with age. Our novel metric of spindle asymmetry suggested that peak spindle asymmetry occurs at 6-9 months of age.Clinical Relevance- Here we provide a robust characterization of the development of EEG brain rhythms during infancy. This can be used as a basis of comparison for studies of infant neurological disease, including epilepsy, autism, developmental delay, and trisomy 21.

Diazepam induced sleep spindle increase correlates with cognitive recovery in a child with epileptic encephalopathy

BACKGROUND

Continuous spike and wave of sleep with encephalopathy (CSWS) is a rare and severe developmental electroclinical epileptic encephalopathy characterized by seizures, abundant sleep activated interictal epileptiform discharges, and cognitive regression or deceleration of expected cognitive growth. The cause of the cognitive symptoms is unknown, and efforts to link epileptiform activity to cognitive function have been unrevealing. Converging lines of evidence implicate thalamocortical circuits in these disorders. Sleep spindles are generated and propagated by the same thalamocortical circuits that can generate spikes and, in healthy sleep, support memory consolidation. As such, sleep spindle deficits may provide a physiologically relevant mechanistic biomarker for cognitive dysfunction in epileptic encephalopathies.

CASE PRESENTATION

We describe the longitudinal course of a child with CSWS with initial cognitive regression followed by dramatic cognitive improvement after treatment. Using validated automated detection algorithms, we analyzed electroencephalograms for epileptiform discharges and sleep spindles alongside contemporaneous neuropsychological evaluations over the course of the patient's disease. We found that sleep spindles increased dramatically with high-dose diazepam treatment, corresponding with marked improvements in cognitive performance. We also found that the sleep spindle rate was anticorrelated to spike rate, consistent with a competitively shared underlying thalamocortical circuitry.

CONCLUSIONS

Epileptic encephalopathies are challenging electroclinical syndromes characterized by combined seizures and a deceleration or regression in cognitive skills over childhood. This report identifies thalamocortical circuit dysfunction in a case of epileptic encephalopathy and motivates future investigations of sleep spindles as a biomarker of cognitive function and a potential therapeutic target in this challenging disease.

Infant functional networks are modulated by state of consciousness and circadian rhythm

Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hr) from 19 healthy infants. Networks were subject specific, as intersubject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ∼24 hr and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.

Delta power robustly predicts cognitive function in Angelman syndrome

Objective

Angelman syndrome (AS) is a severe neurodevelopmental disorder caused by loss of function of the maternally inherited UBE3A gene in neurons. Promising disease‐modifying treatments to reinstate UBE3A expression are under development and an early measure of treatment response is critical to their deployment in clinical trials. Increased delta power in EEG recordings, reflecting abnormal neuronal synchrony, occurs in AS across species and correlates with genotype. Whether delta power provides a reliable biomarker for clinical symptoms remains unknown.

Methods

We analyzed combined EEG recordings and developmental assessments in a large cohort of individuals with AS (N = 82 subjects, 133 combined EEG and cognitive assessments, 1.08–28.16 years; 32F) and evaluated delta power as a biomarker for cognitive function, as measured by the Bayley Cognitive Score. We examined the robustness of this biomarker to varying states of consciousness, recording techniques and analysis procedures.

Results

Delta power predicted the Bayley Scale cognitive score (P < 10⁻⁵, R² = 0.9374) after controlling for age (P < 10⁻²⁴), genotype:age (P < 10⁻¹¹), and repeat assessments (P < 10⁻⁸), with the excellent fit on cross validation (R² = 0.95). There were no differences in model performance across states of consciousness or bipolar versus average montages (ΔAIC < 2). Models using raw data excluding frontal channels outperformed other models (ΔAIC > 4) and predicted performance in expressive (P = 0.0209) and receptive communication (P < 10⁻³) and fine motor skills (P < 10⁻⁴).

Interpretation

Delta power is a simple, direct measure of neuronal activity that reliably correlates with cognitive function in AS. This electrophysiological biomarker offers an objective, clinically relevant endpoint for treatment response in emerging clinical trials.

Scalp high-frequency oscillation rates are higher in younger children

High-frequency oscillations in scalp EEG are promising non-invasive biomarkers of epileptogenicity. However, it is unclear how high-frequency oscillations are impacted by age in the paediatric population. We prospectively recorded whole-night scalp EEG in 30 children and adolescents with focal or generalized epilepsy. We used an automated and clinically validated high-frequency oscillation detector to determine ripple rates (80-250 Hz) in bipolar channels. Children < 7 years had higher high-frequency oscillation rates (P = 0.021) when compared with older children. The median test-retest reliability of high-frequency oscillation rates reached 100% (iqr 50) for a data interval duration of 10 min. Scalp high-frequency oscillation frequency decreased with age (r = -0.558, P = 0.002), whereas scalp high-frequency oscillation duration and amplitude were unaffected. The signal-to-noise ratio improved with age (r = 0.37, P = 0.048), and the background ripple band activity decreased with age (r = -0.463, P = 0.011). We characterize the relationship of scalp high-frequency oscillation features and age in paediatric patients. EEG intervals of ≥ 10 min duration are required for reliable measurements of high-frequency oscillation rates. This study is a further step towards establishing scalp high-frequency oscillations as a valid epileptogenicity biomarker in this vulnerable age group.

Potentiation of cord blood cell therapy with erythropoietin for children with CP: a 2 × 2 factorial randomized placebo-controlled trial

Background

Concomitant administration of allogeneic umbilical cord blood (UCB) infusion and erythropoietin (EPO) showed therapeutic efficacy in children with cerebral palsy (CP). However, no clinical studies have investigated the effects of UCB and EPO combination therapy using a 2 × 2 four-arm factorial blinded design with four arms. This randomized placebo-controlled trial aimed to identify the synergistic and individual efficacies of UCB cell and EPO for the treatment of CP.

Methods

Children diagnosed with CP were randomly segregated into four groups: (A) UCB+EPO, (B) UCB+placebo EPO, (C) placebo UCB+EPO, and (D) placebo UCB+placebo EPO. Based on the UCB unit selection criteria of matching for ≥ 4/6 of human leukocyte antigen (HLA)-A, -B, and DRB1 and total nucleated cell (TNC) number of ≥ 3 × 10⁷/kg, allogeneic UCB was intravenously infused and 500 IU/kg human recombinant EPO was administered six times. Functional measurements, brain imaging studies, and electroencephalography were performed from baseline until 12 months post-treatment. Furthermore, adverse events were closely monitored.

Results

Eighty-eight of 92 children enrolled (3.05 ± 1.22 years) completed the study. Change in gross motor performance measure (GMPM) was greater in group A than in group D at 1 month (△2.30 vs. △0.71, P = 0.025) and 12 months (△6.85 vs. △2.34, P = 0.018) post-treatment. GMPM change ratios were calculated to adjust motor function at the baseline. Group A showed a larger improvement in the GMPM change ratio at 1 month and 12 months post-treatment than group D. At 12 months post-treatment, the GMPM change ratios were in the order of groups A, B, C, and D. These results indicate synergistic effect of UCB and EPO combination better than each single therapy. In diffusion tensor imaging, the change ratio of fractional anisotropy at spinothalamic radiation was higher in group A than group D in subgroup of age ≥ 3 years. Additionally, higher TNC and more HLA-matched UCB units led to better gross motor outcomes in group A. Adverse events remained unchanged upon UCB or EPO administration.

Conclusions

These results indicate that the efficacy of allogeneic UCB cell could be potentiated by EPO for neurological recovery in children with CP without harmful effects.

Trial registration

ClinicalTrials.gov, NCT01991145, registered 25 November 2013.

Slow-wave activity and sigma activities are associated with psychomotor development at 8 months of age

STUDY OBJECTIVES

The electrophysiological properties of non-rapid eye movement sleep (NREM) EEG are homeostatically modulated on global and local use-dependent levels. Furthermore, the local NREM quality reflects age-dependent brain maturation and individual, age-independent, and psychomotor potential. Cortical maturation and its electrophysiological marker, Slow-wave activity (SWA), as well as sleep spindles are known to change in topography and quality during the early years of life, but their associations with psychomotor development in infants are unknown. Therefore, we aimed to evaluate the local properties of SWA and spindles (sigma power) and ascertain whether they correlate with psychomotor development in 8-month-old infants.

METHODS

Ambulatory polysomnographies were recorded in 56 infants at 8 months of age to calculate the local SWA and sigma powers. The associations between the SWA and sigma powers and psychomotor development (Bayley-III) were examined in 36 of these infants.

RESULTS

In both hemispheres, the highest SWA and sigma powers were found occipitally and centrally, respectively, with higher powers in the right hemisphere than in the left. The Bayley-III correlated with local SWA and sigma powers: the occipital SWA and centro-occipital sigma correlated with cognitive scales, and the frontal and occipital SWA and centro-occipital sigma correlated with language and fine motor scales. Most of the correlations were unilateral.

CONCLUSIONS

In 8-month-old infants, the NREM sleep quality shows local differences that are mostly attributable to the topical phase of brain maturation. The local NREM parameters correlate with psychomotor development.

Nonrapid eye movement sleep and risk for autism spectrum disorder in early development: A topographical electroencephalogram pilot study

OBJECTIVE

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder that emerges in the beginning years of life (12-48 months). Yet, an early diagnosis of ASD is challenging as it relies on the consistent presence of behavioral symptomatology, and thus, many children are diagnosed later in development, which prevents early interventions that could benefit cognitive and social outcomes. As a result, there is growing interest in detecting early brain markers of ASD, such as in the electroencephalogram (EEG) to elucidate divergence in early development. Here, we examine the EEG of nonrapid eye movement (NREM) sleep in the transition from infancy to toddlerhood, a period of rapid development and pronounced changes in early brain function. NREM features exhibit clear developmental trajectories, are related to social and cognitive development, and may be altered in neurodevelopmental disorders. Yet, spectral features of NREM sleep are poorly understood in infants/toddlers with or at high risk for ASD.

METHODS

The present pilot study is the first to examine NREM sleep in 13- to 30-month-olds with ASD in comparison with age-matched healthy controls (TD). EEG was recorded during a daytime nap with high-density array EEG.

RESULTS

We found topographically distinct decreased fast theta oscillations (5-7.25 Hz), decreased fast sigma (15-16 Hz), and increased beta oscillations (20-25 Hz) in ASD compared to TD.

CONCLUSION

These findings suggest a possible functional role of NREM sleep during this important developmental period and provide support for NREM sleep to be a potential early marker for ASD.

Mental Sleep Activity and Disturbing Dreams in the Lifespan

Sleep significantly changes across the lifespan, and several studies underline its crucial role in cognitive functioning. Similarly, mental activity during sleep tends to covary with age. This review aims to analyze the characteristics of dreaming and disturbing dreams at different age brackets. On the one hand, dreams may be considered an expression of brain maturation and cognitive development, showing relations with memory and visuo-spatial abilities. Some investigations reveal that specific electrophysiological patterns, such as frontal theta oscillations, underlie dreams during sleep, as well as episodic memories in the waking state, both in young and older adults. On the other hand, considering the role of dreaming in emotional processing and regulation, the available literature suggests that mental sleep activity could have a beneficial role when stressful events occur at different age ranges. We highlight that nightmares and bad dreams might represent an attempt to cope the adverse events, and the degrees of cognitive-brain maturation could impact on these mechanisms across the lifespan. Future investigations are necessary to clarify these relations. Clinical protocols could be designed to improve cognitive functioning and emotional regulation by modifying the dream contents or the ability to recall/non-recall them.

Beta oscillations in the sensorimotor cortex correlate with disease and remission in benign epilepsy with centrotemporal spikes

INTRODUCTION

Benign epilepsy with centrotemporal spikes (BECTS) is a common form of childhood epilepsy with the majority of those afflicted remitting during their early teenage years. Seizures arise from the lower half of the sensorimotor cortex of the brain (e.g. seizure onset zone) and the abnormal epileptiform discharges observed increase during NREM sleep. To date no clinical factors reliably predict disease course, making determination of ongoing seizure risk a significant challenge. Prior work in BECTS have shown abnormalities in beta band (14.9-30 Hz) oscillations during movement and rest. Oscillations in this frequency band are modulated by state of consciousness and thought to reflect intrinsic inhibitory mechanisms.

METHODS

We used high density EEG and source localization techniques to examine beta band activity in the seizure onset zone (sensorimotor cortex) in a prospective cohort of children with BECTS and healthy controls during sleep. We hypothesized that beta power in the sensorimotor cortex would be different between patients and healthy controls, and that beta abnormalities would improve with resolution of disease in this self-limited epilepsy syndrome. We further explored the specificity of our findings and correlation with clinical features. Statistical testing was performed using logistic and standard linear regression models.

RESULTS

We found that beta band power in the seizure onset zone is different between healthy controls and BECTS patients. We also found that a longer duration of time spent seizure-free (corresponding to disease remission) correlates with lower beta power in the seizure onset zone. Exploratory spatial analysis suggests this effect is not restricted to the sensorimotor cortex. Exploratory frequency analysis suggests that this phenomenon is also observed in alpha and gamma range activity. We found no relationship between beta power and the presence or rate of epileptiform discharges in the sensorimotor cortex or a test of sensorimotor performance.

CONCLUSION

These results provide evidence that cortical beta power in the seizure onset zone may provide a dynamic physiological biomarker of disease in BECTS.

Characterization of electroencephalography signals for estimating saliency features in videos

Understanding the functions of the visual system has been one of the major targets in neuroscience for many years. However, the relation between spontaneous brain activities and visual saliency in natural stimuli has yet to be elucidated. In this study, we developed an optimized machine learning-based decoding model to explore the possible relationships between the electroencephalography (EEG) characteristics and visual saliency. The optimal features were extracted from the EEG signals and saliency map which was computed according to an unsupervised saliency model (Tavakoli and Laaksonen, 2017). Subsequently, various unsupervised feature selection/extraction techniques were examined using different supervised regression models. The robustness of the presented model was fully verified by means of ten-fold or nested cross validation procedure, and promising results were achieved in the reconstruction of saliency features based on the selected EEG characteristics. Through the successful demonstration of using EEG characteristics to predict the real-time saliency distribution in natural videos, we suggest the feasibility of quantifying visual content through measuring brain activities (EEG signals) in real environments, which would facilitate the understanding of cortical involvement in the processing of natural visual stimuli and application developments motivated by human visual processing.

Abnormal coherence and sleep composition in children with Angelman syndrome: a retrospective EEG study

Background

Angelman syndrome (AS) is a neurodevelopmental disorder characterized by intellectual disability, speech and motor impairments, epilepsy, abnormal sleep, and phenotypic overlap with autism. Individuals with AS display characteristic EEG patterns including high-amplitude rhythmic delta waves. Here, we sought to quantitatively explore EEG architecture in AS beyond known spectral power phenotypes. We were motivated by studies of functional connectivity and sleep spindles in autism to study these EEG readouts in children with AS.

Methods

We analyzed retrospective wake and sleep EEGs from children with AS (age 4-11) and age-matched neurotypical controls. We assessed long-range and short-range functional connectivity by measuring coherence across multiple frequencies during wake and sleep. We quantified sleep spindles using automated and manual approaches.

Results

During wakefulness, children with AS showed enhanced long-range EEG coherence across a wide range of frequencies. During sleep, children with AS showed increased long-range EEG coherence specifically in the gamma band. EEGs from children with AS contained fewer sleep spindles, and these spindles were shorter in duration than their neurotypical counterparts.

Conclusions

We demonstrate two quantitative readouts of dysregulated sleep composition in children with AS-gamma coherence and spindles-and describe how functional connectivity patterns may be disrupted during wakefulness. Quantitative EEG phenotypes have potential as biomarkers and readouts of target engagement for future clinical trials and provide clues into how neural circuits are dysregulated in children with AS.

Abnormal coherence and sleep composition in children with Angelman syndrome: a retrospective EEG study

Background

Angelman syndrome (AS) is a neurodevelopmental disorder characterized by intellectual disability, speech and motor impairments, epilepsy, abnormal sleep, and phenotypic overlap with autism. Individuals with AS display characteristic EEG patterns including high-amplitude rhythmic delta waves. Here, we sought to quantitatively explore EEG architecture in AS beyond known spectral power phenotypes. We were motivated by studies of functional connectivity and sleep spindles in autism to study these EEG readouts in children with AS.

Methods

We analyzed retrospective wake and sleep EEGs from children with AS (age 4–11) and age-matched neurotypical controls. We assessed long-range and short-range functional connectivity by measuring coherence across multiple frequencies during wake and sleep. We quantified sleep spindles using automated and manual approaches.

Results

During wakefulness, children with AS showed enhanced long-range EEG coherence across a wide range of frequencies. During sleep, children with AS showed increased long-range EEG coherence specifically in the gamma band. EEGs from children with AS contained fewer sleep spindles, and these spindles were shorter in duration than their neurotypical counterparts.

Conclusions

We demonstrate two quantitative readouts of dysregulated sleep composition in children with AS—gamma coherence and spindles—and describe how functional connectivity patterns may be disrupted during wakefulness. Quantitative EEG phenotypes have potential as biomarkers and readouts of target engagement for future clinical trials and provide clues into how neural circuits are dysregulated in children with AS.

Electronic supplementary material

The online version of this article (10.1186/s13229-018-0214-8) contains supplementary material, which is available to authorized users.

Social, motor, and cognitive development through the lens of sleep network dynamics in infants and toddlers between 12 and 30 months of age

Widespread change in behavior and the underlying brain network substrate is a hallmark of early development. Sleep plays a fundamental role in this process. Both slow waves and spindles are key features of nonrapid eye movement sleep (NREM) that exhibit pronounced developmental trajectories from infancy to adulthood. Yet, these prominent features of NREM sleep are poorly understood in infants and toddlers in the age range of 12 to 30 months. Moreover, it is unknown how network dynamics of NREM sleep are associated with outcomes of early development. Addressing this gap in our understanding of sleep during development will enable the subsequent study of pathological changes in neurodevelopmental disorders. The aim of the current study was to characterize the sleep topography with high-density electroencephalography in this age group. We found that δ, θ, and β oscillations and sleep spindles exhibited clear developmental changes. Low δ and high θ oscillations correlated with motor, language, and social skills, independent of age. These findings suggest an important role of network dynamics of NREM sleep in cortical maturation and the associated development of skills during this important developmental period.

Network dynamics in the healthy and epileptic developing brain

Electroencephalography (EEG) allows recording of cortical activity at high temporal resolution. EEG recordings can be summarized along different dimensions using network-level quantitative measures, such as channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different timescales and can be tracked dynamically. Here we describe the dynamics of network state transitions in EEG recordings of spontaneous brain activity in normally developing infants and infants with severe early infantile epileptic encephalopathies (n = 8, age: 1–8 months). We describe differences in measures of EEG dynamics derived from band power, and correlation-based summaries of network-wide brain activity. We further show that EEGs from different patient groups and controls may be distinguishable on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the sharpness of switching from one correlation pattern to another show the largest differences between groups. These findings reveal that the early epileptic encephalopathies are associated with characteristic dynamic features at the network level. Quantitative network-based analyses like the one presented here may in the future inform the clinical use of quantitative EEG for diagnosis.

Local changes in computational non-rapid eye movement sleep depth in infants

OBJECTIVE

Deep NREM sleep and its hallmark EEG phenomenon slow wave activity (SWA) are under homeostatic control in adults. SWA is also locally regulated as it increases in the brain areas that have been used intensively. Moreover, in children, SWA is a marker of cortical maturation. In the present study the local properties of NREM sleep depth were evaluated using the quantitative mean frequency method. We aimed to study if age is related to NREM sleep depth in young infants. In addition, we studied if young infants have local differences in their NREM sleep.

METHODS

Ambulatory over-night polysomnographies were recorded in 59 healthy and full-term infants at the age of one month. The infants were divided into two age groups (<44 weeks and ≥44 weeks) to allow maturational evaluations.

RESULTS

The quantitative sleep depth analysis showed differences between the age groups. In addition, there were local sleep depth differences within the age groups.

CONCLUSIONS

The sleep depth change with age is most likely related to cortical maturation, whereas the local sleep depth gradients might also reflect the use-dependent properties of SWA.

SIGNIFICANCE

The results support the idea that young infants have frontal cortical processing.

Different maturational changes of fast and slow sleep spindles in the first four years of life

OBJECTIVE/BACKGROUND

Massive changes in brain morphology and function in the first years of life reveal a postero-anterior trajectory of cortical maturation accompanied by regional modifications of NREM sleep. One of the most sensible marker of this maturation process is represented by electroencephalographic (EEG) activity within the frequency range of sleep spindles. However, direct evidence that these changes actually reflect maturational modifications of fast and slow spindles still lacks. Our study aimed at answering the following questions: 1. Do cortical changes at 11.50 Hz frequency correspond to slow spindles? 2. Do fast and slow spindles show different age trajectories and different topographical distributions? 3. Do changes in peak frequency explain age changes of slow and fast spindles?

PATIENTS/METHODS

We measured the antero-posterior changes of slow and fast spindles in the first 60 min of nightly sleep of 39 infants and children (0-48 mo.).

RESULTS

We found that (A) changes of slow spindles from birth to childhood mostly affect frontal areas (B) variations of fast and slow spindles across age groups go in opposite direction, the latter progressively increasing across ages; (C) this process is not merely reducible to changes of spindle frequency.

CONCLUSIONS

As a main finding, our cross-sectional study shows that the first form of mature spindle (i.e., corresponding to the adult phasic event of NREM sleep) is marked by the emergence of slow spindles on anterior regions around the age of 12 months.

Physiological Ripples (± 100 Hz) in Spike-Free Scalp EEGs of Children With and Without Epilepsy

Pathological high frequency oscillations (HFOs, >80 Hz) are considered new biomarkers for epilepsy. They have mostly been recorded invasively, but pathological ripples (80-250 Hz) can also be found in scalp EEGs with frequent epileptiform spikes. Physiological HFOs also exist. They have been recorded invasively in hippocampus and neocortex. There are no reports of spontaneously occurring physiological HFOs recorded with scalp EEG. We aimed to study ripples in spike-free scalp EEGs. We included 23 children (6 with, 17 without epilepsy) who had an EEG without interictal epileptiform spikes recorded during sleep. We differentiated true ripples from spurious ripples such as filtering effects of sharp artifacts and high frequency components of muscle artifacts by viewing ripples simultaneously in bipolar and average montage and double-checking the unfiltered signal. We calculated mean frequency, duration and root mean square amplitude of the ripples, and studied their shape and distribution. We found ripples in EEGs of 20 out of 23 children (4 with, 16 without epilepsy). Ripples had a regular shape and occurred mostly on central and midline channels. Mean frequency was 102 Hz, mean duration 70 ms, mean root mean square amplitude 0.95 µV. Ripples occurring in normal EEGs of children without epilepsy were considered physiological; the similarity in appearance suggested that the ripples occurring in normal EEGs of children with epilepsy were also physiological. The finding that it is possible to study physiological neocortical ripples in scalp EEG paves the way for investigating their occurrence during brain development and their relation with cognitive functioning.

Developmental Changes in Sleep Oscillations during Early Childhood

Although quantitative analysis of the sleep electroencephalogram (EEG) has uncovered important aspects of brain activity during sleep in adolescents and adults, similar findings from preschool-age children remain scarce. This study utilized our time-frequency method to examine sleep oscillations as characteristic features of human sleep EEG. Data were collected from a longitudinal sample of young children (n = 8; 3 males) at ages 2, 3, and 5 years. Following sleep stage scoring, we detected and characterized oscillatory events across age and examined how their features corresponded to spectral changes in the sleep EEG. Results indicated a developmental decrease in the incidence of delta and theta oscillations. Spindle oscillations, however, were almost absent at 2 years but pronounced at 5 years. All oscillatory event changes were stronger during light sleep than slow-wave sleep. Large interindividual differences in sleep oscillations and their characteristics (e.g., "ultrafast" spindle-like oscillations, theta oscillation incidence/frequency) also existed. Changes in delta and spindle oscillations across early childhood may indicate early maturation of the thalamocortical system. Our analytic approach holds promise for revealing novel types of sleep oscillatory events that are specific to periods of rapid normal development across the lifespan and during other times of aberrant changes in neurobehavioral function.

Delta rhythmicity is a reliable EEG biomarker in Angelman syndrome: a parallel mouse and human analysis

Background

Clinicians have qualitatively described rhythmic delta activity as a prominent EEG abnormality in individuals with Angelman syndrome, but this phenotype has yet to be rigorously quantified in the clinical population or validated in a preclinical model. Here, we sought to quantitatively measure delta rhythmicity and evaluate its fidelity as a biomarker.

Methods

We quantified delta oscillations in mouse and human using parallel spectral analysis methods and measured regional, state-specific, and developmental changes in delta rhythms in a patient population.

Results

Delta power was broadly increased and more dynamic in both the Angelman syndrome mouse model, relative to wild-type littermates, and in children with Angelman syndrome, relative to age-matched neurotypical controls. Enhanced delta oscillations in children with Angelman syndrome were present during wakefulness and sleep, were generalized across the neocortex, and were more pronounced at earlier ages.

Conclusions

Delta rhythmicity phenotypes can serve as reliable biomarkers for Angelman syndrome in both preclinical and clinical settings.

Electronic supplementary material

The online version of this article (doi:10.1186/s11689-017-9195-8) contains supplementary material, which is available to authorized users.

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram

BACKGROUND

High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges.

NEW METHOD

The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis.

RESULTS

We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability.

COMPARISON WITH EXISTING METHOD

The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable.

CONCLUSIONS

Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable.

Frequency shift in topography of spontaneous brain rhythms from childhood to adulthood

It has been described that the frequency ranges at which theta, mu and alpha rhythms oscillate is increasing with age. The present report, by analyzing the spontaneous EEG, tries to demonstrate whether there is an increase with age in the frequency at which the cortical structures oscillate. A topographical approach was followed. The spontaneous EEG of one hundredand seventy subjects was recorded. The spectral power (from 0.5 to 45.5 Hz) was obtained by means of the Fast Fourier Transform. Correlations of spatial topographies among the different age groups showed that older groups presented the same topographical maps as younger groups, but oscillating at higher frequencies. The results suggest that the same brain areas oscillate at lower frequencies in children than in older groups, for a broad frequency range. This shift to a higher frequency with age would be a trend in spontaneous brain rhythm development.

Ambiguous function words do not prevent 18-month-olds from building accurate syntactic category expectations: An ERP study

To comprehend language, listeners need to encode the relationship between words within sentences. This entails categorizing words into their appropriate word classes. Function words, consistently preceding words from specific categories (e.g., the ball_NOUN, I speak_VERB), provide invaluable information for this task, and children's sensitivity to such adjacent relationships develops early on in life. However, neighboring words are not the sole source of information regarding an item's word class. Here we examine whether young children also take into account preceding sentence context online during syntactic categorization. To address this question, we use the ambiguous French function word la which, depending on sentence context, can either be used as determiner (the, preceding nouns) or as object clitic (it, preceding verbs). French-learning 18-month-olds' evoked potentials (ERPs) were recorded while they listened to sentences featuring this ambiguous function word followed by either a noun or a verb (thus yielding a locally felicitous co-occurrence of la + noun or la + verb). Crucially, preceding sentence context rendered the sentence either grammatical or ungrammatical. Ungrammatical sentences elicited a late positivity (resembling a P600) that was not observed for grammatical sentences. Toddlers' analysis of the unfolding sentence was thus not limited to local co-occurrences, but rather took into account non-adjacent sentence context. These findings suggest that by 18 months of age, online word categorization is already surprisingly robust. This could be greatly beneficial for the acquisition of novel words.

The Disrupted Connectivity Hypothesis of Autism Spectrum Disorders: Time for the Next Phase in Research

During the past decade, the disrupted connectivity theory has generated considerable interest as a pathophysiological model for autism spectrum disorders (ASD). This theory postulates that deficiencies in the way the brain coordinates and synchronizes activity amongst different regions may account for the clinical symptoms of ASD. This review critically examines the current structural and functional connectivity data in ASD and evaluates unresolved assumptions and gaps in knowledge that limit the interpretation of these data. Collectively, studies very often show group alterations in what are thought of as measures of cerebral connectivity, though the patterns of findings vary considerably. We argue that there are three principle needs in this research agenda. First, further basic research is needed to understand the links between measures commonly used (DTI, fMRI, EEG) and other (histological, computational) levels of analysis. Second, speculated causes of inconsistencies in the literature (age, clinical heterogeneity) demand studies that directly evaluate these interpretations. Finally, the field needs well-specified mechanistic models of altered cerebral communication in ASD whose predictions can be tested on multiple levels of analyses.

Mapping changes in cortical activity during sleep in the first 4 years of life

A coherent body of evidence supports the notion that sleep is a local and use-dependent process. Significant changes in brain morphology and function occur in the first years of life, revealing a postero-anterior trajectory of cortical maturation. On this basis, a recent study demonstrated that regional cortical maturation between early childhood and late adolescence is reflected in regional changes of sleep slow wave activity (SWA) during non-rapid eye movement (NREM) sleep. Our hypothesis is that changes of electroencephalogram (EEG) rhythms during sleep from birth to childhood are also mirrored by parallel regional changes in the EEG rhythms of sleep according to the assumption of a postero-anterior gradient in cortical maturation. We studied all-night EEG of 39 healthy, full-term, infants and children aged between 0 and 48 months, evaluating regional differences in NREM sleep. We confirmed the strictly local nature of sleep with frequency-specific regional differences. Specifically, we found a general shift of maxima of the upper alpha activity from occipital to prefrontal regions, expressed mainly by the ~11 Hz frequency. This shift corresponds to a postero-anterior trajectory of the so-called 'slow spindles'. The theta and alpha EEG activity of the frontal cortex exhibits a clear, positive, correlation with age. We conclude that specific local differences during NREM sleep, parallel cortical maturation also in the first 4 years of life.

EEG functional connectivity is partially predicted by underlying white matter connectivity

Over the past decade, networks have become a leading model to illustrate both the anatomical relationships (structural networks) and the coupling of dynamic physiology (functional networks) linking separate brain regions. The relationship between these two levels of description remains incompletely understood and an area of intense research interest. In particular, it is unclear how cortical currents relate to underlying brain structural architecture. In addition, although theory suggests that brain communication is highly frequency dependent, how structural connections influence overlying functional connectivity in different frequency bands has not been previously explored. Here we relate functional networks inferred from statistical associations between source imaging of EEG activity and underlying cortico-cortical structural brain connectivity determined by probabilistic white matter tractography. We evaluate spontaneous fluctuating cortical brain activity over a long time scale (minutes) and relate inferred functional networks to underlying structural connectivity for broadband signals, as well as in seven distinct frequency bands. We find that cortical networks derived from source EEG estimates partially reflect both direct and indirect underlying white matter connectivity in all frequency bands evaluated. In addition, we find that when structural support is absent, functional connectivity is significantly reduced for high frequency bands compared to low frequency bands. The association between cortical currents and underlying white matter connectivity highlights the obligatory interdependence of functional and structural networks in the human brain. The increased dependence on structural support for the coupling of higher frequency brain rhythms provides new evidence for how underlying anatomy directly shapes emergent brain dynamics at fast time scales.