Dataset Information

Developmental Changes in Sleep Oscillations during Early Childhood.

ABSTRACT: 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.

SUBMITTER: Olbrich E

PROVIDER: S-EPMC5563422 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Developmental Changes in Sleep Oscillations during Early Childhood.

Olbrich Eckehard E Rusterholz Thomas T LeBourgeois Monique K MK Achermann Peter P

Neural plasticity 20170815

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 (<i>n</i> = 8; 3 males) at ages 2, 3, and 5 years. Following sleep stage scoring, we detect ...[more]

PMID: 28845310

Similar Datasets

Project description:Slow oscillations, a defining characteristic of the nonrapid eye movement sleep electroencephalogram (EEG), proliferate across the scalp in highly reproducible patterns. In adults, the propagation of slow oscillations is a recognized fingerprint of brain connectivity and excitability. In this study, we (1) describe for the first time maturational features of sleep slow oscillation propagation in children (n = 23; 2-13 years) using high-density (hd) EEG and (2) examine associations between sleep slow oscillatory propagation characteristics (ie, distance, traveling speed, cortical involvement) and white matter myelin microstructure as measured with multicomponent Driven Equilibrium Single Pulse Observation of T1 and T2-magnetic resonance imaging (mcDESPOT-MRI). Results showed that with increasing age, slow oscillations propagated across longer distances (average growth of 0.2 cm per year; R(21) = 0.50, p < .05), while traveling speed and cortical involvement (ie, slow oscillation expanse) remained unchanged across childhood. Cortical involvement (R(20) = 0.44) and slow oscillation speed (R(20) = -0.47; both p < .05, corrected for age) were associated with myelin content in the superior longitudinal fascicle, the largest anterior-posterior, intrahemispheric white matter connectivity tract. Furthermore, slow oscillation distance was moderately associated with whole-brain (R(21) = 0.46, p < .05) and interhemispheric myelin content, the latter represented by callosal myelin water fraction (R(21) = 0.54, p < .01, uncorrected). Thus, we demonstrate age-related changes in slow oscillation propagation distance, as well as regional associations between brain activity during sleep and the anatomical connectivity of white matter microstructure. Our findings make an important contribution to knowledge of the brain connectome using a noninvasive and novel analytic approach. These data also have implications for understanding the emergence of neurodevelopmental disorders and the role of sleep in brain maturation trajectories.

Project description:IntroductionDuring early childhood, typical human motor behavior reveals a gradual transition from automatic motor patterns to acquired motor skills, by the continuous interplay between nature and nurture. During the wiring and shaping of the underlying motor networks, insight into the neurological phenotype of developmental motor patterns is incomplete. In healthy, typically developing children (0-3 years of age), we therefore aimed to investigate the neurological phenotype of developmental motor patterns.MethodsIn 32 healthy, typically developing children (0-3 years), we video-recorded spontaneous motor behavior, general movements (GMs), and standardized motor tasks. We classified the motor patterns by: (a) the traditional neurodevelopmental approach, by Gestalt perception and (b) the classical neurological approach, by the clinical phenotypic determination of movement disorder features. We associated outcomes by Cramer's V.ResultsDevelopmental motor patterns revealed (a) choreatic-like features (?3 months; associated with fidgety GMs (r = 0.732) and startles (r = 0.687)), (b) myoclonic-like features (?3 months; associated with fidgety GMs (r = 0.878) and startles (r = 0.808)), (c) dystonic-like features (0-3 years; associated with asymmetrical tonic neck reflex (r = 0.641) and voluntary movements (r = 0.517)), and (d) ataxic-like features (>3 months; associated with voluntary movements (r = 0.928)).ConclusionsIn healthy infants and toddlers (0-3 years), typical developmental motor patterns reveal choreatic-, myoclonic-, dystonic- and ataxic-like features. The transient character of these neurological phenotypes is placed in perspective of the physiological shaping of the underlying motor centers. Neurological phenotypic insight into developmental motor patterns can contribute to adequate discrimination between ontogenetic and initiating pathological movement features and to adequate interpretation of therapeutic interactions.

Project description:BackgroundInnate immune responses are fine-tuned by small noncoding RNA molecules termed microRNAs (miRs) that modify gene expression in response to the environment. During acute infections, miRs can be secreted in extracellular vesicles (EV) to facilitate cell-to-cell genetic communication. The purpose of this study was to characterize the baseline population of miRs secreted in EVs in the airways of young children (airway secretory microRNAome) and examine the changes during rhinovirus (RV) infection, the most common cause of asthma exacerbations and the most important early risk factor for the development of asthma beyond childhood.MethodsNasal airway secretions were obtained from children (?3 yrs. old) during PCR-confirmed RV infections (n = 10) and age-matched controls (n = 10). Nasal EVs were isolated with polymer-based precipitation and global miR profiles generated using NanoString microarrays. We validated our in vivo airway secretory miR data in an in vitro airway epithelium model using apical secretions from primary human bronchial epithelial cells (HBEC) differentiated at air-liquid interface (ALI). Bioinformatics tools were used to determine the unified (nasal and bronchial) signature airway secretory miRNAome and changes during RV infection in children.ResultsMultiscale analysis identified four signature miRs comprising the baseline airway secretory miRNAome: hsa-miR-630, hsa-miR-302d-3p, hsa- miR-320e, hsa-miR-612. We identified hsa-miR-155 as the main change in the baseline miRNAome during RV infection in young children. We investigated the potential biological relevance of the airway secretion of hsa-mir-155 using in silico models derived from gene datasets of experimental in vivo human RV infection. These analyses confirmed that hsa-miR-155 targetome is an overrepresented pathway in the upper airways of individuals infected with RV.ConclusionsComparative analysis of the airway secretory microRNAome in children indicates that RV infection is associated with airway secretion of EVs containing miR-155, which is predicted in silico to regulate antiviral immunity. Further characterization of the airway secretory microRNAome during health and disease may lead to completely new strategies to treat and monitor respiratory conditions in all ages.

Dataset Information

Developmental Changes in Sleep Oscillations during Early Childhood.

Publications

Developmental Changes in Sleep Oscillations during Early Childhood.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets