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What the Sleep EEG can tell us about behavioral development in babies

Text: Valeria Jaramillo

EEG trace of 6-month-old infant. Slow waves are colored in blue, spindles in red.

In our new study we looked at how sleep electroencephalographic (EEG) markers reflecting thalamocortical connectivity are related to behavioral development in infants. Thalamocortical connectivity refers to the neuronal wiring between two brain regions, the thalamus and the cortex. This wiring is important to integrate sensory and motor signals, and to regulate consciousness and alertness. Altered thalamocortical connections are tied to many neurodevelopmental disorders, such as autism spectrum disorder and attention-deficit/hyperactivity disorder (ADHD). Since thalamocortical connections are established in the last 3 months of pregnancy and are modified extensively in the first 2-3 months after birth, the thalamocortical system is especially vulnerable to environmental influences during this critical period.

Slow waves and spindles that can be recorded using EEG are the most prominent oscillations in non-rapid eye movement (NREM) sleep and emerge in the first months of life postpartum. Since they are generated by the thalamocortical system they reflect thalamocortical connectivity. Further, they are potentially involved in synaptic plasticity processes – the making and breaking of connections between neurons which is crucial for the functioning of the brain and the acquisition of new skills. 

We used high-density electroencephalography (hdEEG) with over 100 electrodes during sleep to evaluate three potential features as predictors for behavioral development in infants:

  • Slow wave slope: the steepness of slow waves, indicator of the strength of the connections (synapses) between neurons, which increases across early infancy and decreases across adolescence, reflecting the typical synaptic dynamics of cortical maturation.
  • Spindle density: the number of spindles per minute, which is associated with memory consolidation during sleep and general cognitive abilities, spindles are thought to play a role in the development of microcircuitry of the sensorimotor brain network.
  • Coupling of slow waves with spindles: the temporal association of slow waves with spindles, which is thought to promote memory consolidation during sleep, and increases from childhood to adolescence, but until now it was unclear whether slow waves and spindles occur in a temporally coupled manner in infants. Our new results show that slow waves and spindles are temporally coupled already in infancy.

Although the EEG features were not associated with behavioral development at the same age timepoint of 6 months, we found that spindle density measured at 6 months predicted behavioral skills at 12 and 24 months, especially in the motor domain. Interestingly, spindle frequency, which reflects how fast spindles are, was also predictive of later behavioral development. This suggests that specific sleep EEG features in early infancy could represent promising biomarkers for later developmental outcomes that could be used to identify infants at risk for disorders based on their early thalamocortical network signatures. Additional studies are needed to refine the use of clinical EEG markers.

Source:
Jaramillo V*, Schoch SF*, Markovic A, Kohler M, Huber R, Lustenberger C, Kurth S (2023). An infant sleep electroencephalographic marker of thalamocortical connectivity predicts behavioral outcome in late infancy. NeuroImage, 1;269:119924.
*equal contribution

Thanks to:
OpenAI ChatGPT for support in texting this blog
Picture from bethL on pixabay


1 comment Add New Comment

  1. Salome says:

    We were asked the following questions in relation to this article:
    «Are those characteristics causal, or indicative of upstream effects? If causal, how do you change them? If not, what are the exposures that cause them, and how do we change that.»

    We believe that in addressing the next core questions, it is important to take both a prospective and a retrospective approach.

    Retrospectively, identifying risk factors can be achieved through epidemiological studies that integrate contextual variables within large population data, beginning during pregnancy. This can capture contextual, genetic, behavioral, and nutritional aspects – as well as the expression of circadian rhythm across generations.

    For predetermined at-risk groups, such secondary effects may occur. Due to the limited availability of disease-modifying treatments, there is a growing focus on prioritizing prevention in pediatric healthcare. Infancy and early childhood are particularly amenable to intervention, and early therapeutic interventions are then more effective in reducing both developmental and economic burdens. For instance, in the case of Autism, applying behavioral therapy before the age of 5 years results in significant and long-lasting effects throughout an individual’s lifespan (see reference below).

    In addition, closed-loop acoustic stimulation protocols may systematically influence brain oscillations during sleep, as evidenced by pilot data in childhood epilepsy (see reference below). Furthermore, early nutritional interventions (likely via the gut microbiota) are potential targets for modulating neurodevelopmental connectivity and may have effects on the thalamic network.

    Lastly, the oscillatory features of deep sleep can influence synaptic strength, and potentially lead to changes in cortical connectivity (as discussed in reference 10.1016/j.neuroimage.2012.03.053). Therefore, supporting children’s systemic rhythm evolution during their transition to a 24-h-society can likely facilitate neurodevelopmental aspects. A new paper (currently under revision) shows how infants’ sleep habits are intertwined with sleep neurophysiology. Sleep can be tailored to be age-appropriate through educational and behavioral strategies for parents, such as the Zurich 3-step concept.

    If you have any thoughts, feel free to write us! We are open to further discussion.

    10.1016/j.heliyon.2022.e12259
    10.1016/j.pcl.2020.02.007
    10.3389/fnhum.2019.00166
    10.1038/s41598-021-83125-z
    10.1101/2021.11.08.467800
    10.5664/jcsm.4536
    10.1016/j.neuroimage.2012.03.053

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