The developing brain revealed during sleep

Blumberg, Mark S.; Dooley, James C.; Sokoloff, Greta

doi:10.1016/j.cophys.2019.11.002

Cited by 37 publications

(36 citation statements)

References 79 publications

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“…However, AS-related EEG activity does occur in premature infants and newborns [16,17]; at those earlier ages, activity takes the form of "spindle bursts," brief thalamocortical oscillations that resemble sleep spindles in some ways, including their dominant frequency [18]. As studied most extensively in infant rats, AS-related twitches are produced by motor structures in the brainstem and trigger sensory feedback that cascades throughout the brain, from medulla to sensorimotor cortex [2]. As in human infants, spindle bursts in rats disappear postnatally, apparently due to the onset of inhibitory mechanisms in the thalamus [19].…”

Section: Resultsmentioning

confidence: 99%

“…In humans and other mammals, the stillness of sleep is punctuated by bursts of rapid eye movements (REMs) and myoclonic twitches of the limbs [1]. Contrary to the notion that twitches are mere by-products of dreams, sensory feedback arising from twitching limbs provides a rich and unique source of activation to the developing sensorimotor system [2]. In fact, it is partly because of the behavioral activation of REM sleep that this state is also called active sleep (AS), in contrast with the behavioral quiescence that gives quiet sleep (QS)-the second major stage of sleep-its name.…”

Section: Discussionmentioning

confidence: 99%

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Twitches emerge postnatally during quiet sleep in human infants and are synchronized with sleep spindles

Sokoloff

Dooley

Glanz

et al. 2021

Preprint

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Sleep, the predominant state of early infancy, is divided into two sub-states: active sleep (AS; or REM sleep) and quiet sleep (QS; or non-REM sleep). Behaviorally, AS is distinguished from QS by the presence of rapid eye movements (REMs) and abundant twitches across the body. Here, in the early postnatal period in human infants, we report the unexpected developmental emergence of twitches during QS. These twitches are unaccompanied by REMs and occur synchronously with sleep spindles, a hallmark of QS. As QS-related twitching increases with age, sleep spindle rate also increases along the sensorimotor strip. The emerging synchrony between subcortically generated twitches and cortical oscillations suggests the development of functional connectivity among distant sensorimotor structures, with potential implications for more sensitive identification of atypical developmental trajectories.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Twitches emerge postnatally during quiet sleep in human infants and are synchronized with sleep spindles

Sokoloff

Dooley

Glanz

et al. 2021

Preprint

Self Cite

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show abstract

“…1 Like the spontaneous activity that arises from the sensory periphery in other modalities (e.g., retinal waves), 2 sensory feedback arising from twitches is well suited to drive activitydependent development of the sensorimotor system. 3 It is partly because of the behavioral activation of REM sleep that this state is also called active sleep (AS), in contrast with the behavioral quiescence that gives quiet sleep (QS)-the second major stage of sleep-its name. In human infants, for which AS occupies 8 h of each day, 4 twitching helps to identify the state; [5][6][7][8] nonetheless, we know little about the structure and functions of twitching across development.…”

Section: Discussionmentioning

confidence: 99%

Twitches emerge postnatally during quiet sleep in human infants and are synchronized with sleep spindles

et al. 2021

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“…Early circuits are characterized by high excitatory convergence and absent or even excitatory GABAergic inputs (Chen and Regehr, 2000 ; Ben-Ari et al, 2007 ; Murata and Colonnese, 2020 ). As result of these early cellular and circuit configurations the developing thalamocortex produces unique patterns of neural activity, such as “spindle-bursts” and “early gamma” which are generated in response to spontaneous activity in the sense organ (Leighton and Lohmann, 2016 ; Luhmann and Khazipov, 2018 ; Blumberg et al, 2020 ). In-vivo recordings in the maturating visual cortex showed intracellular membrane potential dynamics during the early developmental period that include prominent plateau potentials at the neural cell body: prolonged spikeless depolarizations with the membrane potential above −20 mV during spindle-bursts (Colonnese, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Polynomial, piecewise-Linear, Step (PLS): A Simple, Scalable, and Efficient Framework for Modeling Neurons

Tikidji-Hamburyan

Colonnese

2021

Front. Neuroinform.

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Biological neurons can be modeled with different levels of biophysical/biochemical details. The accuracy with which a model reflects the actual physiological processes and ultimately the information function of a neuron, can range from very detailed to a schematic phenomenological representation. This range exists due to the common problem: one needs to find an optimal trade-off between the level of details needed to capture the necessary information processing in a neuron and the computational load needed to compute 1 s of model time. An increase in modeled network size or model-time, for which the solution should be obtained, makes this trade-off pivotal in model development. Numerical simulations become incredibly challenging when an extensive network with a detailed representation of each neuron needs to be modeled over a long time interval to study slow evolving processes, e.g., development of the thalamocortical circuits. Here we suggest a simple, powerful and flexible approach in which we approximate the right-hand sides of differential equations by combinations of functions from three families: Polynomial, piecewise-Linear, Step (PLS). To obtain a single coherent framework, we provide four core principles in which PLS functions should be combined. We show the rationale behind each of the core principles. Two examples illustrate how to build a conductance-based or phenomenological model using the PLS-framework. We use the first example as a benchmark on three different computational platforms: CPU, GPU, and mobile system-on-chip devices. We show that the PLS-framework speeds up computations without increasing the memory footprint and maintains high model fidelity comparable to the fully-computed model or with lookup-table approximation. We are convinced that the full range of neuron models: from biophysical to phenomenological and even to abstract models, may benefit from using the PLS-framework.

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The developing brain revealed during sleep

Cited by 37 publications

References 79 publications

Twitches emerge postnatally during quiet sleep in human infants and are synchronized with sleep spindles

Twitches emerge postnatally during quiet sleep in human infants and are synchronized with sleep spindles

Twitches emerge postnatally during quiet sleep in human infants and are synchronized with sleep spindles

Polynomial, piecewise-Linear, Step (PLS): A Simple, Scalable, and Efficient Framework for Modeling Neurons

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