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      Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood

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          Abstract

          The neurodevelopmental period spanning early-to-middle childhood represents a time of significant growth and reorganisation throughout the cortex. Such changes are critical for the emergence and maturation of a range of social and cognitive processes. Here, we utilised both eyes open and eyes closed resting-state electroencephalography (EEG) to examine maturational changes in both oscillatory (i.e., periodic) and non-oscillatory (aperiodic, ‘1/ f-like’) activity in a large cohort of participants ranging from 4-to-12 years of age (N = 139, average age=9.41 years, SD=1.95). The EEG signal was parameterised into aperiodic and periodic components, and linear regression models were used to evaluate if chronological age could predict aperiodic exponent and offset, as well as well as peak frequency and power within the alpha and beta ranges. Exponent and offset were found to both decrease with age, while aperiodic-adjusted alpha peak frequency increased with age; however, there was no association between age and peak frequency for the beta band. Age was also unrelated to aperiodic-adjusted spectral power within either the alpha or beta bands, despite both frequency ranges being correlated with the aperiodic signal. Overall, these results highlight the capacity for both periodic and aperiodic features of the EEG to elucidate age-related functional changes within the developing brain.

          Highlights

          • Resting-state EEG was recorded from children aged 4–12 years.

          • The EEG signal was parameterized into periodic and aperiodic components.

          • Aperiodic exponent and offset were found to decrease with age.

          • A developmental increase in aperiodic-adjusted alpha peak frequency was observed.

          • These results highlight age-related functional changes within the developing brain.

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          Most cited references85

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          EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

          We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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            Synaptic pruning by microglia is necessary for normal brain development.

            Microglia are highly motile phagocytic cells that infiltrate and take up residence in the developing brain, where they are thought to provide a surveillance and scavenging function. However, although microglia have been shown to engulf and clear damaged cellular debris after brain insult, it remains less clear what role microglia play in the uninjured brain. Here, we show that microglia actively engulf synaptic material and play a major role in synaptic pruning during postnatal development in mice. These findings link microglia surveillance to synaptic maturation and suggest that deficits in microglia function may contribute to synaptic abnormalities seen in some neurodevelopmental disorders.
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              Neuronal oscillations in cortical networks.

              G Buzsáki (2004)
              Clocks tick, bridges and skyscrapers vibrate, neuronal networks oscillate. Are neuronal oscillations an inevitable by-product, similar to bridge vibrations, or an essential part of the brain's design? Mammalian cortical neurons form behavior-dependent oscillating networks of various sizes, which span five orders of magnitude in frequency. These oscillations are phylogenetically preserved, suggesting that they are functionally relevant. Recent findings indicate that network oscillations bias input selection, temporally link neurons into assemblies, and facilitate synaptic plasticity, mechanisms that cooperatively support temporal representation and long-term consolidation of information.
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                Author and article information

                Contributors
                Journal
                Dev Cogn Neurosci
                Dev Cogn Neurosci
                Developmental Cognitive Neuroscience
                Elsevier
                1878-9293
                1878-9307
                22 January 2022
                April 2022
                22 January 2022
                : 54
                : 101076
                Affiliations
                [0005]Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia
                Author notes
                [* ]Correspondence to: Cognitive Neuroscience Unit, School of Psychology, Deakin University, 221 Burwood Hwy, Burwood, Victoria 3125, Australia. a.hill@ 123456deakin.edu.au
                Article
                S1878-9293(22)00020-2 101076
                10.1016/j.dcn.2022.101076
                8800045
                35085871
                8c32ddd6-8f7c-44f6-9443-d70d13f5d0c8
                © 2022 The Authors. Published by Elsevier Ltd.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 18 October 2021
                : 10 January 2022
                : 21 January 2022
                Categories
                Original Research

                Neurosciences
                eeg, electroencephalography,e/i, excitation/inhibition,fooof, fitting oscillations and one over f,gaba, γ-aminobutyric acid,ica, independent component analysis,meg, magnetoencephalography,psd, power spectral density,roi, region of interest,tms, transcranial magnetic stimulation,eeg,aperiodic activity,oscillations,neurodevelopment,neurophysiology,spectral power

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