4.5 Article

Longitudinal changes in aperiodic and periodic activity in electrophysiological recordings in the first seven months of life

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ELSEVIER SCI LTD
DOI: 10.1016/j.dcn.2020.100895

关键词

EEG; Infants; Aperiodic exponent; Neuronal oscillations

资金

  1. Whitehall Foundation, USA [201712-73]
  2. NIH National Institute of General Medical Sciences, USA [R01GM134363-01]
  3. Halicioglu Data Science Institute Fellowship, USA

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Neuronal oscillations play a significant role in early human development, but their assessment is confounded by aperiodic neuronal activity. Studies have shown that the aperiodic signal gradually shifts from childhood into adulthood, highlighting its importance. Longitudinal analysis of EEG data from infants shows progressive flattening of the EEG power spectrum starting in early development.
Neuronal oscillations emerge in early human development. These periodic oscillations are thought to rapidly change in infancy and stabilize during maturity. Given their numerous connections to physiological and cognitive processes, understanding the trajectory of oscillatory development is important for understanding healthy human brain development. This understanding is complicated by recent evidence that assessment of periodic neuronal oscillations is confounded by aperiodic neuronal activity, an inherent feature of electrophysiological recordings. Recent cross-sectional evidence shows that this aperiodic signal progressively shifts from childhood through early adulthood, and from early adulthood into later life. None of these studies, however, have been performed in infants, nor have they been examined longitudinally. Here, we analyzed longitudinal non-invasive EEG data from 22 typically developing infants, ranging between 38 and 203 days old. We show that the progressive flattening of the EEG power spectrum begins in very early development, continuing through the first months of life. These results highlight the importance of separating the periodic and aperiodic neuronal signals, because the aperiodic signal can bias measurement of neuronal oscillations. Given the infrequent, bursting nature of oscillations in infants, we recommend using quantitative time domain approaches that isolate bursts and uncover changes in waveform properties of oscillatory bursts.

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