Spontaneous cortical dynamics from the first years to the golden years

Abstract

In the largest and most expansive lifespan magnetoencephalography (MEG) study to date (n = 434, 6 to 84 y), we provide critical data on the normative trajectory of resting-state spontaneous activity and its temporal dynamics. We perform cutting-edge analyses to examine age and sex effects on whole-brain, spatially-resolved relative and absolute power maps, and find significant age effects in all spectral bands in both types of maps. Specifically, lower frequencies showed a negative correlation with age, while higher frequencies positively correlated with age. These correlations were further probed with hierarchical regressions, which revealed significant nonlinear trajectories in key brain regions. Sex effects were found in absolute but not relative power maps, highlighting key differences between outcome indices that are generally used interchangeably. Our rigorous and innovative approach provides multispectral maps indicating the unique trajectory of spontaneous neural activity across the lifespan, and illuminates key methodological considerations with the widely used relative/absolute power maps of spontaneous cortical dynamics.

Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer’s Disease continuum?

“…Another limitation of the current work is the absence of age matching between the SCD and MCI groups. While this is an inherent property of the cohort, due to the later occurrence of MCI in the Alzheimer’s continuum ( Bessi et al, 2018 , Smailovic et al, 2019 ), the different ages of the two groups may enhance some of the effects found in the identified markers ( Rempe et al, 2023 , Zappasodi et al, 2015 ). However, the differences found between SCD and MCI in microstate C duration and occurrence have an opposite sign compared to the age-related differences in the available literature ( Koenig et al, 2002 ), corroborating our conclusions.…”

Development of peak alpha frequency reflects a distinct trajectory of neural maturation in autistic children

“…Considering prior literature indicating that the effects of age and cognitive ability on PAF are likely nonlinear (Dickinson et al, 2022; Rempe et al, 2023) and that these relations differ based on the diagnostic group (Dickinson et al, 2018; Dickinson et al, 2022; Edgar et al, 2015; Edgar et al, 2019), a generalized additive model (GAM; Hastie & Tibshirani, 1990) was fit using the mcgv package in R (Wood, 2011) to assess the joint effect of age, cognitive ability, and diagnostic status on PAF. In contrast to linear regression which estimates constant regression coefficients (β coefficients) for effects of interest, GAMs allow for more flexible modeling and detection of nonlinear effects by estimating smooth basis functions for predictors interest.…”

“…As such, alpha band activity provides a physiological index of neural and cognitive development (Grandy et al, 2013; Klimesch, 1999). Studies examining the developmental profile of alpha band activity have indicated age‐related increases in relative alpha power (defined as total alpha power divided by sum of power across all frequency bands) throughout childhood that are particularly pronounced in posterior regions of the brain (Cragg et al, 2011; Gasser et al, 1988; Rempe et al, 2023). Further, several studies have shown a significant positive correlation between relative alpha power and performance on standardized measures of intelligence (Fonseca et al, 2006; Schmid et al, 2002), a relationship presumed to reflect the overall increases in cognitive sophistication that take place during maturation (Klimesch, 1999).…”

“…Prior work investigating relations among age, cognitive ability, and PAF have utilized analytic methods assuming linear relations among variables (e.g., comparing differences in the linear relation between age and PAF in autistic and non‐autistic children) despite evidence that these relations are likely nonlinear (Dickinson et al, 2022; Freschl et al, 2022; Rempe et al, 2023). Statistical methods assuming linear, straight‐line relationships between two variables suggest a constant increase in the dependent variable (PAF) across the specified range of the independent variable (e.g., age or cognitive ability).…”

A comparison of resting‐state eyes‐closed and dark‐room alpha‐band activity in children

“…Compared to electroencephalography (EEG), MEG is less sensitive to conductivity differences between the brain, cerebral spinal fluid, skull, and scalp, and thus MEG is often preferred for source localization (Chen et al, 2019; Hämäläinen et al, 1993). For recent discussions on the use of MEG and source localization to study RS activity in children, see Ott et al (2021) and Rempe et al (2023).…”