Effect of age on the relative magnitude spectrum across space and frequency.

A) Model-predicted spectra (averaged across all sensors) for 4 equally spaced ages across the participant age range with inset histogram of participant ages within CamCAN. B) Spectrum of t-values quantifying the age effect across space and frequency. Non-parametric permutations with maximum statistics to control for multiple comparisons across sensors and frequency bins. While this permutation testing was not cluster-based, contiguous clusters of significant sensors were computed post-hoc for visualisation The largest 6 spatially and spectrally contiguous areas of statistically significant effects are highlighted in frequency by black bands at the top of the spectrum and highlighted in space by sensors marked with a white circle in the adjoining topography. C) A 2D frequency-by-space map of all statistically significant effects. Sensor-Frequency combinations that do not reach statistical significance have a faded colour scale. Blue regions indicate decreasing spectral power with age and red regions indicate increases.

Model projected age effect retains non-linear properties of decrease in alpha peak frequency and magnitude.

A) Model predicted spectra (averaged across all sensors) for the oldest and youngest participants in CamCAN with the alpha peak for intermediate ages overlaid in the thick line. B) The parameter estimates of age that combine to describe the peak frequency shift seen in A). C) Scatter plot of individual alpha peak frequencies against age. A linear regression line fitted directly to the individual alpha peaks is shown in red and the GLM-Spectrum derived alpha peaks from the model projected spectra is shown in blue. D) As C for individual alpha peak magnitude.

Cohen’s F 2 effect size for age.

A) Cohen’s F 2 effect size for age across space and frequency. Each line is a sensor, with a colour matched to the sensors in the topography shown in the inset. The six topographies show spatial distributions across the sensor array at the peak frequency of the six significant effects identified in Figure 1B. B) Contours showing the relationship between effect size and sample size for five different experimental power levels. As sample sizes get larger, there is sufficient power to reliably detect smaller effect sizes. C) Effect size spectra with bootstrapped 95% confidence intervals at the peak sensor for the four largest effects identified in Figure 1B.

Summary of significant spatio-spectral regions containing an age effect in CamCAN.

The estimated sample sizes are the range of sample sizes needed to have an 80% chance of detecting the effect in a future sample, assuming that both the effects here are true and that the effect sizes are well estimated. Sample size ranges are computed from bootstrapped 95% confidence intervals for effect size estimate. The lower bound of the 95% confidence intervals can be taken as a smallest effect size of interest. The sample size forcasts are not informative about the present results and serve only as a guide for future study planning.

Summary of significant spatio-spectral regions containing an age effect in CamCAN.

The estimated sample sizes are the range of sample sizes needed to have an 80% chance of detecting the effect in a future sample, assuming that both the effects here are true and that the effect sizes are well estimated. Sample size ranges are computed from bootstrapped 95% confidence intervals for effect size estimate. The sample size forcasts are not informative about the present results and serve only as a guide for future study planning.

Replicability of the parameter estimates, t-statistics, and effect sizes of ageing on the neuronal power spectrum.

Ai) Spectrum of parameter estimates across frequency (averaged over all sensors) for each of the four datasets. Aii) Correlation matrix indicating similarity of the sensor-averaged frequency profile of the age effect between datasets. Bi & Bii) As A for null hypothesis test statistics. Bi & Bii) As A for effect sizes.

The spatial and spectral profile of the effect of age on resting-state brain electrophysiology is replicable across datasets.

A) GLM-Spectrum parameter estimates quantifying the age effect across space and frequency for the four datasets in the replicability analysis with distribution of participant ages shown in the inset figure. The four datasets share the key features highlighted in Figure 1. B) As A) for t-statistics testing the hypothesis that the parameter estimate of the age effect is different to zero. C) As B) but visualised as a 2d image in which each row indicates a single sensor with their y-axis position sorted by spatial location on the anterior-posterior axis. D) as A) for Cohen’s F 2 effect sizes for the age regressor in the GLM-Spectrum.

The age effect computed on the absolute magnitude of the power spectrum.

A) Model-predicted spectra (averaged across all sensors) for 4 equally spaced ages across the participant age range with inset histogram of participant ages within CamCAN. B) The effect size for the age regressor computed on the absolute magnitude of the power spectrum. C top Spectrum of t-values quantifying the age effect across space and frequency. Non-parametric permutations with maximum statistics to control for multiple comparisons across sensors and frequency bins. While this permutation testing was not cluster-based, contiguous clusters of significant sensors were computed post-hoc for visualisation The largest 6 spatially and spectrally contiguous areas of statistically significant effects are highlighted in frequency by black bands at the top of the spectrum and highlighted in space by sensors marked with a white circle in the adjoining topography. C bottom A 2D frequency by space map of all statistically significant effects. Sensor-Frequency combinations that do not reach statistical significance have a faded colour scale. Blue regions indicate decreasing spectral power with age and red regions indicate increases.

Covariate effect sizes and their impact on the ageing effect.

A) Box and whisker plot with paired kernel density plot for the distributions of Cohen’s F 2 effect size for each alternative covariate, when the alternative covariate is included as the only regressor in a GLM, along with an intercept term. The Cohen’s F 2 distributions are collected over every single sensor-frequency pair from the sensor-space dataset (i.e. over 102 sensors and 189 frequency bins). Full GLM spectrum visualisations of the results are included in the appendix. For comparison, the same is shown for the age using a GLM that contains regressors for age and an intercept. B) Change in the Cohen’s F 2 effect size of age between 1) a GLM that includes an age regressor plus intercept, and 2) a model that includes an age regressor, a single alternative covariate regressor and intercept. This is shown for each alternative covariate in turn. Age is excluded from this panel. C) Pearson’s correlation coefficient quantifying the univariate linear relationship between age and each covariate. Age and sex are excluded from this panel.

The age effect is reduced heterogeneously across space and frequency by including GGMV in the model.

A) The Cohen’s F2 spectrum for age using a GLM that contains regressors for age and an intercept. Replicated from Figure 3. B)as A) when a Grey Matter Volume regressor is added to the GLM. Accounting for grey matter volume broadly reduces the effect size of age across the spectra, but some moderate effects remain. C) Spectrum of t-values quantifying the age effect across space and frequency replicated from Figure 1. Non-parametric permutations with maximum statistics to control for multiple comparisons across sensors and frequency bins. While this permutation testing was not cluster-based, contiguous clusters of significant sensors were computed post-hoc for visualisation The largest 6 spatially and spectrally contiguous areas of statistically significant effects are highlighted in frequency by black bands at the top of the spectrum and highlighted in space by sensors marked with a white circle in the adjoining topography D) As C) for the age effect in a model that includes a global grey matter volume covariate. Most effects are reduced in size with smaller contiguous statistically significant regions surviving the post-hoc clustering procedure.

Effect of age on the relative magnitude spectrum across space and frequency in LCMV source reconstructed and parcellated data.

A) Model-predicted spectra (averaged across all sensors) for 4 equally spaced ages across the participant age range. B) Spectrum of t-values quantifying the age effect across space and frequency. Source topographies are shown in the frequency bands with significant effects at sensorspace. The permutation statistics are not repeated at source space. C) A 2D frequency-by-space map of all statistically significant effects. Sensor-Frequency combinations that do not reach statistical significance have a faded colour scale. Blue regions indicate decreasing spectral power with age and red regions indicate increases.

GLM spectrum of parameter estimates for all covariates.

GLM spectrum of t-statistics for all covariates.

GLM spectrum of effect sizes for all covariates.

GLM spectrum of change in estimates age effect size when including covariate in the model.

Age itself is excluded from this figure.

GLM-Spectrum results showing the effect of age estimated for two different sensor normalisations and three different SSS head position correction types.

A) The age effect for the absolute magnitude with SSS applied without head position correction. B) The age effect for the relative magnitude with SSS applied without head position correction. C) The age effect for the absolute magnitude with SSS applied with head position correction applied within each dataset (−movecomp in maxfilter software). D) The age effect for the relative magnitude with SSS applied with head position correction applied within each dataset (− movecomp in maxfilter software). E) The age effect for the absolute magnitude with SSS applied with head position correction applied within each dataset (−movecomp in maxfilter software) and head position alignment to a reference datafile (−trans in maxfilter software). F) The age effect for the relative magnitude with SSS applied with head position correction applied within each dataset (−movecomp in maxfilter software) and head position alignment to a reference datafile (− trans in maxfilter software). G) The spectral generalisation of the topography of the absolute magnitude age effect. H) The spectral generalisation of the topography of the relative magnitude age effect.