Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: ( a ) Overview of the Spectral Parameterization Resolved in Time (SPRiNT) approach: At each time bin along a neurophysiological time series (black trace) n overlapping time windows are Fourier-transformed to yield an estimate of spectral contents, which is subsequently parameterized using specparam . The procedure is replicated across time over sliding, overlapping windows to generate a parameterized spectrogram of neural activity. ( b ) Simulation challenge I: We simulated 10,000 time series composed of the same time-varying spectral (aperiodic and periodic) features, with different realizations of additive noise. ( c ) Simulation challenge II: We simulated another 10,000 time series, each composed of different time-varying spectral (aperiodic and periodic) ground-truth features with additive noise. All simulated time series were used to evaluate the respective performances of SPRiNT and the wavelet- specparam alternative.

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques: Transformation Assay, Activity Assay

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: ( a ) Ground-truth spectrogram (left) and averaged modelled spectrograms produced by the wavelet- specparam approach (middle) and SPRiNT (right; n=10,000). ( b ) Aperiodic parameter estimates (lines: median; shaded regions: first and third quartiles, n=10,000) across time from wavelet- specparam (left) and SPRiNT (right; black: ground truth; blue: exponent; yellow: offset). ( c ) Absolute error (and detection performance) of alpha and beta-band rhythmic components for wavelet- specparam (left) and SPRiNT (right). Violin plots represent the sample distributions (n=10,000; blue: alpha peak; yellow: beta peak; white circle: median, grey box: first and third quartiles; whiskers: range).

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques: Produced

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: ( a ) Results from the temporally smoothed wavelet -specparam approach for the alpha (top) and beta (bottom) rhythmic components for each estimated parameter (from left to right: centre frequency, spectral peak amplitude, and SD). Grey dashed line: ground truth; coloured line: median; shaded region: first and third quartiles. Bar plots in left panels: probability of detecting an oscillatory peak within respective frequency ranges at each time bin. ( b ) Same display for the results obtained with Spectral Parameterization Resolved in Time (SPRiNT). All with n=10,000 simulations.

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques:

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: ( a ) Aperiodic parameter estimates (lines: median; shaded regions: first and third quartiles, n=1000) across time from wavelet -specparam with full width at half maximum (FWHM) = 2 s (left) and wavelet- specparam with FWHM = 4 s (right; black dash: ground truth; blue: exponent; yellow: offset). ( b ) Absolute error (and detection performance) of alpha and beta-band rhythmic components for wavelet -specparam with FWHM = 2 s (left) and wavelet- specparam with FWHM = 4 s (right). Violin plots represent the sample distributions (n=1000; blue: alpha peak; yellow: beta peak; white circle: median, grey box: first and third quartiles; whiskers: range).

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques:

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: ( a ) Aperiodic parameter estimates (lines: median; shaded regions: first and third quartiles, n=1000) across time from unsmoothed wavelet- specparam (left) and SPRiNT without outlier peak removal (right; black dash: ground truth; blue: exponent; yellow: offset). ( b ) Absolute error (and detection performance) of alpha and beta-band periodic components for unsmoothed wavelet- specparam (left) and SPRiNT without outlier peak removal (right). Violin plots represent the sample distributions (n=1000; blue: alpha peak; yellow: beta peak; white circle: median, grey box: first and third quartiles; whiskers: range).

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques:

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: ( a ) Mean periodogram and specparam models for eyes-closed (blue) and eyes-open (yellow) resting-state EEG activity (from electrode Oz; n=178). ( b ) Logistic regressions showed that specparam -derived eyes-closed alpha-peak amplitude was predictive of age group, but mean eyes-closed alpha-peak amplitude derived from SPRiNT was not. ( c ) Example of intrinsic dynamics in alpha activity during the eyes-closed period leading to divergent SPRiNT and specparam models (participant sub-016). In a subset of participants (<10%), we observed strong intermittence of the presence of an alpha peak. Since an alpha peak was not consistently present in the eyes-closed condition, and specparam -derived alpha-peak amplitude (0.77 a.u.; light blue) is lower than SPRiNT-derived mean alpha-peak amplitude (1.06 a.u.; dark blue), as the latter only includes time samples featuring a detected alpha peak. ( d ) Logistic regression showed that temporal variability in eyes-open alpha centre frequency predicts age group. Left: mean SPRiNT spectrogram (n=178) and sample distribution of eyes-open alpha centre frequency (participant sub-067). Right: variability (SD) in eyes-open alpha centre frequency separated by age group. Note: no alpha peaks were detected in the eyes-open period for one participant (boxplot line: median; boxplot limits: first and third quartiles; whiskers: range). Sample sizes: younger adults (age: 20–40 years): 121; older adults (age: 55–80 years): 56. Figure 4—source data 1. Spectral parameters and age group by participant.

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques: Activity Assay, Derivative Assay

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: Logistic regression model of specparam parameters for predicting condition (eyes-closed vs eyes-open).

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques:

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: Eyes-open logistic regression model parameters for predicting age group, specparam .

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques:

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: Eyes-closed logistic regression model parameters for predicting age group, specparam .

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques: