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[Preprint]. 2023 Jun 12:2023.06.11.544491.
doi: 10.1101/2023.06.11.544491.

Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes

Martina Kopčanová  1 , Luke Tait  2   3 , Thomas Donoghue  4 , George Stothart  5 , Laura Smith  6 , Aimee Arely Flores Sandoval  7   8 , Paula Davila-Perez  9   10 , Stephanie Buss  11   12 , Mouhsin M Shafi  11   12 , Alvaro Pascual-Leone  12   13 , Peter J Fried  11   12 , Christopher S Y Benwell  1
Affiliations

Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes

Martina Kopčanová et al. bioRxiv. .

Update in

Abstract

Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer's disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasise the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.

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Conflict of interest statement

Competing interests Dr. A. Pascual-Leone is partly supported by grants from the National Institutes of Health (R01AG076708, R03AG072233) and BrightFocus Foundation. Dr. A. Pascual-Leone serves as a paid member of the scientific advisory boards for Neuroelectrics, Magstim Inc., TetraNeuron, Skin2Neuron, MedRhythms, and Hearts Radiant. He is co-founder of TI solutions and co-founder and chief medical officer of Linus Health. None of these companies have any interest in or have contributed to the present work. Dr. A Pascual-Leone is listed as an inventor on several issued and pending patents on the real-time integration of transcranial magnetic stimulation with electroencephalography and magnetic resonance imaging, and applications of noninvasive brain stimulation in various neurological disorders; as well as digital biomarkers of cognition and digital assessments for early diagnosis of dementia. The remaining authors declare no competing financial interests.

Figures

Figure 1.
Figure 1.
Example of a parametrised spectrum and hypothesised periodic and aperiodic changes. A) An example power spectrum (gray solid line) parametrised into periodic and aperiodic components (dashed line). The periodic component (over and above the aperiodic component) and each identified peak can be characterised by a peak center frequency (CF), power over and above the aperiodic component (PW), and bandwidth (BW). The aperiodic component (dashed line) is characterised by the offset (intercept) and exponent (slope) respectively. B) Periodic change hypothesis, with simulated data representing healthy controls (HC) in gray and AD spectra in black. The aperiodic component for HC (gray dashed line) and AD (purple dashed line) are overlapping. This panel illustrates how changes in the purely periodic activity (above the aperiodic component) can give rise to low frequency power increase and high frequency power decrease without any changes in aperiodic component. C) An illustration of how changes in aperiodic component (dashed lines), without any concurrent changes in the periodic activity, could result in an overall increase in spectral power at low frequencies and a decrease in higher frequencies.
Figure 2:
Figure 2:
Canonical spectral power changes. A-B Mean full scalp power spectra for each diagnostic group. Shaded areas represent the standard error of the mean. Cohort1: green = HC, blue = AD; Cohort2: yellow = HC, purple = AD. C-D Comparison of the original SPR computed from the raw power spectra showed a significant difference between the groups in both cohorts. E-F No significant difference was found when considering low frequencies (delta + theta) alone in cohorts 1 and 2. G-H High frequency (alpha + beta) power increased significantly in cohort 1 but did not differ significantly in cohort 2. *** p < .0001, ** p < .001, * p < .05, ns p > .05.
Figure 3:
Figure 3:
Group averages of parametrized power spectra. A Cohort 1. Mean full scalp power spectra for each diagnostic group after ‘specparam’ parametrization. The final ‘specparam’ model fits are in green (HC) and blue (AD). Each power spectrum further consists of periodic activity (shaded area) and the aperiodic component (dashed line). B Cohort 2 (yellow = HC; purple = AD).
Figure 4:
Figure 4:
Periodic activity in full-scalp power spectra is altered in AD, exhibiting a shift in activity from higher to lower frequencies. A-B Cohort 1 results. A The group averaged spectra after the aperiodic activity has been subtracted from the raw spectra for each participant (AD: blue, HC: green). The shaded areas represent standard error. B Between-group comparison of periodic parameters showed power at the dominant alpha (5–15Hz) peak is significantly reduced in AD, whilst its frequency and bandwidth are not. C-D Cohort 2 results. C Group averaged periodic components of the power spectrum (AD: purple, HC: yellow). The shaded area represents standard error. D Peak alpha (5–15Hz) power differed between groups, whilst center frequency and bandwidth did not. CF: peak center frequency, PW: power over and above the aperiodic component, and BW: bandwidth
Figure 5:
Figure 5:
A-B Comparison of the aperiodic-adjusted SPR (log-transformed) showed a significant between-group difference in both cohorts. C-D Low frequency power (3–8 Hz) did not differ in cohorts 1&2, whilst E-F high frequency power (alpha + beta) decreased in AD relative to HC (this difference was statistically significant in cohort 1). *** p < .0001, ** p < .001, * p < .05, ns p > .05.
Figure 6:
Figure 6:
Aperiodic parameters of individual global power spectra do not differ between diagnostic groups. A-C Cohort 1. A Aperiodic component of the power spectrum (bold) averaged across individuals within each diagnostic group from Cohort 1. Individual full scalp aperiodic components are also plotted (AD = blue; HC = green). B-C Comparison of the aperiodic offset and exponent between AD and HC within Cohort 1 showed no significant differences after controlling for participant age with an ANCOVA (p > .05). D-F Cohort 2. D plots group averaged aperiodic component over individual components from each diagnostic group within Cohort 2 (AD = purple; HC = yellow). E-F Between-group comparisons controlling for age also showed no significant differences between HC and AD groups within Cohort 2 (p > .05).
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