Spectral power ratio as a measure of EEG changes in mild cognitive impairment due to Alzheimer's disease: a case-control study

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

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 emphasize the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.

Beta to theta power ratio in EEG periodic components as a potential biomarker in mild cognitive impairment and Alzheimer's dementia

BACKGROUND

Alzheimer's dementia (AD) is associated with electroencephalography (EEG) abnormalities including in the power ratio of beta to theta frequencies. EEG studies in mild cognitive impairment (MCI) have been less consistent in identifying such abnormalities. One potential reason is not excluding the EEG aperiodic components, which are less associated with cognition than the periodic components. Here, we investigate whether aperiodic and periodic EEG components are disrupted differently in AD or MCI vs. healthy control (HC) individuals and whether a periodic based beta/theta ratio differentiates better MCI from AD and HC groups than a ratio based on the full spectrum.

METHODS

Data were collected from 44 HC (mean age (SD) = 69.1 (5.3)), 114 MCI (mean age (SD) = 72.2 (7.5)), and 41 AD (mean age (SD) = 75.7 (6.5)) participants. Aperiodic and periodic components and full spectrum EEG were compared among the three groups. Receiver operating characteristic curves obtained via logistic regression classifications were used to distinguish the groups. Last, we explored the relationships between cognitive performance and the beta/theta ratios based on the full or periodic spectrum.

RESULTS

Aperiodic EEG components did not differ among the three groups. In contrast, AD participants showed an increase in full spectrum and periodic relative powers for delta, theta, and gamma and a decrease for beta when compared to HC or MCI participants. As predicted, MCI group differed from HC participants on the periodic based beta/theta ratio (Bonferroni corrected p-value = 0.036) measured over the occipital region. Classifiers based on beta/theta power ratio in EEG periodic components distinguished AD from HC and MCI participants, and outperformed classifiers based on beta/theta power ratio in full spectrum EEG. Beta/theta ratios were comparable in their association with cognition.

CONCLUSIONS

In contrast to a full spectrum EEG analysis, a periodic-based analysis shows that MCI individuals are different on beta/theta ratio when compared to healthy individuals. Focusing on periodic components in EEG studies with or without other biological markers of neurodegenerative diseases could result in more reliable findings to separate MCI from healthy aging, which would be valuable for designing preventative interventions.

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

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.