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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).
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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 high...
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Context 1
... p < .05, ns p > .05. Figure 3 plots the grand average spectra for both AD and HC groups after full-scalp individual participant spectra were decomposed into periodic and aperiodic activity using the 'specparam' toolbox (Donoghue, Haller, et al., 2020). This allowed us to estimate both aperiodic parameters (offset and exponent) and periodic parameters (including peak power, center frequency, and bandwidth) at the individual level. ...
Citations
... Recent studies have shown that the EEG power spectrum's 1/f-like aperiodic activity partially reflects the overall cortical balance of excitation and inhibition [9,15]. Abnormalities in this measure have been consistently reported [16] and have been linked to cognitive function [17]. Therefore, we used this spontaneous-excitation/inhibition measure to further investigate the relationship between cortical excitability and p-tau181 concentration. ...
In early-stage Alzheimer's disease (AD) amyloid-β (Aβ) deposition can induce neuronal hyperactivity, thereby potentially triggering activity-dependent neuronal secretion of phosphorylated tau (p-tau), ensuing tau aggregation and spread. Therefore, cortical excitability is a candidate biomarker for early AD detection. Moreover, lowering neuronal excitability could potentially complement strategies to reduce Aβ and tau buildup. There is, however, a lack of understanding of the relationship between cortical excitability and p-tau increase in vivo. Therefore, in a sample of 658 healthy middle-aged (between the ages of 40 and 65) participants of the Barcelona Brain Health Initiative cohort study, we examined the relation of blood-based tau, phosphorylated at amino acid 181 (p-tau181), reflecting neuronal p-tau secretion; neurofilament light chain (NfL), as a passively released control for p-tau181; and electroencephalography (EEG) markers of cortical excitability. A subsample of 47 participants also completed a controlled brain perturbation approach via transcranial magnetic stimulation (TMS) with concurrent EEG. Results show that both spontaneous (i.e., resting-state) and perturbation-based TMS-EEG markers, are associated with blood p-tau181, particularly in older individuals. The perturbation-based marker was a significantly more sensitive predictor of p-tau181 concentration than the spontaneous resting state EEG-based marker. The relationships observed are not present for the NfL control. These results show that relationships between p-tau181 and cortical excitability are present in healthy middle-aged subjects and that p-tau181 increases may reflect activity-dependent secretion.
... EEG has shown great prowess in detecting brain disorders including Alzheimer in previous studies. [23,24,25] EEG is a non-invasive, portable, cost-effective method to measure the electrical activity of the human brain [26]. Combining EEG and machine learning algorithms to automatically detect Alzheimer patients from healthy patients has recently been studied by numerous researchers. ...
Alzheimer's disease (AD) is a neurodegenerative disease of brain tissue, currently incurable, which leads to the progressive and irreversible loss of mental functions, particularly memory. It is rare to detect Alzheimer to an early stage. However, early diagnosis can allow a faster treatment and thus improve the patient's well-being. Electroencephalogram (EEG) is a non-invasive and cost-effective tool that measures electrical activity in the brain. In this study, we aimed to create an automatic detection method by combining several powerful EEG biomarkers which, to our knowledge, were never put together namely Power Spectral Density, Tsallis entropy and changes in the EEG amplitude. The features were then put into a Support Vector Machine (SVM) for the identifications of Alzheimer patients and healthy controls (CN). Using a five-fold cross-validation strategy across the entire frequency band, the classification accuracy reached 83.08%, with a sensitivity of 78% and a specificity of 90% while it reached 78.46% accuracy ,75% sensitivity and 83% specificity using the leave one subject out cross validation.
... Additionally, a reduction in IAF has been associated with Alzheimer's disease patients when compared to healthy controls Bennys et al., 2001;Benwell et al., 2020;Brenner et al., 1986;Coben et al., 1983;Moretti et al., 2004). However, a more recent study did not find this effect after controlling for aperiodic activity (Kopčanová et al., 2023;bioRxiv). ...
Background
Dementia and mild cognitive impairment are characterised by symptoms of cognitive decline, which are typically assessed using neuropsychological assessments (NPAs), such as the Mini-Mental State Examination (MMSE) and Frontal Assessment Battery (FAB). Magnetoencephalography (MEG) is a novel clinical assessment technique that measures brain activities (summarised as oscillatory parameters), which are associated with symptoms of cognitive impairment. However, the relevance of MEG and regional cerebral blood flow (rCBF) data obtained using single-photon emission computed tomography (SPECT) has not been examined using clinical datasets. Therefore, this study aimed to investigate the relationships among MEG oscillatory parameters, clinically validated biomarkers computed from rCBF, and NPAs using outpatient data retrieved from hospital records.
Methods
Clinical data from 64 individuals with mixed pathological backgrounds were retrieved and analysed. MEG oscillatory parameters, including relative power (RP) from delta to high gamma bands, mean frequency, individual alpha frequency, and Shannon’s spectral entropy, were computed for each cortical region. For SPECT data, three pathological parameters—‘severity’, ‘extent’, and ‘ratio’—were computed using an easy z-score imaging system (eZIS). As for NPAs, the MMSE and FAB scores were retrieved.
Results
MEG oscillatory parameters were correlated with eZIS parameters. The eZIS parameters associated with Alzheimer’s disease pathology were reflected in theta power augmentation and slower shift of the alpha peak. Moreover, MEG oscillatory parameters were found to reflect NPAs. Global slowing and loss of diversity in neural oscillatory components correlated with MMSE and FAB scores, whereas the associations between eZIS parameters and NPAs were sparse.
Conclusion
MEG oscillatory parameters correlated with both SPECT (i.e. eZIS) parameters and NPAs, supporting the clinical validity of MEG oscillatory parameters as pathological and symptomatic indicators. The findings indicate that various components of MEG oscillatory characteristics can provide valuable pathological and symptomatic information, making MEG data a rich resource for clinical examinations of patients with cognitive impairments. SPECT (i.e. eZIS) parameters showed no correlations with NPAs. The results contributed to a better understanding of the characteristics of electrophysiological and pathological examinations for patients with cognitive impairments, which will help to facilitate their co-use in clinical application, thereby improving patient care.
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.
