At Starlab, we use EEG to unlock insights into brain function, and one of the most powerful tools in our Cognitive Task Battery is Resting-State EEG. Unlike tasks that measure responses to stimuli, resting-state EEG captures the brain’s spontaneous activity, providing a baseline measure of neural function.
But why is this important? Resting-state EEG helps us study how the brain changes with aging and neurodegenerative diseases like Alzheimer’s and Parkinson’s.
In this post, we explore:
✅ How resting-state EEG reveals early signs of neurodegeneration.
✅ How aging affects brain rhythms differently from disease.
✅ How our research confirms these effects, using two datasets:
Data from our partners at the Center for Research and Technology Hellas (CERTH).
The Lemon Dataset (Max Planck Institute Leipzig Mind-Brain-Body Dataset).
Resting-State EEG & Neurodegeneration
Decades of research show that Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) are linked to a slowing of the EEG spectrum, characterized by:
📉 Increased low-frequency activity (delta & theta waves).
📉 Reduced high-frequency power (alpha & beta waves).
This slowing pattern reflects changes in brain function and has been proposed as a biomarker for cognitive decline. By analyzing EEG spectral features, we can track how neural activity shifts across different stages of neurodegeneration.
One of the figures accompanying this blog post shows the Power Spectral Density (PSD) extracted from data shared by our partners at CERTH, comparing EEG activity between:
Healthy Controls (HC)
Subjective Cognitive Decline (SCD)
Mild Cognitive Impairment (MCI)
Alzheimer’s Disease (AD) patients
This data helps us examine how EEG spectral slowing progresses from early cognitive complaints (SCD) to more advanced stages of dementia (AD).
The Effect of Aging on Brain Rhythms
But what about healthy aging?
Our research confirms that aging also affects brain rhythms—but in a different way than neurodegeneration. Specifically, we have found that:
📉 The alpha peak frequency slows down with age.
🚫 However, there is NO increase in slow-frequency activity (delta & theta waves).
This distinction is crucial:
Pathological cognitive decline (like AD) shows both a slowing of alpha AND an increase in slow waves.
Healthy aging only shows alpha slowing, without excess slow activity.
To illustrate this effect, our second figure shows the PSD extracted from the Lemon Dataset (Max Planck Institute Leipzig Mind-Brain-Body Dataset). This dataset allows us to examine age-related EEG changes in healthy individuals, reinforcing the difference between normal aging and disease progression.
Why Does This Matter?
Understanding these spectral changes in EEG is essential for:
✅ Early detection of neurodegenerative diseases – EEG can provide early warning signs before clinical symptoms appear.
✅ Tracking cognitive changes over time – Longitudinal EEG studies help us measure how neural activity evolves.
✅ Differentiating between normal aging & disease progression – Not all brain changes are pathological!
What’s Next?
In future posts, we will share insights from our ongoing research on how spectral changes in MCI patients with positive amyloid biomarkers compare to those without them. This work is part of our collaboration with ACE Alzheimer Center Barcelona, helping us refine EEG-based biomarkers for early Alzheimer’s detection.
Stay tuned for more updates!
More details on the use of EEG for Cognitive Decline and Alzheimer's Disease here:
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