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Related Concept Videos

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Boxin Sun1,2, Ming Gu1,2, Amanda Cook Maher2,3

  • 1Michigan State University, East Lansing, MI, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 24, 2025
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Summary
This summary is machine-generated.

Electroencephalography (EEG) effective connectivity and blood-based pTau-217 show promise in detecting Alzheimer's disease risk. These biomarkers are related and can be combined for improved early detection of amyloid positivity.

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Area of Science:

  • Neuroscience
  • Biomarker Discovery
  • Alzheimer's Disease Research

Background:

  • Positron emission tomography (PET) amyloid imaging is a standard for early Alzheimer's disease (AD) risk detection.
  • Plasma pTau-217 is emerging as a blood-based biomarker reflecting AD pathology.
  • Electroencephalography (EEG) biomarkers show potential for predicting early neurodegeneration.

Purpose of the Study:

  • To investigate the relationship between EEG-based brain effective connectivity and plasma pTau-217 levels.
  • To compare the performance of EEG effective connectivity and pTau-217 in discriminating amyloid-positive from amyloid-negative individuals.
  • To assess the combined utility of EEG and pTau-217 for predicting amyloid status.

Main Methods:

  • Utilized a dataset of 22 normal cognition participants (65-88 years old), including 7 amyloid-positive and 15 amyloid-negative individuals identified via Aβ-PET.
  • Recorded 3-minute eyes-closed resting-state EEG and collected blood for plasma pTau-217 measurement (SIMOA AlzPath v2 assay).
  • Applied causalized convergent cross mapping (cCCM) to analyze EEG effective connectivity and employed machine learning for amyloid status prediction.

Main Results:

  • Amyloid-positive individuals showed altered effective connectivity compared to amyloid-negative individuals, with both decreased and increased connectivity observed.
  • Effective connectivity between certain EEG regions strongly correlated (>0.6) with plasma pTau-217 levels.
  • Predictive accuracy for amyloid status was 86.36% for EEG, 90.91% for pTau-217, and 95.45% when both biomarkers were combined.

Conclusions:

  • EEG-based effective connectivity and plasma pTau-217 are closely related biomarkers for predicting amyloid status.
  • These biomarkers demonstrate complementary potential in identifying individuals at risk for AD.
  • Combining EEG and pTau-217 biomarkers can enhance the early detection of Alzheimer's disease risk.