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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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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Ming Gu1,2, Boxin Sun1,2, Voyko Kavcic1,3,4

  • 1Michigan Alzheimer's Disease Research Center, Ann Arbor, MI, USA.

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

Combining electroencephalography (EEG) and NIH Toolbox-Cognition Battery (NIHTB-CB) data significantly improves the accuracy of detecting mild cognitive impairment (MCI). This combined approach offers a more sensitive method for early Alzheimer

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Early identification of individuals at risk for Alzheimer's disease and related dementias is crucial for timely interventions and enhanced quality of life.
  • This study aimed to enhance the classification accuracy of normal cognition (NC) and mild cognitive impairment (MCI) by integrating electroencephalography (EEG) data with the NIH Toolbox-Cognition Battery (NIHTB-CB).

Purpose of the Study:

  • To evaluate the diagnostic performance of EEG alone, NIHTB-CB alone, and a combined EEG + NIHTB-CB approach for differentiating between normal cognition and mild cognitive impairment.
  • To determine if incorporating cognitive assessment scores can improve the discriminative power of EEG-based analyses for MCI detection.

Main Methods:

  • Utilized data from 71 participants (40 NC, 31 MCI) with both 64-channel resting-state EEG and NIHTB-CB assessments.
  • NIHTB-CB included tests of crystallized and fluid abilities, processing speed, attention, working memory, and visual memory.
  • Compared classification accuracy using k-fold cross-validation (k=3, 5, 10) for EEG alone, NIHTB-CB alone, and the combined approach, averaging results over 50 random test set selections.

Main Results:

  • The combined EEG + NIHTB-CB model achieved the highest 10-fold test accuracy at 78.82%, outperforming EEG alone (75.49%) and NIHTB-CB alone (65.89%).
  • The 10-fold cross-validation accuracy for the combined model, utilizing all samples for feature selection, reached 93.1%.
  • A similar trend favoring the combined approach was observed for k=3 and k=5 cross-validation.

Conclusions:

  • The integration of NIHTB-CB cognitive scores with EEG data significantly enhances the accuracy of mild cognitive impairment detection compared to either method alone.
  • The combined EEG + NIHTB-CB model demonstrates superior sensitivity for MCI detection, highlighting the synergistic value of multimodal data.
  • Increased sample size contributes to more reliable feature selection and classification in cognitive impairment studies.