Biomarkers
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View abstract on 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
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.
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