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Study on cognitive impairment evaluation based on photoelectric neural information
Zehua Wang1, Ye Zhang1, Ning Ma1
1School of Biological Science and Medical Engineering, Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China.
Journal of Alzheimer'S Disease Reports
|March 24, 2025
Summary
Brain activity patterns in mild cognitive impairment (MCI) vary with cognitive load. Left occipital and temporal regions are key indicators for evaluating MCI, showing distinct responses compared to healthy controls.
Area of Science:
- Neuroscience
- Cognitive Science
- Medical Imaging
Background:
- Investigating cognitive load-dependent brain activation in pre-Alzheimer's disease is crucial.
- Mild cognitive impairment (MCI) represents an early stage of cognitive decline.
- Multimodal neuroimaging offers advanced tools for studying brain function.
Purpose of the Study:
- To evaluate brain activity patterns under varying cognitive loads in individuals with MCI.
- To identify differences in brain activation between MCI patients and healthy controls (HC).
Main Methods:
- Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) were employed.
- Data were collected from 20 MCI patients and 24 healthy controls under four cognitive load conditions.
- Analysis included brain activity features and correlation analyses between modalities.
Main Results:
- Significant condition and group effects were observed in left occipital and temporal regions during encoding.
- Healthy controls exhibited decreased clustering coefficients and local efficiencies with increasing cognitive load, unlike MCI patients.
- MCI patients showed correlations between left occipital/temporal activation skewness and left occipital electrical features.
Conclusions:
- Brain activity patterns in MCI are demonstrably dependent on cognitive load.
- Left occipital and left temporal regions are critical for evaluating MCI.
- These findings highlight potential biomarkers for early detection and monitoring of cognitive decline.
Keywords:
Alzheimer's diseasebrain networkelectroencephalographyfunctional near-infrared spectroscopyMore Related Videos
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