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Microstate feature fusion for distinguishing AD from MCI
Yupan Shi1,2, Qinying Ma3,4, Chunyu Feng1,2
1Institute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang, China.
Abstract:
Electroencephalogram (EEG) microstates provide powerful tools for identifying EEG features due to their rich temporal information. In this study, we tested whether microstates can measure the severity of Alzheimer's disease (AD) and mild cognitive impairment (MCI) in patients and effectively distinguish AD from MCI. We defined two features using transition probabilities (TPs), and one was used to evaluate between-group differences in microstate parameters to assess the within-group consistency of TPs and MMSE scores. Another feature was used to distinguish AD from MCI in machine learning models. Tests showed that there were between-group differences in the temporal characteristics of microstates, and some kinds of TPs were significantly correlated with MMSE scores within groups. Based on our newly defined time-factor transition probabilities (TTPs) feature and partial accumulation strategy, we obtained promising scores for accuracy, sensitivity, and specificity of 0.938, 0.923, and 0.947, respectively. These results provide evidence for microstates as a neurobiological marker of AD.
Insights
Electroencephalogram (EEG) microstates show promise in measuring Alzheimer's disease (AD) severity and distinguishing it from mild cognitive impairment (MCI). These EEG features offer a potential neurobiological marker for AD diagnosis.
Area of Science:
- Neuroscience
- Computational Neuroscience
Background:
- Electroencephalogram (EEG) microstates offer rich temporal information for identifying neural features.
- Alzheimer's disease (AD) and mild cognitive impairment (MCI) require sensitive diagnostic markers.
Purpose of the Study:
- To investigate EEG microstates for measuring AD severity and differentiating AD from MCI.
- To evaluate the utility of microstate transition probabilities (TPs) and a novel time-factor transition probabilities (TTPs) feature.
Main Methods:
- Defined two features based on transition probabilities (TPs) to analyze microstate parameters.
- Assessed between-group differences in microstate temporal characteristics and within-group correlations with MMSE scores.
- Employed machine learning models with the TTPs feature and a partial accumulation strategy for classification.
Main Results:
- Significant between-group differences in microstate temporal characteristics were observed.
- Certain TPs correlated significantly with Mini-Mental State Examination (MMSE) scores within patient groups.
- The TTPs feature achieved high accuracy (0.938), sensitivity (0.923), and specificity (0.947) in distinguishing AD from MCI.
Conclusions:
- EEG microstates demonstrate potential as a neurobiological marker for Alzheimer's disease.
- Microstate analysis, particularly using TTPs, shows efficacy in assessing disease severity and aiding differential diagnosis between AD and MCI.
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