Brain research bulletin · 2026
EEG microstate dynamics across sleep stages in Alzheimer's disease: A pilot study.
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Hu Guanqun, Tong Yujiao, Liu Lingfeng, Zhang Ying, Zhang Meiyun
Аннотация
INTRODUCTION: Alzheimer's disease (AD) is characterized by progressive cognitive decline and brain network dysfunction. EEG microstates offer a sight into rapid network dynamics. However, microstate alterations in AD during waking and different sleep stages remain unexplored.
METHODS: Overnight EEG was recorded from 15 AD patients and 15 healthy controls. Sleep stages (Wake, N1, N2, N3, REM) were manually scored. Microstate analysis extracted temporal parameters and transition probabilities separately for each sleep stage using a common set of grand-mean template maps derived from all stages. Linear mixed models assessed group differences, and correlations examined associations with MMSE.
RESULTS: AD patients showed significantly longer durations compared to HC (Group main effect: F(1,28) = 6.78, p_FDR = 0.042) in mean duration of class A, with the largest difference during wakefulness (p = 0.018, q = 0.046). Mean occurrence of class D was significantly lower in AD (F(1,28) = 5.44, p_FDR = 0.049), with significant reductions during wakefulness and N1. Mean Occurrence of class C showed both a significant Group main effect (F(1,28) = 5.28, p_FDR = 0.049) and a Group × Sleep Stage interaction (F(4112) = 3.31, p_FDR = 0.047) were observed, with AD patients showing lower occurrence during wakefulness and N1. For transition probabilities, AD patients showed significantly increased corrected Class B→A transition (DeltaTM_B→A), but reduced corrected Class D→B transition (DeltaTM_D→B) and raw Class C→D transition (OrgTM_C→D) (all p_FDR < 0.05). Critically, the Group × Sleep Stage interaction for the corrected Class A→D transition (DeltaTM_A→D) reached significance (F(4112) = 6.12, p_FDR = 0.015), with the group difference largest during wakefulness (p = 0.004, q = 0.029). Partial correlations revealed that corrected B→A transition probability negatively correlated with MMSE (r = -0.642, p < 0.001); MeanOccurrence_C (r = 0.487, p = 0.003) and MeanOccurrence_D (r = 0.532, p = 0.001) also showed significant correlations with MMSE.
CONCLUSIONS: This is the first study to report that AD patients exhibit altered microstate parameters and transition probabilities across sleep stages. Present findings suggest that sleep-stage-resolved microstate analysis may offer a new tool for assessing AD.
METHODS: Overnight EEG was recorded from 15 AD patients and 15 healthy controls. Sleep stages (Wake, N1, N2, N3, REM) were manually scored. Microstate analysis extracted temporal parameters and transition probabilities separately for each sleep stage using a common set of grand-mean template maps derived from all stages. Linear mixed models assessed group differences, and correlations examined associations with MMSE.
RESULTS: AD patients showed significantly longer durations compared to HC (Group main effect: F(1,28) = 6.78, p_FDR = 0.042) in mean duration of class A, with the largest difference during wakefulness (p = 0.018, q = 0.046). Mean occurrence of class D was significantly lower in AD (F(1,28) = 5.44, p_FDR = 0.049), with significant reductions during wakefulness and N1. Mean Occurrence of class C showed both a significant Group main effect (F(1,28) = 5.28, p_FDR = 0.049) and a Group × Sleep Stage interaction (F(4112) = 3.31, p_FDR = 0.047) were observed, with AD patients showing lower occurrence during wakefulness and N1. For transition probabilities, AD patients showed significantly increased corrected Class B→A transition (DeltaTM_B→A), but reduced corrected Class D→B transition (DeltaTM_D→B) and raw Class C→D transition (OrgTM_C→D) (all p_FDR < 0.05). Critically, the Group × Sleep Stage interaction for the corrected Class A→D transition (DeltaTM_A→D) reached significance (F(4112) = 6.12, p_FDR = 0.015), with the group difference largest during wakefulness (p = 0.004, q = 0.029). Partial correlations revealed that corrected B→A transition probability negatively correlated with MMSE (r = -0.642, p < 0.001); MeanOccurrence_C (r = 0.487, p = 0.003) and MeanOccurrence_D (r = 0.532, p = 0.001) also showed significant correlations with MMSE.
CONCLUSIONS: This is the first study to report that AD patients exhibit altered microstate parameters and transition probabilities across sleep stages. Present findings suggest that sleep-stage-resolved microstate analysis may offer a new tool for assessing AD.