Since the first demonstrations of network hyperexcitability in scientific models of
Alzheimer's disease, a growing body of clinical studies have identified subclinical epileptiform activity and associated
cognitive decline in patients with
Alzheimer's disease. An obvious problem presented in these studies is lack of sensitive measures to detect and quantify network hyperexcitability in human subjects. In this study we examined whether altered neuronal synchrony can be a
surrogate marker to quantify network hyperexcitability in patients with
Alzheimer's disease. Using magnetoencephalography (MEG) at rest, we studied 30
Alzheimer's disease patients without subclinical epileptiform activity, 20
Alzheimer's disease patients with subclinical epileptiform activity and 35 age-matched controls. Presence of subclinical epileptiform activity was assessed in patients with
Alzheimer's disease by long-term video-EEG and a 1-h resting MEG with simultaneous EEG. Using the resting-state source-space reconstructed MEG signal, in patients and controls we computed the global imaginary coherence in alpha (8-12 Hz) and delta-theta (2-8 Hz) oscillatory frequencies. We found that
Alzheimer's disease patients with subclinical epileptiform activity have greater reductions in alpha imaginary coherence and greater enhancements in delta-theta imaginary coherence than
Alzheimer's disease patients without subclinical epileptiform activity, and that these changes can distinguish between
Alzheimer's disease patients with subclinical epileptiform activity and
Alzheimer's disease patients without subclinical epileptiform activity with high accuracy. Finally, a principal component regression analysis showed that the variance of frequency-specific neuronal synchrony predicts longitudinal changes in Mini-Mental State Examination in patients and controls. Our results demonstrate that quantitative neurophysiological measures are sensitive
biomarkers of network hyperexcitability and can be used to improve diagnosis and to select appropriate patients for the right
therapy in the next-generation clinical trials. The current results provide an integrative framework for investigating network hyperexcitability and network dysfunction together with cognitive and clinical correlates in patients with
Alzheimer's disease.