P1-388: Resting-state oscillatory brain dynamics in Alzheimer's disease

Abstract

Alzheimer's disease (AD) is the most common form of dementia, nevertheless it lacks a definitive antemortem diagnostic test. Altered resting-state oscillatory brain activity may reflect underlying neuropathological changes, and its characterization may lead to new diagnostic possibilities. Due to its superior temporal and spatial resolution compared to EEG, magnetoencephalography (MEG) is a promising technique for this purpose. The present study using MEG and spectral analysis was set up to study differences in relative power of oscillatory brain activity in ten distinct cortical regions. Whole-head 151-channel MEG was recorded in 18 AD patients (mean age 72.1 years, SD 5.6; 7 females; mean MMSE 19.2, range: 13–25) and 18 healthy controls (mean age 69.1, SD 6.8, 11 females; mean MMSE 29, range: 27–30) during a no-task eyes-closed resting state. MEG channels were grouped in ten distinct cerebral regions (central, frontal, occipital, parietal and temporal; both left and right), and relative power was calculated using the Fast Fourier Transformation. The analysis was done both globally and for the specific regions separately, for the delta (0.5–4 Hz), theta (4–8 Hz), alpha1 (8–10 Hz), alpha2 (10–13 Hz), beta (13–30 Hz), and the gamma (30–50 Hz) frequency band. Relative power analysis showed a widespread slowing of oscillatory brain activity, with significant differences in the delta, alpha1, alpha2 and beta frequency bands, particularly in the temporo-parietal regions. Greatest diagnostic accuracy was found in the beta band, especially in the right occipital area (sensitivity 94%, specificity 78%). Correlations of relative power and MMSE scores were generally weak, but with a positive trend in the alfa1 band (R=0.53). Resting-state oscillatory brain activity is different in Alzheimer's disease. Our findings confirm the well-known slowing of brain activity, and show that widespread regional differences between AD and healthy controls can be found using MEG and standard spectral analysis techniques. By analyzing specific cortical regions, this study gives a more detailed topographical view of abnormal oscillatory activity in AD, which may eventually contribute to a more definitive diagnosis in AD.