Quantifying synchrony patterns in the EEG of Alzheimer’s patients with linear and non-linear connectivity markers

Abstract

We analyzed the relation of several synchrony markers in the electroencephalogram (EEG) and Alzheimer’s disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores. The study sample consisted of 79 subjects diagnosed with probable AD. All subjects were participants in the PRODEM-Austria study. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. We employed quadratic least squares regression to describe the relation between MMSE and the EEG markers. Factor analysis was used for estimating a potentially lower number of unobserved synchrony factors. These common factors were then related to MMSE scores as well. Most markers displayed an initial increase of EEG synchrony with MMSE scores from 26 to 21 or 20, and a decrease below. This effect was most prominent during the cognitive task and may be owed to cerebral compensatory mechanisms. Factor analysis provided interesting insights in the synchrony structures and the first common factors were related to MMSE scores with coefficients of determination up to 0.433. We conclude that several of the proposed EEG markers are related to AD severity for the overall sample with a wide dispersion for individual subjects. Part of these fluctuations may be owed to fluctuations and day-to-day variability associated with MMSE measurements. Our study provides a systematic analysis of EEG synchrony based on a large and homogeneous sample. The results indicate that the individual markers capture different aspects of EEG synchrony and may reflect cerebral compensatory mechanisms in the early stages of AD.

Systematic Review on Resting-State EEG for Alzheimer’s Disease Diagnosis and Progression Assessment

“…As detailed in Table 6 , 56 from a total of 97 studies exploring the difference between two populations or more are balanced in relation to the number of subjects. Age, education, and gender are also possible confounding factors that influence AD diagnosis [ 145 , 162 ]. From the 97 studies that included a healthy control group, 65 matched groups for age, 27 for gender, and 25 for years of education.…”

“…Throughout this review, we found that several studies do not present a detailed characterization of the cohorts participating in the study. Variables such as age, gender, and education level have been demonstrated to be confounding factors in AD [ 145 , 162 ]. As such, it is recommended to provide as much information as possible on the study participants, indicating whether or not there are statistically significant differences in demographic variables between groups.…”

Analysis of long range dependence in the EEG signals of Alzheimer patients

“…Synchrony measures show decreased EEG synchrony in MCI patients (Dauwels et al 2010). EEG synchrony markers have been correlated with Mini-Mental State Examination (MMSE) that measures the severity of AD (Waser et al 2016). A decrease of beta and lower alpha synchronization is observed in multi-channel EEG of mild AD patients (Pijnenburg et al 2004;Stam et al 2005).…”

EEG Window Length Evaluation for the Detection of Alzheimer’s Disease over Different Brain Regions

“…In particular, methods have been proposed for the automated discrimination of AD patients from healthy elderly subjects [10,11,12,13,14,15,16], frontotemporal dementia [17], vascular dementia [18], Mild Cognitive Impairment ( MCI ) [19,20], or even epilepsy [21]. Generally, the EEG activity is analyzed from each electrode site [6,22] or from electrode clusters [7,8,23]. Studies concerning the structural and functional asymmetry have reported that an early onset of AD affects different lobes [24].…”