Study of the MEG background activity in Alzheimer's disease patients with scaling analysis methods

NREM Sleep EEG Characteristics Correlate to the Mild Cognitive Impairment in Patients with Parkinsonism

Early identification and diagnosis of mild cognitive impairment (MCI) in patients with parkinsonism (PDS) are critical. The aim of this study was to identify biomarkers of MCI in PDS using conventional electroencephalogram (EEG) power spectral analysis and detrended fluctuation analysis (DFA). In this retrospective study, patients with PDS who underwent an overnight polysomnography (PSG) study in our hospital from 2019 to 2020 were enrolled. Patients with PDS assessed by clinical examination and questionnaires were divided into two groups: the PDS with normal cognitive function (PDS-NC) group and the PDS with MCI (PDS-MCI) group. Sleep EEG signals were extracted and purified from the PSG and subjected to a conventional power spectral analysis, as well as detrended fluctuation analysis (DFA) during wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Forty patients with PDS were enrolled, including 25 with PDS-NC and 15 with PDS-MCI. Results revealed that compared with PDS-NC patients, patients with PDS-MCI had a reduced fast ratio ((alpha + beta)/(delta + theta)) and increased DFA during NREM sleep. DFA during NREM was diagnostic of PDS-MCI, with an area under the receiver operating characteristic curve of 0.753 (95% CI: 0.592-0.914) (p < 0.05). Mild cognitive dysfunction was positively correlated with NREM-DFA (r = 0.426, p = 0.007) and negatively correlated with an NREM-fast ratio (r = -0.524, p = 0.001). This suggested that altered EEG activity during NREM sleep is associated with MCI in patients with PDS. NREM sleep EEG characteristics of the power spectral analysis and DFA correlate to MCI. Slowing of EEG activity during NREM sleep may reflect contribution to the decline in NREM physiological function and is therefore a marker in patients with PDS-MCI.

A Comprehensive Review of Magnetoencephalography (MEG) Studies for Brain Functionality in Healthy Aging and Alzheimer's Disease (AD)

Neural oscillations were established with their association with neurophysiological activities and the altered rhythmic patterns are believed to be linked directly to the progression of cognitive decline. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution. Single channel, connectivity as well as brain network analysis using MEG data in resting state and task-based experiments were analyzed from existing literature. Single channel analysis studies reported a less complex, more regular and predictable oscillations in Alzheimer's disease (AD) primarily in the left parietal, temporal and occipital regions. Investigations on both functional connectivity (FC) and effective (EC) connectivity analysis demonstrated a loss of connectivity in AD compared to healthy control (HC) subjects found in higher frequency bands. It has been reported from multiplex network of MEG study in AD in the affected regions of hippocampus, posterior default mode network (DMN) and occipital areas, however, conclusions cannot be drawn due to limited availability of clinical literature. Potential utilization of high spatial resolution in MEG likely to provide information related to in-depth brain functioning and underlying factors responsible for changes in neuronal waves in AD. This review is a comprehensive report to investigate diagnostic biomarkers for AD may be identified by from MEG data. It is also important to note that MEG data can also be utilized for the same pursuit in combination with other imaging modalities.

Early disturbances of gamma band dynamics in mild cognitive impairment

Recent studies have indicated that gamma band oscillations participate in the temporal binding needed for the synchronization of cortical networks involved in short-term memory and attentional processes. To date, no study has explored the temporal dynamics of gamma band in the early stages of dementia. At baseline, gamma band analysis was performed in 29 cases with mild cognitive impairment (MCI) during the n-back task. Based on phase diagrams, multiple linear regression models were built to explore the relationship between the cognitive status and gamma oscillation changes over time. Individual measures of phase diagram complexity were made using fractal dimension values. After 1 year, all cases were assessed neuropsychologically using the same battery. A total of 16 MCI patients showed progressive cognitive decline (PMCI) and 13 remained stable (SMCI). When adjusted for gamma values at lag -2, and -3 ms, PMCI cases displayed significantly lower average changes in gamma values than SMCI cases both in detection and 2-back tasks. Gamma fractal dimension of PMCI cases displayed significantly higher gamma fractal dimension values compared to SMCI cases. This variable explained 11.8% of the cognitive variability in this series. Our data indicate that the progression of cognitive decline in MCI is associated with early deficits in temporal binding that occur during the activation of selective attention processes.