Open your eyes and you will see. Changes in “eyes-open” versus “eyes-closed” small-world properties of EEG functional connectivity in amnesic mild cognitive impairment

Differentiating amnestic from non-amnestic mild cognitive impairment subtypes using graph theoretical measures of electroencephalography

The purpose of this study was to explore different patterns of functional networks between amnestic mild cognitive impairment (aMCI) and non-aMCI (naMCI) using electroencephalography (EEG) graph theoretical analysis. The data of 197 drug-naïve individuals who complained cognitive impairment were reviewed. Resting-state EEG data was acquired. Graph analyses were performed and compared between aMCI and naMCI, as well as between early and late aMCI. Correlation analyses were conducted between the graph measures and neuropsychological test results. Machine learning algorithms were applied to determine whether the EEG graph measures could be used to distinguish aMCI from naMCI. Compared to naMCI, aMCI showed higher modularity in the beta band and lower radius in the gamma band. Modularity was negatively correlated with scores on the semantic fluency test, and the radius in the gamma band was positively correlated with visual memory, phonemic, and semantic fluency tests. The naïve Bayes algorithm classified aMCI and naMCI with 89% accuracy. Late aMCI showed inefficient and segregated network properties compared to early aMCI. Graph measures could differentiate aMCI from naMCI, suggesting that these measures might be considered as predictive markers for progression to Alzheimer’s dementia in patients with MCI.

Anaesthesia-dependent oscillatory EEG features in the super-elderly

OBJECTIVE

Although the characteristics of electroencephalograms (EEGs) have been reported to change with age, anaesthesia-dependent oscillatory features and reactivity of the super-elderly EEG to anaesthesia have not been examined in detail.

METHODS

Participants comprised 20 super-elderly patients (age; mean ± standard deviation, 87.1 ± 3.8 years) and 20 young adult patients (35.5 ± 8.5 years). At three levels of sevoflurane anaesthesia (minimum alveolar concentration [MAC] of 0.3, 0.7, and 1.4), oscillatory features of the frontal EEG were examined by analysing quadratic phase coupling (bicoherence) and power spectrum in α and δ-θ areas and compared in an anaesthesia-dependent manner, using the Friedman test.

RESULTS

Among super-elderly individuals, bicoherences in the δ-θ area showed anaesthesia-dependent increases (median [interquartile range], 12.9% [5.2%], 19.2% [9.1%], 23.3% [8.7%]; 0.3, 0.7, 1.4 MAC sevoflurane, p = 0.000), whereas bicoherence in the α area did not change at these different anaesthesia levels (11.2% [3.9%], 12.5% [4.4%], 14.1% [5.7%], respectively; p = 0.142), counter to the results found in young adult patients, where both δ-θ and α bicoherences changed with anaesthesia.

CONCLUSIONS

In the super-elderly, δ-θ bicoherence of EEG shows anaesthesia- dependent changes, whereas α activity remains small irrespective of anaesthesia level.

SIGNIFICANCE

Quantification of δ-θ bicoherence is a candidate for anaesthesia monitoring in the super-elderly.