Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals.

The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group; the functional connectivity patterns were different in the left hemisphere between two groups; the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (-3.89%); (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients.

Altered EEG microstate dynamics in mild cognitive impairment and Alzheimer's disease.

OBJECTIVE: Resting-state EEG microstate is a promising neurophysiological tool to explore the temporal dynamics of cognitive activity. Till now, the microstate syntax is far from being fully understood in mild cognitive impairment (MCI) and Alzheimer's disease (AD). We aim to investigate the possible explanation for the alterations of transition probabilities in microstate syntax between different stages of cognitive impairment. METHODS: The artefact-corrected resting-state EEG in patients with MCI (n = 46), AD (n = 43) and healthy controls (HC, n = 43) were used for microstate analysis. Four microstates were labeled A-D according to the study (Koenig et al., 2002). RESULTS: Microstate duration, occurrence and coverage showed overall differences between HC, MCI and AD. Duration and coverage B increased significantly in AD compared with HC and MCI. Coverage C decreased significantly in AD compared with MCI. Microstate syntax had specialized single transitions in MCI and AD. Transitions between symmetrical (C and D) and asymmetrical (A and B) classes showed a decreased pattern. It was only in MCI that an increased transition from A to C was found and only in AD an increased transition from A to B was found. Besides, a negative spearman's correlation was found between the transition probability from A to B and Mini-Mental State Examination (MMSE) scores. CONCLUSION: Altered resting-state EEG microstates in particular specialized single transitions in microstate syntax were showed in MCI and AD. SIGNIFICANCE: For the first time, we discovered which single transitions between pairs of microstates play an important role in microstate syntax in different stages of cognitive impairment.

[Electroencephalogram characteristics under successful cognitive reappraisal in emotion regulation].

Cognitive reappraisal is an important strategy for emotion regulation. Studies show that even healthy people may not be able to implement this strategy successfully, but the underlying neural mechanism behind the behavioral observation of success or failure of reappraisal is unclear. In this paper, 28 healthy college students participated in an experiment of emotional regulation with the cognitive reappraisal strategy. They were asked to complete the cognitive psychological questionnaires before the experiment. Their behavioral scores and scalp electroencephalogram (EEG) signals were collected simultaneously during the experiment. We divided all the subjects into two groups, according to the statistical test of valence scores. Then we analyzed their questionnaires, early event-related potential (ERP) components N200, P200, and late positive potential (LPP), and calculated the correlation between the valence score and the amplitude of LPP. The results showed that, in both groups, compared with negative-watching, the reappraisal induced larger N200 and P200 components and there were two modulation patterns ("increase" and "decrease") of the reappraisal effect on the amplitude of early LPP (300-1 000 ms after stimulus onset). Moreover, correlation analysis showed that significant positive correlation between two differences in the successful group, i.e., the greater difference in the valence scoresin between reappraisal and negative-watching, the greater difference in the amplitude of early LPP between reappraisal and negative-watching; but no such effect was found in the failure group. These results indicated that, whether reappraisal was successful or not, no significant effect on early ERP components was found; and there were different patterns of the reappraisal effect on early LPP. The difference between successful and failure groups was mainly reflected in early LPP, that is, the EEG characteristics and behavioral scores of successful group were significantly positively correlated. Furthermore, the small sample analysis showed that this correlation only existed in the pattern of "increase". In the future, more research of this modulation mode is necessary in order to find more stable EEG characteristics under successful cognitive reappraisal in emotion regulation.