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Abstract Over the past few years, it has become standard to describe brain anatomical and functional organisation in terms of complex networks, wherein single brain regions or modules and their connections are respectively identified with network nodes and the links connecting them. Often, the goal of a given study is not that of modelling brain activity but, more basically, to discriminate between experimental conditions or populations, thus to find a way to compute differences between them. This in turn involves two important aspects: defining discriminative features and quantifying differences between them. Here we show that the ranked dynamical stability of network features, from links or nodes to higher-level network properties, discriminates well between healthy brain activity and various pathological conditions. These easily computable properties, which constitute local but topographically aspecific aspects of brain activity, greatly simplify inter-network comparisons and spare the need for network pruning. Our results are discussed in terms of microstate stability. Some implications for functional brain activity are discussed.

Abstract Alterations in P300 amplitude and latency, as well as neuropsychological tests, are informative to detect early signs of the affected high cognitive processing in Mild Cognitive Impairment (MCI). In the present study, we examined P300 latency and amplitude elicited by visual oddball paradigm in 20 participants with MCI and age, education, and sex-matched healthy controls from frontal, central, and parietal midline electrodes. We performed a mixed-design ANOVA to compare P300 amplitude and latency between groups during target and non-target stimulus presentation. We also assessed the correlation between our electrophysiology findings and neuropsychological tests. Our results indicated that in healthy individuals P300 is elicited earlier in target stimulus processing compared to non-target stimulus processing. On the contrary, in the MCI group, P300 latency was increased during target processing compared to non-target stimulus processing. Moreover, P300 latency in target processing is prolonged in the MCI group compared to controls. Also, our correlation results showed a significant correlation between P300 peak latency and amplitude, and attention required cognitive tasks. In conclusion, our results provide evidence that high-order cognitive processes that are involved in stimulus processing slows down in individuals with MCI due to the high working memory demand for neural processing.

Earlier research has suggested gender differences in event-related potentials/oscillations (ERPs/EROs). Yet, the alteration in event-related oscillations (EROs) in the delta and theta frequency bands have not been explored between genders across the three age groups of adulthood, i.e., 18–50, 51–65, and >65 years. Data from 155 healthy elderly participants who underwent a neurological examination, comprehensive neuropsychological assessment (including attention, memory, executive function, language, and visuospatial skills), and magnetic resonance imaging (MRI) from past studies were used. The delta and theta ERO powers across the age groups and between genders were compared and correlational analyses among the ERO power, age, and neuropsychological tests were performed. The results indicated that females displayed higher theta ERO responses than males in the frontal, central, and parietal regions but not in the occipital location between 18 and 50 years of adulthood. The declining theta power of EROs in women reached that of men after the age of 50 while the theta ERO power was more stable across the age groups in men. Our results imply that the cohorts must be recruited at specified age ranges across genders, and clinical trials using neurophysiological biomarkers as an intervention endpoint should take gender into account in the future.

Objective: This study investigated cortical thickness differences and their relationships with cognitive functions in Parkinson's disease (PD) with mild cognitive impairment (MCI) and cognitively normal (CN). Materials and Methods: Twenty-two patients with PD-MCI, 23 with PD-CN, and 23 healthy controls with structural brain magnetic resonance imaging scans and complete neuropsychological tests were enrolled in this study. Cortical thickness analysis was performed using the Statistical Parametric Mapping 12 software package. Correlations with cognitive functions were examined. Results: Cortical thickness was significantly lower in the PD-CN and PD-MCI patient groups than in healthy controls in the left precuneus and isthmuscingulate cortex, right pars orbitalis, insula, and lateral orbitofrontal cortex. In addition, the PD-MCI group also exhibited cortical thinning in the left superior temporal gyrus, transverse temporal cortex, supramarginal gyrus, and bilateral posterior cingulate cortex compared with healthy controls. Correlation analyses among cortical thickness and cognitive scores of PD also revealed moderate associations between memory and the posterior cingulate cortex; language and the precuneus; and executive functions and the insula and isthmus-posterior cingulate cortices. Conclusion: MCI in PD may be related to cortical alterations in the posterior cingulate cortex and the left temporoparietal cortex, which has been associated with subtle cognitive deficits in PD.

Abstract Background Biomarkers of Alzheimer’s disease (AD) and mild cognitive impairment (MCI, or prodromal AD) are highly significant for early diagnosis, clinical trials and treatment outcome evaluations. Electroencephalography (EEG), being noninvasive and easily accessible, has recently been the center of focus. However, a comprehensive understanding of EEG in dementia is still needed. A primary objective of this study is to investigate which of the many EEG characteristics could effectively differentiate between individuals with AD or prodromal AD and healthy individuals. Methods We collected resting state EEG data from individuals with AD, prodromal AD, and normal cognition. Two distinct preprocessing pipelines were employed to study the reliability of the extracted measures across different datasets. We extracted 41 different EEG features. We have also developed a stand-alone software application package, Feature Analyzer, as a comprehensive toolbox for EEG analysis. This tool allows users to extract 41 EEG features spanning various domains, including complexity measures, wavelet features, spectral power ratios, and entropy measures. We performed statistical tests to investigate the differences in AD or prodromal AD from age-matched cognitively normal individuals based on the extracted EEG features, power spectral density (PSD), and EEG functional connectivity. Results Spectral power ratio measures such as theta/alpha and theta/beta power ratios showed significant differences between cognitively normal and AD individuals. Theta power was higher in AD, suggesting a slowing of oscillations in AD; however, the functional connectivity of the theta band was decreased in AD individuals. In contrast, we observed increased gamma/alpha power ratio, gamma power, and gamma functional connectivity in prodromal AD. Entropy and complexity measures after correcting for multiple electrode comparisons did not show differences in AD or prodromal AD groups. We thus catalogued AD and prodromal AD-specific EEG features. Conclusions Our findings reveal that the changes in power and connectivity in certain frequency bands of EEG differ in prodromal AD and AD. The spectral power, power ratios, and the functional connectivity of theta and gamma could be biomarkers for diagnosis of AD and prodromal AD, measure the treatment outcome, and possibly a target for brain stimulation.