In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pairwise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.

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In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. Coherence is introduced to measure the pair-wise normalized linear synchrony and functional correlations between two EEG signals in different frequency domains, and graph analysis is further used to investigate the influence of AD on the functional connectivity of human brain. Data analysis results show that, compared with the control group, the pair-wise coherence of AD group is significantly decreased, especially for the theta and alpha frequency bands in the frontal and parieto-occipital regions. Furthermore, functional connectivity among different brain regions is reconstructed based on EEG, which exhibit obvious small-world properties. Graph analysis demonstrates that the local functional connections between regions for AD decrease. In addition, it is found that small-world properties of AD networks are largely weakened, by calculating its average path lengths, clustering coefficients, global efficiency, local efficiency, and small-worldness. The obtained results show that both pair-wise coherence and functional network can be taken as effective measures to distinguish AD patients from the normal, which may benefit our understanding of the disease.

Key points Slow periodic activity can propagate with speeds around 0.1 m s−1 and be modulated by weak electric fields. Slow periodic activity in the longitudinal hippocampal slice can propagate without chemical synaptic transmission or gap junctions, but can generate electric fields which in turn activate neighbouring cells. Applying local extracellular electric fields with amplitude in the range of endogenous fields is sufficient to modulate or block the propagation of this activity both in the in silico and in the in vitro models. Results support the hypothesis that endogenous electric fields, previously thought to be too small to trigger neural activity, play a significant role in the self‐propagation of slow periodic activity in the hippocampus. Experiments indicate that a neural network can give rise to sustained self‐propagating waves by ephaptic coupling, suggesting a novel propagation mechanism for neural activity under normal physiological conditions. Abstract Slow oscillations are a standard feature observed in the cortex and the hippocampus during slow wave sleep. Slow oscillations are characterized by low‐frequency periodic activity (<1 Hz) and are thought to be related to memory consolidation. These waves are assumed to be a reflection of the underlying neural activity, but it is not known if they can, by themselves, be self‐sustained and propagate. Previous studies have shown that slow periodic activity can be reproduced in the in vitro preparation to mimic in vivo slow oscillations. Slow periodic activity can propagate with speeds around 0.1 m s−1 and be modulated by weak electric fields. In the present study, we show that slow periodic activity in the longitudinal hippocampal slice is a self‐regenerating wave which can propagate with and without chemical or electrical synaptic transmission at the same speeds. We also show that applying local extracellular electric fields can modulate or even block the propagation of this wave in both in silico and in vitro models. Our results support the notion that ephaptic coupling plays a significant role in the propagation of the slow hippocampal periodic activity. Moreover, these results indicate that a neural network can give rise to sustained self‐propagating waves by ephaptic coupling, suggesting a novel propagation mechanism for neural activity under normal physiological conditions.

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