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Abnormalities of rhythms and phase lag index in the data-driven cortical network model of Alzheimer's disease

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Abstract

The typical hallmark of electroencephalogram (EEG) in Alzheimer’s disease (AD) is a slowing of rhythms and perturbations in synchrony. However, the mechanism of AD electrophysiological abnormalities is still ambiguous. Synapse deficiency has been considered as an evident neuropathological change in AD that is closely associated with cognitive decline. The main purpose of this work is to explore how synapse deficiency in AD affects these electrophysiological features using neural computational techniques. First, based on the Diffusion Tensor Imaging data, a connectivity matrix of a structural brain network is constructed by means of a pipeline toolbox called PANDA. Using this data-driven connectivity matrix, a cortical network model with 90 cortical areas is then be built in which each cortical area is modeled by a neuron mass model. Subsequently, by reducing the synaptic strength parameter to mimic synapse deficiency in AD, our results show that the synapse deficiency does not only cause a leftward shift of the dominant frequency, but also induces a decrease in the alpha rhythm and an increase in the theta rhythm. Further, the influence of synapse deficiency on phase synchrony is investigated by the phase lag index (PLI). When the synaptic strength parameter is reduced, the alpha-band PLI decreases and theta-band PLI increases. Moreover, a statistical analysis of the differences between the simulated AD and healthy control (HC) in terms of synchronization and rhythms is performed. The results demonstrate that there are significant differences between simulated AD and HC groups. All the above simulation results are consistent with the EEG changes of AD in the physiological experiments. Finally, a strong statistical correlation between PLI and relative power is revealed using Pearson’s correlation analysis. This study reveals a close relationship between synapse deficiency and electrophysiological abnormalities in AD, which may provide new insight for the early diagnosis of AD.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 11972217, 12372062). JK acknowledges support from the Ministry of Science and Higher Education of the Russian Federation within the framework of state support for the creation and development of World-Class Research Centers “Digital biodesign and personalized healthcare” (No. 075-15-2020-926).

Funding

This work is funded by the National Natural Science Foundation of China (Grant Nos. 11972217, 12372062). JK acknowledges support from the Ministry of Science and Higher Education of the Russian Federation within the framework of state support for the creation and development of World-Class Research Centers “Digital biodesign and personalized healthcare” (No. 075-15-2020-926).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, 710062, People’s Republic of China

    SiLu Yan & XiaoLi Yang

  2. Potsdam Institute for Climate Impact Research, Potsdam, Germany

    Jürgen Kurths

  3. Department of Physics, Humboldt University of Berlin, Berlin, Germany

    Jürgen Kurths

  4. Centre for Analysis of Complex Systems, World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia

    Jürgen Kurths

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  1. SiLu Yan
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All authors contributed to the study conception and design. Data collection and analysis were performed by SY and XY. The first draft of the manuscript was written by SY and XY. JK proposed constructive advice and polished the language. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to XiaoLi Yang.

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Yan, S., Yang, X. & Kurths, J. Abnormalities of rhythms and phase lag index in the data-driven cortical network model of Alzheimer's disease. Nonlinear Dyn 111, 21289–21306 (2023). https://doi.org/10.1007/s11071-023-08968-9

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