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DOI 10.1007/s00234-023-03241-7

Медицинская библиотека · Neuroradiology · 2023

Involvement of the default mode network in patients with transient global amnesia: multilayer network.

Lee Dong Ah, Lee Ho-Joon, Park Kang Min

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Аннотация

INTRODUCTION

We aimed to investigate the alterations in the multilayer network in patients with transient global amnesia (TGA).

METHODS

We enrolled 124 patients with TGA and 80 healthy controls. Both patients with TGA and healthy controls underwent a three-teslar brain magnetic resonance imaging (MRI). A gray matter layer matrix was created using a morphometric similarity network derived from the T1-weighted imaging, and a white matter layer matrix was constructed using structural connectivity based on the diffusion tensor imaging. A multilayer network analysis was performed by applying graph theoretical analysis.

RESULTS

There were no significant differences in global network measures between the groups. However, several regions, related to the default mode network, showed significant differences in nodal network measures between the groups. Multi-richness in the left pars opercularis, multi-rich-club degree in the right posterior cingulate gyrus, and weighted multiplex participation in the right posterior cingulate gyrus were higher in patients with TGA compared with healthy controls (15.47 vs. 12.26, p = 0.0005; 41.68 vs. 37.16, p = 0.0005; 0.90 vs. 0.80, p = 0.0005; respectively). The multiplex core-periphery in the left precuneus was higher (0.96 vs. 0.84, p = 0.0005), whereas that in the transverse temporal gyrus was lower in patients with TGA compared with healthy controls (0.00 vs. 0.02, p = 0.0005).

CONCLUSION

We newly find the alterations in the multilayer network in patients with TGA compared with healthy controls, which shows the involvement of the default mode network. These changes may be related to the pathophysiology of TGA.

Для цитирования: Lee Dong Ah, Lee Ho-Joon, Park Kang Min. Involvement of the default mode network in patients with transient global amnesia: multilayer network.. Neuroradiology. 2023. DOI: 10.1007/s00234-023-03241-7.

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