Connectivity of EEG and fMRI Network in the Resting State in Healthy People and Patients with Post-Traumatic Disorder of Consciousness

被引：1

作者：

A. S. Zigmantovich ^{[1
]}

E. V. Sharova ^{[1
]}

M. M. Kopachka ^{[2
]}

A. S. Smirnov ^{[2
]}

E. V. Alexandrova ^{[2
]}

E. L. Masherov ^{[2
]}

E. M. Troshina ^{[2
]}

I. N. Pronin ^{[2
]}

L. B. Oknina ^{[1
]}

机构：

[1] Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow

[2] Burdenko National Medical Research Center of Neurosurgery, Ministry of Health of Russia, Moscow

来源：

Human Physiology | 2024年 / 50卷 / 1期

关键词：

disorders of consciousness; EEG; fMRI resting networks; functional connectivity; rhythmic transcranial magnetic stimulation (rTMS); traumatic brain injury;

D O I：

10.1134/S0362119723700640

中图分类号：

学科分类号：

摘要：

Abstract: Recovery of consciousness in patients with post-comatose unconscious states after severe traumatic brain injury and the search for their objective markers are among the urgent medical and social problems. To clarify the information content and the degree of consistency of changes in hemodynamic and bioelectrical parameters, in this work we carried out comparative studies of fMRI networks and EEG connectivity at rest in healthy subjects, as well as in patients with post-traumatic disorders of consciousness before and after therapeutic rhythmic transcranial magnetic stimulation (rTMS). It was shown that the characteristics of the functional connectivity of fMRI and EEG at rest are among the informative markers of neuroplasticity during depression of consciousness. A certain topographic correspondence between the fMRI networks and the EEG integral connectivity pattern at rest was established, regardless of the modification of the latter assessment: in the continuous recording mode or pseudo-EP. At the same time, the method of independent fMRI components more clearly reveals the features of the state of individual neural networks, and the indicators of EEG functional connectivity (range 1–15 Hz) are more informative in assessing the integral neural network characteristics and their changes during treatment. © Pleiades Publishing, Inc. 2024. ISSN 0362-1197, Human Physiology, 2024, Vol. 50, No. 1, pp. 1–14. Pleiades Publishing, Inc., 2024.

引用

页码：1 / 14

页数：13

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