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3 June 2019 The investigation of synchronization between different areas of the brain in locomotor activity
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Proceedings Volume 11067, Saratov Fall Meeting 2018: Computations and Data Analysis: from Nanoscale Tools to Brain Functions; 110670A (2019) https://doi.org/10.1117/12.2527732
Event: International Symposium on Optics and Biophotonics VI: Saratov Fall Meeting 2018, 2018, Saratov, Russian Federation
Abstract
In the present paper we describe that the synchronization of various patterns of brain activity in the process of locomotor activity differs for groups of conditionally healthy subjects and patients. We study the difference between healthy subjects, patients in post-stroke dynamics and patients with chronic hypertension. Conditionally healthy individuals demonstrate the normal (typical) character of brain activity when moving with the hand. Impaired blood supply to the brain, apparently, leads to a stationary increase in the frequency of oscillatory activity recorded on the EEG, which is observed for patients with both background recording and physical activity. Patients with paresis caused by cerebral hemorrhages and disorders of the corresponding mechanisms in the acute period after stroke recovery demonstrate an uncompensated destruction of the mechanism for controlling the motor functions of the hands, leading to an increase in low-frequency activity patterns during attempts to make a movement. Patients with arterial hypertension and cerebral ischemic disease in the course of the motor act show an increase in the amplitude of the patterns in the frequency range (8 - 12 Hz). The analyses of EEG data is based on wavelet math methods. The study of the level of EEG activity changes may serve as some diagnostic criterion for assessing changes in brain activity during the development of degenerative processes.
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M. Zhuravlev, A. Runnova, and Yu. Kryuchkov " The investigation of synchronization between different areas of the brain in locomotor activity", Proc. SPIE 11067, Saratov Fall Meeting 2018: Computations and Data Analysis: from Nanoscale Tools to Brain Functions, 110670A (3 June 2019); https://doi.org/10.1117/12.2527732
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