Evidence map›Paper›PMID 39585069›Full record

ArticleNeurology international2024

Wavelet-Detected Changes in Nocturnal Brain Electrical Activity in Patients with Non-Motor Disorders Indicative of Parkinson's Disease.

Anastasiya E Runnova, Maksim O Zhuravlev, Anton R Kiselev, Ruzanna R Parsamyan, Margarita A Simonyan, Oxana M Drapkina

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Article in Neurology international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Anastasiya E RunnovaNational Medical Research Center for Therapy and Preventive Medicine, Moscow 101990, Russia.ORCID 0000-0002-2102-164X
Maksim O ZhuravlevNational Medical Research Center for Therapy and Preventive Medicine, Moscow 101990, Russia.ORCID 0000-0002-8620-1609
Anton R KiselevNational Medical Research Center for Therapy and Preventive Medicine, Moscow 101990, Russia.ORCID 0000-0003-3967-3950
Ruzanna R ParsamyanDepartment of Biophysics and Digital Technologies, Saratov State Medical University, Saratov 410012, Russia.
Margarita A SimonyanDepartment of Biophysics and Digital Technologies, Saratov State Medical University, Saratov 410012, Russia.
Oxana M DrapkinaNational Medical Research Center for Therapy and Preventive Medicine, Moscow 101990, Russia.ORCID 0000-0003-0323-2635

Funding

Russian Ministry of Health 122013100209-5
6 · The paper itself

Abstract

Background/Objectives-Parkinson's disease (PD) is the second most common neurodegenerative disorder caused by the destruction of neurons in the substantia nigra of the brain. Clinical diagnosis of this disease, based on monitoring motor symptoms, often leads to a delayed start of PD therapy and control, where over 60% of dopaminergic nerve cells are damaged in the brain substantia nigra. The search for simple and stable characteristics of EEG recordings is a promising direction in the development of methods for diagnosing PD and methods for diagnosing the preclinical stage of PD development. Methods-42 subjects participated in work, of which 4 female/10 male patients were included in the group of patients with non-motor disorders, belonging to the risk group for developing PD (median age: 62 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 143 and 80)/(median age: 68 years, height: 170 cm, weight: 73.9 kg, pulse: 75, BPsys and BPdia: 143 and 82). The first control group of healthy participants included 6 women (median age: 33 years, height: 161 cm, weight: 66 kg, pulse: 80, BPsys and BPdia: 110 and 80)/8 men (median age: 36.3 years, height: 175 cm, weight: 69 kg, pulse: 78, BPsys and BPdia: 120 and 85). The second control group of healthy participants included 8 women (median age: 74 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 145 and 82)/6 men (median age: 51 years, height: 172 cm, weight: 72.5 kg, pulse: 74, BPsys and BPdia: 142 and 80). Wavelet oscillatory pattern estimation is performed on patients' nocturnal sleep recordings without separating them into sleep stages. Results-Amplitude characteristics of oscillatory activity in patients without motor disorders and the prodromal PD stage are significantly reduced both in terms of changes in the number of patterns and in terms of their duration. This pattern is especially pronounced for high-frequency activity, in frequency ranges close to 40 Hz. Conclusions-The success of the analysis of the electrical activity of the brain, performed over the entire duration of the night recording, makes it promising to further use during daytime monitoring the concept of oscillatory wavelet patterns in patients with non-motor disorders, belonging to the risk group for developing PD. The daytime monitoring system can become the basis for developing screening tests to detect neurodegenerative diseases as part of routine medical examinations.

Indexed as

EEG markersoscillational patternsParkinson’s diseasepolysomnographysignal processingsleepwavelet analysis

Identifiers

PMID39585069
PMCPMC11587428

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