Method and apparatus for providing activity information of cerebral glymphatic system

Abstract

Disclosed in the present specification are a novel and effective method and apparatus for measuring the level of cerebral glymphatic system activity, and a method and apparatus for screening a glymphatic system activity index. In an aspect, the method and apparatus of the present invention enable measurement of the activity of the glymphatic system with a high predictive value and accuracy, and the method and apparatus of the present invention enable discovery of polysomnography, brainwaves, and near-infrared spectroscopy indices for determining the activity of the glymphatic system.

Description

Method and device for providing information on cerebral lymphatic system activity

[0001] Described herein are novel and effective methods and devices for providing cerebral glial lymphatic activity information.

[0002] Recently, it has been discovered that the central nervous system also has a pathway to discharge metabolic products inside brain cells to stably maintain brain cell function, and this has been named the 'glymphatic system'. The glymphatic system is a system in which cerebrospinal fluid (CSF) moves along the perivascular space, seeps into the brain parenchyma through aquaporin-4 (AQP-4) water channels expressed on astrocyte endfeet surrounding the cerebral vasculature, mixes with the interstitial fluid, and removes waste products through solute exchange.

[0003] The brain is a very active metabolic system, accounting for 20% of the body's metabolic output. Therefore, maintaining proper glio-lymphatic system activity is crucial for maintaining and promoting brain health. In particular, the glio-lymphatic system plays a crucial role in the removal of pathological proteins, such as amyloid beta and tau, within the brain parenchyma, which are the cause of neurodegenerative diseases such as Alzheimer's disease. Furthermore, glio-lymphatic system activity is known to increase by more than 60% during sleep and to be suppressed upon awakening. Therefore, if glio-lymphatic system function is chronically impaired or its activation is impaired during sleep, waste products, including amyloid beta and tau, cannot be properly removed, increasing the risk of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease.

[0004] Given the critical role of the glial lymphatic system in both normal brain function and pathophysiology, objective and quantitative measurement of glial lymphatic system activity is necessary. Existing quantitative methods for measuring glial lymphatic system activity include two-photon microscopy, a quantitative method used in animal experiments; however, its invasiveness precludes its application to humans. In humans, methods for measuring glial lymphatic system activity include administering a contrast agent (gadobutrol) into the cerebrospinal fluid followed by repeated brain magnetic resonance imaging (MRI), or administering a contrast agent intravenously followed by repeated MRI to identify glial lymphatic system activity and pathways. However, intrathecal injection of contrast agents is not FDA-approved, real-time continuous MRI measurement is difficult, tracking dynamic changes during the wake-sleep transition and sleep stages is impossible, measurement interferes with natural sleep, and measurement of glial lymphatic system function in various pathological conditions with impaired blood-brain barrier function is not possible. Accordingly, the present inventors have developed a method for quantitatively measuring and monitoring the activity of the glial lymphatic system by accurately measuring the amount of brain water in real time and noninvasively based on near-infrared spectroscopy (NIRS).

[0005] Previously, it was known that the elasticity of cerebral arterioles was a factor determining the activity of the glio-lymphatic system. The elasticity of cerebral arterioles allows cerebrospinal fluid to move along the inter-arterial spaces and perfuse the brain parenchyma. Therefore, as blood vessels age, the compliance of cerebral arteries decreases, which in turn impairs the function of the glio-lymphatic system. Therefore, vascular risk factors such as hypertension, smoking, atherosclerosis, heart failure, and renal failure have been suggested as risk factors for the decline of glio-lymphatic system function. These risk factors share a common mechanism of impairing the elasticity of cerebral arterioles through chronic vascular remodeling. However, it is difficult to reverse this chronic mechanism through risk factor correction, which is a limitation.

[0006] Humans spend an average of eight hours sleeping per day, and sleep significantly impacts their overall health and quality of life. According to a global sleep survey, 60% of the entire population is affected by sleep disorders and sleep disorders. Among these, insomnia (27%), snoring (24%), and sleep apnea (9%) were the most common types of sleep disorders. Decreased quality and quantity of sleep are known to increase the risk of major diseases such as dementia, cancer, stroke, cardiovascular disease, hypertension, and diabetes. In particular, sleep disorders are known to increase the risk of dementia. Insomnia increases the risk of dementia by 1.5 times, sleep apnea by 1.2 times, sleep pattern changes by 1.9 times, and sleep behavior disorders (such as REM sleep behavior disorder) by 5.5 times. These risks are equal to or higher than the major risk factors for dementia that are already known.

[0007] The existing pathophysiological mechanism hypotheses for dementia caused by sleep disorders included decreased physical activity due to sleep disturbances, depression, decreased concentration, increased amyloid beta and tau protein production due to neuronal hyperactivity, and neuroinflammation. However, recently, decreased glial lymphatic system activity due to sleep disorders has emerged as a major cause of dementia. Sleep activates the protein clearance mechanism through the glial lymphatic system. According to a mouse study, a large amount of cerebrospinal fluid flows into the brain parenchyma immediately after sleep, and at the same time, about 70% of amyloid beta is cleared during the first 30 minutes of sleep. This amyloid beta clearance rate is about twice as fast as when maintained in a wakeful state, suggesting that sleep plays a key role in the protein clearance mechanism through the glial lymphatic system.

[0008] [Prior Art Literature]

[0009] [Patent Document]

[0010] (Patent Document 0001) Korean Patent Application No. 10-2022-0166047 (Application Date: December 1, 2022)

[0011] [Cross-reference to related applications]

[0012] This application claims the benefit and priority of Korean Patent Application No. 10-2023-0070436, filed May 31, 2023, entitled “Method for Measuring Cerebral Glial Lymphatic Activity,” and Korean Patent Application No. 10-2024-0071504, filed May 31, 2024, entitled “Method and Device for Providing Cerebral Glial Lymphatic Activity Information,” the entire contents of which are incorporated herein by reference.

[0013] The purpose of the present invention is to provide a novel method and device capable of providing information on glymphatic system activity and its determining factors and related indicators.

[0014]

[0015] To achieve the above purpose,

[0016] In one aspect, the present invention provides a method for providing information on lymphatic system activity or activity indicators, the method comprising: analyzing a change in one or more electroencephalographic indices and / or one or more near-infrared spectroscopy indices associated with a change in brain water content in the brain of a subject; and providing lymphatic system activity or activity indicator information based on the analysis results.

[0017] In another aspect, the present invention provides a device for measuring lymphatic drainage, comprising: a first data processing unit that receives and analyzes information on at least one of near-infrared spectroscopy indices including very-low frequency oscillation and / or low frequency oscillation of the brain of a subject administered with a sedative; and an electroencephalographic indices including alpha [8-12 Hz] and / or beta [13-30 Hz] frequency bands of an electroencephalographic spectrum; and a second data processing unit that receives information on brain water content of the subject and an analysis result of the first data processing unit to provide information on lymphatic drainage activity.

[0018] In another aspect, the present invention provides a computer-readable recording medium storing program instructions readable by a computer and operable by the computer, wherein when the program instructions are executed by a processor of the computer, the processor performs the above-described method.

[0019] In one aspect, the method and device of the present invention can measure glymphatic system activity with high predictability and accuracy.

[0020] In another aspect, the method and device of the present invention can noninvasively measure glymphatic system activity.

[0021] In another aspect, the method and device of the present invention can suggest a physiological mechanism for regulating glymphatic system activity.

[0022] In another aspect, the method and device of the present invention can be used to discover polysomnography, electroencephalography and near-infrared spectroscopy indices that determine glymphatic system activity.

[0023] In another aspect, since the method and device of the present invention can provide standard data values ​​that can be judged as within the normal range of lymphatic activity, it is also possible to provide information on whether it is normal or abnormal.

[0024] In another aspect, based on the measured glial lymphatic activity, information can be provided regarding health indicators (which may include lifestyle-related factors), diagnosis and / or progression, pathophysiology (including pathogenesis and cause), and / or treatment effects of normal aging and neurodegenerative brain diseases such as Alzheimer's disease and Parkinson's disease or other nervous system diseases.

[0025] Figure 1 is a general experimental schematic. In the experiment, the influence of overall sleep indices obtained from polysomnography on the total change in brain water during sleep and the change in brain water in each stage of sleep measured by near-infrared spectroscopy is analyzed. Specifically, in order to discover the change in brain water in each stage of sleep and the sleep factors that affect it, a 5-minute time window is set, and various polysomnography indices, EEG indices, and near-infrared spectroscopy indices are acquired for each window, and then the average value of the change in brain water in the 5-minute amount at 30 minutes and 40 minutes after the change in sleep state and the influence of the above indices on the average value of the change in brain water in the amount of the above are analyzed.

[0026] Figures 2a to 2c relate to a method for measuring changes in the amount of brain water in each sleep stage based on near-infrared spectroscopy. Figure 2a relates to a method for simultaneously measuring near-infrared spectroscopy (NIRS) and polysomnography (PSG). Figure 2b is an overview of near-infrared spectroscopy, and each laser wavelength band is composed of wavelengths that show specific absorbance for oxyhemoglobin (HbO2), reduced hemoglobin (HbR), and water, respectively. Through this, the amounts of oxyhemoglobin, reduced hemoglobin, and water in the brain parenchyma can be calculated. Figure 2c relates to a method for measuring multi-channel brain water using NIRS.

[0027] Figures 3a to 3c relate to a method for analyzing changes in brain water content by sleep stage and changes in brain water content by 5-minute time window. Figure 3a measures changes in brain water content by matching changes in each sleep stage with a 5-minute time window. Figure 3b compares time-series changes in brain water content by 5-minute time window for 60 minutes after a change in sleep stage. Figure 3c measures changes in brain water content by 5 minutes immediately before the change and 5 minutes at a specific point in time (45 minutes in this figure) after the change in each sleep stage.

[0028] Figures 4a to 4c relate to methods for acquiring and analyzing EEG and polysomnographic data. Figure 4a illustrates the process of acquiring EEG and various sleep indicator data and waveforms during polysomnographic testing. Figure 4b illustrates the acquisition of EEG waveform and amplitude data in real time for each EEG channel. Figure 4c relates to connectivity analysis performed based on the EEG data.

[0029] Fig. 5a relates to spectrum power analysis. Figs. 5b and 5c relate to extremely low frequency vibration, low frequency vibration confirmation and quantitative evaluation methods. Specifically, Fig. 5b confirms that extremely low frequency vibration (high peak of 0.02 Hz in this analysis) occurs during sleep through spectrum power analysis based on real-time waveform data such as hemodynamic indicators (blood flow), and Fig. 5c quantifies the absolute power and relative power of the extremely low frequency vibration and low frequency vibration and compares and analyzes the power changes of the extremely low frequency vibration and low frequency vibration by each factor (age and sleep state in the figure).

[0030] Figure 6 shows an analysis of the change in the average brain water content between 5 minutes (First) and 30 minutes (Last) immediately after administration of the sedative.

[0031] Figure 7 shows the results of an analysis confirming that brain water content increased 30 minutes after administration of the sedative dexmedetomidine compared to the baseline.

[0032] Figure 8 is an example of an implementation of power spectrum analysis that evaluates the power of extremely low frequency vibration and low frequency vibration frequency bands by Fourier transforming raw time series data of brain water content changes.

[0033] Fig. 9 is an example of an implementation of power spectrum analysis that converts raw time series data of brain waves and evaluates power for each frequency band.

[0034]

[0035] Hereinafter, exemplary implementation examples of the present invention will be described in detail.

[0036] Definition of terms

[0037] In this specification, information on lymphatic activity includes information on lymphatic activity or activity index.

[0038] In this specification, the activity of the lymphatic system may refer to the quantitative and / or qualitative activity level of the lymphatic system.

[0039] In this specification, the lymphatic system activity indicator is a marker related to the activity of the lymphatic system, and may mean one or more of various sleep indicators including polysomnography indicators, electroencephalography indicators, and brain spectroscopy indicators.

[0040] In this specification, terms such as "unit," "module," "device," "terminal," "server," or "system" are intended to refer to a combination of hardware and software driven by the hardware. For example, the hardware may be a data processing device including a CPU or other processor, such as a mobile device or PC. Furthermore, the software driven by the hardware may refer to a running process, object, executable, thread of execution, program, etc.

[0041]

[0042] Description of exemplary implementations

[0043] The present invention, in one aspect, is a method for providing information on the activity or activity index of the glio-lymphatic system, which is performed by a processor, comprising the steps of: analyzing a change in one or more electroencephalographic indices and / or one or more near-infrared spectroscopy indices associated with a change in brain water content in the brain of a subject; and providing glio-lymphatic system activity or activity index information based on the analysis results.

[0044] In an exemplary embodiment, the analysis step may include analyzing changes in polysomnographic parameters associated with changes in brain water content.

[0045] In an exemplary embodiment, the sleep index obtained through the polysomnography may be at least one selected from the group consisting of total sleep time (minutes), sleep efficiency (%), arousal index ( / h), wake after sleep onset (min), apnea-hypopnea index ( / h), oxygen desaturation index (%), pulse rate, respiration rate, and oxygen saturation.

[0046] In an exemplary embodiment, the method may include: analyzing very-low frequency oscillations and / or low frequency oscillations of a brain of a subject; providing brain water content information based on the analysis; and providing glial lymphatic activity information based on the brain water content information.

[0047] In an exemplary embodiment, the subject may be a subject whose level of lymphatic activity has been increased, particularly by administration of a sedative, such as dexmedetomidine, as described below.

[0048] In an exemplary embodiment, the frequency of the ultra-low frequency vibration is 0.0067 Hz or more and less than 0.04 Hz, and the frequency of the low frequency vibration is 0.04 Hz or more and 0.1 Hz or less.

[0049] In an exemplary embodiment, the degree of increase in the relative power of the extremely low frequency and / or low frequency vibration is correlated with the degree of increase in brain water content and / or gliolymphatic activity. This means that the degree of increase in the relative power of the extremely low frequency and / or low frequency vibration is a factor measuring the physiological mechanism of the increase in brain water content and / or gliolymphatic activity, i.e., a biomarker. Therefore, the degree of increase in brain water content and / or gliolymphatic activity can be predicted based on the degree of increase in the relative power of the extremely low frequency and / or low frequency vibration.

[0050] In an exemplary embodiment, the analysis of the brainwave index change may include analysis of the power of the brainwave spectrum, and in the step of analyzing the brainwave spectrum power, the absolute power (uV2) and / or relative power (%) of one or more frequency bands selected from the group consisting of delta [1-4 Hz], theta [4-7 Hz], alpha [8-12 Hz], and beta [13-30 Hz] may be measured.

[0051] In an exemplary embodiment, in the step of analyzing the brainwave spectrum power, the absolute power (uV2) of the alpha [8-12 Hz] and / or beta [13-30 Hz] frequency bands can be measured.

[0052] In an exemplary embodiment, the degree of absolute power decrease in the alpha and / or beta frequency bands is correlated with the degree of increase in brain water content and / or gliolymphatic activity. This means that the absolute power decrease in the alpha and / or beta frequency bands is a factor measuring the physiological mechanism of the increase in brain water content and / or gliolymphatic activity, i.e., a biomarker. Accordingly, the degree of increase in brain water content and / or gliolymphatic activity can be predicted based on the degree of absolute power decrease in the alpha and / or beta frequency bands.

[0053] In one exemplary embodiment, the method and / or device may analyze connectivity within two or more frequency bands selected from the group consisting of delta [1-4 Hz], theta [4-7 Hz], alpha [8-12 Hz] and beta [13-30 Hz] of the brainwave spectrum.

[0054] In an exemplary embodiment, the method may further include a step of analyzing connectivity within the frequency bands of the brainwave spectrum, particularly alpha [8-12 Hz] and beta [13-30 Hz], wherein the step of analyzing connectivity within the frequency bands may measure one or more indices selected from the group consisting of a weighted phase lag index (wPLI), an imaginary part of coherency (iCoh), and a phase locking value (PLV).

[0055] In an exemplary embodiment, the near-infrared spectroscopy index may include the occurrence of extremely low-frequency vibrations and low-frequency vibrations on each laser of the near-infrared spectroscopy and the index of oxyhemoglobin (HbO2), reduced hemoglobin (HbR), and water content calculated therefrom, as well as the absolute power and relative power.

[0056] Each laser wavelength band of the near-infrared spectroscopy technique may be one or more selected from a group consisting of wavelength bands that each exhibit specific absorbance for oxyhemoglobin (HbO2), reduced hemoglobin (HbR), and water. Analysis of the laser wavelength band signal of the near-infrared spectroscopy technique can be used to calculate the amount of oxyhemoglobin, reduced hemoglobin, and water in the brain parenchyma.

[0057] Meanwhile, in exemplary embodiments of the present invention, a device for measuring lymphatic drainage system activity is provided, including a first data processing unit that receives and analyzes information on at least one of: near-infrared spectroscopy indices including very-low frequency oscillation and / or low frequency oscillation of the brain of a subject administered with a sedative; and brain wave indices including alpha [8-12 Hz] and / or beta [13-30 Hz] frequency bands of the brain wave spectrum; and a second data processing unit that receives information on brain water content of the subject and an analysis result of the first data processing unit and provides information on lymphatic drainage system activity.

[0058] In addition, exemplary embodiments of the present invention provide a computer-readable recording medium storing program instructions readable by a computer and operable by the computer, wherein when the program instructions are executed by a processor of the computer, the processor performs the above-described method.

[0059] The computer may be any computing device, such as a personal computer, desktop computer, laptop computer, notebook, smartphone, or the like, or may be integrated into any such device. The computer may be a device having one or more alternative and special purpose processors, memory, storage space, and networking components (either wireless or wired). The computer may run an operating system, such as, for example, Microsoft's Windows-compatible operating system, Apple's OS X or iOS, a Linux distribution, or Google's Android OS. The computer program consists of a set of instructions that can be executed on the computer, and the name thereof may vary depending on the form of the computer. For example, if the form of the computer is a smartphone, the computer program may be referred to as an app.

[0060] In exemplary embodiments, a computer-readable recording medium on which a program for implementing the above-described method is recorded may include any type of recording device that stores data that can be read by a computer. Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. In addition, the computer-readable recording medium may be distributed across network-connected computer systems, so that the computer-readable code can be stored and executed in a distributed manner. In addition, functional programs, codes, and code fragments for implementing the present invention will be readily understood by those skilled in the art to which the present embodiment pertains.

[0061]

[0062] Hereinafter, the composition and effects of the present invention will be described in more detail with examples. However, the examples below are provided for illustrative purposes only to aid understanding of the present invention and are not intended to limit the scope and scope of the present invention.

[0063] [Example]

[0064] Sleep polygraphy and near-infrared spectroscopy monitoring methods

[0065] Near-infrared spectroscopy (NIRS) monitoring is performed simultaneously with polysomnography. Polysomnography provides comprehensive indices, including total sleep time (minutes), sleep efficiency (%), arousal index ( / h), wake after sleep onset (min), apnea-hypopnea index ( / h), and oxygen desaturation index (%).

[0066] Sleep stages (wake, N1, N2, N3, REM) are classified by 30-second epochs. Transition from wakefulness to sleep is considered to occur when the first sleep epoch (N1, N2, N3) occurs in wakefulness and the sleep epoch continues for more than 2 minutes. Transitions from sleep to wakefulness, from NREM sleep to REM sleep, and from REM sleep to NREM sleep are also determined by the same criteria.

[0067] Time windows are set at 5-minute intervals based on the sleep stage transition point. Each time window point is divided into “5 minutes before transition” and “50 minutes after transition” at 5-minute intervals. Sleep indices (arousal index, / h), apnea-hypopnea index (apnea-hypopnea index, / h), and hypoxia index (oxygen desaturation index, %) are extracted for each window.

[0068] In addition, the change in brain water amount is measured for each window using near-infrared spectroscopy. This is a relative value measured based on the average value for 5 minutes before sleep stage conversion. For each window, the absolute power (uV) for each frequency band (delta [1-4 Hz], theta [4-7 Hz], alpha [8-12 Hz], beta [13-30 Hz]) is measured based on spectral analysis of EEG data obtained from polysomnography. 2 ) and relative power (%) are measured.

[0069] Connectivity analysis is performed within each frequency band for each window. Connectivity analysis includes indicators such as weighted phase lag index (wPLI), imaginary part of coherency (iCoh), and phase locking value (PLV).

[0070] For each window, spectrum analysis is performed on indicators such as pulse, oxygen saturation, respiratory rate, and airflow. Through this, the occurrence of very low frequency oscillations (0.01-0.1 Hz) and low frequency oscillations (0.1-0.5 Hz) in each indicator is measured, as well as absolute and relative power.

[0071] For each window, a spectrum analysis is performed on the signals of each laser wavelength using the near-infrared spectroscopy technique and the changes in the amount of oxyhemoglobin (HbO2), reduced hemoglobin (HbR), and water calculated from them. Through this, the occurrence of very low frequency oscillations (0.01-0.1 Hz) and low frequency oscillations (0.1-0.5 Hz) on each indicator, as well as the absolute and relative power, are measured.

[0072]

[0073] Analysis of sleep indices, brainwave indices, and near-infrared spectroscopy indices

[0074] Sleep indices, EEG indices, and near-infrared spectroscopy indices analyzed for each 5-minute window may include: (1) arousal index ( / h), apnea-hypopnea index ( / h), and oxygen desaturation index (%); (2) absolute power (uV) for each frequency band (delta, theta, alpha, beta). 2) and relative power (%), (3) connectivity indices such as weighted phase lag index (wPLI), imaginary part of coherency (iCoh), and phase locking value (PLV) for each frequency band (delta, theta, alpha, beta), (4) absolute power (uV2) and relative power (%) for each frequency band of polysomnography indices such as pulse, oxygen saturation, respiratory rate, and inspiratory pressure (airflow), (5) occurrence of extremely low-frequency oscillations and low-frequency oscillations in indices such as pulse, oxygen saturation, respiratory rate, and inspiratory pressure, and absolute power and relative power, (6) occurrence of extremely low-frequency oscillations and low-frequency oscillations in indices such as oxyhemoglobin (HbO2), reduced hemoglobin (HbR), and water quantity calculated through each laser of near-infrared spectroscopy, and absolute power and relative power.

[0075] The correlation between the indicators for each window during the above 5 minutes and the change in brain water amount by near-infrared spectroscopy is evaluated.

[0076] In situations of transition from wakefulness to NREM sleep, transition from sleep to wakefulness, transition from NREM sleep to REM sleep, and transition from REM sleep to NREM sleep, the average brain water content for 30 minutes after the transition, the average brain water content for 5 minutes at the 30-minute point, the average brain water content for 40 minutes after the transition, and the average brain water content for 5 minutes at the 30-minute point are measured compared to the average brain water content for 5 minutes before the transition.

[0077] The correlation between the average brain water content for 30 minutes after sleep state transition, the change in the average brain water content for 5 minutes at the 30-minute point, the average value of the sleep and brain wave indices for 30 minutes, and the 5-minute window value at the 30-minute point is analyzed.

[0078] The correlation between the average brain water content for 40 minutes after sleep state transition, the 5-minute average brain water content change index at the 40-minute point, the 40-minute average value of the above-mentioned sleep indices and EEG indices, and the 5-minute window value at the 40-minute point is analyzed.

[0079] Through this, we can discover the physiological mechanisms that regulate the activity of the glymphatic system and discover EEG and polysomnography indices that determine the activity of the glymphatic system.

[0080]

[0081] Analysis of EEG and near-infrared spectroscopy indices according to drug use

[0082] The EEG indices analyzed for each window of 5 minutes, and the near-infrared spectroscopy indices include: (1) absolute power (uV) for each frequency band (delta [1-4Hz], theta [4-7Hz], alpha [8-12Hz], beta [13-30Hz]) based on spectrum analysis of EEG data. 2 ) and relative power (%), (2) occurrence of brain wave ultra-low frequency vibration and low frequency vibration and absolute power, relative power, (3) occurrence of ultra-low frequency vibration and low frequency vibration on each laser of near-infrared spectroscopy and the index of oxyhemoglobin (HbO2), reduced hemoglobin (HbR), and water quantity calculated through the laser, and absolute power and relative power.

[0083] The correlation between the indicators for each window during the above 5 minutes and the change in brain water amount by near-infrared spectroscopy is evaluated.

[0084] In situations where sedation is induced using dexmedetomidine and / or propofol, which are commonly used sedatives in the operating room, the average brain water value for 30 minutes after sedation is measured compared to the average brain water value for 5 minutes immediately after sedation, and the change in the average brain water value for 5 minutes at the 30-minute point is measured.

[0085] The correlation between the average brain water content for 30 minutes after sedative administration, the change in the average brain water content for 5 minutes at the 30-minute point, the average sleep and brain wave indices for 30 minutes, and the 5-minute window value at the 30-minute point is analyzed.

[0086] Through this, we can discover the physiological mechanisms that regulate the activity of the glymphatic system, and discover EEG indicators, brain spectroscopy indicators, and polysomnography indicators that determine the activity of the glymphatic system.

[0087] As shown in Figure 6, the average brain water content change between 5 minutes (First) and 30 minutes (Last) immediately after sedative administration can be measured. The average brain water content change according to the use and type of sedative is shown in Table 1 below.

[0088] Changes in the average brain water content according to the use and type of sedative MeanStd. Error95% Confidence IntervalLower BoundUpper BoundNo sedation1.024.396.2261.822Propofol.503.276-.0531.058Dexmedetomidine1.420.259.8991.942

[0089] In addition, as shown in Figure 7, it was confirmed that the brain water content increased compared to the baseline at 30 minutes after administration of the sedative dexmedetomidine. Power spectrum analysis is performed by Fourier transforming the raw time series data of brain water content changes and evaluating the power in the ELF and LF frequency bands (Figure 8). In the above analysis, ELF of brain water content data is defined as 0.0067-0.04 Hz, and LF as 0.04-0.2 Hz.

[0090] Alternatively, the raw time series data of brain waves is converted to perform power spectrum analysis by evaluating the power for each frequency band (Fig. 9).

[0091] As shown in Table 1 below, when dexmedetomidine was administered, brain water content increased more significantly than when propofol was administered to induce sedation. A trend toward a greater increase in brain water content was also observed in the group that did not receive sedation, although not significantly so.

[0092] Changes in brain water content MeanDifferenceStd. ErrorSig.95% Confidence Interval for DifferenceLower BoundUpper BoundNo sedationPropofol.521.480.283-.4461.489Dexmedetomidine-.396.477.411-1.357.565PropofolNo sedation-.521.480.283-1.489.446Dexmedetomidine-.917.385.021-1.692-.143DexmedetomidineNo sedation.396.477.411-.5651.357Propofol.917 * .385.021.1431.692

[0093] Sedation with the sedative drug dexmedetomidine increased cerebral water content, as measured by near-infrared spectroscopy, and was significantly higher than that with propofol. This supports the idea that gliolymphatic system activity can be activated by specific drugs. Furthermore, it highlights the potential utility of drugs such as dexmedetomidine for the treatment of major diseases through gliolymphatic system activation. Furthermore, this finding could enable the identification of biomarkers that assess the presence and extent of gliolymphatic system activation.

[0094] As shown in Table 2, the increase in brain water content at 30 minutes compared to baseline was associated with a decrease in absolute power in the alpha and beta bands of the EEG.

[0095] Key results of association analysis adjusting for factors such as gender and age Delta_abs_ratioTheta_abs_ratioAlpha_abs_ratioBeta_abs_ratioDelta(brain water content change)Correlation.205.234-.453-.453Significance (2-tailed).204.147.003.003df38383838

[0096] Considering that the alpha and beta bands in the EEG are frequency bands corresponding to brain activity in an aroused state, it was confirmed that brain water content increased depending on the degree to which aroused EEG was reduced by sedation, that is, depending on the sedation itself and depth.

[0097] Table 3 presents an association analysis that identifies biomarkers associated with increases in brain water content at 30 minutes compared to baseline, adjusting for factors such as gender and age. Increased brain water content was found to be positively correlated with very low-frequency (VLF) oscillations of brain water content.

[0098] Discovery of biomarkers through association analysis that adjusts for factors such as gender and ageAbsVLF1_ratioAbsVLF1_deltaRelVLF1_deltaDelta(brain water content change)Correlation.086.219.495Significance (2-tailed).599.174.001df383838

[0099] This suggests that the glymphatic activation mechanism, measured by increased brain water content, is regulated by ELF vibrations within the brain, and that increases in cerebral water content measured by near-infrared spectroscopy, ELF and / or low-frequency vibrations, may be biomarkers for evaluating glymphatic activation and key mechanisms for inducing glymphatic activation. Therefore, ELF and / or low-frequency vibrations may be used as biomarkers for evaluating whether and to what extent the glymphatic activation mechanism is activated.

Claims

1. A method for providing information on lymphatic activity or activity indicators, performed by a processor, A step of analyzing changes in one or more electroencephalographic indices and / or one or more near-infrared spectroscopy indices associated with changes in brain water content in the brain of a subject; and A method for providing lymphatic system activity information, comprising: providing lymphatic system activity or activity index information based on the above analysis results.

2. In paragraph 1, The method comprises the steps of analyzing very-low frequency oscillation and / or low frequency oscillation of the brain of a subject; A step of providing brain water content information based on the above analysis; and A method comprising the step of providing information on lymphatic activity based on the brain water content information.

3. In paragraph 2, A method wherein the frequency of the above ultra-low frequency vibration is 0.0067 Hz or more and less than 0.04 Hz.

4. In paragraph 2, A method wherein the frequency of the above low-frequency vibration is 0.04 Hz or more and 0.1 Hz or less.

5. In paragraph 2, A method for predicting the degree of increase in brain water content and lymphatic activity based on the degree of increase in relative power of the above-mentioned ultra-low frequency and / or low frequency vibration.

6. In paragraph 1, A method in which the above brain wave index change analysis includes brain wave spectrum power analysis.

7. In paragraph 6, A method for measuring the absolute power (uV2) of the alpha [8-12 Hz] and / or beta [13-30 Hz] frequency bands in the step of analyzing the above brain wave spectrum power.

8. In paragraph 7, A method for predicting the degree of brain water content and lymphatic activity increase by the degree of absolute power decrease in the alpha and / or beta frequency bands.

9. In paragraph 7, A method further comprising the step of analyzing connectivity within the alpha [8-12 Hz] and beta [13-30 Hz] frequency bands of the brainwave spectrum.

10. In paragraph 9, A method for measuring at least one index selected from the group consisting of a weighted phase lag index (wPLI), an imaginary part of coherency (iCoh), and a phase locking value (PLV) in a step of analyzing connectivity within the above frequency band.

11. In paragraph 1, A method wherein the subject is a subject to which a sedative has been administered.

12. In paragraph 1, The above analysis step is a method for analyzing changes in polysomnographic parameters associated with changes in brain water content.

13. A computer-readable recording medium storing program instructions that are readable by a computer and operable by the computer, wherein when the program instructions are executed by a processor of the computer, the processor performs a method according to any one of claims 1 to 12.

14. A first data processing unit that receives and analyzes information about one or more of the following: near-infrared spectroscopy indices including very-low frequency oscillation and / or low frequency oscillation of the brain of a subject administered with a sedative; and electroencephalographic indices including alpha [8-12 Hz] and / or beta [13-30 Hz] frequency bands of the electroencephalographic spectrum; and A device for measuring lymphatic activity, comprising a second data processing unit that receives information on brain water content of the subject and analysis results of the first data processing unit and provides information on lymphatic activity.

15. In paragraph 14, The device wherein the sedative is dexmedetomidine.

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