Devices, systems, and methods for quantifying neuroinflammation

Abstract

Disclosed and contemplated herein is a bispectral EEG (BSEEG) method that can detect patients with delirium and can detect delirium associated with systemic inflammation. In various implementations, a mouse model is used to detect delirium associated with systemic inflammation induced by lipopolysaccharide (LPS) injection.

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims the benefit of U.S. Provisional Application No. 63 / 124,524, filed on December 11, 2020, entitled "Devices, Systems, and Method for Quantifying Neuro - Inflammation", under 35 U.S.C. § 119(e), and for all purposes, the entire content thereof is incorporated herein by reference.

[0002] This disclosure relates to quantifying neuro - inflammation, and in particular, to the use of bispectral electroencephalogram (BSEEG) for quantifying neuro - inflammation.

Background Art

[0003] Delirium is not only common but also dangerous, and it is widely recognized that the one - year mortality rate reaches 4%. Furthermore, delirium is associated with prolonged hospital stays, reduced likelihood of discharge to home, and long - term cognitive impairment. Additionally, delirium entails a significant economic burden. That is, one case of delirium is estimated to cost about $60,000. There are 2 - 3 million cases of delirium annually in the United States alone, and the annual economic loss associated with delirium is about $150 billion. Therefore, delirium is a significant burden for elderly patients, healthcare providers, hospital systems, and the healthcare economy.

[0004] Despite the fact that delirium has been presented as a burden, little is known about its pathophysiology, and no effective preventive measures or treatment methods have been identified. Furthermore, the diagnosis of delirium largely depends on the subjective assessment of mental state based on psychiatric interviews.

[0005] Therefore, there is a need in the art for devices, systems, and methods for systematically examining variables related to delirium, including neuro - inflammation.

Summary of the Invention

[0006] This specification discloses various methods for examining and quantifying neuroinflammation and associated delirium in patients.

[0007] In the various embodiments described herein, one or more computer systems may be configured to perform a specific operation or action by installing software, firmware, hardware, or a combination thereof on the system that causes the system to perform an action during operation. One or more computer programs may be configured to perform a specific operation or action by including instructions that cause a data processing device to perform an action when executed by the device. Various implementations include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0008] Example 1 is a bispectral EEG evaluation system comprising at least one sensor, a screening device, and a platform tool, wherein the system is configured to record EEG to obtain raw EEG data, calculate the recorded BSEEG score, and compare the recorded BSEEG score with the clinical inflammation threshold.

[0009] Example 2 is the same system as in Example 1, in which raw EEG data is recorded over a certain period of time.

[0010] Example 3 is the system according to any one of Examples 1-2, further comprising calculating the maximum BSEEG score.

[0011] Example 4 is the system according to any one of Examples 1 to 3, further comprising outputting the recorded BSEEG score.

[0012] Example 5 is the system described in any of Examples 1 to 4, wherein the system is configured to update the clinical inflammation threshold via a machine learning model.

[0013] Example 6 is the system described in any of Examples 1 to 5, wherein the platform tool is a web-based tool.

[0014] Example 7 is the system according to any one of Examples 1 to 6, further configured to quantify the level of electroencephalogram abnormalities and neuroinflammation over time.

[0015] Example 8 is a system according to any of Examples 1 to 7, in which the recorded BSEEG score is calculated from raw EEG data via power spectral density analysis.

[0016] Example 9 is a system according to any of Examples 1 to 8, wherein the recorded BSEEG score is the ratio of EEG signals in the range of 3 Hz to 10 Hz.

[0017] Example 10 further includes calculating a baseline BSEEG score, according to any of Examples 1 to 9.

[0018] Example 11 further includes calculating a standardized BSEEG score, as described in any of Examples 1 to 10.

[0019] Example 12 provides a system for quantifying neuroinflammation, comprising at least two sensors configured to record EEG signals indicating electroencephalogram frequencies, a processor, and at least one tool configured to operate on the processor, wherein the tool is configured to record EEG signals, perform spectral density analysis on the EEG signals to calculate a BSEEG score, and compare the BSEEG score to a clinical inflammation threshold to determine the amount of neuroinflammation.

[0020] In Example 13, the system according to Example 12, wherein at least one tool is configured to determine a baseline BSEEG score.

[0021] In Example 14, the system according to any one of Examples 12 to 13, wherein at least one tool is configured to calculate a standardized BSEEG score.

[0022] In Example 15, the system according to any one of Examples 12 to 14, wherein at least one tool is configured to calculate a maximum BSEEG score over a given period.

[0023] In Example 16, the system according to any one of Examples 12 to 15, wherein at least one tool is configured to calculate an average BSEEG score over a given period.

[0024] In Example 17, the system according to any one of Examples 12 to 16, further comprising a display configured to display an output including an average BSEEG score, a maximum BSEEG score, a baseline BSEEG score, and a standardized BSEEG score.

[0025] In Example 18, the system according to any one of Examples 12 to 17, wherein the tool is further configured to quantify the level of electroencephalogram abnormality and the level of neuroinflammation over time.

[0026] In Example 19, the system according to any one of Examples 12 to 18, wherein the given period is about 12 hours.

[0027] In Example 20, an inflammation evaluation method includes recording an EEG to obtain raw EEG data, calculating a recorded BSEEG score, and comparing the recorded BSEEG score with a clinical inflammation threshold.

[0028] The implementation of the technology described may include hardware, a method or process, or computer software on a computer-accessible medium. Although multiple embodiments are disclosed, further embodiments of the present disclosure will become apparent to those skilled in the art from the following detailed description, which illustrates and describes exemplary embodiments of the invention. As implemented, the present disclosure can be modified in various obvious ways without departing from the spirit and scope of the present disclosure. Therefore, the drawings and detailed description should be regarded as illustrative in nature and not restrictive.

Brief Description of the Drawings

[0029] [Figure 1] It is a system diagram according to one implementation. [Figure 2A] It is a flowchart of a system according to one implementation. [Figure 2B] It is a flowchart for various system calculations according to one implementation. [Figure 3] An experimental schedule is shown that includes an LPS injection to induce systemic inflammation to evaluate the EEG head-mounted placement, baseline EEG measurement, and subsequent brain responses measured by EEG changes. [Figure 4] A diagram of electrode placement on a mouse's head is shown. A total of four electrodes (two EEG, ground, and reference) were placed according to standard procedures. [Figure 5] A typical pattern of BSEEG scores in mice after LPS injection is shown. EEG1 and EEG2 were recorded from the head-mounted positions as shown in FIG. 2, and independent recordings were taken from the same mouse in the same experiment. BSEEG scores from both channels increased after LPS injection. The BSEEG scores remained abnormally elevated for several days until they returned to baseline. Also, the within-day variability seen before LPS administration decreased during the period of BSEEG elevation, often consistent with the clinical picture of delirious patients who experience abnormal sleep cycles. The example shown here is of young mice injected with 1 mg / kg of LPS. [Figure 6]This study demonstrates a reduction in diurnal variability of standardized BSEEG (sBSEEG) scores after injection of various doses of LPS. The mean sBSEEG score from EEG2 (frontal) in young mice increased after LPS injection. The score remained abnormally elevated for several days before returning to baseline. Furthermore, the diurnal variability observed before LPS administration decreased during the period of sBSEEG elevation, consistent with the clinical presentation of delirium patients often experiencing abnormal sleep cycles. [Figure 7] This figure shows dose-dependent changes in BSEEG scores in mice after LPS injection. Maximum BSEEG scores after various LPS injection doses are compared. A dose-dependent increase in BSEEG scores after LPS injection is demonstrated. Error bars represent standard errors. [Figure 8] The experimental schedule includes EEG head-mount surgery, EEG recording, and UP-LPS injection. [Figure 9] Typical BSEEG results in young mice before and after saline injection are shown. [Figure 10] The sBSEEG results of young mice before and after saline injection are shown. [Figure 11] This shows a comparison of BSEEG and sBSEEG results in young mice before and after UP-LPS injection. [Figure 12] This study shows a dose-dependent increase in sBSEEG scores after UP-LPS injection in young mice. [Figure 13A] This study shows a dose-dependent increase in sBSEEG scores after UP-LPS injection in young mice. [Figure 13B] This study shows a dose-dependent increase in sBSEEG scores after UP-LPS injection in aged mice. [Figure 14] This shows a comparison of the increase in sBSEEG scores in both young and aged mice after injection of 2.0 mg / kg of UP-LPS. [Modes for carrying out the invention]

[0030] This specification discloses various methods, as well as related systems and devices, for the use of bispectral EEG (BSEEG) with algorithms for evaluating, studying, and quantifying cognitive impairments such as delirium and neuroinflammation. In certain implementations, the methods and systems may be used to predict mortality. In various implementations, the disclosed BSEEG methods can objectively quantify the level of neuroinflammation induced by systemic inflammation via lipopolysaccharide (LPS).

[0031] Disclosed and intended herein are further bispectral EEG (BSEEG) approaches. In certain embodiments, the disclosed systems, devices, and methods can utilize the detection of patients with delirium and determine its association with systemic inflammation. In certain further embodiments, the disclosed methods, systems, and devices provide a more objective and efficient approach than commonly used behavioral tests or pathological examinations for the detection of delirium. Furthermore, in certain implementations, web-based tools for BSEEG and sBSEEG scoring may be used in conjunction with the methods, as further discussed herein.

[0032] While we do not wish to bind to a specific mechanism, neuroinflammation has been suggested to play a significant role in the pathophysiology of delirium. Evidence suggests that microglia, resident immune cells of the central nervous system that contribute to neuroinflammation, are readily activated and release inflammatory cytokines induced by peripheral infections. Several animal studies have shown that aged rodents treated with LPS exhibit increased levels of activated microglia and inflammatory cytokine protein compared to adult rodents. While these molecular and pathological changes have been observed in behavioral changes such as cognitive impairment, few studies have shown a relationship between microglial activation and EEG changes. Since the methods and systems discussed herein can quantify EEG slowdown by calculating BSEEG scores, the disclosed methods may enable a more accurate assessment of the correlation between microglial activation and electrophysiological brain dysfunction induced by LPS or other types of invasiveness.

[0033] Previous animal studies using pro-inflammatory cognitive changes have relied on behavioral tests without objective and biological methods to quantify the severity of cognitive impairment. As shown and described herein, a mouse model is used to detect delirium with systemic inflammation induced by lipopolysaccharide (LPS) injection. Using BSEEG data, electrophysiological changes in the brain in response to LPS injection that induces a systemic inflammatory response in C57Bl / 6 mice were objectively quantified.

[0034] Previously disclosed approaches to detect delirium have utilized bispectral EEG (BSEEG) and algorithms in clinical studies. These earlier approaches effectively differentiate between patients with and without delirium and, in certain implementations, predict long-term mortality.

[0035] In various implementations, the disclosed systems and methods include, for example, a screening device 8 shown in Figure 1. In these implementations, the screening device 8 is configured to receive signals from one or more sensors 12A, 12B. In this implementation, one or more sensors 12A, 12B are, but are not limited to, brain sensors such as electrodes placed on patient 2. One or more sensors 12A, 12B are configured to measure the brain activity of patient 2, for example, via electroencephalography (EEG). The signals may then be processed to extract one or more features of the signals. Sensors 12A, 12B may be wired or wireless. One or more features may be analyzed to determine one or more values ​​for each of the one or more features. The values ​​may include a BSEEG score, a standardized BSEEG score (sBSEEG score), and a maximum BSEEG score. The values ​​may be compared with various data to determine the severity of neuroinflammation and delirium.

[0036] In certain implementations, the screening device 8 and / or the disclosed system and method include one or more computers configured to perform a specific operation or action by installing software, firmware, hardware, or a combination thereof on the system / device 8, which causes the system / device 8 to perform an action during operation. One or more computer programs may be configured to perform a specific operation or action by including instructions that cause the device to perform an action when executed by the data processing device.

[0037] Referring here to Figures 2A and 2B, the approach disclosed herein comprises a series of steps, each of which is optional, and particular steps may be performed in any order or iteratively. In one step, the EEG is recorded (box 10) to obtain raw EEG data (box 12). In a further processing step (box 20), the EEG recording is transformed via power spectral density (box 22) to calculate a BSEEG score (box 24). In certain implementations, the BSEEG score (box 24) is calculated using a BSEEG algorithm similar to those disclosed in PCT application PCT / US16 / 64937, filed December 5, 2017, titled "Apparatus, Systems and Methods for Predicting, Screening and Monitoring of Encephalopathy Delirium," PCT application PCT / US19 / 51276, filed September 16, 2019, titled "Systems and Methods for Detection of Delirium Risk Using Epigenetic Markers," and PCT application PCT / US20 / 26914, filed April 6, 2020, titled "Apparatus, Systems and Methods for Predicting, Screening and Monitoring of Mortality and Other Conditions," each of which is incorporated herein by reference.

[0038] In other words, in one step, after recording the raw EEG signal (box 12), the data can be exported to an EDF file or the like by default software for the Pinnacle Sirenia EEG system or other suitable file types and systems, as can be easily understood by those skilled in the art. The exported file can be uploaded to a platform tool such as a web-based tool or other software application, and the BSEEG score (box 24) can be visualized and the results viewed, as can be easily understood.

[0039] In another optional step, once consecutive data from several days of recordings is uploaded, the sBSEEG score (box 26) may be calculated using the baseline data. The sBSEEG score is calculated as the change in BSEEG score from the baseline measurement (box 28).

[0040] In a further analysis step (box 30), the average BSEEG score (box 32) can be calculated over a given period, for example, over a 12-hour period. Then, using this average BSEEG score (box 32), the maximum BSEEG score (box 34) can be calculated for separate periods within the period from which the average was obtained. For example, the maximum BSEEG score may be equal to a comparison of the hourly BSEEG scores over the 12 hours from which the average BSEEG score was calculated with the average BSEEG score. The highest value after the comparison for each period is the maximum BSEEG score (box 34).

[0041] In a specific implementation, in an optional step, the recorded BSEEG score and / or sBSEEG score are compared to a clinical threshold, such as the clinical inflammation threshold, where results exceeding the clinical inflammation threshold are reported for clinical use via platform tools, etc. (Box 40).

[0042] In certain embodiments, the threshold step (box 40) includes determining an inflammation threshold. In these and other implementations, the inflammation threshold may be based on patient or patient group historical EEG data, medical record data, and additional data sources, as understood.

[0043] In a further optional step, the recorded BSEEG score, sBSEEG score, and / or threshold comparison are processed and analyzed by system 10 and output in the output step (box 50). The output data can be read and interpreted by a physician, patient, or other stakeholder. High sBSEEG and BSEEG scores indicate a higher level of neuroinflammation, and exceeding a specified threshold indicates abnormal brain activity. In various implementations, the output information is displayed on device 8, for example, as shown in Figure 1.

[0044] Various implementations enable improved assessment of delirium using electroencephalography (EEG), which can capture spreading slow waves such as delta waves, characteristic of human delirium. A bispectral EEG method using an algorithm has been disclosed in previously incorporated references, which effectively distinguishes patients with and without delirium by processing EEG using spectral power density analysis and capturing slow waves compared to high frequencies. In a human study of the BSEEG method in elderly patients admitted to the University of Iowa Hospitals and Clinics, sensitivity and specificity exceeded 80%, and the receiver operating characteristic (ROC) was approximately 0.8. These results suggest that the BSEEG score may be a biomarker of delirium.

[0045] Many previous EEG studies have used anticholinergic agents such as atropine and biperiden to induce EEG delirium features in animal models. However, this scenario is associated only with rare cases of anticholinergic overdose. Disclosed herein is a delirium model in which lipopolysaccharide (LPS) is infused to model systemic pro-inflammatory delirium. Furthermore, the disclosed quantification methods are clinically relevant and directly translatable into clinical settings to understand the pathophysiology of delirium in humans, specifically the effects of peripheral inflammation / infection leading to inflammation in the central nervous system.

[0046] Patients in a delirious state have recovered from systemic inflammation such as pneumonia and urinary tract sepsis. The data disclosed herein demonstrate that electrophysiological abnormalities or delirium can be objectively and accurately quantified by using BSEEG in conjunction with the algorithm.

[0047] Further disclosed herein is an EEG-based technique capable of capturing the electrophysiological features characteristic of systemic inflammation-associated delirium. A web-based tool may be used to create widespread applicability of the disclosed method and system. In certain implementations, users can upload their own EEG recordings to visualize changes in BSEEG scores over time. In various implementations, the web-based tool can quantify the level of electroencephalogram abnormalities and neuroinflammation over time.

[0048] As described in the examples herein, BSEEG was used in mice to detect electrophysiological changes consistent with delirium in humans. Furthermore, aged mice have been shown to be more sensitive to LPS than younger mice, as reflected by elevated BSEEG scores. To evaluate dose-dependent EEG changes from LPS injection, BSEEG responses to a range of LPS concentrations were assessed. These age-related and LPS dose-related differences are consistent with the fact that older patients or patients with severe systemic inflammation, such as serious infections or highly invasive surgery, are at higher risk of developing delirium.

[0049] Because delirium can be caused by multifactorial etiologies, including infection, surgery, and cholinergic drug overdose, it is ideal to compare different initiation methods to compare BSEEG changes with various etiologies. Furthermore, inhibition or activation of microglia may alter BSEEG score changes in response to LPS or other triggers. Certain drug therapies may suppress the development of delirium and may also prevent the increase in BSEEG scores in response to LPS, such as ramelteon, suvorexant, and dexamethasone.

[0050] Further implementations can evaluate the relationship between BSEEG scores and behavioral changes, and these methods may involve using a wireless EEG system to simultaneously measure both EEG and behavioral responses from the same patient. Such approaches would provide valuable information for assessing the consistency of EEG changes and behavioral changes; that is, observed behavioral changes can be compared / correlated with BSEEG and sBSEEG scores. Additionally, to better assess the usefulness of BSEEG in evaluating brain dysfunction or delirium, it is important to evaluate the correlation between BSEEG scores and pathological data such as microglia activation, and molecular data such as cytokine levels and gene expression. In various implementations, such correlations enable an understanding of the pathophysiological mechanisms of brain dysfunction and neuroinflammation.

[0051] In specific implementations, other systems, methods, and devices described herein may be reviewed by using machine learning models to identify the characteristics of BSEEG / sBSEEG scores, establish parameters and thresholds, and refine thresholds and standards to improve the accuracy of the system and device 8. In these implementations, the models are used to correlate digital BSEEG data in computing machines such as servers and databases.

[0052] In general, various machine learning approaches may be coded for execution on processors, server / computing devices, databases, third-party servers, and other computing or electronic storage devices that communicate with devices 8 and / or sensors 12A, 12B during operation.

[0053] The model may be run on BSEEG data recorded from or otherwise observed by a patient, such as patient movements recorded via motion capture through wearable devices or video imaging, and other recorded data such as medical records.

[0054] In various implementations, the data may include, but is not limited to, BSEEG data, raw EEG data, power spectral density transformed data, and one or more of the other implementations, as would be understood.

[0055] Accordingly, various systems and methods using machine learning models may transmit and / or receive information from various computing devices and patients' electronic medical records ("EMRs") via gateways or other connectivity mechanisms for use in monitoring, screening, or predicting delirium / inflammation. In certain embodiments, the systems and methods may utilize EMR data to improve the accuracy of delirium monitoring, screening, or prediction performed in conjunction with the screening device 8 and associated systems and methods.

[0056] In various implementations, BSEEG scores may also be loaded into the computer's computer storage to generate a suitable tree algorithm or logistic regression equation. Once generated, the tree algorithm, which may take the form of a large set of if-then conditions, may then be coded using any common computing language for test implementations. For example, the if-then conditions can be captured and compiled to generate a machine-executable module that, when in operation, accepts new data and outputs results, which may include computed predictions or other graphical representations.

[0057] The output may be in the form of a graph showing predictions or probability values, along with relevant statistical indicators such as p-values ​​and chi-scores. In various implementations, these results can be reintroduced within the learning module or elsewhere to continuously improve the system's functionality, including updating various thresholds used throughout. These implementations also understand that the respective data values ​​and readings can be trended to improve the performance of the device, system, and method. In these implementations, for example, a continuous stream of trending data and a trend over time may be identified, which can be used to provide additional optional evaluation steps. In various implementations, the model may provide additional programs to improve accuracy, which may include aggregating and adjusting the various thresholds described herein.

[0058] As used herein, the terms “value” and “feature” are interchangeable and can refer to raw data and analyzed data, whether numerical, time-scale, graphical, or otherwise. In various implementations as described herein, a value such as the number of high frequencies may be compared to a threshold. Alternatively or additionally, the ratio of two or more values ​​may be compared to a threshold. The threshold may be a predetermined value. The threshold may be based on statistical information regarding the presence, absence, or likelihood of subsequent delirium onset, such as information from an individual population. In certain embodiments, the threshold may be predetermined for one or more patients. In certain embodiments, the threshold may be consistent for all patients. In certain embodiments, the threshold may be specific to one or more characteristics of a patient, such as current health, age, sex, race, medical history, or other medical conditions. In certain embodiments, the threshold may be adjusted based on the patient’s EMR physiological data.

[0059] In certain embodiments, the threshold may be the ratio of high-frequency waves to low-frequency waves. In certain embodiments, the threshold may be the ratio of high-frequency waves to low-frequency waves over a period of time. Throughout this disclosure, the ratio is referred to as the ratio of high-frequency waves to low-frequency waves, but it will be understood that the ratio may also be the ratio of low-frequency waves to high-frequency waves, as long as the format of the ratio is consistent throughout the process. For example, the comparison may be the ratio of high-frequency waves to low-frequency waves, or vice versa, i.e., low-frequency / high-frequency.

[0060] One or more characteristics or values ​​may be predetermined. For example, the range of high-frequency waves may be predetermined to be greater than a set value. Similarly, the range of low-frequency waves may be predetermined to be less than a set value. The set value may be the same for all patients or may vary depending on specific patient characteristics.

[0061] Other features or values ​​of one or more signals may be extracted. For example, the signal-to-noise ratio may also be determined for other applications. Data quality may be assessed by looking for non-physiological frequencies of electrical activity. Data acquisition and / or interpretation may be limited to be stopped when data quality falls below an acceptable level. [Examples]

[0062] Example 1 Methods and materials Animals and breeding Male wild-type C57Bl / 6 mice, in early adulthood (approximately 2-3 months) and aged (approximately 18-19 months), were purchased from Jackson Laboratory. The mice were housed in the animal facilities of the University of Iowa. All mice were housed in plexiglass cages, separated by sex, with free access to food and water. Ambient temperature and relative humidity within the facilities were strictly controlled. The animals were maintained in a 12:12 light-dark cycle within the animal facilities. Animal studies were conducted according to protocols approved by the UI Animal Experimentation Committee.

[0063] Drugs and reagents LPS (Sigma, E. coli origin, serotype O111:B4, catalog number L2630) was obtained and dissolved in injectable saline. The LPS and saline were injected into the peritoneal cavity of mice.

[0064] Experimental Design Figure 3 shows the experimental schedule for EEG head-mount placement surgery, EEG recording, and inflammation experiments. Baseline measurements were taken on day 1. Saline solution was injected on day 2, and LPS was injected on day 4. Various doses of LPS were injected as described herein.

[0065] EEG electrode head-mounted placement surgery EEG electrodes were positioned on the mouse heads using a standard protocol, as shown in Figure 4, followed by a one-week recovery period after head-mount surgery. EEG recordings were stable before the start of the inflammation experiment. A wired EEG system (Sirenia EEG system, Pinnacle Technologies, Inc., Lawrence, KS) was used in the experiment according to standard procedures.

[0066] Inflammation experiment The schedule was as follows: Day 1, baseline measurement; Day 2, saline injection; Day 3, recovery; Day 4, LPS injection; and Day 5 onward, recovery. For each mouse, at 8:00 AM, the following doses of LPS were injected into three cohorts of 3-4 male mice: 0.5, 1, and 2 mg / kg for young mice, and 0.25, 0.5, and 1 mg / kg for aged mice. Different dose ranges of LPS were established between the young and aged groups because the maximum dose of 2 mg / kg was expected to be lethal for aged mice. Each age group included a total of 9-10 mice. Results were compared between the groups.

[0067] Calculation of BSEEG score EEG was recorded in wild-type early adult mice (approximately 2-3 months old) and aged mice (approximately 18-19 months old) after LPS injection. The EEG recordings were converted to power spectral density to calculate the BSEEG score.

[0068] EEG Signal Processing - This algorithm was implemented to obtain an EEG score for measuring the presence and severity of delirium in patients. Animal EEG recordings were analyzed using an algorithm similar to those used in previously disclosed human studies. Raw EEG recordings were digitally converted to power spectral density to calculate the BSEEG score. Specifically, SleepPro (Sirenia EEG system, Pinnacle Technologies, Inc., Lawrence, KS) software was used to export the raw data after the Fast Fourier Transform, and then processed through the algorithm to calculate the BSEEG score. The BSEEG score is derived through the calculation of the ratio of 3Hz power to 10Hz power. As shown in Figure 5, the BSEEG score was plotted on the y-axis and the recording time was plotted on the x-axis.

[0069] Standardized BSEEG (sBSEEG) Score - To standardize the change in BSEEG score from baseline, the mean BSEEG score for the first 12 hours at baseline for each mouse (daytime on day 1) was set to zero, and the degree of change from that baseline was defined as the standardized BSEEG (sBSEEG) score.

[0070] Maximum BSEEG Score - To compare the changes in BSEEG scores after various doses of LPS injection, the increase in maximum BSEEG score from EEG2 (forehead) after LPS injection was calculated. The increase in BSEEG score was calculated as follows: (BSEEG score after LPS injection every hour) - (Average BSEEG score over the first 12 hours). Next, the maximum value was defined as the increase in the maximum BSEEG score. The increase in the maximum BSEEG score was used to compare different groups based on the dose of injected LPS.

[0071] statistical analysis To investigate the dose-effect relationship between BSEEG scores and compare young and aged mouse groups, a linear regression model was fitted that included dose levels, groups, and level-group interactions. In estimating the coefficients within the model, a generalized estimation equation with a composite symmetric working correlation structure was used to account for within-subject correlations. The Wald trial was performed at a significance level of 0.05. All statistical analyses were performed in R.

[0072] Web-based BSEEG score calculator A web-based tool has been developed to calculate BSEEG scores from raw EEG signal data, which was obtained through the Pinnacle Sirenia EEG system.

[0073] result Typical patterns of BSEEG score changes after LPS injection Studies in young mice (2–3 months, n=9) and aged mice (18–19 months, n=10) implanted with two EEG channels revealed diurnal variation over a 24-hour period, both at baseline and after LPS vehicle injection. Figure 5 depicts a typical example of BSEEG scores before and after LPS injection. For brevity, data for day 3 (1 day after saline injection) have been omitted. During the day, while the mice were sleeping, BSEEG scores were slightly higher and relatively stable. During the night, when the mice were active, BSEEG scores were, on average, lower and unstable. This diurnal variation pattern was not affected after saline injection.

[0074] Injection of mice with LPS increased BSEEG scores and slowed EEG activity, with scores peaking at approximately 12 hours and reduced diurnal variation (Figure 5). LPS injection dose-dependently increased BSEEG scores and reduced diurnal variation. The mean BSEEG score increased more significantly in the aged mouse group as the dose increased.

[0075] Changes in sBSEEG scores and loss of diurnal variation over several days after LPS injection To highlight diurnal variation, the data points in Figure 6 represent the mean sBSEEG from 12-hour EEG2 (frontal) scores over several days. The mean sBSEEG scores across three mice in each LPS dose group over the long term are shown on the y-axis. The pattern of diurnal variation during the first three days was very consistent across multiple experiments with the nine mice tested. The pattern did not change even after saline injection (day 2) (Figure 6, days 1-3). After LPS injection on day 4, the diurnal pattern decreased and remained abnormal for approximately three days after LPS exposure (day 6). By day 7, diurnal variation appeared to be returning.

[0076] Increased dose-dependent BSEEG score in response to LPS Maximum BSEEG scores from EEG2 (forehead) after various doses of LPS injection were compared (0.5, 1, and 2 mg / kg in juvenile mice; 0.25, 0.5, and 1 mg / kg in aged mice) (Figure 7). According to the Wald test with a significance level of 0.05, mean scores significantly increased with increasing dose levels in both the juvenile mouse group (p < 0.0001) and the aged mouse group (p < 0.0001). Additionally, the interaction effect was statistically significant (p = 0.004), with a positive coefficient estimate. This indicates that BSEEG scores increased much faster in the aged group as the dose increased (Figure 7).

[0077] Web-based BSEEG score calculator After recording the raw EEG signal, the data can be exported as an EDF file by the Pinnacle Sirenia EEG system or by default software for other suitable file types and systems. The EDF file can then be uploaded to a web-based tool, and the BSEEG score can be visualized. If consecutive data from recordings over several days is uploaded, the sBSEEG score can also be calculated using the baseline data from day 1.

[0078] Consideration Using BSEEG data, we objectively quantified electrophysiological changes in the brain in response to LPS injections that induce a systemic inflammatory response in C57Bl / 6 mice. This type of LPS injection animal model is widely used to assess the consequences of systemic inflammation. Disclosed herein is the ability to quantify delirium in animal models using EEG and BSEEG in specific implementations.

[0079] This approach is supported by the success of previously disclosed human studies that used BSEEG to distinguish between patients with and without delirium. Using previously disclosed algorithms, various features of EEG signals were tested before developing an algorithm to distinguish between patients with and without delirium. Through this, a clear and quantitative difference in the severity of delirium was found between patients who experienced delirium and those who did not. In this study on a mouse model, the same algorithm was applied to the analysis of EEG signals measured from mice.

[0080] The data from this example demonstrate that the BSEEG method can be used to evaluate electrophysiological activity in mice that is similar to EEG features characteristic of delirium, consistent with previous findings using BSEEG in humans. An increase in BSEEG score indicates "EEG slow waves" suggestive of brain dysfunction. LPS injection reduced the diurnal variability of BSEEG scores; that is, BSEEG scores were relatively stable and slightly elevated during the day, but unstable and low at night (Figures 3 and 4). These LPS-induced BSEEG changes are consistent with sleep / wake cycle disturbances in patients with delirium.

[0081] Furthermore, aged mice responded more strongly to LPS than juvenile mice in a dose-dependent manner (Figure 7). These age-related and LPS dose-related differences are consistent with the fact that older patients, or patients with severe systemic inflammation such as severe infections or highly invasive surgery, are at higher risk of developing delirium.

[0082] Example 2 Materials and Methods - In this example, C57BL / 6 mice, including both young (8-week-old) and aged (72-week-old) mice, were studied. This study also used ultrapurity LPS (Invivo Gen) at various doses ranging from 0.5 to 2.0 mg / kg administered intraperitoneally.

[0083] The experimental schedule is shown in Figure 8. In the experiment, EEG head-mount surgery was performed two weeks before EEG recording. EEG recording began on day 0 and continued for 5 days. On day 2, the mice were injected with UP-LPS.

[0084] To ensure understanding, both wired and wireless EEG systems were used.

[0085] Analysis – EEG data was analyzed by recording the raw EEG signal. This raw EEG data was then processed using appropriate software to determine the BSEEG score (3Hz / 10Hz). In some cases, a web-based BSEEG calculator was used.

[0086] Results - Figure 9 shows the BSEEG scores of young mice over several days. On day 2, the mice were injected with saline. Figure 10 shows the sBSEEG scores from the data. This data shows the diurnal variation in young mice.

[0087] Figure 11 shows the BSEEG and sBSEEG scores of young mice that received UP-LPS injections at the indicated time. As can be seen, in particular, the diurnal variation in the sBSEEG data ceased after LPS injection.

[0088] Figure 12 shows the dose-dependent increase in sBSEEG scores after UP-LPS injection in young mice. This is because the sBSEEG score was obtained as the dose of UP-LPS increased after administration.

[0089] Figures 13A and 13B show dose-dependent changes in sBSEEG scores after UP-LPS injection in both young (Figure 13A) and aged (Figure 13B) mice. As can be seen in both young and aged mice, there is an increase in sBSEEG scores as the dose of UP-LPS administered increases. The rate of increase was higher in aged mice compared to young mice.

[0090] Figure 14 shows that a 2.0 mg / kg dose of UP-LPS resulted in an increase in BSEEG scores in both young and aged mice. UP-LPS induced a greater increase in BSEEG scores in aged mice compared to young mice.

[0091] While this disclosure has described various embodiments, those skilled in the art will recognize that modifications may be made in form and detail without departing from the spirit and scope of this disclosure.

Claims

1. A system for evaluating bispectral EEG, a) At least two sensors that measure raw EEG data, b) A device that receives raw EEG data from at least two of the sensors, c) comprising a platform tool, wherein the platform tool is Obtaining the aforementioned raw EEG data, The acquired raw EEG data is converted into spectral density, and the BSEEG score is calculated by dividing the power of the 3 Hz low-frequency component of the spectral density by the power of the 10 Hz high-frequency component. Calculate the baseline BSEEG score, which is the mean of the BSEEG score during the period before inflammatory stimulation and during inactivity. The process involves calculating a standardized BSEEG score by subtracting the baseline BSEEG score from the BSEEG score calculated from the raw EEG data recorded over a certain period of time, When the standardized BSEEG score exceeds a predetermined clinical inflammation threshold, it is determined that the nerve is in an inflamed state. A system configured to perform the following actions.

2. The system according to claim 1, wherein the raw EEG data is recorded over a certain period of time.

3. The system according to claim 1, further comprising determining the maximum sBSEEG score as the BSEEG score, which is the BSEEG score with the largest difference between the BSEEG score and the baseline BSEEG score at predetermined intervals after inflammatory stimulation.

4. The system according to claim 1, further comprising outputting the calculated BSEEG score and the standardized BSEEG score.

5. The system according to claim 1, wherein the platform tool is a web-based tool.

6. The system according to claim 1, further configured to quantify the level of electroencephalogram abnormalities and the level of neuroinflammation over time.

7. The system according to claim 1, wherein the recorded BSEEG score is calculated from raw EEG data via power spectral density analysis.

8. The system according to claim 7, wherein the recorded BSEEG score is the ratio of the EEG power in the 3 Hz frequency band to the EEG power in the 10 Hz frequency band.

9. A system for quantifying neuroinflammation, (a) at least two sensors configured to record EEG signals indicating electroencephalogram frequencies, (b) Processor and (c) comprising at least one tool configured to operate on the processor, wherein the at least one tool is Recording the EEG signal, The BSEEG score is calculated by performing spectral density analysis on the EEG signal and dividing the power of the 3 Hz low-frequency component among the frequency components of the obtained spectral density by the power of the 10 Hz high-frequency component among the frequency components. Calculate the baseline BSEEG score, which is the mean of the BSEEG score during the period before inflammatory stimulation and during inactivity. The process involves calculating a standardized BSEEG score by subtracting the baseline BSEEG score from the BSEEG score calculated from the EEG signals recorded over a certain period of time, When the standardized BSEEG score exceeds a predetermined clinical inflammation threshold, it is determined that the nerve is in an inflamed state. A system configured to perform the following actions.

10. The system according to claim 9, wherein at least one tool is configured to calculate the BSEEG score with the largest difference between the BSEEG score and the baseline BSEEG score as the maximum sBSEEG score.

11. The system according to claim 10, further comprising a display configured to display an output including the maximum sBSEEG score, the baseline BSEEG score, and the BSEEG score.

12. The system according to claim 9, wherein the tool is further configured to quantify the level of electroencephalogram abnormalities and the level of neuroinflammation over time.

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