A neuro-immunotherapy response assessment data analysis method

Abstract

The present application relates to the technical field of medical data processing, in particular to a kind of neuroimmunotherapy response evaluation data analysis method, comprising: obtaining the multidimensional data of candidate subject in treatment cycle, according to multidimensional data to carry out data analysis, to determine the neuro-immune transmitter collaborative fluctuation degree of candidate subject, central-peripheral nerve collaborative index and timing coordination index of neuroimmunity;The neuro-immune transmitter collaborative fluctuation degree of candidate subject, central-peripheral nerve collaborative index and timing coordination index are carried out data fusion processing, and the treatment response index of candidate subject is obtained.By the treatment response index of candidate subject determined by the present application, the defect that complex timing change of neuro-immune network dynamic interaction is difficult to capture can be overcome, and the accuracy and reliability of neuroimmunotherapy response evaluation can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of medical data processing technology, and more specifically to a method for analyzing data on the response of neuroimmunotherapy. Background Technology

[0002] Neuroimmune regulation is a core mechanism for maintaining homeostasis, and its dysfunction is closely related to the occurrence and development of various major diseases, including autoimmune diseases, neurodegenerative diseases, and tumors. However, the efficacy of neuroimmunotherapy varies greatly among individuals. Some patients respond rapidly and sustainably to treatment, while others develop primary or secondary resistance, or even experience severe adverse reactions such as cytokine storms due to over-activation of the immune response. Therefore, accurately assessing patients' responsiveness to neuroimmunotherapy and adjusting treatment plans in a timely manner are crucial for improving efficacy and reducing risks.

[0003] However, clinical assessment currently relies mainly on traditional biomarkers (such as cytokine levels and the proportion of immune cell subsets) and imaging features. These indicators are mostly static single-point detections, which are difficult to capture the complex temporal changes of dynamic interactions in the neural-immune network, resulting in insufficient accuracy and reliability of responsiveness assessment. Summary of the Invention

[0004] To address the aforementioned technical problems of insufficient accuracy and reliability in existing neuroimmunotherapy response assessments, the present invention aims to provide a data analysis method for neuroimmunotherapy response assessments. The specific technical solution adopted is as follows: One embodiment of the present invention provides a method for analyzing data on the response to neuroimmunotherapy, the method comprising the following steps: The vagus nerve discharge frequency sequence, EEG alpha wave power sequence, IL-6 content sequence, NE content sequence, heart rate variability sequence, and skin conductance level sequence of the candidate subjects were obtained daily during the treatment cycle; the treatment cycle included the baseline period and the treatment period. The neuro-immune neurotransmitter synergistic variability was determined based on the daily vagal nerve discharge frequency subsequence, IL-6 content subsequence, NE content subsequence, and skin conductance level subsequence of the candidate subjects during the treatment period; the neuro-immune neurotransmitter synergistic variability was used at least to characterize the possibility that the candidate subjects were affected by spontaneous regulatory mechanisms during neuroimmunotherapy. The central-peripheral synergy index is determined based on the daily EEG alpha wave power subsequence, skin conductance level subsequence, and heart rate variability subsequence of the candidate subjects during the pending period; the pending period is the baseline period and the treatment period, and the central-peripheral synergy index is used at least to characterize the coordination between central sedation and peripheral stress response of the candidate subjects; The temporal coordination index of neuroimmunity was determined based on the daily vagal nerve firing frequency subsequence and IL-6 content subsequence of the candidate subjects during the treatment period; the temporal coordination index was used to characterize at least the rhythmic synchronization of the vagal nerve firing frequency and IL-6 content of the candidate subjects. The neuro-immune neurotransmitter synergistic fluctuation, central-peripheral nerve synergistic index, and temporal coordination index of the candidate subjects are fused to obtain the treatment response indicators of the candidate subjects.

[0005] Furthermore, determining the synergistic fluctuation of neuro-immune neurotransmitters includes: Determine the rate of change of vagal nerve firing frequency, IL-6 content, NE content, and skin conductance level at each time point; The first maximum cross-correlation function value of the vagus nerve discharge frequency change rate sequence and the skin conductance level change rate sequence is calculated to obtain the first time lag. The body stress coefficient is determined based on the deviation between the first maximum cross-correlation function value and the first time lag. Calculate the second maximum cross-correlation function value of the IL-6 content change rate sequence and the NE content change rate sequence to obtain the second time lag; determine the lag increase rate based on the second time lag; The stress coefficient of the body and the hysteresis increase rate are fused to obtain the neuro-immune neurotransmitter synergistic fluctuation of the candidate subjects.

[0006] Further, determining the lag increase rate based on the second time lag includes: The time delay in the decrease of IL-6 levels in the target subject is obtained; the target subject is a healthy human body that does not require neuroimmunotherapy, and the time delay is equal to the time point from when the NE level increases to when the IL-6 level decreases. Calculate the difference between the second time lag and the delay time, and normalize the difference to obtain the lag increase rate.

[0007] Furthermore, determining the central-peripheral nerve synergy index includes: Based on the daily EEG alpha wave power subsequence and skin conductance level subsequence of the candidate subject within the pending period, the phase difference of the candidate subject at each time point within the pending period is determined; the phase difference is equal to the difference between the instantaneous phase angle of the EEG alpha wave power and the instantaneous phase angle of the skin conductance level at the same time point; The synchronization enhancement rate of the candidate subjects is determined based on the phase difference at each time point within the waiting period; the synchronization enhancement rate is used to characterize at least the coordination between the brain sedation state and sympathetic nerve excitation. The heart rate variability improvement rate of the candidate subjects is determined based on the daily heart rate variability subsequences during the treatment period; the heart rate variability improvement rate is determined by the difference between the heart rate variability of the candidate subjects during the treatment period and the heart rate variability at the baseline period. The synchronization enhancement rate and heart rate variability enhancement rate of the candidate subjects are fused to obtain the central-peripheral nerve synergy index of the candidate subjects.

[0008] Furthermore, determining the synchronization enhancement rate of candidate subjects includes: The phase difference of the candidate subjects at each time point within the pending period is processed to obtain the phase synchronization index of the candidate subjects within the pending period. The synchronization improvement rate of the candidate subjects was determined based on the phase synchronization index of the candidate subjects during the treatment period, the phase synchronization index during the baseline period, and the phase synchronization index of the target subjects.

[0009] Further, obtaining the phase synchronization index of the candidate subject within the pending period includes: The phase difference of the candidate subject at each time point within the undetermined period is subjected to a complex exponential transformation to obtain the complex vector at each time point within the undetermined period. The mean magnitude of the complex vector at all time points within the pending period is calculated as the phase synchronization index of the candidate subject within the pending period.

[0010] Furthermore, the determination of the temporal coordination index of neuroimmunity includes: Obtain the trough of vagal nerve discharge frequency and the peak of IL-6 content at the first treatment of each candidate subject during the treatment period; Based on the trough and peak times corresponding to each treatment, the rhythm consistency index of the candidate subjects is determined; The vagus nerve discharge frequency subsequence and IL-6 content subsequence of the candidate subjects during the treatment period were divided into intervals to obtain each first value interval and each second value interval; the first value interval is the value interval of the vagus nerve discharge frequency subsequence, and the second value interval is the value interval of the IL-6 content subsequence. The coupling strength of the candidate subjects is determined based on each first value interval and each second value interval; The rhythm consistency index and coupling strength of the candidate subjects are fused to determine the temporal coordination index of neuroimmunity.

[0011] Further, determining the rhythm consistency index of candidate subjects includes: Calculate the time difference between the peak time and the trough time corresponding to the same treatment session; The circular mean of the candidate subjects is obtained by processing the data of each time difference using the circular mean calculation method. The circumferential mean of the candidate subjects is normalized, and the normalized value is determined as the rhythm consistency index of the candidate subjects.

[0012] Further, determining the coupling strength of candidate subjects includes: By combining each first value interval and each second value interval of the same day in pairs, we can obtain the corresponding interval combinations for each day during the treatment period. Based on the frequency of occurrence of each interval combination each day during the treatment period, the joint probability distribution of each interval combination each day is determined, and then the marginal probability distribution of each day is determined. Determine the mutual information value between the sequence formed by all joint probability distributions corresponding to the same day and the sequence formed by all marginal probability distributions; The average value of the mutual information over all days is normalized, and the normalized value is used as the coupling strength of the candidate subjects.

[0013] Furthermore, after obtaining the treatment response indicators of the candidate subjects, the following are also included: Set a first response indicator threshold and a second response indicator threshold; the first response indicator threshold is less than the second response indicator threshold. When the treatment response index of a candidate subject is less than the first response index threshold, the candidate subject is determined to be in the first response level. When a candidate subject's treatment response index is greater than or equal to the first response index threshold and less than or equal to the second response index threshold, the candidate subject is determined to be in the second response level. When a candidate subject's treatment response index is greater than the second response index threshold, the candidate subject is determined to be in the third response level. The response effect of the first response level is lower than that of the second response level, and the response effect of the second response level is lower than that of the third response level.

[0014] The present invention has the following beneficial effects: This invention provides a data analysis method for assessing the responsiveness of neuroimmunotherapy. First, it quantifies the temporal regulatory delay and fluctuation correlation between neurotransmitters and immune factors by calculating synergistic fluctuations, filling the gap in analyzing the contradictions in neurotransmitter-immune dual-channel regulation and improving the numerical accuracy and reliability of subsequent treatment response indicators determined based on synergistic fluctuations. Second, it quantifies the dynamic coordination between central sedation and peripheral sympathetic excitation through phase synchronization analysis of EEG alpha waves and skin conductance, reflecting the overall neuroregulatory effect of treatment. Next, it comprehensively assesses the long-term synergistic state of neuroimmune rhythms by capturing the synchronicity of key nodes in IL-6 and vagal nerve rhythms, as well as the nonlinear correlation across the entire diurnal cycle. Finally, by integrating synergistic patterns and combining neuroimmune neurotransmitter synergistic fluctuations, central-peripheral nerve synergistic indices, and temporal coordination indices, it maps the transition of the neuroimmune network from disorder to homeostasis, reflecting the neuroimmune treatment responsiveness of candidate subjects. The treatment response indicators for candidate subjects determined by this invention can effectively improve the accuracy and reliability of responsiveness assessment. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a method for evaluating the response of neuroimmunotherapy data according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the implementation of step S2 in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the implementation of step S3 in an embodiment of the present invention; Figure 4 This is a flowchart illustrating the implementation of step S4 in an embodiment of the present invention. Detailed Implementation

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0018] The application scenarios targeted by this invention can be: Currently, the assessment of neuroimmunotherapy responsiveness mainly relies on static single-point monitoring, which struggles to capture the complex temporal changes in the dynamic interactions of the neuro-immune network. Furthermore, it neglects the synergistic influence of multiple dimensions, including neurophysiological signals, neurotransmitter concentrations, and psychological stress states, leading to insufficient accuracy and reliability in the responsiveness assessment. Therefore, one embodiment of this invention provides a data analysis method for neuroimmunotherapy responsiveness assessment, such as... Figure 1 As shown, it includes the following steps: S1, acquire the daily vagus nerve discharge frequency sequence, EEG alpha wave power sequence, IL-6 content sequence, NE content sequence, heart rate variability sequence, and skin conductance level sequence of the candidate subjects during the treatment cycle.

[0019] Here, candidate subjects refer to patients who require neuroimmunotherapy. For example, patients with moderately active rheumatoid arthritis can be selected as candidate subjects, with an age range of 35-65 years, to exclude those with a history of severe cardiovascular disease, nerve conduction disorders, and mental illness. The treatment cycle includes the baseline period, the treatment period, and the follow-up period. This embodiment mainly focuses on data analysis during the baseline and treatment periods; to reduce computational load, data related to the follow-up period are not included here.

[0020] The treatment regimen for candidate subjects may include: vagal nerve electrical stimulation 3 times a week for 20 minutes each time, with the stimulation intensity starting from 1 mA and increasing by 0.5 mA each week according to the patient's tolerance, up to a maximum of 3 mA; combined with subcutaneous injection of biological agents once every 2 weeks, with the dosage standardized according to the patient's weight, for a total treatment cycle of 12 weeks, including a 4-week baseline period (before treatment), an 8-week treatment period, and a 4-week follow-up period after the end of treatment (follow-up visit period).

[0021] For candidate subjects during the baseline and treatment periods, the following metrics were collected daily at fixed intervals, including: Non-invasive electrodes were attached to the surface projection area of ​​the vagus nerve on the left side of the candidate's neck and connected to a neurophysiological recorder to continuously record the vagus nerve discharge frequency during normal and stimulated periods. The stimulated period refers to the time corresponding to each treatment session involving vagus nerve electrical stimulation.

[0022] Electroencephalogram (EEG) electrodes were placed on both frontal lobe regions of the candidate subjects to continuously record alpha wave power values ​​during treatment, reflecting the central nervous system sedation state. Alpha waves are defined as those between 8 and 13 Hz.

[0023] Vacuum blood collection tubes and matching enzyme-linked immunosorbent assay (ELISA) kits were prepared in advance to measure changes in IL-6 and NE levels in subjects at intervals of 6:00 AM (corresponding to the pre-morning peak of IL-6), 12:00 PM (mid-rhythm), and 8:00 PM (rhythm trough). Both could be fitted with continuous change curves using the least squares method over time. The three points mentioned above each day are sufficient to define the core characteristics of a circadian rhythm, such as phase, amplitude, and median. The continuous change curves are used to visually characterize the diurnal rhythm patterns of IL-6 and NE.

[0024] Equipped with a wrist-worn wearable device, it records 24-hour heart rate variability (HRV), which reflects autonomic nerve tension; and records 24-hour skin conductance level (SCL), which reflects sympathetic nerve excitation, i.e. psychological stress level.

[0025] It should be noted that, in the embodiments of the present invention, in order to facilitate subsequent data processing and avoid differences in units and magnitudes between data, the collected indicator data are all standardized to eliminate the influence of units in data calculations. The standardized indicator data ranges from 0 to 1.

[0026] In addition, the implementation frequency of data collection for different dimensions of indicators can be set by the implementer according to the specific implementation situation, and no specific limit is made here.

[0027] Thus, this embodiment obtained daily vagal nerve discharge frequency sequences, EEG alpha wave power sequences, IL-6 content sequences, NE content sequences, heart rate variability sequences, and skin conductance level sequences for candidate subjects during the treatment cycle. All elements in each sequence are standardized data.

[0028] S2. The neuro-immune neurotransmitter synergistic variability was determined based on the daily vagal nerve discharge frequency subsequence, IL-6 content subsequence, NE content subsequence, and skin conductance level subsequence of the candidate subjects during the treatment period.

[0029] Here, the neuro-immune neurotransmitter synergistic variability is used at least to characterize the likelihood that candidate subjects are affected by spontaneous regulatory mechanisms during neuroimmunotherapy.

[0030] By decoupling the neuroimmunotherapy mechanism, response patterns can be extracted. The nervous system regulates immunity through neurotransmitters. The core mechanism involves the specific binding of neurotransmitters to receptors on the surface of immune cells, initiating intracellular signaling pathways and ultimately altering the activity of immune cells and the pattern of cytokine secretion. The regulation of immunity through neurotransmitters can be broken down into: upstream triggering signal → neural activation → neurotransmitter release → immune response. This mode of immune regulation exhibits a natural temporal delay.

[0031] In neuroimmunotherapy, potential triggers include external electrical stimulation and spontaneous psychological stress. Electrical stimulation excites the vagus nerve, leading to increased norepinephrine (NE) release, which in turn inhibits T cell activity and regulates immunity. Simultaneously, spontaneous psychological stress in the subject indirectly affects immunity, increasing skin conductance level (SCL) followed by increased NE release. It's worth noting that NE is a common neurotransmitter in both processes. To avoid excessive stress, IL-6 levels begin to decline approximately 30 minutes after the NE increase, allowing immune function to naturally return to normal and reducing the risk of inflammation.

[0032] Therefore, this embodiment can determine the neuro-immune neurotransmitter synergistic fluctuation of candidate subjects during the treatment period based on the vagal nerve discharge frequency subsequence, IL-6 content subsequence, NE content subsequence, and skin conductance level subsequence.

[0033] As an exemplary implementation, step S2 described above can be achieved through... Figure 2 The steps shown are as follows: S21, determine the rate of change of vagal nerve discharge frequency, IL-6 content, NE content, and skin conductance level at each time point.

[0034] In one embodiment, for any type of indicator data, assuming a uniform distribution of time intervals, the difference between the indicator data at the current moment and the indicator data at the moment before the current moment is calculated as the rate of change of the indicator data at the current moment. Each time point in the sequence can be considered the current moment; a time point is equivalent to a moment. In this embodiment, the change at the first moment is not analyzed.

[0035] In another embodiment, for any type of indicator data, the time interval between the current moment and the previous moment and the difference in indicator data are obtained, and the ratio of the difference in indicator data to the time interval is used as the rate of change of indicator data at the current moment.

[0036] Among them, any type of indicator data can be vagal nerve discharge frequency, IL-6 content, NE content, or skin conductance level. The calculation method for the rate of change of different types of indicator data is the same, and will not be elaborated here.

[0037] S22, calculate the first maximum cross-correlation function value of the vagus nerve discharge frequency change rate sequence and the skin conductance level change rate sequence to obtain the first time lag, and determine the body stress coefficient based on the deviation between the first maximum cross-correlation function value and the first time lag.

[0038] Here, the body stress coefficient can at least characterize the likelihood that candidate subjects are prone to spontaneous psychological stress when stimulated, and it is used to determine the neuro-immune neurotransmitter synergistic fluctuation of candidate subjects.

[0039] In one embodiment, the rate of change of vagal nerve discharge frequency and the rate of change of skin conductance level are first obtained at each time point during the treatment period. Then, based on the sequence of vagal nerve discharge frequency change rate and the sequence of skin conductance level change rate, a correlation function is used to determine the maximum cross-correlation function value, denoted as the first maximum cross-correlation function value. The corresponding first time lag can be determined from the first maximum cross-correlation function value. Finally, the body's stress coefficient is determined based on the deviation between the first maximum cross-correlation function value and the first time lag. The calculation methods for the maximum cross-correlation function value and the time lag are existing technologies and will not be elaborated here.

[0040] In this embodiment, if the first time lag is not zero, the ratio of the first maximum cross-correlation function value to the first time lag is used as the body stress coefficient. If the first time lag is equal to zero, it indicates that there is no significant delay between the two signal sequences during this period. In this case: the second time lag corresponding to the second largest peak in the cross-correlation function can be selected and substituted into the fractional calculation to replace the first time lag; or, the body stress coefficient can be directly determined to be the preset maximum threshold, and this moment can be recorded as a synchronous response state. The preset maximum threshold can also be the first maximum cross-correlation function value itself (i.e., the ratio is set to 1), or a configurable constant between 0.8 and 1.2 can be preset according to the actual application scenario.

[0041] It should be noted that the greater the body's stress coefficient, the greater the correlation between the rate of change in vagal nerve discharge and the rate of change in skin conductance, and the lower the lag, indicating that the subject is more likely to experience spontaneous psychological stress when stimulated, the probability of the two regulatory pathways running in parallel is greater, and the greater the synergistic fluctuation of neuro-immune neurotransmitters in the candidate subject.

[0042] S23, calculate the second maximum cross-correlation function value of the IL-6 content change rate sequence and the NE content change rate sequence to obtain the second time lag; determine the lag increase rate based on the second time lag.

[0043] In one embodiment, the rate of change of IL-6 and NE levels at each time point during the treatment period are first obtained. Then, based on the IL-6 and NE level change rate sequences, a correlation function is used to determine the maximum cross-correlation function value, denoted as the second maximum cross-correlation function value. The corresponding second time lag can be determined from the second maximum cross-correlation function value. If neurotransmitter transmission is normal, the second time lag can characterize the delay in natural regulation.

[0044] When the body's stress coefficient is high, the two triggering mechanisms correspond to two regulatory pathways operating in parallel, accelerating NE release, inducing receptor desensitization, and prolonging the natural immune regression time, i.e., increasing the second time lag. Therefore, it is necessary to determine the rate of increase in lag based on the second time lag, including: The first step is to obtain the time delay in the decrease of IL-6 levels in the target subjects. The target subjects are healthy individuals who do not require neuroimmunotherapy, and the delay time is equal to the time point from when NE levels increase to when IL-6 levels decrease.

[0045] The second step is to calculate the difference between the second time lag and the delay time, and then normalize the difference to obtain the lag increase rate.

[0046] In one embodiment, a maximum-minimum normalization method can be used for normalization. Of course, implementers can also use other normalization methods, without specific limitations. The maximum-minimum normalization can be determined by identifying the differences between the second time lag of multiple historical subjects and the IL-6 level decrease delay time of the target subject, i.e., selecting the maximum and minimum values ​​from multiple differences.

[0047] Furthermore, to improve the numerical stability of subsequent ratio calculations and avoid calculation anomalies caused by the normalized signal amplitude being too small or even approaching zero, this embodiment adds a lower bound bias rule when normalizing the rate of change sequence. The specific implementation of this lower bound bias rule is as follows: after completing the conventional normalization mapping, a preset positive bias constant is superimposed on the normalization result, ensuring that all normalized values ​​are greater than zero, thereby providing a non-zero lower bound for subsequent ratio calculations. The value of this positive bias constant should be much smaller than the effective amplitude range of the signal to ensure that it does not substantially affect the relative distribution of the original data. For example, its value can be taken as a very small amount within the normalized amplitude order of magnitude. S24, data fusion processing is performed on the body's stress coefficient and hysteresis increase rate to obtain the neuro-immune neurotransmitter synergistic fluctuation of the candidate subjects.

[0048] In one embodiment, the product of the body's stress coefficient and the rate of increase in hysteresis is determined, and the product is used as the neuro-immune neurotransmitter synergistic variability of the candidate subject.

[0049] It should be noted that the greater the synergistic variability of neuro-immune neurotransmitters, the more susceptible the individual candidate is to the influence of spontaneous regulatory mechanisms during neuroimmunotherapy, resulting in higher synergistic variability and consequently weaker treatment response in the candidate.

[0050] Thus, this embodiment obtained the neuro-immune neurotransmitter synergistic fluctuation of the candidate subjects.

[0051] S3. The central-peripheral synergy index was determined based on the daily EEG alpha wave power subsequence, skin conductance level subsequence, and heart rate variability subsequence of the candidate subjects during the pending period.

[0052] Here, the period to be determined is the baseline period and the treatment period. The central-peripheral synergy index is used at least to characterize the coordination between central sedation and peripheral stress response in candidate subjects.

[0053] The central nervous system, specifically the brain's calm state, needs dynamic coordination with the excitation level of the peripheral sympathetic nervous system; this is an important indicator of the body's homeostasis. In patients with rheumatoid arthritis, chronic inflammation disrupts this coordination. For example, when the brain should be in a calm state (corresponding to increased alpha waves), the sympathetic nervous system remains overexcited (corresponding to elevated skin conductance), leading to psychosomatic dysregulation.

[0054] Therefore, this embodiment extracts the phase relationship between EEG alpha waves and skin conductance levels to quantify the coordination between brain sedation and sympathetic nerve excitation, so as to reflect the overall regulatory effect of treatment on the nervous system and determine the central-peripheral nerve synergy index.

[0055] As an exemplary implementation, step S3 described above can be achieved through... Figure 3 The steps shown are as follows: S31, Based on the daily EEG alpha wave power subsequence and skin conductance level subsequence of the candidate subject within the pending period, determine the phase difference of the candidate subject at each time point within the pending period.

[0056] Here, the phase difference is equal to the difference between the instantaneous phase angle of the EEG alpha wave power and the instantaneous phase angle of the skin conductance level at the same time point.

[0057] In one embodiment, the daily EEG alpha wave power subsequence and skin conductance level subsequence of the candidate subject within the specified time period are extracted. The instantaneous phase angle of the EEG alpha wave power and skin conductance level at each time point is determined using Hilbert transform. The difference between the instantaneous phase angle of the EEG alpha wave power and the instantaneous phase angle of the skin conductance level at the same time point is calculated to obtain the phase difference of the candidate subject at each time point within the specified time period. The implementation process of determining the instantaneous phase angle using Hilbert transform is prior art and will not be elaborated here.

[0058] It should be noted that the phase difference can reflect the consistency of the fluctuations in the alpha wave of the brain and the skin conductance. For example, when the brain sedation signal is at its peak (phase 0°), the peripheral stress signal is at its trough (phase 180°), and the phase difference is -180°. This indicates that the two are in reverse synchronization. When sedation is enhanced, stress is weakened, which means that the current treatment is in an ideal state.

[0059] S32, determine the synchronization enhancement rate of the candidate subjects based on the phase difference at each time point within the waiting period.

[0060] Here, the synchronicity enhancement rate is used at least to characterize the coordination between the brain's sedation state and sympathetic nerve excitation, that is, the improvement of phase synchronicity by treatment, which is used to determine the central-peripheral synergy index of candidate subjects.

[0061] As an exemplary implementation, step S32 described above can be achieved through the following steps: The first step is to process the phase difference of the candidate subjects at each time point within the pending period to obtain the phase synchronization index of the candidate subjects within the pending period.

[0062] In one embodiment, the phase difference of the candidate subject at each time point within the pending period is subjected to a complex exponential transformation to obtain a complex vector at each time point within the pending period; the mean magnitude of the complex vectors at all time points within the pending period is calculated as the phase synchronization index of the candidate subject within the pending period.

[0063] The complex exponential transformation converts angular information into a unit complex vector. If the phase difference at all time points is very close, the converted complex vectors will all point in almost the same direction. Adding up these vectors with almost identical directions will result in a long average vector with a magnitude close to 1. In other words, the closer the phase synchronization exponent is to 1, the more stable the phase difference changes, i.e., the higher the synchronization. Conversely, the closer the phase synchronization exponent is to 0, the more random the phase changes, indicating poor synchronization.

[0064] It should be noted that complex exponential transformation is a standard and necessary method for handling mathematical operations on circumferential variables (such as phase and direction). By converting angles into vectors, the stability index characterizing synchronicity can be correctly calculated, that is, the phase synchronization index can be determined.

[0065] The second step is to determine the synchronization improvement rate of the candidate subjects based on their phase synchronization index during the treatment period, their phase synchronization index during the baseline period, and the phase synchronization index of the target subjects.

[0066] Referring to the process for determining the phase synchronization index of candidate subjects during the waiting period, the phase synchronization index of candidate subjects during the treatment period and during the baseline period, as well as the phase synchronization index of target subjects, can be obtained.

[0067] In one embodiment, the difference between the phase synchronization index of the candidate subject during the treatment period and the phase synchronization index during the baseline period is calculated, and the ratio of the difference between the two phase synchronization indices to the phase synchronization index of the target subject is used as the synchronization improvement rate of the candidate subject, wherein the phase synchronization index of the target subject can be equal to the average of the phase synchronization indices of multiple target subjects.

[0068] As an example, the formula for calculating the concurrency enhancement rate of candidate subjects can be: In the formula, T represents the synchronicity enhancement rate of candidate subjects. The phase synchronization index represents the target subject's... This indicates the phase synchronization index of the candidate subjects during the treatment period. This indicates the phase synchronization index of the candidate subjects during the baseline period.

[0069] In the formula for calculating the synchronization improvement rate, This indicates the difference in phase synchronization between the treatment period and the baseline period for candidate subjects. A higher phase synchronization index indicates improved synchronicity between the candidate's alpha waves and skin conductance; in healthy individuals... Used with In comparison, the range of the synchronicity enhancement rate can be limited to 0 to 1; the synchronicity enhancement rate can represent the treatment effect of candidate subjects during treatment relative to healthy individuals.

[0070] S33, determine the heart rate variability enhancement rate of the candidate subjects based on the daily heart rate variability subsequence of the candidate subjects within the pending period.

[0071] Here, the increase in heart rate variability can at least characterize the improvement in autonomic tone (HRV) during treatment, which is used to determine the central-peripheral synergy index of candidate subjects.

[0072] In one embodiment, the mean heart rate variability of the candidate subject during the treatment period and at the baseline period are calculated separately to obtain the mean heart rate variability of the candidate subject during the treatment period and the mean heart rate variability of the candidate subject at the baseline period; based on the mean heart rate variability of the candidate subject during the treatment period and the mean heart rate variability of the candidate subject at the baseline period, the heart rate variability improvement rate is determined.

[0073] As an example, the formula for calculating the rate of increase in heart rate variability in candidate subjects can be: In the formula, X represents the rate of increase in heart rate variability of the candidate subjects, and log represents the logarithmic function. This represents the mean heart rate variability of candidate subjects during the treatment period. This represents the mean heart rate variability of the candidate subjects during the baseline period.

[0074] In the formula for calculating the heart rate variability elevation rate (HRV), the larger the HRV elevation rate, the greater the likelihood of an increase in HRV. An increase in HRV can indicate an enhanced autonomic nervous system regulation capacity. Using a logarithmic function for data transformation can enhance the sensitivity to small changes in heart rate variability, thereby improving the numerical accuracy of the HRV elevation rate for candidate subjects.

[0075] S34. Data fusion processing was performed on the synchronization enhancement rate and heart rate variability enhancement rate of the candidate subjects to obtain the central-peripheral nerve synergy index of the candidate subjects.

[0076] In one embodiment, the product of the candidate subject's synchronicity enhancement rate and heart rate variability enhancement rate is calculated as the candidate subject's central-peripheral synergy index.

[0077] It should be noted that the larger the central-peripheral synergy index, the more coordinated the central sedation and peripheral stress response tend to be, and the stronger the treatment response of the candidate subjects will be.

[0078] Thus, this embodiment obtained the central-peripheral nerve synergy index of the candidate subjects.

[0079] S4. The temporal coordination index of neuroimmunity was determined based on the subsequences of vagal nerve discharge frequency and IL-6 content of candidate subjects during the treatment period.

[0080] Here, the temporal coordination index of neuroimmunity is used at least to characterize the rhythmic synchronization of vagal nerve firing frequency and IL-6 content in candidate subjects.

[0081] The circadian rhythms of the nervous and immune systems exhibit intrinsic synergy. IL-6, a key pro-inflammatory cytokine, exhibits a morning-high, evening-low rhythm that aligns with the body's morning immune defense needs, such as responding to potential pathogen accumulation at night. Conversely, the vagus nerve, a core pathway for neuroregulation of immunity, displays an evening-high, morning-low firing frequency rhythm that effectively suppresses excessive inflammation at night by enhancing nerve conduction, thus complementing the IL-6 rhythm. This pattern of weakened neural inhibition during periods of immune activity and enhanced neural inhibition during periods of immune rest is a natural manifestation of maintaining immune homeostasis and a complex, long-term expression of the balanced response to neuroimmunotherapy during treatment. Disruption of this synergy leads to a vicious cycle of pathogen accumulation and regulatory imbalance. Therefore, an effective treatment is characterized by enhanced synchronicity between these two rhythms, necessitating the extraction of temporal coordination characteristics of neuroimmunology.

[0082] As an exemplary implementation, step S4 described above can be achieved through... Figure 4 The steps shown are as follows: S41, obtain the trough of vagal nerve discharge frequency and the peak of IL-6 content for the first time during each treatment of the candidate subject during the treatment period.

[0083] For example, for any given treatment, the first trough of the vagal nerve discharge frequency occurred at 7:10 AM, while the first peak of the IL-6 content occurred at 7:00 AM, a difference of 10 minutes.

[0084] If the first occurrence of a trough or peak value cannot be detected, the time corresponding to the lowest point of the vagal nerve discharge frequency within the preset time window corresponding to the current treatment is identified as the approximate value of the trough value, and the time corresponding to the highest point of IL-6 content is identified as the approximate value of the peak value. These are used as substitute times for subsequent comparisons.

[0085] S42. Determine the rhythm consistency index of candidate subjects based on the trough and peak times corresponding to each treatment.

[0086] Here, the rhythm consistency index is used at least to characterize the rhythmic interaction features of the nervous and immune systems, and it is used to determine the temporal coordination index of the neuroimmune system.

[0087] As an exemplary implementation, step S42 described above can be achieved through the following steps: The first step is to calculate the time difference between the peak and trough times during the same treatment.

[0088] In one embodiment, the difference between the peak time and the trough time corresponding to the same treatment is calculated to obtain the time difference for each treatment.

[0089] The second step involves processing the data at each time difference using a circular mean calculation method to obtain the circular mean of the candidate subjects.

[0090] In one embodiment, since the treatment period is periodic, the time difference between each treatment is calculated using a ring mean: first, the time difference is converted into radians, such as... In radians; for each time difference in radians, calculate its sine and cosine values ​​to obtain the mean of the sine and cosine values ​​for all time difference radians; using the four-quadrant arctangent function, determine the correct quadrant based on the signs of the mean sine and cosine values ​​to obtain a quadrant located in... The values ​​between these values ​​are denoted as the average angle; finally, the average angle is converted back from radians to the original data unit to obtain the circular mean. The calculation method and specific implementation process of the circular mean are existing technologies and will not be elaborated here.

[0091] It should be noted that by using a circular mean, the distortion of the time difference across midnight by the linear mean can be avoided. For example, the time difference between 23:00 and 1:00 is 2 hours according to the circular mean, instead of 22 hours according to the nonlinear calculation.

[0092] The third step is to normalize the circumferential mean of the candidate subjects and determine the normalized value as the rhythm consistency index of the candidate subjects.

[0093] In one embodiment, the circumscribed mean of candidate subjects is normalized using a maximum-minimum normalization method to obtain a normalized value, which is then used as the rhythm consistency index of the candidate subjects. The maximum and minimum values ​​in the maximum-minimum normalization can be obtained from the circumscribed means of multiple historical subjects.

[0094] It should be noted that the rhythmic interaction between the nervous and immune systems is not a match at a single point in time, but a dynamic coupling throughout the entire circadian cycle. Therefore, it is necessary to divide the entire treatment process into intervals and determine the coupling strength of candidate subjects based on the division results, as shown in steps S43 to S44 below.

[0095] S43, divide the vagus nerve discharge frequency subsequence and IL-6 content subsequence of the candidate subjects during the treatment period into intervals to obtain each first value interval and each second value interval.

[0096] Here, the first value range is the value range of the vagal nerve discharge frequency subsequence, and the second value range is the value range of the IL-6 content subsequence.

[0097] In one embodiment, the vagus nerve discharge frequency subsequence and IL-6 content subsequence during the treatment period are updated to obtain new vagus nerve discharge frequency subsequence and new IL-6 content subsequence for each day.

[0098] Specifically, the daily vagus nerve firing frequency subsequence and IL-6 content subsequence of candidate subjects during the treatment period were converted into data sequences at 24 time points, i.e., one vagus nerve firing frequency and IL-6 content corresponding to each hour. For example, the average of the vagus nerve firing frequency and IL-6 content at all time points corresponding to each hour was calculated to obtain the new vagus nerve firing frequency subsequence and new IL-6 content subsequence corresponding to each day.

[0099] In one embodiment, the new vagus nerve discharge frequency subsequence and the new IL-6 content subsequence corresponding to each day are divided into intervals to obtain each first value interval and each second value interval corresponding to each day.

[0100] Specifically, the two new sequences are discretized, and the range of values ​​for vagal nerve firing frequency and IL-6 content is divided into several intervals. Specifically, the difference between the maximum and minimum values ​​in the new sequences is calculated, and then the ratio of the difference between the maximum and minimum values ​​to the preset number of intervals is calculated to obtain the boundary of each interval of the two new sequences. Then, the new sequences are divided using the interval boundaries to obtain the first value interval and the second value interval corresponding to each day.

[0101] Among them, the number of preset equal intervals for a new sequence can be set to 10. At this time, the new vagal nerve discharge frequency subsequence corresponding to each day has 10 value intervals, which are called the first value interval; the new IL-6 content subsequence also has 10 value intervals, which are called the second value interval; the determination of the first value interval and the second value interval can also be achieved through other calculation methods, which are not specifically limited here.

[0102] S44. Determine the coupling strength of the candidate subjects based on each first value interval and each second value interval.

[0103] Here, the coupling strength is used at least to characterize the degree of proximity of the nonlinear correlation between two rhythms (i.e., the rhythms corresponding to vagal nerve firing frequency and IL-6 content) and the state of health.

[0104] As an exemplary implementation, step S44 described above can be achieved through the following steps: The first step is to combine each of the first and second value intervals for the same day in pairs to obtain the corresponding interval combinations for each day during the treatment period.

[0105] In one embodiment, the first value intervals and the second value intervals corresponding to the same day are randomly paired to obtain interval combinations. An interval combination includes one first value interval and one second value interval.

[0106] The second step is to determine the joint probability distribution of each interval combination for each day based on the frequency of occurrence of each interval combination during the treatment period, and then determine the marginal probability distribution for each day.

[0107] In one embodiment, the occurrence frequency of each interval combination during the treatment period is obtained by statistically analyzing the interval combinations corresponding to each day. Based on the occurrence frequency of each interval combination, the number of days in the treatment period, and the number of elements in the new sequence, the joint probability distribution of each interval combination corresponding to each day is determined, and the marginal probability distribution corresponding to each day is determined based on the joint probability distribution.

[0108] As an example, the formula for calculating the joint probability distribution of the interval combination corresponding to the i-th first value interval and the j-th second value interval can be: In the formula, Let represent the joint probability distribution of the interval combinations corresponding to the i-th first value interval and the j-th second value interval. This represents the number of times the interval combination corresponding to the i-th first value interval and the j-th second value interval appears during the treatment period, m represents the number of days in the treatment period, and n represents the number of elements in the new vagus nerve discharge frequency subsequence or the new IL-6 content subsequence.

[0109] In obtaining the joint probability distribution Then, the marginal probability distribution of the i-th first value interval is further obtained. Marginal probability distribution of the j-th second value interval The calculation process for the marginal probability distribution is existing technology and will not be elaborated here.

[0110] The third step is to determine the mutual information value between the sequence formed by all joint probability distributions corresponding to the same day and the sequence formed by all marginal probability distributions.

[0111] In one embodiment, the joint probability distribution sequence and marginal probability distribution sequence for each day are obtained, and the mutual information value between the joint probability distribution sequence and the marginal probability distribution sequence for the same day is calculated. The calculation process for the mutual information value is prior art and will not be elaborated here.

[0112] The fourth step is to normalize the average mutual information value for all days, and use the normalized value as the coupling strength of the candidate subjects.

[0113] In one embodiment, the average of all mutual information values ​​during the treatment period is calculated, and the average mutual information values ​​for all days are normalized using max-min normalization. The normalized value is then used as the coupling strength of the candidate subject. The maximum and minimum values ​​in the max-min normalization can be obtained by averaging the mutual information values ​​of multiple historical subjects during the treatment period.

[0114] In another preferred embodiment, the average mutual information value of all days during the treatment period is calculated as the first average value; the average mutual information value of the target subject over a certain period is obtained in the same way as the second average value; the ratio of the first average value and the second average value is normalized to obtain the coupling strength of the candidate subject. The period corresponding to the target subject is consistent with the number of days in the treatment period of the candidate subject, and the normalization process can be consistent with the processing process in the previous embodiment.

[0115] S45, the rhythm consistency index and coupling strength of candidate subjects are fused to determine the temporal coordination index of neuroimmunity.

[0116] In one embodiment, the temporal coordination index of neuroimmunity is determined based on the product of the candidate subject's rhythm consistency index and coupling strength. As an example, the formula for calculating the temporal coordination index of neuroimmunity can be: In the formula, S represents the temporal coordination index of the neuroimmune system of the candidate subject, L represents the rhythm consistency index of the candidate subject, and O represents the coupling strength of the candidate subject.

[0117] It should be noted that the values ​​of the rhythm consistency index and coupling strength are both between 0 and 1. If two data points between 0 and 1 are directly multiplied, the value of the timing coordination index will become smaller and smaller. However, the square root calculation can avoid this.

[0118] Thus, this embodiment obtained the temporal coordination index of the neuroimmunity of the candidate subjects.

[0119] S5. Data fusion processing was performed on the neuro-immune neurotransmitter synergistic fluctuation, central-peripheral nerve synergistic index, and temporal coordination index of the candidate subjects to obtain the treatment response indicators of the candidate subjects.

[0120] Here, the treatment response index is used at least to characterize the responsiveness of candidate subjects during the treatment process. The larger the treatment response index, the stronger the treatment effectiveness of the candidate subject.

[0121] The hallmark of effective treatment is the system's shift from disorder to equilibrium. Therefore, it is crucial to uncover potential characteristics reflecting whether neuroimmunotherapy activates homeostatic regulation by analyzing the correlation features of factors across different dimensions. This involves identifying the neuro-immune neurotransmitter synergistic fluctuation, the central-peripheral nerve synergistic index, and the temporal coordination index of neuroimmunity. Typically, effective neuroimmunotherapy reduces synergistic fluctuation, stabilizes the central-peripheral nerve synergistic index, and enhances the temporal coordination index, ultimately leading to homeostasis.

[0122] As an example, the formula for calculating the treatment response of candidate subjects can be: In the formula, Z represents the treatment responsiveness of the candidate subject, W represents the central-peripheral nerve synergy index of the candidate subject, S represents the temporal coordination index of the neuroimmune system of the candidate subject, and D represents the neuro-immune neurotransmitter synergy volatility of the candidate subject. The volatility in the treatment responsiveness calculation formula cannot be zero.

[0123] In the formula for calculating treatment responsiveness, the central-peripheral synergy index reflects the dynamic balance between cerebral sedation and peripheral stress, while the temporal coordination index reflects the long-term rhythmic synergy of neuroimmune responses. Higher values ​​of both indicate better responsiveness to neuroimmune therapy. Furthermore, the neuro-immune neurotransmitter synergy fluctuation is an early marker of homeostasis disruption. The larger the value of D and the smaller the value of D, the greater the treatment response index of the candidate subject.

[0124] Next, to facilitate comparison and determination of response levels, the treatment response of candidate subjects was normalized, including: A large number of subjects who received treatment were obtained. The treatment responsiveness of each sample was obtained according to the above treatment responsiveness determination process. The maximum and minimum values ​​of the treatment responsiveness of all samples were taken. The range of the maximum and minimum values ​​was used to normalize the treatment responsiveness of the candidate subjects to obtain the treatment response index of the candidate subjects.

[0125] After obtaining the treatment response indicators of candidate subjects, a method for analyzing neuroimmunotherapy response assessment data also includes: The first step is to set the threshold values ​​for the first and second response metrics. The threshold value for the first response metric is lower than the threshold value for the second response metric.

[0126] In one embodiment, the first response indicator threshold can be set to 0.3 and the second response indicator threshold can be set to 0.7. The implementer can set the values ​​of the first response indicator threshold and the second response indicator threshold according to the specific actual situation, and no specific limitation is made here.

[0127] The second step is to compare the treatment response indicators of the candidate subjects with the threshold values ​​of the first response indicator and the second response indicator to determine the response level of the candidate subjects.

[0128] In one embodiment, when a candidate subject's treatment response index is less than a first response index threshold, the candidate subject is determined to be at the first response level; when a candidate subject's treatment response index is greater than or equal to the first response index threshold and less than or equal to a second response index threshold, the candidate subject is determined to be at the second response level; and when a candidate subject's treatment response index is greater than the second response index threshold, the candidate subject is determined to be at the third response level. The response effect of the first response level is lower than that of the second response level, and the response effect of the second response level is lower than that of the third response level.

[0129] It should be noted that if a candidate subject is at the first response level, it indicates an abnormal response to neuroimmunotherapy, meaning that homeostasis has not improved and there may be neurotransmitter regulation disorders or neuro-immune disconnect, requiring consideration of changing the treatment plan. If a candidate subject is at the second response level, it indicates a partial response to neuroimmunotherapy, meaning that homeostasis has partially recovered but there are shortcomings (such as fluctuation control meeting the target but insufficient rhythm coordination), requiring targeted optimization of treatment. If a candidate subject is at the third response level, it indicates a high response to neuroimmunotherapy, meaning that the neuro-immune network is close to homeostasis, with small fluctuations, good neural coordination, and rhythm synchronization, indicating that the treatment is effective.

[0130] Thus, this embodiment achieves the assessment of the neuroimmunotherapy response of candidate subjects.

[0131] In summary, this invention provides a data analysis method for assessing the responsiveness of neuroimmunotherapy. First, by calculating synergistic fluctuations, it quantifies the temporal regulatory delays and fluctuation correlations between neurotransmitters and immune factors, filling the gap in analyzing the contradictions in neurotransmitter-immune dual-channel regulation and improving the numerical accuracy and reliability of subsequent treatment response indicators determined based on synergistic fluctuations. Second, through phase synchronization analysis of EEG alpha waves and skin conductance, it quantifies the dynamic coordination between central sedation and peripheral sympathetic excitation, reflecting the overall neuroregulatory effect of treatment. Next, by capturing the synchronicity of IL-6 and key nodes of vagal nerve rhythms, as well as the nonlinear correlation across the entire diurnal cycle, it achieves a comprehensive assessment of the long-term synergistic state of neuroimmune rhythms. Finally, by integrating synergistic patterns and combining neuroimmune neurotransmitter synergistic fluctuations, central-peripheral nerve synergistic indices, and temporal coordination indices, it maps the transition of the neuroimmune network from disorder to homeostasis, reflecting the neuroimmune treatment responsiveness of candidate subjects. The treatment response indicators for candidate subjects determined by this invention can effectively improve the accuracy and reliability of responsiveness assessment.

Claims

1. A method for analyzing data on the response to neuroimmunotherapy, characterized in that, Includes the following steps: The vagus nerve discharge frequency sequence, EEG alpha wave power sequence, IL-6 content sequence, NE content sequence, heart rate variability sequence, and skin conductance level sequence of the candidate subjects were obtained daily during the treatment cycle; the treatment cycle included the baseline period and the treatment period. The neuro-immune neurotransmitter synergistic variability was determined based on the daily vagal nerve discharge frequency subsequence, IL-6 content subsequence, NE content subsequence, and skin conductance level subsequence of the candidate subjects during the treatment period; the neuro-immune neurotransmitter synergistic variability was used at least to characterize the possibility that the candidate subjects were affected by spontaneous regulatory mechanisms during neuroimmunotherapy. The central-peripheral synergy index is determined based on the daily EEG alpha wave power subsequence, skin conductance level subsequence, and heart rate variability subsequence of the candidate subjects during the pending period; the pending period is the baseline period and the treatment period, and the central-peripheral synergy index is used at least to characterize the coordination between central sedation and peripheral stress response of the candidate subjects; The temporal coordination index of neuroimmunity was determined based on the daily vagal nerve firing frequency subsequence and IL-6 content subsequence of the candidate subjects during the treatment period; the temporal coordination index was used to characterize at least the rhythmic synchronization of the vagal nerve firing frequency and IL-6 content of the candidate subjects. The neuro-immune neurotransmitter synergistic fluctuation, central-peripheral nerve synergistic index, and temporal coordination index of the candidate subjects are fused to obtain the treatment response indicators of the candidate subjects.

2. The method for analyzing data on the response to neuroimmunotherapy according to claim 1, characterized in that, The determination of neuro-immune neurotransmitter synergistic fluctuation includes: Determine the rate of change of vagal nerve firing frequency, IL-6 content, NE content, and skin conductance level at each time point; The first maximum cross-correlation function value of the vagus nerve discharge frequency change rate sequence and the skin conductance level change rate sequence is calculated to obtain the first time lag. The body stress coefficient is determined based on the deviation between the first maximum cross-correlation function value and the first time lag. Calculate the second maximum cross-correlation function value of the IL-6 content change rate sequence and the NE content change rate sequence to obtain the second time lag; determine the lag increase rate based on the second time lag; The stress coefficient of the body and the hysteresis increase rate are fused to obtain the neuro-immune neurotransmitter synergistic fluctuation of the candidate subjects.

3. The method for analyzing data on the response to neuroimmunotherapy according to claim 2, characterized in that, The step of determining the lag increase rate based on the second time lag includes: The time delay in the decrease of IL-6 levels in the target subject is obtained; the target subject is a healthy human body that does not require neuroimmunotherapy, and the time delay is equal to the time point from when the NE level increases to when the IL-6 level decreases. Calculate the difference between the second time lag and the delay time, and normalize the difference to obtain the lag increase rate.

4. The method for analyzing data on the response to neuroimmunotherapy according to claim 3, characterized in that, The determination of the central-peripheral nerve synergy index includes: Based on the daily EEG alpha wave power subsequence and skin conductance level subsequence of the candidate subject within the pending period, the phase difference of the candidate subject at each time point within the pending period is determined; the phase difference is equal to the difference between the instantaneous phase angle of the EEG alpha wave power and the instantaneous phase angle of the skin conductance level at the same time point; The synchronization enhancement rate of the candidate subjects is determined based on the phase difference at each time point within the waiting period; the synchronization enhancement rate is used to characterize at least the coordination between the brain sedation state and sympathetic nerve excitation. The heart rate variability improvement rate of the candidate subjects is determined based on the daily heart rate variability subsequences during the treatment period; the heart rate variability improvement rate is determined by the difference between the heart rate variability of the candidate subjects during the treatment period and the heart rate variability at the baseline period. The synchronization enhancement rate and heart rate variability enhancement rate of the candidate subjects are fused to obtain the central-peripheral nerve synergy index of the candidate subjects.

5. The method for analyzing data on the response to neuroimmunotherapy according to claim 4, characterized in that, The determination of the synchronization enhancement rate of candidate subjects includes: The phase difference of the candidate subjects at each time point within the pending period is processed to obtain the phase synchronization index of the candidate subjects within the pending period. The synchronization improvement rate of the candidate subjects was determined based on the phase synchronization index of the candidate subjects during the treatment period, the phase synchronization index during the baseline period, and the phase synchronization index of the target subjects.

6. The method for analyzing data on the response to neuroimmunotherapy according to claim 5, characterized in that, The process of obtaining the phase synchronization index of the candidate subject within the pending period includes: The phase difference of the candidate subject at each time point within the undetermined period is subjected to a complex exponential transformation to obtain the complex vector at each time point within the undetermined period. The mean magnitude of the complex vector at all time points within the pending period is calculated as the phase synchronization index of the candidate subject within the pending period.

7. The method for analyzing data on the response to neuroimmunotherapy according to claim 6, characterized in that, The determination of the temporal coordination index of neuroimmunity includes: Obtain the trough of vagal nerve discharge frequency and the peak of IL-6 content at the first treatment of each candidate subject during the treatment period; Based on the trough and peak times corresponding to each treatment, the rhythm consistency index of the candidate subjects is determined; The vagus nerve discharge frequency subsequence and IL-6 content subsequence of the candidate subjects during the treatment period were divided into intervals to obtain each first value interval and each second value interval; the first value interval is the value interval of the vagus nerve discharge frequency subsequence, and the second value interval is the value interval of the IL-6 content subsequence. The coupling strength of the candidate subjects is determined based on each first value interval and each second value interval; The rhythm consistency index and coupling strength of the candidate subjects are fused to determine the temporal coordination index of neuroimmunity.

8. The method for analyzing data on the response to neuroimmunotherapy according to claim 7, characterized in that, The determination of the rhythm consistency index of candidate subjects includes: Calculate the time difference between the peak time and the trough time corresponding to the same treatment session; The circular mean of the candidate subjects is obtained by processing the data of each time difference using the circular mean calculation method. The circumferential mean of the candidate subjects is normalized, and the normalized value is determined as the rhythm consistency index of the candidate subjects.

9. The method for analyzing data on the response to neuroimmunotherapy according to claim 7, characterized in that, The determination of the coupling strength of candidate subjects includes: By combining each first value interval and each second value interval of the same day in pairs, we can obtain the corresponding interval combinations for each day during the treatment period. Based on the frequency of occurrence of each interval combination each day during the treatment period, the joint probability distribution of each interval combination each day is determined, and then the marginal probability distribution of each day is determined. Determine the mutual information value between the sequence formed by all joint probability distributions corresponding to the same day and the sequence formed by all marginal probability distributions; The average value of the mutual information over all days is normalized, and the normalized value is used as the coupling strength of the candidate subjects.

10. The method for analyzing data on the response to neuroimmunotherapy according to claim 1, characterized in that, After obtaining the treatment response indicators for candidate subjects, the following are also included: Set a first response indicator threshold and a second response indicator threshold; the first response indicator threshold is less than the second response indicator threshold. When the treatment response index of a candidate subject is less than the first response index threshold, the candidate subject is determined to be in the first response level. When a candidate subject's treatment response index is greater than or equal to the first response index threshold and less than or equal to the second response index threshold, the candidate subject is determined to be in the second response level. When a candidate subject's treatment response index is greater than the second response index threshold, the candidate subject is determined to be in the third response level. The response effect of the first response level is lower than that of the second response level, and the response effect of the second response level is lower than that of the third response level.

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