Prediction and / or prevention use of abnormal low-frequency beta neural oscillations

Abstract

The invention belongs to the field of neurobiology and neuroengineering, and provides a method for predicting and preventing dyskinesia diseases (such as Parkinson's disease) caused by dopamine system damage before clinical symptoms appear by taking abnormal low-frequency beta neural oscillation of a specific brain region as a biomarker. The method is used for predicting abnormal high-frequency beta neural oscillation occurring after the diseases (especially the period after motion symptoms appear); wherein the monitoring and intervention target brain region of the abnormal low-frequency beta oscillation is striatum, and the abnormal high-frequency beta oscillation is visible in the striatum, the motor cortex and the subthalamic nucleus.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of neurobiology and neuroengineering, and particularly relates to a use for predicting and / or preventing abnormal low-frequency beta neural oscillation. BACKGROUND

[0002] Dopamine system plays a core role in the motor control of central nervous system, especially the dopaminergic pathway from substantia nigra pars compacta (SNpc) to striatum, the functional status of which directly affects the excitatory-inhibitory balance of basal ganglia. When this pathway is damaged, the body can have a series of clinical manifestations characterized by motor dysfunction, collectively known as dopaminergic system-related movement disorders.

[0003] Among these diseases, Parkinson's disease (PD) is the most common and most studied one, and its pathological features include progressive loss of dopaminergic neurons, significant reduction of dopamine content in striatum, and abnormal neural rhythm activity of basal ganglia network. In addition to Parkinson's disease, multiple system atrophy (MSA), progressive supranuclear palsy (PSP), Lewy body dementia (DLB), drug-induced Parkinsonism, toxic injury, and vascular Parkinsonism can also be classified as dopaminergic system-related movement disorders.

[0004] Existing movement disorder diagnosis mainly relies on clinical symptom evaluation and imaging examination, and these methods have low sensitivity in the early stage of the disease, making it difficult to discover the initial stage of the pathological process in time. In addition, the lack of quantitative indicators related to disease progression that can be obtained through non-invasive or minimally invasive electrophysiological methods also limits the development of individualized treatment strategies.

[0005] Therefore, it is urgent to realize early prediction, progression monitoring and intervention evaluation of Parkinson's disease and other movement disorders. SUMMARY

[0006] The present application provides a use for predicting and / or preventing abnormal low-frequency beta neural oscillation, aiming to use abnormal low-frequency beta neural oscillation in a specific target brain area as a biomarker to predict the occurrence of clinical motor symptoms of Parkinson's disease and other dopaminergic system-related movement disorders before the occurrence of clinical motor symptoms, and to prevent related diseases through monitoring of the biomarker.

[0007] The application provides a use of abnormal low-frequency beta neural oscillation, characterized in that: the abnormal low-frequency beta neural oscillation is used for predicting and / or preventing a movement disorder disease with impaired dopamine system, and / or predicting abnormal high-frequency beta neural oscillation in the late stage of movement or after the onset of symptoms of the movement disorder disease with impaired dopamine system, wherein a target brain area where the abnormal low-frequency beta neural oscillation exists comprises a striatum; and a target brain area where the abnormal high-frequency beta neural oscillation exists comprises any one or a combination of a striatum, a motor cortex and a subthalamic nucleus.

[0008] The use provided by the application also has the characteristics that: the abnormal low-frequency beta neural oscillation is also used as a trigger signal for closed-loop intervention in a prodromal stage of the movement disorder disease with impaired dopamine system.

[0009] The use provided by the application also has the characteristics that: the target brain area where the abnormal low-frequency beta neural oscillation exists also comprises a motor cortex and / or a subthalamic nucleus.

[0010] The use provided by the application also has the characteristics that: the low-frequency beta is 10-15 Hz; and the high-frequency beta is 20-30 Hz.

[0011] The use provided by the application also has the characteristics that: the impaired dopamine system is dopamine depletion, and / or the movement disorder disease with impaired dopamine system is Parkinson's disease.

[0012] The application also provides a method for predicting and / or preventing a movement disorder disease with impaired dopamine system based on low-frequency beta neural oscillation, comprising:

[0013] (a) collecting neural electrical signals in a target brain area;

[0014] (b) analyzing a signal power spectrum and determining whether low-frequency beta oscillation is abnormally increased;

[0015] (c) when the low-frequency beta oscillation is abnormally increased, performing disease risk assessment and / or triggering closed-loop intervention.

[0016] The target brain area comprises a striatum.

[0017] The method provided by the application also has the characteristics that: the target brain area can also comprise a motor cortex and / or a subthalamic nucleus.

[0018] The method provided by the application also has the characteristics that: the closed-loop intervention comprises:

[0019] continuously detecting low-frequency beta neural oscillation power of the target brain area during the intervention process;

[0020] when the power exceeds a preset threshold, immediately triggering a regulation measure for a target neuron group,

[0021] The target neuron group is a neuron group related to the generation or maintenance of the low-frequency beta neural oscillation, preferably cholinergic interneurons in the striatum;

[0022] The regulation measure includes inhibiting the neuron group by closed-loop optogenetics to prevent or slow down movement disorders of the dopamine system and / or the occurrence of pathologically abnormal high-frequency beta oscillation, and can also include deep brain stimulation or other neural regulation methods capable of reducing low-frequency beta neural oscillation;

[0023] The trigger threshold of the closed-loop intervention can be set according to the determination standard of the abnormal low-frequency beta neural oscillation, preferably the same, but can also be adjusted according to the intervention strategy.

[0024] The method provided by the application also has the following characteristics: the determination of the abnormal low-frequency beta neural oscillation includes:

[0025] Power spectrum analysis is performed on the collected neural electrical signals to calculate the power ratio of the 10-15 Hz frequency band;

[0026] The power ratio is compared with the reference value of the individualized baseline or the healthy control database;

[0027] When the power ratio is higher than the reference value plus the standard deviation of the preset multiple, it is determined that there is an abnormal low-frequency beta neural oscillation.

[0028] Beneficial effects:

[0029] Based on the experimental observation of chronic 6-OHDA animal models and ChAT-Cre transgenic rats, it is found that the striatum (dStr) appears abnormal low-frequency beta neural oscillation in the premotor phase of movement disorders of the dopamine system (such as Parkinson's disease), and the energy increase of the early low-frequency beta can effectively predict the abnormal high-frequency beta appearing in the later stage of the movement disorders of the dopamine system and the subsequent movement disorders, that is, the application finds that the abnormal low-frequency beta neural oscillation can be effectively used as a biomarker for predicting the movement disorders of the dopamine system and the abnormal high-frequency beta neural oscillation in the later stage of the disease;

[0030] Based on the closed-loop intervention system of the biomarker, the embodiment of inhibiting the cholinergic interneurons (ChAT-positive interneurons, ChINs) by optogenetics proves that it can delay the occurrence of high-frequency beta and the decline of motor function, and can effectively prevent or slow down the development of movement disorders of the dopamine system. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1Quantitative analysis of time-frequency energy density map and low / high beta power of dStr, M1, STN in chronic 6-OHDA model with disease course staging;

[0032] Figure 2 The curve of rotarod behavior and its correlation with low / high beta power in chronic 6-OHDA model with disease course staging.

[0033] Figure 3 Regression / prediction analysis of early dStr low-beta to late high-beta in chronic 6-OHDA model.

[0034] Figure 4 Phase-locking analysis of dStr ChAT-positive neuron firing phase to low-beta in chronic 6-OHDA model (Rayleigh test).

[0035] Figure 5 Long-term effects of closed-loop optogenetic intervention based on low-beta triggering on LFP (low / high-beta) and behavioral outcomes in chronic 6-OHDA model. DETAILED DESCRIPTION

[0036] The specific embodiments of the present application are described below. For the specific methods or materials used in the examples, those skilled in the art can make routine substitutions and selections based on the technical ideas of the present application, and are not limited to the specific descriptions in the examples of the present application.

[0037] The methods used in the examples are conventional methods unless otherwise specified; the materials, reagents, etc. used are commercially available unless otherwise specified.

[0038] The relevant professional terms or words in this text are explained as follows:

[0039] 1. Dopaminergic system impairment

[0040] As used herein, "dopaminergic system impairment" refers to any condition that results in abnormal structure or function of the dopaminergic system due to neurodegeneration, vascular lesion or injury, toxic / metabolic damage, drug effect (e.g. long-term dopamine receptor antagonists), genetic factors or other causes. The impairment can manifest as one or a combination of the following (including but not limited to): reduced number of dopaminergic neurons in the substantia nigra pars compacta (SNpc); decreased dopamine content in the striatum; impaired function or expression of dopamine transporter (DAT); abnormal dopamine receptors or downstream signaling pathways; or corresponding evidence observed in histological, molecular, imaging, biochemical or electrophysiological tests. The term is defined inclusively to cover abnormalities in dopamine pathway function caused by different mechanisms.

[0041] In the present application, "dopaminergic system impairment" includes but is not limited to dopamine depletion, abnormal dopamine receptor or transporter function, functional blockage caused by drugs or toxicity, and vascular basal ganglia damage.

[0042] 2. Dopamine depletion

[0043] "Dopamine depletion" is a specific form of "dopaminergic system impairment" and refers specifically to a decrease in dopamine content or number of dopaminergic neurons, such as reduced SNpc neuron count, decreased dopamine or metabolite concentration in the striatum, or significantly reduced DAT binding. Dopaminergic system impairment or depletion can cause movement deficits, which are diseases characterized by abnormal motor control, including hypokinetic (e.g. Parkinson's disease) and hyperkinetic (e.g. Huntington's disease) disorders. Parkinson's disease (PD) is a neurodegenerative disease characterized by progressive loss of SNpc dopaminergic neurons and is a representative disease of hypokinetic movement disorder caused by dopamine depletion.

[0044] Dopamine depletion is a common pathological basis for several movement disorders, among which Parkinson's disease is the most common type of primary neurodegenerative disease, the characteristic pathology of which is progressive dopamine depletion. However, other diseases or causes can also lead to dopamine depletion and cause similar movement disorder phenotypes (including but not limited to multiple system atrophy, progressive supranuclear palsy, Lewy body disease, drug-induced Parkinsonism, toxic / metabolic injury and cerebrovascular lesions, etc.). Therefore, the biomarkers and methods of implementation described in the present application are preferably implemented in the context of Parkinson's disease, but are not limited to the strict sense of "Parkinson's disease" diagnosis, and the scope of protection includes all indications accompanied by dopamine depletion and leading to movement disorder phenotypes.

[0045] 3. Movement deficits

[0046] Definition: A group of neurological diseases or symptoms affecting motor control, manifestations including tremor, bradykinesia, muscle rigidity, involuntary movements, etc.

[0047] In this application, it mainly refers to Parkinsonian motor signs, which is the target symptom category for prediction / prevention and intervention in the present application.

[0048] 4. Biomarker

[0049] Definition: A feature or indicator (including molecular, cellular, tissue, imaging or electrophysiological indicators, etc.) that can be objectively measured and used as an indicator of normal physiological processes, pathological processes or responses to treatment.

[0050] In this application, it refers to a quantitative indicator that can be used to indicate the risk of occurrence or the state of progression of movement disorders caused by damage to the dopamine system, or it refers to a quantitative indicator that can be used to predict abnormal high-frequency beta neural oscillations after the late stage of movement or the appearance of symptoms of movement disorders caused by damage to the dopamine system, such as "abnormal low-beta oscillations" in the striatum and its derived indicators (PSD%, burst characteristics, etc.).

[0051] 5. Electrophysiological biomarker / Electrophysiological metric

[0052] Definition: A quantifiable feature obtained by recording the electrical activity of nerves or muscles (such as LFP, EEG, ECoG, single or multi-unit discharges) to reflect tissue function or pathological state.

[0053] Quantitative indices derived from LFP / EEG / ECoG / unit discharge recordings, such as %PSD in beta band, burst features, coherence, PLV, etc., are used to predict / assess disease course and trigger closed-loop intervention.

[0054] 6. Premotor phase

[0055] Definition: The stage of disease course before the appearance of clinically recognizable motor symptoms (e.g. bradykinesia, rigidity, gait abnormalities), pathological or network functional changes have already occurred but behavioral manifestations are not yet obvious.

[0056] In this application, the term "Stage 1" and the like in the application represent a stage characterized by the observation of an abnormal increase in low-beta energy in the striatum, but no significant decrease in kinematic tests such as rotarod, and a typical manifestation in the application is an abnormal increase in dStr low-beta energy without a decrease in rotarod.

[0057] This application is also referred to as early, pre-stage, etc.

[0058] 7. Late phase

[0059] This application refers to the stage of disease progression to the appearance of symptoms of impaired movement or exacerbation of symptoms of impaired movement, accompanied by an abnormal increase in high-beta in this application, which usually corresponds to Stage 3-5 (see the "Stage / Period" item).

[0060] 8. Low-frequency beta (also referred to as Low-beta, low-β, LB, or low-frequency beta, etc. in the text)

[0061] Definition: Lower sub-band of beta band (low end of beta band, the coverage of the frequency band can be 10-20 Hz, preferably 10-15 Hz).

[0062] In this application, as an early electrophysiological biomarker, it first appears in the "pre-motor phase" of animal models.

[0063] In this specification, the so-called "abnormal low-frequency beta neural oscillation" refers to neural rhythmic oscillation in the low end of the beta band, and when the power, frequency band relative energy (PSD%), burst frequency, single burst duration or duty cycle and other electrophysiological characteristics of this frequency band are statistically significantly increased or other pathological changes compared with the baseline of the same subject or matched healthy controls, it is called "abnormal high-frequency beta neural oscillation". This abnormality can be observed in the local field potential (LFP), electrocorticography (ECoG) or scalp electroencephalogram (EEG) recordings in the specific target brain area, and can be used to indicate the risk or progression status of movement disorders related to abnormal function of the dopamine system (including but not limited to dopamine depletion).

[0064] 9. High-frequency beta (also denoted as High-beta, high-β, HB, or high-frequency beta)

[0065] Definition: Higher sub-band of beta band (upper end of beta band, which can be taken as 20-30 Hz).

[0066] In this application, as a representative of late pathological beta oscillation, it is often associated with the appearance of motor symptoms (such as bradykinesia, rigidity).

[0067] In this specification, the so-called "abnormal high-frequency beta neural oscillation" refers to the neural rhythmic oscillation in the high end of the beta band. When the power, frequency band relative energy (PSD%), burst frequency, single burst duration, or duty cycle of the electrical physiological characteristics in this frequency band are statistically significantly increased or other pathological changes compared with the baseline of the same subject or matched healthy controls, it is called "abnormal high-frequency beta neural oscillation". This abnormality can be observed in a specific target brain area through local field potential (LFP), electrocorticography (ECoG), or scalp electroencephalography (EEG) recordings. The quantitative indicators include but are not limited to PSD%, burst count / duration / duty cycle of Hilbert envelope, cross-region coherence, and phase locking value (PLV). Abnormal high-frequency beta oscillation is often associated with abnormal function of dopamine system (including but not limited to dopamine depletion), and can be used as an electrophysiological marker of disease progression or the status of motor disorders (such as Parkinson-like symptoms).

[0068] 10. Local field potential (Local Field Potential, LFP)

[0069] Low-frequency neural electrical signals recorded by implanted electrodes reflect the activity of adjacent neuronal populations (postsynaptic currents, membrane currents, etc.), which are used to calculate PSD, PSD%, burst, etc.

[0070] 11. Electroencephalography (Electroencephalography, EEG) and electrocorticography (Electrocorticography, ECoG)

[0071] EEG is a non-invasive recording on the scalp, and ECoG is a subdural / cortical surface recording. It can be used as a non / semi-invasive alternative to LFP to detect beta oscillation (different spatial resolution and signal-to-noise ratio).

[0072] 12. Power spectral density (Power Spectral Density, PSD) and PSD%

[0073] PSD represents the power density of the frequency distribution of the signal; PSD% = target band integrated power E_l / total analysis bandwidth integrated power E_t x 100%, used for different stage / individual / electrode normalized comparison.

[0074] Threshold determination formula: when PSD% > μ0 + n·σ0, it is judged as abnormal,

[0075] Wherein, μ0 is the baseline / healthy mean, σ0 is the standard deviation, and n is the preset multiple (preferably 2, which can be adjusted according to application). The formula can also be used as the setting basis of the closed-loop trigger threshold (see “Threshold / trigger threshold”).

[0076] 13. Burst (β-burst)

[0077] Short high-power β-band event. Usually use band-pass Hilbert transform to get instantaneous amplitude (envelope), and define single burst with threshold and minimum duration.

[0078] In this application, the duration, frequency and duty cycle of burst can be used as important indicators for determining “abnormality”, for example, single burst > 400 ms is considered as abnormal long burst.

[0079] 14. Hilbert transform (Hilbert transform)

[0080] Extract the instantaneous phase and instantaneous amplitude (envelope) of narrowband signal, which is used to identify burst and calculate phase locking.

[0081] 15. Welch method (Welch method)

[0082] A spectrum estimation technique (windowing, windowing, overlapping averaging) to reduce variance. Preferred parameters of the embodiment: window length 1s, 50% overlap, Hamming window; can also be adjusted according to device / scene.

[0083] 16. Friedman test plus Dunn multiple comparison

[0084] Repeated measurement / multi-time point non-parametric comparison and post-correction method, used to test the difference of low-β / high-β relative to baseline in each stage (Baseline, Stages 1-5).

[0085] Statistical significance mark: *p<0.05; **p<0.01; ***p<0.005; ****p<0.0001.

[0086] 17. Rayleigh test (Rayleigh test)

[0087] Circular statistical method, test whether the phase angle distribution deviates from uniform, used to determine whether the neuronal firing is significantly locked to the beta phase

[0088] 18. Phase-Locking Value (PLV) / Coherence

[0089] An index to measure the phase / spectral synchrony of two signals, used to evaluate the synchronization and propagation characteristics of beta oscillations among dStr, M1, STN.

[0090] 19. ChAT-Cre, Cholinergic interneurons (ChINs)

[0091] ChAT-Cre: a transgenic animal line with Cre driven by choline acetyltransferase promoter; ChINs: cholinergic interneurons in striatum. In this application, eNpHR3.0 is specifically expressed in ChINs to implement optogenetic inhibition.

[0092] 20. rAAV2 / 9-EF1a-DIO-eNpHR3.0-mCherry

[0093] Recombinant AAV vector (AAV2 ITR, AAV9 capsid), EF1a promoter, DIO construct, expressing inhibitory opsin eNpHR3.0 and mCherry, for cell type-specific inhibition and tracing of ChINs.

[0094] 21. Optogenetics and closed-loop optogenetic intervention

[0095] Optogenetics: control neuronal activity with light-sensitive proteins; Closed-loop optogenetics: automatically output light stimulation triggered by real-time biomarkers (such as low-beta), achieving precise timing of inhibition / activation.

[0096] 22. TTL (Transistor-Transistor Logic) trigger signal

[0097] Digital level control signal (such as 5V TTL), used to trigger light source / stimulation device from decision module.

[0098] 23. Deep brain stimulation (DBS) and adaptive deep brain stimulation (aDBS)

[0099] DBS: implantation of electrodes to stimulate target nuclei (e.g. STN) with electrical pulses; aDBS: closed-loop DBS that dynamically adjusts the intensity / timing of stimulation based on real-time biomarkers (e.g. beta power). The present application is compatible with the strategy of using beta band as the closed-loop trigger signal and the concept of aDBS.

[0100] 24. Rotarod (Rotating rod test)

[0101] Behavioral test to assess motor coordination and balance in rodents, measured as duration on a rotating rod; very sensitive to changes in motor ability, thus used to quantify changes in motor function over the course of the disease and correlate with beta metrics.

[0102] 25. Baseline (Baseline) and Control (Control)

[0103] Baseline: reference record before intervention / lesion (used to calculate mu, sigma and set thresholds); Control: sham operation / untreated or contralateral hemisphere, to verify specificity and statistical significance.

[0104] 26. 6-OHDA (6-hydroxydopamine) model

[0105] Definition: neurotoxin 6-hydroxydopamine selectively destroys dopaminergic neurons, used to construct a Parkinson's-like animal model.

[0106] In the present application, chronic, low-dose, intermittent injections are used to simulate progressive dopamine loss, i.e. to establish a chronic dopamine depletion PD rat model (chronic dopamine (DA) degeneration), thereby reproducing the staging changes from prodromal to clinical stages.

[0107] 27. SPF (Specific Pathogen Free)

[0108] Animal facility / animal level, refers to not carrying the specified pathogen, to ensure the repeatability and safety of the experiment.

[0109] 28. Closed-loop stimulation

[0110] Closed-loop stimulation: a mode of neural stimulation that is automatically triggered based on real-time detection of biomarkers (e.g. abnormally low frequency beta or burst); this mode uses the output of the detection module as a trigger condition, and implements electrical or optical stimulation on the target tissue when the pre-set decision rule is met, aiming to specifically suppress or intervene pathological brain electrical activity. Closed-loop stimulation usually includes five modules of detection, decision, trigger, stimulation and feedback / record, and constraints are placed on parameters such as trigger delay, minimum trigger interval, trigger intensity and duration to ensure safety and effectiveness.

[0111] 29. Random delay

[0112] Control strategy of random delay of trigger after detection of trigger signal within a preset time window, to rule out non-specific effects caused by timing coupling, to verify the causality of closed-loop trigger.

[0113] 30. Dorsal striatum (dStr), primary motor cortex (M1) and subthalamic nucleus (STN)

[0114] dStr (dorsal striatum): core area of early marker and closed-loop target of the present application;

[0115] M1 (primary motor cortex): high-β related area in the middle and late stages;

[0116] STN (subthalamic nucleus): classic DBS target nucleus, high-β related area in the middle and late stages.

[0117] 31. Target neuronal population

[0118] Neuronal population related to low-β generation or maintenance, which is the direct object of intervention; preferably ChINs of dStr, and of course, other candidate neuronal populations related to low-β pathological rhythm.

[0119] 32. Stage

[0120] The present application divides the course of the disease into:

[0121] Baseline: before modeling / intervention;

[0122] Stage 1 (pre-motor prodromal phase, also known as early stage): dStr low-β abnormally elevated, behavior not significantly impaired;

[0123] Stage 2 (pre-motor prodromal phase, also known as early stage): network pathology deepens, behavior still not significantly impaired;

[0124] Stage 3-5 (post-motor phase, also known as late stage): multi-region high-β abnormalities are significantly abnormal, motor impairment is aggravated, and high-β appears in Stage 3 (first seen in dStr) accompanied by rotarod decline.

[0125] 33. Threshold / Trigger threshold

[0126] The "abnormality determination threshold" and the "closed-loop trigger threshold" can be consistent in principle, or can be adjusted moderately according to engineering / safety strategies (such as optimization of sensitivity / specificity). In the context of closed loop, it is required to continuously detect and start / stop stimulation in real time according to the threshold during the intervention process, so as to embody the closed-loop feature.

[0127] The application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0128] In the embodiments of the application, a typical animal model of PD (6-OHDA progressive dopamine depletion rats) is used, but the application is not limited to PD diagnosis or intervention, and is also applicable to all diseases accompanied by damage to the dopamine system (including but not limited to dopamine depletion) and leading to movement disorder phenotypes.

[0129] In the following embodiments, a chronic dopamine depletion PD rat model is established: male wild-type rats (wild-type rats are used for recording in the process of dopamine chronic depletion in experiments) and ChAT-cre strain transgenic rats, about 300 grams, are used. Record electrodes and drug delivery catheters are simultaneously implanted in the brain of the rats, and after recovery for a period of time after the operation, low-dose 6-OHDA is injected into the brain of the rats through the drug delivery catheter multiple times, once every 5 days, and 1.5 micrograms each time. The ChAT-cre strain transgenic rats need to be injected with rAAV2 / 9-Ef1a-DIO-eNpHR 3.0-mCherry virus into the striatum before performing the above operation (which can specifically transfect the cholinergic interneurons of the striatum, and only make the light-sensitive ion channel specifically expressed on the cell membrane of this type of neurons, and later we can specifically control the activity of this type of neurons by means of optical genetic operation by giving 593 nm wavelength laser), then the long-term local field potential LFP of each rat is recorded and the rotarod behavior is monitored, and the rats are cultured in an SPF level environment: before injecting 6-OHDA, the corresponding rats are recorded for three consecutive days, and the average value is taken as the baseline (Baseline) for comparison of each experimental data.

[0130] In the following, different stages mainly include: baseline stage (Baseline), stage 1 (Stage 1), stage 2 (Stage 2), stage 3 (Stage 3), stage 4 (Stage 4), and stage 5 (Stage 5).

[0131] In the embodiments, the relevant statistical significance markers are: *p<0.05; **p<0.01; ***p<0.005; ****p<0.0001.

[0132] Example 1 Chronic dopamine depletion model and beta oscillation changes

[0133] Objective: To verify whether there is an increase in abnormal low-frequency beta oscillation energy (PSD% increase) in the dStr, M1, and STN before the onset of motor symptoms in the chronic 6-OHDA model (chronic dopamine depletion model / chronic dopamine depletion PD rat model).

[0134] Methods: In the chronic dopamine depletion model established as described above, LFP recordings were performed daily in the rat's striatum, motor cortex, and subthalamic nucleus, along with rotarod behavioral experiments. The low-beta PSD% (10-15 Hz) was analyzed and compared to the baseline at each stage using Friedman's test with Dunn's multiple comparisons. The results are shown in Figure 1 and Figure 2 .

[0135] Figure 1 The dynamic changes in low-frequency and high-frequency beta oscillations in the PD model and the extent of damage to dopaminergic neurons are shown, mainly using Friedman's test with Dunn's multiple comparisons, which include:

[0136] A. Experimental design: Figure 1 A shows the design and timeline of the experiment. Low-dose 6-OHDA injections were performed every five days, accompanied by rotarod experiments and electroencephalographic recordings. The experiment lasted for 20 days, divided into different stages (Stage 1 to Stage 5), and LFP signals were recorded in brain regions such as the striatum (dStr), motor cortex (M1), and subthalamic nucleus (STN) during the PD progression. During the experiment, LFP signals from brain regions were used to assess the changes in low-frequency beta (10-15 Hz) and high-frequency beta (20-30 Hz) oscillation energy.

[0137] B. Band-pass filtered signals: Figure 1 B shows example signals of low-frequency and high-frequency beta oscillations. The vertical axis (Amplitude, mV) represents the amplitude of the band-pass filtered signal in millivolts (mV), which indicates the strength of different frequency oscillations. The horizontal axis (Time, seconds) represents the passage of time during the experiment in seconds (s), which is used to show the changes in low-frequency beta and high-frequency beta signals over time during the PD progression. In Figure 1 B, low-frequency beta signals (green arrows) and high-frequency beta signals (red arrows) are extracted and shown to dynamically change in energy during the PD process. It can be seen that as the PD process progresses, there is an increase in low-frequency beta oscillation energy, which then gradually transitions to an abnormal increase in high-frequency beta oscillation energy.

[0138] C. Spectrogram: Figure 1C shows the LFP spectrograms (energy density plots) of dStr, M1 and STN in different stages of PD. The vertical axis (Frequency, Hz) represents frequency, with units of Hertz (Hz), ranging from low to high frequencies (e.g., low beta, theta waves, etc.). The horizontal axis (Time, days) represents different time points or stages of the experiment, with units of days (days), ranging from baseline (before 6-OHDA injection) to Stage 5. From Figure 1 As can be seen in C, as the PD progresses, the energy of low beta oscillations gradually increases and transforms into high beta oscillations. In particular, in the dStr region, the increase in low beta oscillation energy is most significant, and it rapidly transforms into high beta oscillations in the subsequent stage (Stage 3). Overall, as PD progresses, there is an initial excessive increase in low beta energy, followed by a gradual transformation into an abnormal increase in high beta energy.

[0139] D. Quantitative analysis (PSD): Figure 1 D shows the changes in low beta and high beta power in dStr, M1 and STN through quantitative analysis. The vertical axis (Power Spectral Density, PSD (%)) represents the power spectral density, with units of percentage (%). This measures the energy distribution of the signal, showing the energy of low beta (10-15 Hz) and high beta (20-30 Hz) oscillations. The horizontal axis (Stages) represents different stages of PD progression, including baseline and different stages from Stage 1 to Stage 5.

[0140] Figure 1 The analysis in D shows that in Stage 1 (after the first injection of 6-OHDA), the energy of low beta oscillations in all brain regions significantly increased (baseline vs. Stage 1: dStr: n = 6 rats, p = 0.0274; M1: n = 6 rats, p = 0.0274; STN: n = 6 rats, p = 0.0101), and compared to the normal state (baseline):

[0141] dStr (striatum): The high beta power of 6 rats significantly increased in Stage 3 (n = 6 rats, baseline vs. Stage 3, p = 0.0274) and continued to increase, with more significant differences in Stage 4 (n = 6 rats, baseline vs. Stage 4 p = 0.0034) and Stage 5 (n = 6 rats, baseline vs. Stage 5 p = 0.0011);

[0142] M1 (motor cortex): High frequency beta power in 6 rats was significantly increased only at Stage 4 (n=6, baseline vs. Stage 4, p=0.0436) and Stage 5 (n=6, baseline vs. Stage 5, p=0.0034);

[0143] STN (subthalamic nucleus): High frequency beta power in 6 rats was significantly increased only at Stage 5 (n=6, baseline vs. Stage 5, p=0.0436), showing a later increase pattern than dStr and M1.

[0144] It can be seen that the striatum (dStr) showed a significant increase in high frequency beta oscillation energy at Stage 3 (p=0.0274), earlier than M1 and STN, and the change was more persistent. In contrast, the increase in high frequency beta oscillation energy in the motor cortex was later, and showed a significant change only at Stage 5, while the STN showed a later increase pattern than dStr and M1. These results suggest that the striatum shows an abnormal increase in high frequency beta oscillation energy in the early stages of PD development, while the motor cortex and subthalamic nucleus show this change later. This finding supports the special role of the striatum in the pathological process of PD, which may be closely related to the extent of damage to its dopaminergic neurons.

[0145] E. Comparison of low beta and high beta power in different stages

[0146] Figure 1 E. Comparison of low beta and high beta power in different stages Figure 1 D, represents the power spectral density, unit: percentage (%), used to measure the power of low frequency beta (Low Beta, LB) and high frequency beta (High Beta, HB) oscillation. The abscissa (Stages): represents the different stages of PD, used to show the power changes of low frequency beta and high frequency beta oscillation in each stage.

[0147] Figure 1Two-way ANOVA of E shows that the transition of low-frequency beta to high-frequency beta in the PD progression is a dynamic process, and the performance of this transition in different brain regions is different. In particular, in the striatum, the transition of low-frequency beta to high-frequency beta is particularly significant in the 4th and 5th stages, and this process appears relatively late in the motor cortex (M1) and the thalamic nucleus (STN): n = 6 rats; dStr: 4th stage, p = 0.0409; 5th stage, p = 0.0151; M1: 1st stage, p = 0.0074; 5th stage, p = 0.0001; STN: 4th stage, p = 0.0239.

[0148] F. Dopaminergic neuron damage

[0149] Figure 1 F. Dopaminergic neuron damage

[0150] Figure 1 F. Dopaminergic neuron damage

[0151] G. Dopamine damage ratio

[0152] Figure 1 G. Dopamine damage ratio

[0153] From Figure 1 G, it can be seen that as the PD progresses, the damage ratio gradually increases, and finally in Stage 5, the right side DA neuron damage reaches the most severe degree. This data is highly consistent with the changes in oscillatory energy in Figure 2 D and Figure 2 E, indicating that the damage of dopaminergic neurons is closely related in time to the abnormal changes in oscillatory energy.

[0154] Figure 1 F, G Both figures demonstrate that our chronic low dose (1.5 μg / injection) 6-OHDA injection protocol can mimic the slow progression of PD in clinic.

[0155] Figure 1 For the results of the Rotarod test and the changes of β oscillation power during the chronic dopamine depletion process, please see the following:

[0156] A. Schematic diagram of the Rotarod test protocol

[0157] Figure 2 A shows the experimental design of the Rotarod test during the chronic dopamine (DA) depletion process. The time schedule is consistent with Figure 2 , and the experimental procedure is consistent with Figure 2 , except that this figure shows the daily Rotarod test, which is used to evaluate the dynamic changes of motor performance of rats during the PD progression.

[0158] B. Rotarod performance and β oscillation power changes of a single rat Figure 2 B shows the Rotarod performance (blue bars, Runtime) and the changes of low frequency β (LB) and high frequency β (HB) power in dStr of a single rat during the PD progression. X-axis: Time (Days); Left Y-axis: Runtime (s) - the longer the runtime, the stronger the motor performance; Right Y-axis: Norm. PSD (%), Friedman test with Dunn's multiple comparison.

[0159] Figure 2 B shows the Rotarod performance, low β activity and high β activity of a single rat (n = 1) during the chronic DA depletion process. It can be seen that although the low β power increased significantly at Stage 1, the decline in motor performance (i.e., motor impairment) did not occur until Stage 2.

[0160] C. Statistics of Rotarod performance and power changes of all experimental rats Figure 2 C shows the statistical analysis of the motor performance and dStr low β, high β power of 7 rats at different stages. X-axis: Different stages (Baseline, Stage 1-5); Left Y-axis: Runtime (s); Right Y-axis: Norm. PSD (%), Friedman test with Dunn's multiple comparison, Figure 2 C shows that:

[0161] Motor performance (blue boxplot): significant decrease from baseline at stage 3 (p=0.0214), stage 4 (p=0.0010) and stage 5 (p<0.0001) by Friedman test with Dunn's multiple comparisons: n=7 rats, baseline vs. stage 3, p=0.0214; baseline vs. stage 4, p=0.0010; baseline vs. stage 5, p<0.0001;

[0162] dStr low beta power: significant increase from baseline at stage 1 (p=0.0274) by Friedman test with Dunn's multiple comparisons: early abnormal increase in low beta power at stage 1 (6-OHDA injection) (n=6 rats, Friedman test with Dunn's multiple comparisons: baseline vs. stage 1, p=0.0274;

[0163] dStr high beta power: significant increase from baseline at stage 3 (p=0.0274), further increase at stage 4 (p=0.0034) and stage 5 (p=0.0011), indicating that abnormal oscillations in high beta power in dStr occur in synchrony with the appearance of motor impairment (starting at stage 3): n=6 rats, baseline vs. stage 3, p=0.0274; stage 4, p=0.0034; stage 5, p=0.0011.

[0164] Figure 2 B and C illustrate that motor impairment is slow to appear during the development of PD slow depletion and is accompanied by a significant increase in high beta oscillation power in dStr (striatum); and, that a significant increase in low beta oscillation power in dStr occurs before motor impairment is apparent.

[0165] Figure 3 D-F are used to show the correlation analysis of early (Stage 1) excessive low beta power in dStr (D), M1 (E) and STN (F) regions with late (Stage 5) motor impairment, abscissa = Stage 1 low beta power (% PSD), ordinate = Stage 5 running time (s): results show a significant negative correlation between early (i.e. stage 1) low beta oscillation power and late (stage 5) motor performance, i.e. early abnormal low beta oscillation and initial motor performance are significantly correlated with stage 5 motor impairment (n=7 rats, Pearson correlation analysis shows: dStr low beta, p=0.0464; M1 low beta, p=0.0217; STN low beta, p=0.2751):

[0166] Figure 3D-F show that low-frequency beta oscillation energy in stage 1 is associated with dyskinesia in later stages.

[0167] Figure 3 G-I show the correlation analysis of dStr (G), M1 (H) and STN (I) regions full-range over-high beta power and dyskinesia. The abscissa = high beta power (% PSD), the ordinate = running time (s). The results show that the increase of high beta power is highly correlated with motor defects (n = 7 rats, Spearman correlation analysis shows that dStr high beta, p < 0.0001; M1 high beta, p < 0.0001; STN high beta, p < 0.0001). Figure 3 G-I show that high-frequency beta energy is significantly negatively correlated with motor ability, verifying that the present experiment is consistent with PD and abnormal increase of beta energy.

[0168] In summary, the present embodiment establishes a chronic dopamine depletion model by low-dose multiple 6-OHDA injections, and continuously records the LFP signals of the striatum (dStr), motor cortex (M1) and subthalamic nucleus (STN). The results show that the low-frequency beta (10-15 Hz) power increases significantly in the early stage (Stage 1), and the high-frequency beta (20-30 Hz) power increases in the middle and late stages, among which the dStr has the earliest abnormal high-frequency beta. The damage to dopaminergic neurons and the change in beta oscillation progress synchronously, suggesting that the transformation from low beta to high beta is a key dynamic feature of disease development.

[0169] Example 2 Prediction of early low beta to late high beta and dyskinesia

[0170] Objective: According to the results recorded by the model and method of Example 1, evaluate the predictive value of early (Stage 1) striatal (dStr) low-frequency beta (low-beta) power on late (Stage 3-5) high-frequency beta (high-beta) power and Rotarod motor performance decline.

[0171] Methods: Take Stage 1 dStr low beta power (PSD%, and burst characteristics) as the independent variable, and Stage 3-5 high beta power and Rotarod performance in each brain region as the dependent variable, construct a linear regression prediction model. Statistical analysis uses two-way ANOVA or linear regression, goodness of fit (Adjusted R 2 ) and p value are used to evaluate the prediction effect. The specific results are shown in Figure 3 .

[0172] A. Overview of brain region prediction relationship: Figure 3 A shows the brain region distribution of the prediction relationship between early dStr low beta power and late high beta power. The model results show that the goodness of fit (Adj. R 2The higher the β value and the smaller the p-value, the more significant the prediction effect. The results show that low β power in Stage 1 dStr can predict high β power in Stage 3–5 dStr (Adj.R.). 2 (0.730–0.926, p<0.01).

[0173] B. Predictive regression analysis results for each brain region: x-axis: Stage 1 low β power (PSD, %), y-axis: Target stage high β power (PSD, %). Figure 3 B shows the specific linear regression results:

[0174] Stage 1 low β power in dStr significantly predicted high β power in the later stage (dStr) (Stage 3: n = 9 rats, p = 0.0021; Stage 4: n = 9 rats, p = 0.0002; Stage 5: n = 9 rats, p = 2.1 × 10⁻⁶). -5 );

[0175] In stage 5, early low β power in the motor cortex (M1) and subthalamic nucleus (STN) also showed a similar predictive relationship for later high β power (M1 group: n=9, p=1.04×10⁻³; STN group: n=9, p=5.08×10⁻³). -3 )

[0176] Figure 3 A and B indicate that the low β power in the striatum can indeed predict the high-frequency β power that appears later.

[0177] Figure 3 C summarizes the prediction results of M1 and STN high β power based on the low β power of early Stage 1 dStr. It is a schematic diagram of linear regression analysis used to evaluate the predictive value of early low β power of dStr for the later high β power of M1 and STN. Figure 4 C is for Figure 4 The regression summary of D, Figure 5 D provides the specific regression curves corresponding to Figure C, with the horizontal axis representing Stage 1 low β power (PSD, %) and the vertical axis representing the target stage M1 or STN high β power (PSD, %). ​ C and D are visible:

[0178] Early Stage 1dStr low β power significantly predicts later M1 high β power: Stage 4(Adj.R 2 =0.958, p<0.01), Stage 5 (Adj.R 2 =0.876, p<0.001): Group M1: n=9, Stage 4 p=8.85×10 -3 Stage 5 p = 1.29 × 10-4 .

[0179] Early Stage 1 dStr low beta power significantly predicts later STN high beta power: Stage 4 (Adj. R 2 = 0.471, p < 0.05), Stage 5 (Adj. R 2 = 0.965, p < 0.001): STN group: n = 9, p = 0.0247 for Stage 4; p = 1.44 x 10 -6

[0180] ​ C and D show that early Stage 1 dStr low beta power abnormal oscillation can not only predict the earliest high beta power abnormal oscillation in dStr, but also predict the high beta power abnormal oscillation in M1 and STN, showing the feasibility of predicting the prodromal stage of Parkinson's disease, for example, with early Stage 1 dStr low beta power abnormal oscillation.

[0181] In summary, the present embodiment uses early (Stage 1) dStr low beta power as the independent variable, and later (Stage 3-5) high beta power and rotarod performance as the dependent variables for regression analysis. The results show that early dStr low beta significantly predicts the earliest high beta increase in this region and the later high beta abnormality in other brain regions (M1, STN), and is highly correlated with later motor function decline, proving that it can be used as an early biomarker to predict the occurrence of dopamine impairment related movement disorders.

[0182] Example 3 Closed-loop optogenetic intervention based on abnormal low beta oscillation

[0183] Objective: Use low frequency beta oscillation as a trigger signal to inhibit dStr cholinergic interneurons (ChAT+interneurons, ChINs) through closed-loop optogenetic inhibition, verify the functional role of low beta oscillation in the pathological process, and evaluate its intervention effect on the later increase in high beta oscillation energy and the decline in motor function.

[0184] Methods: AAV-DIO-eNpHR3.0 (inhibitory opsin) was injected into the dStr of ChAT-Cre line rats and optical fibers were implanted. Real-time power detection system was used to monitor low beta (10-15 Hz) power, and when the set threshold was reached, TTL signal was triggered to drive 593 nm yellow light to inhibit the discharge of ChINs (closed-loop group); the control group was randomly delayed (Random delay). LFP signals and rotarod performance were recorded, and changes in low beta and high beta (20-30 Hz) power and motor ability were analyzed. Two-way ANOVA was used in the present embodiment.

[0185] Results (Example): The closed-loop group significantly suppressed low-β in dStr upon stimulation, and long-term delayed the rise of late high-β and the decline of Rotarod function (closed-loop group n=7, overall effect significant). It can be seen that: the closed-loop suppression proves the functional role of low-β in the pathological chain, and demonstrates the feasibility of pre-motor intervention based on this marker, with the following specific results:

[0186] ​ : Early low-β phase-locked with ChAT neuron firing:

[0187] A. Experimental design schematic: showing the recording of dStr cholinergic neuron firing and detecting its phase relationship with low-β oscillation before and after Stage 1;

[0188] B. Phase distribution polar plot: Top row: phase distribution of ChAT neuron firing (columnar); Bottom row: results of Rayleigh test (dot), phase (°), spike count. Results show: Baseline (baseline) no significant phase locking (ns); Stage 1: the phase of firing is concentrated around 0°, showing significant phase locking (p<0.0001),

[0189] ​ It is shown that the firing of ChAT neurons in Stage 1 is significantly phase-locked with low-β oscillation, and in the early stage of chronic dopamine depletion, the peak firing of cholinergic interneurons in the striatum is significantly locked with abnormal low-frequency β oscillation, providing a functional target for subsequent closed-loop intervention.

[0190] Then, the abnormal increase of low-frequency β oscillation energy was taken as an indicator (biomarker) to inhibit the cholinergic interneurons in the striatum of ChAT-cre transgenic rats by closed-loop optogenetic inhibition.

[0191] ​ : Intervention effect of closed-loop optogenetic inhibition:

[0192] A. Experimental flowchart: Left: virus injection and optical fiber implantation schematic; Middle: timeline, closed-loop stimulation is implemented at Stage 0-4, each for 5 minutes; Horizontal coordinate: time (days);

[0193] B. Closed-loop control system schematic: showing the closed-loop logic flow of signal detection, power calculation, and triggering light stimulation;

[0194] C. Immediate effect of closed-loop and random stimulation on low beta power: Abscissa: brain region (dStr, M1, STN) ordinate: PSD (%), the closed-loop group significantly reduced the low beta power of dStr, M1, STN at the time of stimulation (p < 0.0001), and the random group had no significant change;

[0195] D. Change in performance on the rotating rod: Abscissa: stage (Baseline, Stage 1-5), ordinate: movement time (seconds), the results show that the closed-loop group significantly delayed the decline in motor ability during the entire PD process (Stage 3-5 performance is higher than that of the random group), and the motor ability of the random group declined faster;

[0196] E. High-frequency beta spectrum: Abscissa: time (days); ordinate: frequency (Hz); color: power intensity, the results show that the closed-loop group: high beta oscillation appears delayed and with lower amplitude at Stage 3-5; the random group: high beta is significantly increased at Stage 3

[0197] F. High-frequency beta power statistics: Abscissa: stage, ordinate: high beta PSD (%), the results show that the closed-loop group: the high beta power of dStr, M1, STN is delayed to rise significantly at Stage 5 (p < 0.05-0.01), and the random group: dStr high beta is significantly increased at Stage 3 (p < 0.01-0.0001)

[0198] The functional verification of this embodiment: at Stage 1, ChAT neuron firing is significantly phase-locked with low beta oscillation, suggesting that low beta abnormalities are closely related to the activity of ChINs; the intervention effect shows that closed-loop inhibition of ChINs can immediately reduce low beta power and significantly delay the rise of high beta power and the appearance of motor disorders in the later stage.

[0199] That is, based on the early closed-loop optogenetic intervention of abnormal low beta oscillation, the rise of pathological high-frequency beta oscillation energy and the appearance of motor disorders in the later stage can be effectively delayed, indicating that abnormal low-frequency beta neural oscillation can be used as a biomarker for preventing motor disorder diseases caused by damage to the dopamine system, and indicating that low beta oscillation is not only a pathological biomarker, but also a target for intervention in the motor prodromal stage.

[0200] In summary, this embodiment uses low beta power as a trigger signal to inhibit dStr cholinergic interneurons by closed-loop optogenetics. Closed-loop intervention can immediately reduce low beta power and significantly delay the rise of high beta power and the appearance of motor disorders in the later stage; the random stimulation group has no such effect. The results verify that abnormal low beta oscillation is not only a predictive indicator, but also a trigger signal for closed-loop intervention in the motor prodromal stage, and has the feasibility of preventive intervention in the early stage of the characteristics of motor disorder diseases caused by damage to the dopamine system.

Claims

1. Use of abnormal low-frequency beta neural oscillation, characterized in that: the abnormal low-frequency beta neural oscillation is used for: predicting and / or preventing a movement disorder with impaired dopamine system, and / or predicting abnormal high-frequency beta neural oscillation in the late stage of movement or after the onset of symptoms of a movement disorder with impaired dopamine system, wherein the target brain region where the abnormal low-frequency beta neural oscillation exists includes the striatum; the target brain region where the abnormal high-frequency beta neural oscillation exists includes any one or a combination of the striatum, the motor cortex, and the subthalamic nucleus.

2. The use according to claim 1, characterized in that: the abnormal low-frequency beta neural oscillation is also used as a trigger signal for closed-loop intervention in the prodromal stage of a movement disorder with impaired dopamine system.

3. The use according to claim 1 or 2, characterized in that: the target brain region where the abnormal low-frequency beta neural oscillation exists also includes the motor cortex and / or the subthalamic nucleus.

4. The use according to any one of claims 1-3, characterized in that: the low-frequency beta is 10-15 Hz; the high-frequency beta is 20-30 Hz.

5. The use according to any one of claims 1-4, characterized in that: the impaired dopamine system is dopamine depletion, and / or the movement disorder with impaired dopamine system is Parkinson's disease.

6. A method for predicting and / or preventing a movement disorder with impaired dopamine system based on low-frequency beta neural oscillation, comprising: (a) collecting neural electrical signals in a target brain region; (b) analyzing the signal power spectrum and determining whether the low-frequency beta oscillation is abnormally elevated; (c) when the low-frequency beta oscillation is abnormally elevated, performing disease risk assessment and / or triggering closed-loop intervention; wherein the target brain region includes the striatum.

7. The method according to claim 6, characterized in that: the target brain region can also include the motor cortex and / or the subthalamic nucleus.

8. The method according to claim 6 or 7, characterized in that: the closed-loop intervention includes: continuously detecting the low-frequency beta neural oscillation power of the target brain region during the intervention process; when the power exceeds a preset threshold, immediately triggering a control measure for the target neuron group, the target neuron group is a neuron group related to the generation or maintenance of the low-frequency beta neural oscillation, preferably cholinergic interneurons in the striatum; the control measure includes inhibiting the neuron group by closed-loop optogenetics to prevent or slow down the movement disorder with impaired dopamine system and / or pathological high-frequency beta neural oscillation, and can also include deep brain stimulation or other neural regulation methods that can reduce low-frequency beta neural oscillation; wherein the trigger threshold of the closed-loop intervention can be set according to the determination standard of abnormal low-frequency beta neural oscillation, preferably the same, but can also be adjusted according to the intervention strategy.

9. The method according to any one of claims 6-8, characterized in that: the abnormal determination of the low-frequency beta neural oscillation includes: performing power spectrum analysis on the collected neural electrical signals to calculate the power proportion in the 10-15 Hz frequency band; comparing the power proportion with the reference value of the individualized baseline or healthy control database; When the power occupation ratio is higher than the reference value plus the preset multiple of the standard deviation, it is determined that the abnormal low-frequency beta neural oscillation exists.

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