FNIRS dynamic brain entropy-based depression targeted nerve regulation method and system

Abstract

The invention relates to the technical field of intelligent medical adjuvant therapy, in particular to a depression targeted nerve regulation method and system based on fNI RS dynamic brain entropy, and aims to overcome the defects that an existing nerve regulation method is fixed in stimulation parameter, lacks real-time feedback, depends on subjective evaluation and the like. According to the method, a multichannel near infrared spectrum optical brain imaging device is adopted to collect a time sequence signal of prefrontal lobe resting hemoglobin; calculating the time sequence signal of the prefrontal lobe static hemoglobin to obtain a static entropy feature and a dynamic entropy feature; calculating a brain entropy ratio according to the static entropy feature and the dynamic entropy feature; and matching the brain entropy ratio with a preset brain entropy threshold value, and adjusting nerve regulation and control stimulation parameters according to the corresponding brain entropy threshold value. Through the brain entropy real-time feedback closed-loop TMS regulation and control technology, the quantitative relation between the brain entropy and the stimulation parameters is established, and a stimulation parameter dynamic adjustment method is provided for the nerve regulation and control technology.

Description

Technical Field

[0001] The present application relates to the field of intelligent medical auxiliary treatment technology, and in particular to a method and system for targeted neural regulation of depression based on fNIRS dynamic brain entropy. Background Art

[0002] Currently, the clinical treatment of depression faces severe challenges. Although the neuromodulatory technology Transcranial Magnetic Stimulation (TMS) can be applied to the treatment of MDD, traditional neuromodulatory technology treatment plans have obvious shortcomings: the fixed parameter stimulation mode fails to take into account individual differences, resulting in 30-40% of patients not responding to treatment; the treatment process lacks real-time brain activity monitoring means, the quantitative relationship between stimulation parameters and changes in neural plasticity has not yet been clarified, and the efficacy evaluation still relies on subjective scales with poor accuracy.

[0003] In recent years, researchers have made important breakthroughs in the study of the pathological mechanisms of MDD. Several studies have shown that MDD patients have characteristic abnormalities in brain entropy in the dorsolateral prefrontal cortex (DLPFC), which is mainly manifested as a significant low entropy state. This abnormal state is closely related to the rigidity of neural activity, specifically manifested as decreased cognitive flexibility and overactivation of the default mode network. More notably, the study found that dynamic entropy characteristic parameters have been confirmed to be predictive indicators of treatment response. This important discovery has laid a solid theoretical foundation for the development of precise neural regulation technology based on real-time monitoring of brain entropy.

[0004] Currently, there are two key flaws in combining real-time brain entropy monitoring technology with neuromodulation technology: first, real-time brain entropy quantification technology has not yet been realized, and traditional analysis methods rely on offline calculations (with a delay of more than 1 minute), which cannot meet the needs of clinical immediate intervention; second, there is currently a lack of a closed-loop control system that dynamically links brain entropy feedback with TMS parameters.

[0005] In response to these technical bottlenecks, there is an urgent need to develop a new technology that integrates real-time fNIRS brain entropy monitoring and adaptive TMS regulation to solve the key technical bottlenecks in the current field of depression diagnosis and treatment. Summary of the Invention

[0006] The embodiments of this specification provide a depression-targeted neuromodulation system and method based on fNIRS dynamic brain entropy, which is used to solve the following technical problems in the prior art: the existing neuromodulation technology has fixed transcranial magnetic stimulation parameters, the selection of stimulation parameters is highly subjective, and there is a lack of real-time brain activity monitoring methods during transcranial magnetic stimulation.

[0007] The embodiments of this specification adopt the following technical solutions:

[0008] According to a first aspect, the present application provides a method for targeted neuromodulation of depression based on fNIRS dynamic brain entropy, which comprises the following steps:

[0009] Step S1: using a multi-channel near-infrared spectroscopy optical brain imaging device to collect the prefrontal cortex resting-state hemoglobin time series signal;

[0010] Step S2: Calculating the prefrontal lobe resting-state hemoglobin time series signal to obtain a static entropy feature of the oxygenated hemoglobin time series signal and a dynamic entropy feature of the oxygenated hemoglobin time series signal, wherein the dynamic entropy feature at least includes a coefficient of variation;

[0011] Step S3: dividing the dynamic entropy feature of the oxygenated hemoglobin time series signal by the static entropy feature of the oxygenated hemoglobin time series signal to obtain a brain entropy ratio;

[0012] Step S4: Match the brain entropy ratio with a preset brain entropy threshold to obtain a corresponding brain entropy threshold, and adjust the neural regulation stimulation parameters according to the brain entropy threshold.

[0013] Furthermore, in step S2, the prefrontal resting hemoglobin time series signal is calculated to obtain the static entropy characteristics of the oxygenated hemoglobin time series signal and the dynamic entropy characteristics of the oxygenated hemoglobin time series signal, specifically including:

[0014] Preprocessing the prefrontal lobe resting-state hemoglobin time series signal, wherein the preprocessing includes at least one or more of head motion removal, filtering, and physiological noise elimination;

[0015] extracting the preprocessed prefrontal resting-state hemoglobin time series signal to at least obtain an oxygenated hemoglobin time series signal;

[0016] Calculating the static entropy characteristics of the oxygenated hemoglobin time series signal according to a preset time series, wherein the preset time series includes all time series;

[0017] The sliding window method is used to dynamically segment the oxyhemoglobin time series signal according to the preset window data to obtain the oxyhemoglobin time segment signals in N consecutive overlapping time segments. The permutation entropy of the oxyhemoglobin time segment signal in each time segment is calculated to obtain the oxyhemoglobin permutation entropy sequence. The oxyhemoglobin permutation entropy sequence is statistically analyzed to extract the dynamic entropy characteristics of the oxyhemoglobin time series signal.

[0018] Preferably, the filtering process adopts band-pass filtering process with a frequency of 0.01-0.5 Hz.

[0019] Furthermore, according to the preset time series, the static entropy characteristics of the oxyhemoglobin time series signal are calculated, specifically including:

[0020] Reconstruct the time series into a phase space vector based on the preset embedding dimension and time delay;

[0021] For each phase space vector, its elements are arranged in ascending order to obtain multiple arrangement patterns;

[0022] Calculate the probability of multiple permutation patterns appearing in a time series;

[0023] The information entropy formula is used to calculate the static entropy characteristics based on probability.

[0024] Furthermore, the dynamic entropy features include the dynamic entropy mean, the dynamic entropy standard deviation, and the coefficient of variation. The dynamic entropy features of the oxyhemoglobin time series signal are divided by the static entropy features of the oxyhemoglobin time series signal to obtain the brain entropy ratio, which specifically includes:

[0025] The sum of the dynamic entropy mean, dynamic entropy standard deviation and coefficient of variation is obtained by summing the values;

[0026] The brain entropy ratio is obtained by dividing the sum of dynamic entropy by the static entropy feature.

[0027] Furthermore, the brain entropy ratio is matched with a preset brain entropy threshold to obtain a corresponding brain entropy threshold, and the neural regulation stimulation parameters are adjusted according to the brain entropy threshold, specifically including:

[0028] If the brain entropy ratio is lower than 0.8 and lasts for 10s±1s, the neuromodulation parameters are switched to iTBS mode, using intermittent theta burst stimulation, 50Hz three-phase pulses, and a stimulation intensity of 80-120% MT;

[0029] If the brain entropy ratio is above 0.9 and lasts for 30s±5s, the neuromodulation parameters are switched to cTBS mode, using continuous theta burst stimulation, setting the frequency to 5 Hz, and the stimulation intensity to 90-110% MT.

[0030] Furthermore, the fNIRS dynamic brain entropy-based targeted neuromodulation method for depression also includes:

[0031] Step S5: updating the neural regulation stimulation parameters every 5 seconds;

[0032] Repeat step S1.

[0033] According to the second aspect, the present application provides a depression-targeted neural regulation system based on fNIRS dynamic brain entropy, wherein the depression-targeted neural regulation system based on fNIRS dynamic brain entropy is used to implement the depression-targeted neural regulation method based on fNIRS dynamic brain entropy as described in any of the above items.

[0034] Furthermore, the fNIRS dynamic brain entropy-based depression-targeted neuromodulation system includes an fNIRS-DLPFC brain entropy dynamic monitoring unit, which includes at least a high-density near-infrared spectroscopy probe array, a multimodal brain entropy real-time calculation module, and a motion artifact correction subsystem.

[0035] Wherein, the high-density near-infrared spectrum probe array adopts 32 channels; and / or

[0036] The sampling rate of the multimodal entropy real-time calculation module is ≥10Hz.

[0037] Furthermore, the fNIRS dynamic brain entropy-based targeted neuroregulatory system for depression includes a neuroregulatory transcranial magnetic stimulation system, which includes at least a TBS / cTBS adaptive switching module. The TBS / cTBS adaptive switching module integrates at least a real-time stimulation strategy decision engine, a parameter automatic adjustment interface, and a safety interruption protection circuit.

[0038] Furthermore, the fNIRS dynamic brain entropy-based targeted neuroregulatory system for depression also includes a brain entropy threshold grading database, which is used to store at least preset brain entropy thresholds and a mapping relationship model between brain entropy and HAMD scale scores.

[0039] According to a third aspect, the present application provides a depression-targeted neuromodulation device based on fNIRS dynamic brain entropy, comprising: at least one processor; and

[0040] a memory communicatively connected to the at least one processor; wherein,

[0041] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the above-mentioned methods for targeted neural regulation of depression based on fNIRS dynamic brain entropy.

[0042] According to a fourth aspect, the present application provides a non-volatile computer storage medium storing computer-executable instructions, which, when executed, can implement any of the above-mentioned methods for targeted neural regulation of depression based on fNIRS dynamic brain entropy.

[0043] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects:

[0044] The embodiments of this specification provide a targeted neuromodulation method for depression based on fNIRS dynamic brain entropy. The method uses an fNIRS system to monitor the time series signal of blood oxygen content in the dorsolateral prefrontal cortex (DLPFC) in real time, adopts a sliding window method and permutation entropy to calculate dynamic brain entropy, and performs millisecond-level precise evaluation of the activity of the dorsolateral prefrontal cortex DLPFC. The static entropy feature (capturing signal complexity) and the dynamic entropy feature (CV value characterizing the stability of neural activity) are integrated to construct a highly discriminative biomarker; and an intelligent closed-loop control system is constructed to dynamically adjust the TMS parameters according to the real-time entropy value, thereby achieving precise and personalized neuromodulation of transcranial magnetic stimulation parameters. By organically combining real-time brain function status monitoring with an adaptive stimulation strategy, the efficacy and safety of TMS in treating major depression are significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0046] Figure 1 A schematic flow chart of a method for targeted neuromodulation of depression based on fNIRS dynamic brain entropy provided in one embodiment of this specification;

[0047] Figure 2 Another flowchart of the fNIRS dynamic brain entropy-based targeted neuromodulation method for depression provided in an embodiment of this specification;

[0048] Figure 3 A schematic diagram of the dynamic brain entropy calculation process of the fNIRS dynamic brain entropy-based depression-targeted neural regulation method provided in an embodiment of this specification;

[0049] Figure 4 A schematic diagram of the optode arrangement of a depression-targeted neural regulation system based on fNIRS dynamic brain entropy provided in an embodiment of this specification;

[0050] Figure 5 Schematic diagram of the hardware interconnection architecture of the fNIRS dynamic brain entropy-based depression-targeted neural regulation system provided in one embodiment of this specification. DETAILED DESCRIPTION

[0051] To make the purpose, technical solutions, and advantages of this specification more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this specification and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0052] The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0053] Near-infrared spectroscopy (fNIRS) studies have confirmed that multi-scale brain entropy of oxygenated hemoglobin (HbO) signals in the DLPFC region is significantly negatively correlated with the severity of major depressive disorder (MDD), and the detection cost is only one-tenth of that of functional magnetic resonance imaging (fMRI). GPU-accelerated algorithms can successfully reduce brain entropy calculation latency from minutes to milliseconds (<300ms), meeting the needs of immediate clinical intervention.

[0054] On the other hand, transcranial magnetic stimulation (TMS) uses pulsed magnetic fields to act on the central nervous system, altering the membrane potential of cortical nerve cells and generating induced currents. Currently, while neuromodulatory transcranial stimulation technology can be used to treat MDD, it suffers from issues such as fixed-parameter stimulation patterns that fail to account for individual differences, a lack of real-time brain activity monitoring during treatment, and a lack of clarity regarding the quantitative relationship between stimulation parameters and changes in neuroplasticity, resulting in poor accuracy.

[0055] In order to solve the above problems, the embodiments of the present application provide a targeted neuromodulation method for depression based on fNIRS dynamic brain entropy. Figure 1 A flow chart of a method for targeted neuromodulation of depression based on fNIRS dynamic brain entropy provided in an embodiment of the present application.

[0056] like Figure 1 As shown, this embodiment provides a method for targeted neuromodulation of depression based on fNIRS dynamic brain entropy, which includes the following steps:

[0057] Step S1: using a multi-channel near-infrared spectroscopy optical brain imaging device to collect the prefrontal cortex resting-state hemoglobin time series signal;

[0058] Step S2: Calculating the prefrontal lobe resting-state hemoglobin time series signal to obtain a static entropy feature of the oxygenated hemoglobin time series signal and a dynamic entropy feature of the oxygenated hemoglobin time series signal, wherein the dynamic entropy feature at least includes a coefficient of variation;

[0059] Step S3: dividing the dynamic entropy feature of the oxygenated hemoglobin time series signal by the static entropy feature of the oxygenated hemoglobin time series signal to obtain a brain entropy ratio;

[0060] Step S4: Match the brain entropy ratio with a preset brain entropy threshold to obtain a corresponding brain entropy threshold, and adjust the neural regulation stimulation parameters according to the brain entropy threshold.

[0061] In this embodiment, a 26-channel near-infrared spectroscopy optical brain imaging device (fNIRS) is preferably used to collect the prefrontal resting-state hemoglobin time series signal of the individual to be tested at a sampling frequency of ≥5 Hz.

[0062] In this embodiment, the prefrontal resting hemoglobin time series signal is calculated to obtain the static entropy characteristics of the oxygenated hemoglobin time series signal and the dynamic entropy characteristics of the oxygenated hemoglobin time series signal, specifically including:

[0063] Preprocessing the prefrontal lobe resting-state hemoglobin time series signal, wherein the preprocessing includes at least one or more of head motion removal, filtering, and physiological noise elimination;

[0064] extracting the preprocessed prefrontal resting-state hemoglobin time series signal to at least obtain an oxygenated hemoglobin time series signal;

[0065] Calculating the static entropy characteristics of the oxygenated hemoglobin time series signal according to a preset time series, wherein the preset time series includes all time series;

[0066] The sliding window method is used to dynamically segment the oxyhemoglobin time series signal according to the preset window data to obtain the oxyhemoglobin time segment signals in N consecutive overlapping time segments. The permutation entropy of the oxyhemoglobin time segment signal in each time segment is calculated to obtain the oxyhemoglobin permutation entropy sequence. The oxyhemoglobin permutation entropy sequence is statistically analyzed to extract the dynamic entropy characteristics of the oxyhemoglobin time series signal.

[0067] In this embodiment, the filtering process preferably adopts band-pass filtering process with a frequency of 0.01-0.5 Hz.

[0068] According to the prefrontal resting hemoglobin time series signal, 26 channels of oxygenated hemoglobin (HbO) time series signal, deoxygenated hemoglobin (HbR) time series signal, and total hemoglobin (HbT) time series signal can be extracted respectively. Figure 2As shown, the static entropy characteristics and dynamic entropy characteristics of the oxyhemoglobin time series signal, the static entropy characteristics and dynamic entropy characteristics of the deoxyhemoglobin time series signal, and the static entropy characteristics and dynamic entropy characteristics of the total hemoglobin time series signal can be calculated respectively. In this embodiment, the brain entropy ratio is calculated using the static entropy characteristics and dynamic entropy characteristics of the oxyhemoglobin time series signal. Of course, it is understandable that the brain entropy ratio can be calculated based on any one of the brain entropy data of oxyhemoglobin, the brain entropy data of deoxyhemoglobin, or the brain entropy data of total hemoglobin, or any two of the brain entropy data can be combined to calculate the brain entropy ratio, or the three brain entropy data can be used to comprehensively calculate the brain entropy ratio.

[0069] In this embodiment, the steps for calculating the static entropy feature specifically include:

[0070] Reconstruct the time series into a phase space vector based on the preset embedding dimension and time delay;

[0071] For each phase space vector, its elements are arranged in ascending order to obtain multiple arrangement patterns;

[0072] Calculate the probability of multiple permutation patterns appearing in a time series;

[0073] The information entropy formula is used to calculate the static entropy characteristics based on probability.

[0074] The calculation steps of the static entropy characteristics are further explained in detail as follows:

[0075] Step S3.1 Phase space reconstruction

[0076] Preset a time series X = {x1, x2, ..., x N}, select the embedding dimension m (usually 3-7) and the time delay τ (usually 1), and reconstruct the preset time series into a phase space vector: X i =(x i , x i+τ ,...,x i+(m-1)τ ), where: i=1, 2,..., N-(m-1)τ.

[0077] Step S3.2 Calculate the arrangement pattern

[0078] For each phase space vector X i , arrange its elements in ascending order to obtain the arrangement pattern π. For example, if X i =(x3, x1, x2), then the arrangement pattern is π=(3, 2, 1).

[0079] Step S3.3: Statistical probability distribution of arrangement patterns

[0080] Calculate all possible permutations π kThe probability of m! types appearing in the time series is P(π k ).

[0081] Step S3.4 Calculate the static entropy characteristics

[0082] The static entropy feature is calculated using the information entropy formula, where the formula is:

[0083]

[0084] For normalization, it is usually divided by ln(m!), the formula is:

[0085] PE norm =PE(m) / ln(m!)

[0086] Among them, PE norm The value range is: 0≤PE norm ≤1, PE norm The closer the value is to 1, the more random the signal is, and the closer it is to 0, the more regular the signal is.

[0087] Step S3.5 calculates the static entropy characteristics of the entire time series

[0088] The preset time series is selected as the entire time series, and according to the above steps S3.1 to S3.4, the static entropy characteristics of the oxygenated hemoglobin time series signal (marked as HbO_Static), the static entropy characteristics of the deoxyhemoglobin time series signal (marked as HbR_Static), and the static entropy characteristics of the total hemoglobin time series signal (marked as HbT_Static) are calculated respectively.

[0089] In this embodiment, if Figure 3 As shown, the dynamic entropy feature calculation steps specifically include:

[0090] First, the oxygenated hemoglobin (HbO) time series is dynamically segmented based on the sliding window method. For example, the window length is set to 100ms and the step length is 50ms, and the complete time series signal is divided into N consecutive overlapping time segments.

[0091] Then, the static entropy characteristics of each time segment are calculated independently to obtain the entropy value sequence (PE1, PE2, ..., PE N ).

[0092] Finally, three dynamic characteristic indicators are extracted through statistical analysis:

[0093] 1) Dynamic entropy mean (μPE) is used to reflect the overall signal complexity. The calculation formula of dynamic entropy mean is:

[0094] μPE=ΣPE i / N;

[0095] 2) The standard deviation of dynamic entropy (σPE) is used to characterize the intensity of dynamic fluctuations in brain function;

[0096] 3) Coefficient of variation. The calculation formula of the coefficient of variation is:

[0097] CV=σPE / μPE

[0098] The dynamic entropy mean, dynamic entropy standard deviation and coefficient of variation are combined as the dynamic entropy feature to obtain the dynamic entropy feature of the oxyhemoglobin time series signal.

[0099] The above calculation process is repeated synchronously to obtain the dynamic entropy characteristics of the deoxyhemoglobin time series signal and the dynamic entropy characteristics of the total hemoglobin time series signal.

[0100] In this embodiment, the dynamic entropy feature uses the coefficient of variation as the primary measurement indicator, which is equal to the dynamic entropy standard deviation divided by the dynamic entropy mean. Of course, it is understandable that the dynamic entropy feature can also use the sum of the dynamic entropy standard deviation, the dynamic entropy mean, and the coefficient of variation as the primary measurement indicator. The dynamic entropy sum is obtained by summing the dynamic entropy mean, the dynamic entropy standard deviation, and the coefficient of variation; the dynamic entropy sum is divided by the static entropy feature to calculate the brain entropy ratio.

[0101] In this embodiment, the brain entropy ratio is matched with a preset brain entropy threshold to obtain a corresponding brain entropy threshold, and the neural regulation stimulation parameters are adjusted according to the brain entropy threshold, specifically including:

[0102] If the brain entropy ratio is lower than 0.8 and lasts for 10s±1s, the neuromodulation parameters are switched to iTBS mode, using intermittent theta burst stimulation, 50Hz three-phase pulses, and a stimulation intensity of 80-120% MT;

[0103] If the brain entropy ratio is above 0.9 and lasts for 30s±5s, the neuromodulation parameters are switched to cTBS mode, using continuous theta burst stimulation, setting the frequency to 5 Hz, and the stimulation intensity to 90-110% MT.

[0104] In this embodiment, the fNIRS dynamic brain entropy-based targeted neuromodulation method for depression also includes:

[0105] Step S5: Update the neural stimulation parameters every 5 seconds, and automatically switch to safe mode in case of abnormal conditions. Abnormal conditions include: entropy mutation detection (ΔE / Δt>10% / s), local temperature monitoring (>38°C alarm), and autonomic nervous system response (HRV abnormality identification).

[0106] Repeat step S1.

[0107] The fNIRS dynamic brain entropy-based targeted neuromodulation method for depression provided in this embodiment monitors the time series signal of blood oxygen content in the dorsolateral prefrontal cortex (DLPFC) in real time through the fNIRS system, calculates dynamic brain entropy using a sliding window method and permutation entropy, and performs millisecond-level precise assessment of DLPFC activity. It integrates static entropy features (capturing signal complexity) and dynamic entropy features (CV values ​​characterizing neural activity stability) to construct a highly discriminative biomarker. It also constructs an intelligent closed-loop control system to dynamically adjust TMS parameters based on real-time entropy values, thereby achieving precise and personalized neuromodulation of transcranial magnetic stimulation parameters. By organically combining real-time brain function status monitoring with an adaptive stimulation strategy, the efficacy and safety of TMS in treating major depression are significantly improved.

[0108] The fNIRS dynamic brain entropy-based targeted neuromodulation method for depression provided in this embodiment constructs a four-level classification system: at the micro level, single-channel dynamic entropy features (6 dimensions / channel) are used to accurately capture local brain function abnormalities; at the meso level, comprehensive brain entropy features (78-dimensional static entropy features, 78-dimensional dynamic entropy features, and 156-dimensional comprehensive brain entropy features) are used; at the macro level, the ratio of dynamic brain entropy to static brain entropy is used to determine TMS stimulation parameters. This effectively addresses the problems of existing TMS treatments, such as the use of fixed stimulation parameters, disregard for individual patient differences, the lack of an objective feedback mechanism during the treatment process, the reliance on subjective scales to evaluate efficacy, the disconnection between neuromodulation and brain function status, and insufficient treatment precision.

[0109] This embodiment also provides a depression-targeted neuroregulatory system based on fNIRS dynamic brain entropy, wherein the depression-targeted neuroregulatory system based on fNIRS dynamic brain entropy is used to implement the above-mentioned depression-targeted neuroregulatory method based on fNIRS dynamic brain entropy.

[0110] In this embodiment, the fNIRS dynamic brain entropy-based targeted neuroregulatory system for depression includes an fNIRS-DLPFC brain entropy dynamic monitoring unit, which includes at least a high-density near-infrared spectral probe array, a multimodal brain entropy real-time calculation module, and a motion artifact correction subsystem.

[0111] like Figure 4 As shown, the high-density near-infrared spectroscopy probe array uses an 11-transmitting × 8-receiving photoelectrode array with a light source wavelength of 735nm-850nm, covering the prefrontal region, forming 26 measurement channels (S represents the light source photoelectrode, D represents the detector photoelectrode) and a spatial distribution of 32 prefrontal cortex channels (purple represents the light source photoelectrode, blue represents the detector photoelectrode), with a sampling rate ≥10Hz.

[0112] The multimodal entropy real-time calculation module can use a field programmable gate array to calculate static entropy, dynamic entropy mean, dynamic entropy standard deviation, and coefficient of variation.

[0113] In this embodiment, if Figure 5 As shown, the fNIRS dynamic brain entropy-based targeted neuroregulatory system for depression also includes a neuroregulatory transcranial magnetic stimulation system, which includes at least a TBS / cTBS adaptive switching module. The TBS / cTBS adaptive switching module integrates at least a real-time stimulation strategy decision engine, a parameter automatic adjustment interface, and a safety interruption protection circuit.

[0114] The neuromodulatory transcranial magnetic stimulation (TMS) system used in this embodiment has the following core parameter characteristics: the stimulation output supports a continuously adjustable frequency of 0.1-25 Hz (with a step accuracy of 0.1 Hz) and an intensity adjustment of 0-120% of the motor threshold (with a step accuracy of 1%), can generate a biphasic decaying oscillation wave with a pulse width of 280 μs, and provides three operating modes: single pulse, repetitive pulse (rTMS), and theta burst stimulation (iTBS / cTBS); the system has a spatiotemporal accuracy of ±50 μs synchronization error and a positioning error of <2 mm (when MRI is registered), a peak magnetic field intensity of 2.5 T (standard coil), and a focal depth of 1.5 -3cm adjustable; equipped with a multi-dimensional safety monitoring system, including 10Hz sampling coil temperature monitoring (38℃ warning / 40℃ emergency stop), magnetic field stability detection (fluctuation <±5%) and an emergency braking mechanism with a response time of <10ms; the device integrates Gigabit Ethernet and TTL interfaces, supports synchronization with multimodal fNIRS / EEG data (delay <5ms), and provides three preset modes: standard rTMS (1-20Hz / 80-120% MT), iTBS (50Hz burst / 70-100% MT) and cTBS (5Hz continuous / 90-110% MT).

[0115] In this embodiment, the fNIRS dynamic brain entropy-based targeted neuroregulatory system for depression also includes a brain entropy threshold grading database, which is used to store at least preset brain entropy thresholds and a mapping relationship model between brain entropy and HAMD scale scores.

[0116] In this embodiment, the fNIRS-based targeted neuromodulation system for depression uses a multimodal data fusion architecture to collect three key types of data:

[0117] Neuroimaging data: The fNIRS-DLPFC brain entropy dynamic monitoring unit captured a 26-channel DLPFC region HbO / HbR / HbT concentration time series in real time, with a sampling rate of ≥10 Hz, to ensure complete acquisition of neurovascular coupling signals.

[0118] Treatment parameter data: Completely record the historical stimulation parameters of neuromodulated transcranial magnetic stimulation (TMS), including frequency (0.1-20 Hz), intensity (80-120% MT), working mode (rTMS / iTBS / cTBS) and duration;

[0119] Clinical evaluation data: Standardized collection of HAMD scale scores and adverse events, such as headaches and epileptic auras.

[0120] (2) Preprocessing process: A three-stage noise reduction scheme is adopted: the first stage processing: multi-scale decomposition is performed using db4 wavelet transform to effectively eliminate motion artifacts and high-frequency noise; the second stage processing: 0.01-0.5Hz bandpass filtering is used to specifically remove heartbeat (about 1Hz) and breathing (0.2-0.3Hz) interference; the third stage processing: physiological noise elimination based on ICA is used to retain the effective signal components of the characteristic frequency band.

[0121] (3) Dynamic brain entropy calculation: The sliding window method (window length 100 ms, step length 50 ms) was used to calculate the static entropy characteristics (PE) of each channel, and the dynamic quotient characteristics were extracted, including: dynamic entropy mean (μPE), dynamic entropy standard deviation (σPE), and coefficient of variation (CV = σPE / μPE).

[0122] (4) Data alignment: Synchronize the entropy feature sequence with the TMS stimulation timestamp to construct input-output pairs:

[0123] Enter X t =[HbO-PE, HbR-PE, HbT-PE] t-k:t ;

[0124] Output Y t =TMS parameter t+1; (k is the lookback time window, the default is 10 seconds).

[0125] Therefore, the neural regulation stimulation parameters are adjusted based on the brain entropy characteristics.

[0126] This embodiment also provides a depression-targeted neuromodulation device based on fNIRS dynamic brain entropy, comprising: at least one processor; and

[0127] a memory communicatively connected to the at least one processor; wherein,

[0128] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned fNIRS dynamic brain entropy-based depression targeted neural regulation method.

[0129] This embodiment also provides a non-volatile computer storage medium storing computer-executable instructions, which, when executed, can implement the above-mentioned fNIRS-based dynamic brain entropy-based targeted neuromodulation method for depression.

[0130] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device and medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0131] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0132] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0136] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0137] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0138] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0139] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0140] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A targeted neuromodulation method for depression based on fNIRS dynamic brain entropy, characterized by: The following steps are involved: Step S1: using a multi-channel near-infrared spectroscopy optical brain imaging device to collect the prefrontal cortex resting-state hemoglobin time series signal; Step S2: Calculating the prefrontal lobe resting-state hemoglobin time series signal to obtain a static entropy feature of the oxygenated hemoglobin time series signal and a dynamic entropy feature of the oxygenated hemoglobin time series signal, wherein the dynamic entropy feature at least includes a coefficient of variation; Step S3: dividing the dynamic entropy characteristic of the oxygenated hemoglobin time series signal by the static entropy characteristic of the oxygenated hemoglobin time series signal to obtain a brain entropy ratio; Step S4: matching the brain entropy ratio with a preset brain entropy threshold to obtain a corresponding brain entropy threshold, and adjusting the neural regulation stimulation parameters according to the brain entropy threshold.

2. The method for targeted neural regulation of depression based on fNIRS dynamic brain entropy according to claim 1, characterized in that: The step S2 calculates the prefrontal lobe resting hemoglobin time series signal to obtain the static entropy feature of the oxygenated hemoglobin time series signal and the dynamic entropy feature of the oxygenated hemoglobin time series signal, specifically including: Preprocessing the prefrontal lobe resting-state hemoglobin time series signal, wherein the preprocessing includes at least one or more of a head motion removal process, a filtering process, and a physiological noise elimination process; Extracting the preprocessed prefrontal lobe resting-state hemoglobin time series signal to obtain at least an oxygenated hemoglobin time series signal; Calculating the static entropy characteristics of the oxyhemoglobin time series signal according to a preset time series, wherein the preset time series includes all time series; The oxyhemoglobin time series signal is dynamically segmented using a sliding window method according to preset window data to obtain oxyhemoglobin time segment signals in N consecutive overlapping time segments. The permutation entropy of the oxyhemoglobin time segment signal in each time segment is calculated to obtain an oxyhemoglobin permutation entropy sequence. The oxyhemoglobin permutation entropy sequence is statistically analyzed to extract the dynamic entropy characteristics of the oxyhemoglobin time series signal.

3. The method for targeted neural regulation of depression based on fNIRS dynamic brain entropy according to claim 2, characterized in that: Calculating the static entropy characteristics of the oxyhemoglobin time series signal according to a preset time series specifically includes: Reconstruct the time series into a phase space vector based on the preset embedding dimension and time delay; For each phase space vector, its elements are arranged in ascending order to obtain multiple arrangement patterns; Calculating the probability of the plurality of arrangement patterns appearing in the time series; The information entropy formula is used to calculate the static entropy feature according to the probability.

4. The method for targeted neural regulation of depression based on fNIRS dynamic brain entropy according to claim 1, characterized in that: The dynamic entropy feature includes a dynamic entropy mean, a dynamic entropy standard deviation, and a coefficient of variation. The dynamic entropy feature of the oxyhemoglobin time series signal is divided by the static entropy feature of the oxyhemoglobin time series signal to obtain a brain entropy ratio, which specifically includes: Summing the dynamic entropy mean, the dynamic entropy standard deviation and the coefficient of variation to obtain a dynamic entropy sum; The dynamic entropy sum is divided by the static entropy feature to obtain the brain entropy ratio.

5. The method for targeted neural regulation of depression based on fNIRS dynamic brain entropy according to claim 1, characterized in that: Matching the brain entropy ratio with a preset brain entropy threshold to obtain a corresponding brain entropy threshold, and adjusting the neural regulation stimulation parameters according to the brain entropy threshold, specifically including: If the brain entropy ratio is lower than 0.8 and lasts for 10s±1s, the neuromodulation parameters are switched to iTBS mode, using intermittent theta burst stimulation, setting 50Hz three-phase pulses, and stimulation intensity of 80-120% MT; If the brain entropy ratio is above 0.9 and lasts for 30s±5s, the neuromodulation parameters are switched to cTBS mode, using continuous theta burst stimulation, setting the frequency to 5 Hz, and the stimulation intensity to 90-110% MT.

6. The method for targeted neural regulation of depression based on fNIRS dynamic brain entropy according to claim 1, characterized in that: The method further comprises: Step S5: updating the neural regulation stimulation parameters every 5 seconds; Repeat step S1.

7. A targeted neuromodulatory system for depression based on fNIRS dynamic brain entropy, characterized by: The fNIRS dynamic brain entropy-based targeted neuroregulatory system for depression is used to implement the fNIRS dynamic brain entropy-based targeted neuroregulatory method for depression as described in any one of claims 1 to 6.

8. The fNIRS dynamic brain entropy-based targeted neuromodulatory system for depression according to claim 7, characterized in that: The system includes an fNIRS-DLPFC brain entropy dynamic monitoring unit, The fNIRS-DLPFC brain entropy dynamic monitoring unit at least includes a high-density near-infrared spectroscopy probe array, a multimodal brain entropy real-time calculation module and a motion artifact correction subsystem. Wherein, the high-density near-infrared spectrum probe array adopts 32 channels; and / or The sampling rate of the multimodal entropy real-time calculation module is ≥10 Hz.

9. The fNIRS dynamic brain entropy-based targeted neuromodulatory system for depression according to claim 7, characterized in that: The system includes a neuromodulatory transcranial magnetic stimulation system, The neuromodulatory transcranial magnetic stimulation system includes at least a TBS / cTBS adaptive switching module, which integrates at least a real-time stimulation strategy decision engine, a parameter automatic adjustment interface, and a safety interruption protection circuit.

10. The depression-targeted neural regulation system based on fNIRS dynamic brain entropy according to claim 7, characterized in that: The system also includes a brain entropy threshold classification database, The brain entropy threshold grading database is at least used to store preset brain entropy thresholds and a mapping relationship model between brain entropy and HAMD scale scores.

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