Functional brain network determination method, device and apparatus for nicotine addiction intervention

Abstract

The present invention is applicable to the field of neuroscience technology and provides a method, device and equipment for determining functional brain networks for nicotine addiction intervention. The method includes: respectively collecting resting-state EEG signals from subjects in two groups under the guidance of a smoking paradigm, preprocessing each collected resting-state EEG signal, performing EEG microstate analysis on all processed EEG signals to obtain a group microstate template and corresponding microstate characteristics, screening out statistically significant microstate transition pairs through two-way interaction analysis of a linear mixed-effects model constructed based on the microstate characteristics, performing EEG source analysis on the group microstate templates corresponding to the microstate transition pairs, and determining functional brain networks related to neurofeedback training for nicotine addiction intervention, thereby more accurately capturing the subtle changes in brain functional state before and after neurofeedback treatment, and providing more objective and quantitative neural markers for revealing its intervention mechanism and optimizing treatment strategies.

Description

Technical Field

[0001] The present invention belongs to the field of neuroscience technology, and in particular relates to a method, device and equipment for determining functional brain networks for intervening in nicotine addiction. Background Art

[0002] Neurofeedback (NF) therapy is a brain function regulation technology based on the principle of operant conditioning. It monitors an individual's EEG or brain function signals in real time and feeds them back to the subject, guiding them to actively regulate their own brain activity, thereby improving neural function and related behavioral performance. This method is essentially a closed-loop training system and is widely used in the auxiliary treatment of various neuropsychiatric diseases such as Attention-Deficit / Hyperactivity Disorder (ADHD), anxiety, depression, chronic pain and addiction.

[0003] In addiction research, neurofeedback is considered a promising non-invasive intervention, particularly suitable for modulating brain networks associated with reward, impulse control, and self-regulation, such as the default mode network (DMN), executive control network (ECN), and limbic system. Compared to traditional medications or behavioral therapies, neurofeedback can more directly affect brain function, offering advantages such as individualization, lack of side effects, and long-term plasticity. Therefore, with the advancement of neuroimaging and signal processing technologies, neurofeedback is gradually becoming a promising neuromodulatory tool for studying and intervening in addictive behaviors. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, device and equipment for determining functional brain networks for nicotine addiction intervention, aiming to solve the problem that the existing technology cannot capture the rapid dynamic changes of brain function and thus cannot effectively reveal the reorganization mechanism of functional networks related to addiction during neurofeedback intervention.

[0005] In a first aspect, the present invention provides a method for determining a functional brain network for nicotine addiction intervention, the method comprising the following steps:

[0006] resting-state EEG signals of nicotine-addicted subjects in an experimental group and a control group were collected under the guidance of a pre-established smoking paradigm, wherein each nicotine-addicted subject in the experimental group underwent neurofeedback training in the smoking paradigm, and each nicotine-addicted subject in the control group did not undergo the neurofeedback training;

[0007] Preprocessing each of the collected resting-state EEG signals to obtain a corresponding processed EEG signal;

[0008] Performing EEG microstate analysis on all the processed EEG signals to obtain a plurality of group microstate templates and a microstate feature of each processed EEG signal;

[0009] constructing a linear mixed-effects model based on the microstate characteristics, and screening out statistically significant microstate transition pairs through a two-way interaction analysis of the linear mixed-effects model;

[0010] A brain power source analysis is performed on the group microstate templates corresponding to the microstate transition pairs to determine the functional brain network related to the neurofeedback training. Based on the activation characteristics of the functional brain network, nicotine-addicted subjects are guided to actively adjust their brain activity patterns through neurofeedback closed-loop training to achieve intervention in nicotine addiction.

[0011] In some embodiments, the step of performing EEG microstate analysis on all the processed EEG signals includes:

[0012] Performing spatiotemporal clustering analysis on all the processed EEG signals to generate the group microstate template;

[0013] According to the group microstate template, feature extraction is performed on each of the processed EEG signals to obtain the microstate features.

[0014] In some embodiments, the step of performing spatiotemporal cluster analysis on all the processed EEG signals includes:

[0015] Calculating the global field power of each processed EEG signal;

[0016] Clustering the EEG topography at the global field power peak to obtain a number of individual microstate templates for each of the nicotine-addicted subjects;

[0017] The individual microstate templates of all the nicotine-addicted subjects are clustered to obtain the group microstate template.

[0018] In some embodiments, the step of extracting features from each of the processed EEG signals according to the group microstate template includes:

[0019] Back-fitting each of the group microstate templates to each of the processed EEG signals to obtain a corresponding EEG microstate sequence;

[0020] performing smoothing processing on the EEG microstate sequence;

[0021] The microstate features are extracted from the smoothed EEG microstate sequence.

[0022] In some embodiments, the model formula of the linear mixed effects model is ,in, represents the characteristic value of the microstate characteristics, represents the time variable, represents the group variable, representing said nicotine-addicted subject, represents the time group interaction term, Used to control individual repeated measurement effects.

[0023] In a second aspect, the present invention provides a functional brain network determination device for nicotine addiction intervention, the device comprising:

[0024] an EEG signal acquisition unit, configured to respectively acquire resting-state EEG signals of nicotine-addicted subjects in an experimental group and a control group under the guidance of a pre-established smoking paradigm, wherein each nicotine-addicted subject in the experimental group undergoes neurofeedback training in the smoking paradigm, and each nicotine-addicted subject in the control group does not undergo the neurofeedback training;

[0025] an EEG signal processing unit, configured to pre-process each of the collected resting-state EEG signals to obtain a corresponding processed EEG signal;

[0026] an EEG signal analysis unit, configured to perform EEG microstate analysis on all the processed EEG signals to obtain a plurality of group microstate templates and a microstate feature of each processed EEG signal;

[0027] A microstate screening unit, configured to construct a linear mixed-effect model based on the microstate characteristics, and screen out statistically significant microstate transition pairs through a two-way interaction analysis of the linear mixed-effect model;

[0028] The functional brain determination unit is used to perform brain power source analysis on the group microstate templates corresponding to the microstate transition pairs, determine the functional brain network related to the neurofeedback training, and guide nicotine-addicted subjects to actively adjust their brain activity patterns through neurofeedback closed-loop training based on the activation characteristics of the functional brain network, thereby achieving intervention in nicotine addiction.

[0029] In some embodiments, the EEG signal analysis unit includes:

[0030] a cluster analysis unit, configured to perform spatiotemporal cluster analysis on all the processed EEG signals to generate the group microstate template;

[0031] A feature extraction unit is used to extract features from each of the processed EEG signals according to the group microstate template to obtain the microstate features.

[0032] In some embodiments, the cluster analysis unit includes:

[0033] a power calculation unit, configured to calculate the global field power of each processed EEG signal;

[0034] a first clustering unit, configured to cluster the EEG topography at the global field power peak to obtain a plurality of individual microstate templates of each of the nicotine-addicted subjects;

[0035] The second clustering unit is used to cluster the individual microstate templates of all the nicotine-addicted subjects to obtain the group microstate template.

[0036] In a third aspect, the present invention further provides a computing device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-described method when executing the computer program.

[0037] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0038] In an embodiment of the present invention, resting-state EEG signals of nicotine-addicted subjects in an experimental group and a control group are collected under the guidance of a smoking paradigm, each collected resting-state EEG signal is preprocessed, and all processed EEG signals are subjected to EEG microstate analysis to obtain several group microstate templates and microstate characteristics of each processed EEG signal. Statistically significant microstate transition pairs are screened out through a two-way interactive analysis of a linear mixed-effects model constructed based on the microstate characteristics. EEG source analysis is performed on the group microstate templates corresponding to the microstate transition pairs to determine the functional brain network related to neurofeedback training. Nicotine addiction intervention is performed based on the functional brain network, thereby more accurately capturing the subtle changes in brain functional state before and after neurofeedback treatment, and providing more objective and quantitative neural markers for revealing its intervention mechanism and optimizing treatment strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 1 is a flow chart of a method for determining a functional brain network for nicotine addiction intervention provided in Example 1 of the present invention;

[0040] Figure 2 Schematic diagram of the two-step microstate clustering results in the functional brain network determination method for nicotine addiction intervention provided in Example 1 of the present invention;

[0041] Figure 3 Schematic diagram of microstate transition pairs with significant interaction effects in the method for determining functional brain networks for nicotine addiction intervention provided in Example 1 of the present invention;

[0042] Figure 4 This is a schematic diagram of the activation of microstate transitions on 8 and 18 typical brain networks in the functional brain network determination method for nicotine addiction intervention provided in Example 1 of the present invention;

[0043] Figure 5 2 is a schematic structural diagram of a functional brain network determination device for nicotine addiction intervention provided in a second embodiment of the present invention;

[0044] Figure 6 It is a structural diagram of the computing device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0046] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof. Furthermore, the terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. The terms "first," "second," and similar terms do not denote any order, quantity, or importance, but are simply used to distinguish one component from another. Terms such as "connected" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positions; changes in the absolute position of the described objects may also change the relative positions of the objects. The term "plurality" refers to two or more, and other quantifiers are used similarly.

[0047] In order to keep the following description of the embodiments of the present invention clear and concise, detailed descriptions of some known functions and components are omitted in this specification.

[0048] The following describes the specific implementation of the present invention in detail with reference to specific embodiments:

[0049] Example 1:

[0050] Figure 1 The following illustrates the implementation process of the method for determining a functional brain network for nicotine addiction intervention provided in Example 1 of the present invention. For ease of illustration, only the portion related to the embodiment of the present invention is shown, which is described in detail as follows:

[0051] In step S101, resting-state EEG signals of nicotine-addicted subjects in the experimental group and the control group are collected under the guidance of a pre-constructed smoking paradigm, wherein each nicotine-addicted subject in the experimental group undergoes neurofeedback training in the smoking paradigm, and each nicotine-addicted subject in the control group does not undergo neurofeedback training.

[0052] The embodiments of the present invention are applicable to computing devices, such as personal computers, servers, and the like. In the embodiments of the present invention, first, nicotine-addicted subjects who meet preset conditions (such as age, gender, smoking years, health status, etc.) are selected, and then the qualified nicotine-addicted subjects (hereinafter referred to as subjects) are randomly assigned to an experimental group and a control group, and a smoking paradigm is constructed for performing neural regulation on the nicotine-addicted subjects in the experimental group and the control group. The experimental process of the smoking paradigm generally includes four stages: basic training, neurofeedback training, post-training behavioral training, and follow-up training. Under the guidance of the smoking paradigm, the experimental group performs neurofeedback training, and the control group does not perform neurofeedback training. The resting-state electroencephalogram (EEG) signals of the experimental group and the control group during the basic training and post-training behavioral training stages are collected, respectively. Among them, the basic training stage is generally used to collect the original state of the subjects before addiction intervention. During the basic training stage, the subjects will be subject to questionnaire assessment and behavioral tasks. The questionnaire assessment refers to the use of standardized scales (such as the Tobacco Craving Questionnaire) to measure the craving of the subjects. The TCQ) quantifies the subjective state of the subject (e.g., smoking craving), and the behavioral task (e.g., smoking cue response task) is used to observe the subject's objective behavioral response (e.g., grasping impulse, choice preference) in a specific situation (e.g., when faced with a smoking picture);

[0053] The neurofeedback training phase is used to guide subjects to autonomously regulate brain activity through real-time feedback to reduce their craving for smoking cues. During the neurofeedback training phase, subjects are sequentially administered a pre-neurofeedback questionnaire assessment (e.g., TCQ), a pre-neurofeedback task (e.g., a smoking cue reactivity task, which measures subjects' physiological or psychological reactions to smoking-related cues (e.g., images of cigarettes, smoking scenes, etc.) (e.g., cravings, heart rate changes, etc.), used to assess cue sensitivity of addictive behavior), neurofeedback training (or treatment), a post-neurofeedback questionnaire assessment (e.g., TCQ), and a post-neurofeedback task (e.g., a smoking cue reactivity task).

[0054] The post-training behavioral training phase is used to capture the short-term behavioral effects of the intervention. By comparing the data from the basic training phase, the researchers analyze whether neurofeedback causes short-term changes in EEG activity and behavioral indicators, and verify the direct impact of NF training on behavior. In this phase, the subjects are re-exposed to smoking-related cues (such as cigarette images and odors) to observe their behavioral responses (such as grasping urges and changes in physiological indicators) to assess whether NF training translates into actual improvements in behavioral control ability.

[0055] The follow-up training phase was used to assess the long-term maintenance of the intervention effects.

[0056] In step S102, each collected resting-state EEG signal is preprocessed to obtain a corresponding processed EEG signal.

[0057] In an embodiment of the present invention, each collected resting-state EEG signal is preprocessed. The specific operations of the preprocessing include but are not limited to useless electrode removal, noise removal, interference removal, artifact removal, segmentation, and bad channel interpolation. Specifically, useless electrodes are first removed from the resting-state EEG signal (including horizontal eye movement and vertical eye movement channels). Then, the signal after useless electrode removal is subjected to common average re-reference to remove zero-mean random noise. After that, the signal after noise removal is low-pass filtered at 1-45 Hz to remove interference from irrelevant artifacts such as lower-frequency electrooculogram signals and higher-frequency electromyography signals, and a 50 Hz notch filter is used to remove power frequency interference. Subsequently, independent component analysis (ICA) is calculated on the signal after interference removal. The ICA matrix is ​​used to remove blink artifacts and horizontal eye movement artifacts. Finally, to ensure the stability of the EEG signal quality and reduce the amount of calculation, the middle 120s-240s of the signal are segmented and the bad channels are interpolated to obtain a clean EEG signal. For ease of description and distinction, the EEG signal obtained after preprocessing is called the processed EEG signal.

[0058] In step S103, all processed EEG signals are subjected to EEG microstate analysis to obtain a number of group microstate templates and microstate features of each processed EEG signal.

[0059] In an embodiment of the present invention, all processed EEG signals are subjected to EEG microstate analysis to reveal the short-term and stable electrophysiological state of the brain in the resting state, and several group microstate templates and microstate characteristics of each processed EEG signal are obtained, wherein the group microstate template is a global optimal microstate template across subjects and treatments, and the microstate characteristics include time coverage (Coverage, Cov), duration (Duration, Dur), occurrence frequency (Occurrence, Occ) and transition probability (Transition Probability, TP). Time coverage refers to the proportion of the total time that a specific microstate remains in the dominant state to the total time of the microstate sequence, duration refers to the average time that a specific microstate remains in the dominant state, occurrence frequency refers to the number of times a specific microstate appears continuously in one second, and transition probability refers to the probability of a specific microstate transitioning to another microstate.

[0060] In a feasible embodiment, the following steps are performed to perform EEG microstate analysis on all processed EEG signals:

[0061] (S103.1) performing spatiotemporal clustering analysis on all processed EEG signals to generate a group microstate template;

[0062] In an embodiment of the present invention, spatiotemporal cluster analysis of all processed EEG signals is performed through the following steps:

[0063] (S103.1.1) Calculate the global field power of each processed EEG signal;

[0064] In the embodiment of the present invention, the formula Calculate the global field power (GFP) of each processed EEG signal, where represents the time point of the processed EEG signal, Indicates the total number of channels of the processed EEG signal, Indicates the ordinal number of the channel, Indicates the Channels at time The voltage value, express The average voltage of all channels at the moment, the GFP peak corresponds to the moment when the EEG topography is most stable and the spatial distribution is most significant, which is the key time point for microstate switching.

[0065] (S103.1.2) Cluster the EEG topography at the global field power peak to obtain several individual microstate templates for each nicotine-addicted subject;

[0066] (S103.1.3) Cluster the individual microstate templates of all nicotine-addicted subjects to obtain a group microstate template.

[0067] In an embodiment of the present invention, the EEG topography at the GFP peak moment is extracted to obtain the optimal signal-to-noise ratio, and the Adaptive Agglomerative Hierarchical Clustering (AAHC) algorithm is used to perform two-step clustering on these EEG topography to identify the average potential field map: first, the EEG topography at the GFP peak of all subjects is subjected to the first step of microstate clustering, and then the individual microstate templates obtained by the first step of microstate clustering are merged as the input of the second step of microstate clustering. The global optimal microstate template finally obtained has high robustness across treatments and subjects. In addition, in the clustering process, the polarity of the EEG signal is ignored, and the number of clusters selected is k = 2:8.

[0068] Specifically, first, the first step of clustering is performed: the EEG topography at the GFP peak moment of each subject is spatially clustered using the polarity-independent AAHC algorithm, and the number of cluster templates is defined as k. During the iterative process of clustering, the cluster centroids with high global explained variance (GEV) and low cross-validation criteria (CV) are identified as the most representative templates, and the number of centroids is the optimal number of cluster templates. After the first step of spatial clustering, all subjects will eventually obtain a set of microstate templates, namely, individual optimal microstate templates. Then, the second step of clustering is performed: all individual optimal microstate templates obtained in the first step of clustering are spatially clustered for the second time k times, and the defined number of clusters is still k. After the second step of spatial clustering, k microstate templates across treatments and subjects for neurodynamic analysis are obtained. Finally, the two indicators of GEV and CV corresponding to each microstate template obtained in the second step of clustering are used. The number of final optimal templates and the corresponding optimal microstate template, that is, the global optimal microstate template, is determined. GEV reflects the quality of the extracted cluster centroid, that is, the extent to which the microstate template can represent the original EEG signal. The larger the value, the better. CV is used to measure the similarity and closeness between microstates. The lower the CV value, the lower the similarity between microstates, the more obvious the distinction, and the better the clustering effect. The ratio of GEV to CV is used to evaluate the quality of the microstate template. When the first-order difference of this ratio (that is, the difference between adjacent ratios) is the largest, it indicates that the corresponding microstate template is the optimal microstate template.

[0069] As an example, in a specific embodiment, according to different cluster numbers k, the group microstate template obtained by two-step clustering is as follows: Figure 2 As shown in (a), according to Figure 2 As can be seen from (b) and (c), the maximum first-order GEV / CV difference indicates that 7 is the optimal number of microstate templates. These 7 templates are marked as MS1, MS2, MS3, MS4, MS5, MS6, and MS7 respectively. These templates can effectively reflect the temporal dynamic changes of EEG signals in nicotine-addicted patients before and after neurofeedback treatment.

[0070] (S103.2) Perform feature extraction on each processed EEG signal based on the group microstate template to obtain microstate features.

[0071] In the embodiment of the present invention, feature extraction is performed on each processed EEG signal through the following steps:

[0072] (S103.2.1) Back-fitting each population microstate template to each processed EEG signal to obtain a corresponding EEG microstate sequence;

[0073] (S103.2.2) Smoothing of EEG microstate sequences;

[0074] (S103.2.3) Extract microstate features from the smoothed EEG microstate sequence.

[0075] In an embodiment of the present invention, each group microstate template is back-fitted to each processed EEG signal. Through a top-down back-fitting process, each processed EEG signal is re-presented as a series of dynamic microstate sequences. Based on the global map dissimilarity (GMD) standard, each sample point in the EEG signal is assigned to a microstate with high spatial similarity. In order to eliminate the influence of noise, the back-fitted microstate sequence is smoothed, that is, the terminal microstate period of less than 30 milliseconds in the back-fitted microstate sequence is reallocated to another microstate. Finally, the corresponding microstate features are extracted to quantify the dynamics of the EEG microstates.

[0076] As an example, in a specific embodiment, the group microstate template is back-fitted and the microstate features are extracted. A total of 10*7=70 features are extracted for each subject, where 10 represents 3 static features Cov, Dur, and Occ and 7 dynamic features TP1, TP2, TP3, TP4, TP5, TP6, and TP7, and 7 represents the number of group microstate templates.

[0077] In a feasible embodiment, the eigenvalues ​​of all extracted microstate features are subjected to residual normality tests and homoscedasticity tests. The test results show that for all features, there is a slight skewness at both ends of the data in the residual normality test. However, considering the large sample size of the data, the slight skewness can be ignored. In the homoscedasticity test, the scatter plots of the residual values ​​and fitted values ​​of all features are evenly distributed around 0, thus satisfying homoscedasticity.

[0078] In step S104, a linear mixed effect model is constructed based on the microstate characteristics, and statistically significant microstate transition pairs are screened out through two-way interaction analysis of the linear mixed effect model.

[0079] In this embodiment of the present invention, a linear mixed effects model (LME) was constructed based on microstate characteristics. The temporal dynamics of microstate characteristics before and after neurofeedback training were analyzed using a two-way interaction analysis within the LME. Statistically significant microstate transition pairs were screened to evaluate the regulatory effect of neurofeedback on smoking cue responses. Specifically, FDR (False Discovery Rate Correction) was used to identify microstate transition pairs with significant interactions (e.g., p < 0.05).

[0080] In a feasible embodiment, the model formula of the linear mixed effect model is: ,in, The eigenvalues ​​representing the characteristics of the microstate, represents the time variable, represents the group variable, Representing nicotine-addicted subjects, represents the time group interaction term, Used to control individual repeated measurement effects.

[0081] In this embodiment of the present invention, based on the microstate characteristics after the residual normality test and the homoscedasticity test, an LME including the factors of time (i.e., before and after neurofeedback training) and group (i.e., experimental group vs. control group) is constructed, which is expressed as ,in, The eigenvalues ​​representing the characteristics of the microstate, represents the time variable, represents the group variable, Representing nicotine-addicted subjects, represents the time group interaction term, It is used to control the individual repeated measurement effect, that is, to control the correlation caused by "intra-individual repeated measurements", so as to model the time*group interaction through LME, while taking into account the repeated measurement structure (multiple measurements for each subject). It is a more rigorous and statistically valid method than multiple t-tests.

[0082] In a feasible embodiment, the interaction and main effects of time and group are analyzed by constructing LME, and the results are shown in Table 1 and Figure 3 As shown in Table 1 and Figure 3 It can be seen that there is a significant Day:Group interaction effect in the probability of mutual conversion between MS1 ​​and MS2 before false positive rate (FDR) correction.

[0083] Table 1

[0084]

[0085] In another feasible embodiment, to reduce the probability of Type I errors, FDR correction was performed on the static features (Cov, Dur, Occ) and the dynamic features (TP), respectively. The corrected results are shown in Table 2. As can be seen from Table 2, after correction, only the conversion probability from MS2 to MS1 showed a significant interaction, indicating that neurofeedback treatment caused the temporal change pattern of the experimental group to be different from that of the control group, suggesting that this feature difference may be related to the treatment effect.

[0086] Table 2

[0087]

[0088] In another feasible embodiment, simple effect analysis is further performed on the transformation pairs with significant interaction effects. Specifically, for inter-group differences, a two-sample t-test is used to analyze whether there are characteristic differences between the experimental group and the control group before and after neurofeedback training; for intra-group differences, a paired t-test is used to analyze the characteristic differences between the control group and the experimental group before and after neurofeedback training. Before the test, all characteristic values ​​are tested to see whether they meet the prerequisites of the t-test. For characteristic values ​​that do not meet the prerequisites, non-parametric test methods are used instead. The test results showed that before neurofeedback training, there were no significant differences in all microstate characteristics between the experimental group and the control group after FDR correction. Therefore, excluding the interference of differences before neurofeedback training, it can be explained that the differences in subsequent treatment effects are more likely to be attributed to the neurofeedback treatment itself. After neurofeedback treatment, there were no significant differences in all characteristics between the experimental group and the control group after correction. This may be caused by the interaction between group and time. The paired t-test results before and after neurofeedback treatment in the experimental group are shown in Table 3. It can be seen from Table 3 that before and after neurofeedback treatment, the probability of transition from MS2 to MS1 in the experimental group increased significantly (t = 3.9652), while no such difference was found in the control group, indicating that this transition pair is an important neural representation for judging the effect of NF treatment.

[0089] Table 3

[0090]

[0091] In step S105, brain power source analysis is performed on the group microstate templates corresponding to the microstate transition pairs to determine the functional brain network related to neurofeedback training. Based on the activation characteristics of the functional brain network, neurofeedback closed-loop training is used to guide nicotine-addicted subjects to actively adjust their brain activity patterns, thereby achieving intervention in nicotine addiction.

[0092] In an embodiment of the present invention, in order to further clarify the physiological functional significance of the group microstate template, brain power analysis is performed on the group microstate template corresponding to the microstate transition pair, and the functional brain network related to neurofeedback training is determined. Based on the activation characteristics of the functional brain network, neurofeedback closed-loop training is used to guide nicotine-addicted subjects to actively adjust their brain activity patterns, thereby achieving intervention in nicotine addiction.

[0093] In a specific embodiment, for the transformation pair MS2 and MS1 with a significant interaction effect, an EEG source analysis is performed using an EEG source tracing method. Specifically, the topographic electro-physiological state source-imaging (TESS) algorithm is used to obtain the position information β values ​​of the two templates in the source space. These values ​​reflect the intensity of neural activity in the brain in a microstate. The β values ​​are then subjected to a single-sample t-test and FDR correction (correction level p < 0.001) to obtain the activation information of the two templates at the voxel level. Then, according to the cortical template (Cortical Template) of Schaefer400 and the subcortical template (Subcortical Template) containing 16 regions of interest (ROIs), the activation information of the two templates at the voxel level is obtained. Templates) were used to assign voxels to each ROI, and the activation information of the two templates at the ROI level was obtained. Finally, the ROIs were assigned to 8 and 18 typical brain networks, and the activation status of the two templates at the network level was obtained, that is, the functional brain networks corresponding to the two templates. Finally, according to the functions of different brain networks, the physiological functional significance and conversion relationship of the two templates were analyzed. The activation status of MS1 and MS2 on 8 and 18 typical brain networks is shown in Figure 2. Figure 4 As shown by Figure 4 (a) shows that among the eight typical brain networks, the activation of MS1 is mainly concentrated in the ventral attention network and the default mode network, which are mainly related to visual information processing, self-related thinking, emotional processing and introspection. The activation of MS2 is mainly distributed in the ventral attention network, the default mode network, the dorsal attention network and the sensorimotor network, with a larger weight, which means that MS2 may be related to vision, sensation, movement, attention maintenance and spatial orientation. The difference in the distribution of activation between MS1 ​​and MS2 may reflect the functional division of labor between emotional processing and cognitive regulation. Figure 4Figure (b) shows that among the 18 representative brain networks, MS1 has higher weights in the default mode network C, default mode network A, and peripheral visual network, indicating abnormalities in self-related thinking, visual processing, and emotion regulation. It also has strong weights in cortical and visual networks, possibly involving underlying emotional responses and visual perceptual processing. MS2, on the other hand, has higher weights in the default mode network A, dorsal attention network A, and executive control network C, indicating increased self-rumination, heightened sensitivity to external stimuli, and compensatory effects of the executive control system. MS1 (with stronger activation in the visual network) is more likely to be involved in visual information processing and self-related thinking, while MS2 (with stronger activation in the dorsal attention network A and salience network A) is more likely to regulate emotion-driven attention, reflecting higher-level cognitive regulatory mechanisms. The increased conversion rate from MS2 to MS1 in nicotine-addicted patients after neurofeedback treatment suggests that neurofeedback treatment has produced positive changes in patients' cognitive regulatory processes. The close connection and mutual transformation between patients' responses to visual stimulation of cigarettes and their self-cognitive regulatory processes fully demonstrates the effectiveness of neurofeedback therapy.

[0094] In an embodiment of the present invention, resting-state EEG signals of nicotine-addicted subjects in the experimental group and the control group are collected under the guidance of a smoking paradigm, each collected resting-state EEG signal is preprocessed, and EEG microstate analysis is performed on all processed EEG signals to obtain several group microstate templates and microstate characteristics of each processed EEG signal. Statistically significant microstate transition pairs are screened out through a two-way interaction analysis of a linear mixed-effects model constructed based on the microstate characteristics. EEG source analysis is performed on the group microstate templates corresponding to the microstate transition pairs to determine the functional brain network associated with neurofeedback training. Nicotine addiction intervention is performed based on the functional brain network, thereby more accurately capturing the subtle changes in brain functional state before and after neurofeedback treatment, and providing more objective and quantitative neural markers for revealing its intervention mechanism and optimizing treatment strategies.

[0095] Example 2:

[0096] Figure 5 The structure of the functional brain network determination device for nicotine addiction intervention provided by the second embodiment of the present invention is shown. For ease of illustration, only the parts related to the embodiment of the present invention are shown, including:

[0097] an EEG signal acquisition unit 51 for respectively acquiring resting-state EEG signals of nicotine-addicted subjects in an experimental group and a control group under the guidance of a pre-established smoking paradigm, wherein each nicotine-addicted subject in the experimental group undergoes neurofeedback training in the smoking paradigm, and each nicotine-addicted subject in the control group does not undergo neurofeedback training;

[0098] The EEG signal processing unit 52 is used to pre-process each collected resting-state EEG signal to obtain a corresponding processed EEG signal;

[0099] The EEG signal analysis unit 53 is used to perform EEG microstate analysis on all processed EEG signals to obtain a number of group microstate templates and microstate features of each processed EEG signal;

[0100] A microstate screening unit 54 is used to construct a linear mixed effect model based on the microstate characteristics, and screen out statistically significant microstate transition pairs through a two-way interaction analysis of the linear mixed effect model;

[0101] The functional brain determination unit 55 is used to perform brain power source analysis on the group microstate templates corresponding to the microstate transition pairs, determine the functional brain network related to the neurofeedback training, and guide nicotine-addicted subjects to actively adjust their brain activity patterns through neurofeedback closed-loop training based on the activation characteristics of the functional brain network, thereby achieving intervention in nicotine addiction.

[0102] Preferably, the EEG signal analysis unit 53 includes:

[0103] Cluster analysis unit, used to perform spatiotemporal cluster analysis on all processed EEG signals and generate group microstate templates;

[0104] The feature extraction unit is used to extract features from each processed EEG signal according to the group microstate template to obtain microstate features.

[0105] Cluster analysis units include:

[0106] a power calculation unit, used to calculate the global field power of each processed EEG signal;

[0107] The first clustering unit is used to cluster the EEG topography at the global field power peak to obtain several individual microstate templates for each nicotine-addicted subject;

[0108] The second clustering unit is used to cluster the individual microstate templates of all nicotine-addicted subjects to obtain a group microstate template.

[0109] In the embodiments of the present invention, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functional distribution can be implemented by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to implement all or part of the functions described above. The various units and modules of the device can be implemented by corresponding hardware or software units. Each unit and module can be an independent software or hardware unit, or can be integrated into a software or hardware unit, which is not intended to limit the present invention. In addition, the specific names of the various functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the device can refer to the corresponding description in the aforementioned method embodiment, which will not be repeated here.

[0110] Example 3:

[0111] Figure 6 The structure of a computing device provided by the third embodiment of the present invention is shown. For ease of description, only the parts related to the embodiment of the present invention are shown.

[0112] The computing device 6 of the embodiment of the present invention includes a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60. When the processor 60 executes the computer program 62, the steps of the above-mentioned method for determining a functional brain network for nicotine addiction intervention are implemented, such as Figure 1 Alternatively, when the processor 60 executes the computer program 62, the functions of each unit in the above-mentioned device embodiments are realized, for example Figure 5 Function of the unit shown.

[0113] In an embodiment of the present invention, resting-state EEG signals of nicotine-addicted subjects in the experimental group and the control group are collected under the guidance of a smoking paradigm, each collected resting-state EEG signal is preprocessed, and EEG microstate analysis is performed on all processed EEG signals to obtain several group microstate templates and microstate characteristics of each processed EEG signal. Statistically significant microstate transition pairs are screened out through a two-way interaction analysis of a linear mixed-effects model constructed based on the microstate characteristics. EEG source analysis is performed on the group microstate templates corresponding to the microstate transition pairs to determine the functional brain network associated with neurofeedback training. Nicotine addiction intervention is performed based on the functional brain network, thereby more accurately capturing the subtle changes in brain functional state before and after neurofeedback treatment, and providing more objective and quantitative neural markers for revealing its intervention mechanism and optimizing treatment strategies.

[0114] The computing device of the embodiment of the present invention may be a personal computer. The steps implemented when the processor 60 of the computing device 6 executes the computer program 62 to implement the functional brain network determination method for nicotine addiction intervention can be referred to the description of the aforementioned method embodiment and will not be repeated here.

[0115] Example 4:

[0116] In an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned method for determining a functional brain network for nicotine addiction intervention are implemented, for example, Figure 1 Alternatively, when the computer program is executed by a processor, the functions of each unit in the above-mentioned device embodiments are realized, for example Figure 5 Function of the unit shown.

[0117] In an embodiment of the present invention, resting-state EEG signals of nicotine-addicted subjects in the experimental group and the control group are collected under the guidance of a smoking paradigm, each collected resting-state EEG signal is preprocessed, and EEG microstate analysis is performed on all processed EEG signals to obtain several group microstate templates and microstate characteristics of each processed EEG signal. Statistically significant microstate transition pairs are screened out through a two-way interaction analysis of a linear mixed-effects model constructed based on the microstate characteristics. EEG source analysis is performed on the group microstate templates corresponding to the microstate transition pairs to determine the functional brain network associated with neurofeedback training. Nicotine addiction intervention is performed based on the functional brain network, thereby more accurately capturing the subtle changes in brain functional state before and after neurofeedback treatment, and providing more objective and quantitative neural markers for revealing its intervention mechanism and optimizing treatment strategies.

[0118] The computer-readable storage medium of the embodiments of the present invention may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EEPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the embodiments of the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0119] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the scope of disclosure involved in the above embodiments is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concepts. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0120] In addition, although adopting specific order to describe each operation, this should not be interpreted as requiring these operations to be executed in the specific order shown or in sequential order.Under certain environment, multitasking and parallel processing may be advantageous.Similarly, although comprising some specific implementation details in the above discussion, these should not be interpreted as limiting the scope of the present invention.Some features described in the context of independent embodiment can also be implemented in single embodiment in combination.On the contrary, the various features described in the context of independent embodiment also can be implemented in multiple embodiments individually or in the mode of any suitable subcombination.

Claims

1. A method for determining functional brain networks for nicotine addiction intervention, characterized in that: The method comprises the following steps: resting-state EEG signals of nicotine-addicted subjects in an experimental group and a control group were collected under the guidance of a pre-established smoking paradigm, wherein each nicotine-addicted subject in the experimental group underwent neurofeedback training in the smoking paradigm, and each nicotine-addicted subject in the control group did not undergo the neurofeedback training; Preprocessing each of the collected resting-state EEG signals to obtain a corresponding processed EEG signal; Performing EEG microstate analysis on all the processed EEG signals to obtain a plurality of group microstate templates and a microstate feature of each processed EEG signal; constructing a linear mixed-effects model based on the microstate characteristics, and screening out statistically significant microstate transition pairs through a two-way interaction analysis of the linear mixed-effects model; Performing brain power source analysis on the group microstate templates corresponding to the microstate transition pairs to determine the functional brain network related to the neurofeedback training, so as to guide nicotine-addicted subjects to actively adjust their brain activity patterns through neurofeedback closed-loop training based on the activation characteristics of the functional brain network.

2. The method according to claim 1, wherein The step of performing EEG microstate analysis on all the processed EEG signals comprises: Performing spatiotemporal clustering analysis on all the processed EEG signals to generate the group microstate template; According to the group microstate template, feature extraction is performed on each of the processed EEG signals to obtain the microstate features.

3. The method according to claim 2, wherein The step of performing spatiotemporal cluster analysis on all the processed EEG signals comprises: Calculating the global field power of each processed EEG signal; Clustering the EEG topography at the global field power peak to obtain a number of individual microstate templates for each of the nicotine-addicted subjects; The individual microstate templates of all the nicotine-addicted subjects are clustered to obtain the group microstate template.

4. The method according to claim 2, wherein The step of extracting features from each of the processed EEG signals according to the group microstate template comprises: Back-fitting each of the group microstate templates to each of the processed EEG signals to obtain a corresponding EEG microstate sequence; performing smoothing processing on the EEG microstate sequence; The microstate features are extracted from the smoothed EEG microstate sequence.

5. The method according to claim 1, wherein The model formula of the linear mixed effect model is: ,in, represents the characteristic value of the microstate characteristics, represents the time variable, represents the group variable, representing said nicotine-addicted subject, represents the time group interaction term, Used to control individual repeated measurement effects.

6. A functional brain network determination device for nicotine addiction intervention, characterized in that: The device comprises: an EEG signal acquisition unit, configured to respectively acquire resting-state EEG signals of nicotine-addicted subjects in an experimental group and a control group under the guidance of a pre-established smoking paradigm, wherein each nicotine-addicted subject in the experimental group undergoes neurofeedback training in the smoking paradigm, and each nicotine-addicted subject in the control group does not undergo the neurofeedback training; an EEG signal processing unit, configured to pre-process each of the collected resting-state EEG signals to obtain a corresponding processed EEG signal; an EEG signal analysis unit, configured to perform EEG microstate analysis on all the processed EEG signals to obtain a plurality of group microstate templates and a microstate feature of each processed EEG signal; A microstate screening unit, configured to construct a linear mixed-effect model based on the microstate characteristics, and screen out statistically significant microstate transition pairs through a two-way interaction analysis of the linear mixed-effect model; The functional brain determination unit is used to perform brain power source analysis on the group microstate template corresponding to the microstate transition pair, determine the functional brain network related to the neurofeedback training, and guide nicotine-addicted subjects to actively adjust their brain activity patterns through neurofeedback closed-loop training based on the activation characteristics of the functional brain network.

7. The device according to claim 6, characterized in that The EEG signal analysis unit comprises: a cluster analysis unit, configured to perform spatiotemporal cluster analysis on all the processed EEG signals to generate the group microstate template; A feature extraction unit is used to extract features from each of the processed EEG signals according to the group microstate template to obtain the microstate features.

8. The device according to claim 7, wherein The cluster analysis unit includes: a power calculation unit, configured to calculate the global field power of each processed EEG signal; a first clustering unit, configured to cluster the EEG topography at the global field power peak to obtain a plurality of individual microstate templates of each of the nicotine-addicted subjects; The second clustering unit is used to cluster the individual microstate templates of all the nicotine-addicted subjects to obtain the group microstate template.

9. A computing device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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