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

Abstract

The invention is applicable to the technical field of neuroscience, and provides a functional brain network determination method, device and equipment for nicotine addiction intervention, and the method comprises the following steps: respectively collecting resting-state electroencephalogram signals of two groups of subjects under the guidance of a smoking normal form, preprocessing the collected resting-state electroencephalogram signals, and determining the resting-state electroencephalogram signals of the two groups of subjects according to the preprocessed resting-state electroencephalogram signals; electroencephalogram micro-state analysis is carried out on all the obtained processed electroencephalogram signals, a group micro-state template and corresponding micro-state features are obtained, and micro-state conversion pairs with statistical significance are screened out through bidirectional interaction analysis of a linear mixing effect model constructed according to the micro-state features; and performing brain power analysis on the group micro-state template corresponding to the micro-state transition pair, and determining a functional brain network related to neural feedback training for nicotine addiction intervention, thereby realizing more precise capture of subtle changes of brain function states before and after neural feedback treatment, and improving the accuracy of the neural feedback treatment. And a more objective and quantitative nerve marker is provided for revealing an intervention mechanism and optimizing a treatment strategy.

Description

Technical Field

[0001] The present invention belongs to the technical field of neuroscience, and particularly relates to a method, device and equipment for determining a functional brain network for nicotine addiction intervention. Background Art

[0002] Neurofeedback (NF) therapy is a brain function regulation technology based on the principle of operant conditioning. By real-time monitoring the electroencephalogram (EEG) or brain function signals of an individual and feeding them back to the subject, it guides the subject to actively regulate their own brain activities, thereby improving nerve function and related behavioral performances. This method is essentially a closed-loop training system and is widely used in the adjuvant 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 potential non-invasive intervention method, especially suitable for regulating brain networks related to reward, impulse control, and self-regulation ability, such as the default mode network (DMN), executive control network (ECN), and limbic system. Compared with traditional drug or behavioral therapies, neurofeedback can act on the brain function state in a more direct way and has advantages such as individuation, no drug side effects, and long-term plasticity. Therefore, with the development of neuroimaging and signal processing technologies, neurofeedback is gradually becoming a promising neuroregulation means for researching 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 a functional brain network for nicotine addiction intervention, aiming to solve the problem that the existing technology cannot capture the rapid dynamic changes of brain functions, resulting in the inability to effectively reveal the reorganization mechanism of the functional network related to addiction during the neurofeedback intervention process.

[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: Collect the resting-state EEG signals of nicotine-addicted subjects in two groups, namely the experimental group and the control group, under the guidance of a pre-constructed smoking paradigm. Among them, 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; Preprocess each of the collected resting-state EEG signals to obtain the corresponding processed EEG signals; Perform electroencephalogram (EEG) microstate analysis on all of the post-treatment EEG signals to obtain a number of population microstate templates and the microstate features of each of the post-treatment EEG signals; Construct a linear mixed-effects model based on the microstate features, and screen out microstate transition pairs with statistical significance through two-way interaction analysis of the linear mixed-effects model; Perform brain source analysis on the population microstate templates corresponding to the microstate transition pairs to determine the functional brain networks 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 networks, and achieve the intervention of nicotine addiction.

[0006] In some embodiments, the step of performing EEG microstate analysis on all of the post-treatment EEG signals includes: Perform spatio-temporal clustering analysis on all of the post-treatment EEG signals to generate the population microstate templates; Extract features from each of the post-treatment EEG signals according to the population microstate templates to obtain the microstate features.

[0007] In some embodiments, the step of performing spatio-temporal clustering analysis on all of the post-treatment EEG signals includes: Calculate the global field power of each of the post-treatment EEG signals; Perform clustering on the EEG topographic maps at the global field power peaks to obtain a number of individual microstate templates for each of the nicotine-addicted subjects; Perform clustering on the individual microstate templates of all of the nicotine-addicted subjects to obtain the population microstate templates.

[0008] In some embodiments, the step of extracting features from each of the post-treatment EEG signals according to the population microstate templates includes: Inverse fit each of the population microstate templates to each of the post-treatment EEG signals to obtain corresponding EEG microstate sequences; Perform smoothing processing on the EEG microstate sequences; Extract the microstate features from the smoothed EEG microstate sequences.

[0009] In some embodiments, the model formula of the linear mixed-effects model is , where represents the eigenvalue of the microstate features, represents the time variable, represents the group variable, represents the nicotine-addicted subject, represents the time-group interaction term, For controlling individual repeated measurement effects.

[0010] In a second aspect, the present invention provides a functional brain network determination device for nicotine addiction intervention, and the device includes: An electroencephalogram (EEG) signal acquisition unit, configured to respectively acquire resting-state EEG signals of nicotine-addicted subjects in two groups, namely an experimental group and a control group, under the guidance of a pre-constructed smoking paradigm. Among them, 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 preprocess each of the acquired resting-state EEG signals to obtain corresponding processed EEG signals; An EEG signal analysis unit, configured to perform EEG microstate analysis on all the processed EEG signals to obtain a plurality of population microstate templates and microstate features of each of the processed EEG signals; A microstate screening unit, configured to construct a linear mixed effects model based on the microstate features, and screen out microstate transition pairs with statistical significance through two-way interaction analysis of the linear mixed effects model; A functional brain determination unit, configured to perform brain source analysis on the population microstate templates corresponding to the microstate transition pairs to determine a 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, thereby realizing the intervention of nicotine addiction.

[0011] In some embodiments, the EEG signal analysis unit includes: A clustering analysis unit, configured to perform spatio-temporal clustering analysis on all the processed EEG signals to generate the population microstate templates; A feature extraction unit, configured to extract features from each of the processed EEG signals according to the population microstate templates to obtain the microstate features.

[0012] In some embodiments, the clustering analysis unit includes: A power calculation unit, configured to calculate the global field power of each of the processed EEG signals; A first clustering unit, configured to cluster the EEG topographies at the global field power peaks to obtain a plurality of individual microstate templates of each nicotine-addicted subject; A second clustering unit, configured to cluster the individual microstate templates of all nicotine-addicted subjects to obtain the population microstate templates.

[0013] In a third aspect, the present invention further provides a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method described above are implemented.

[0014] In a fourth aspect, the present invention further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0015] In the embodiments of the present invention, the resting-state EEG signals of nicotine-addicted subjects in the experimental group and the control group are respectively collected under the guidance of a smoking paradigm. The collected resting-state EEG signals are preprocessed, and EEG microstate analysis is performed on all the processed EEG signals to obtain several group microstate templates and the microstate features of each processed EEG signal. Through two-way interaction analysis of a linear mixed-effects model constructed according to the microstate features, microstate transition pairs with statistical significance are screened out, and brain source analysis is performed on the group microstate templates corresponding to the microstate transition pairs to determine the functional brain networks related to neurofeedback training, so as to perform nicotine addiction intervention based on the functional brain networks, thereby achieving a more refined capture of the subtle changes in the brain function 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

[0016] Figure 1 is a schematic flowchart of a method for determining a functional brain network for nicotine addiction intervention provided in Embodiment 1 of the present invention; Figure 2 is a schematic diagram of the two-microstate clustering result in the method for determining a functional brain network for nicotine addiction intervention provided in Embodiment 1 of the present invention; Figure 3 is a schematic diagram of microstate transition pairs with significant interaction effects in the method for determining a functional brain network for nicotine addiction intervention provided in Embodiment 1 of the present invention; Figure 4 is a schematic diagram of the activation of microstate transition pairs on 8 and 18 typical brain networks in the method for determining a functional brain network for nicotine addiction intervention provided in Embodiment 1 of the present invention; Figure 5 is a schematic structural diagram of a device for determining a functional brain network for nicotine addiction intervention provided in Embodiment 2 of the present invention; Figure 6 is a schematic structural diagram of a computing device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, 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 used to limit the present invention.

[0018] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations. And the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms. Terms such as "first", "second" and similar words do not denote any order, quantity or importance, but are only used to distinguish different components. "Connection" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to represent relative position relationships, and when the absolute position of the object being described changes, the relative position relationship may also change accordingly. The term "plurality" means two or more, and other quantifiers are similar.

[0019] In order to keep the following description of the embodiments of the present invention clear and concise, the detailed description of some known functions and known components is omitted in this specification.

[0020] The following describes the specific implementation of the present invention in detail with reference to specific embodiments: Embodiment 1: Figure 1 The implementation process of the functional brain network determination method for nicotine addiction intervention provided in Embodiment 1 of the present invention is shown. For the convenience of description, only the parts related to the embodiments of the present invention are shown and are described in detail as follows: In step S101, the resting-state electroencephalogram signals of nicotine-addicted subjects in two groups, namely the experimental group and the control group, are respectively collected under the guidance of a pre-constructed smoking paradigm. Among them, 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.

[0021] Embodiments of the present invention are applicable to computing devices, such as personal computers, servers, etc. In the embodiments of the present invention, first, nicotine-addicted subjects meeting preset conditions (such as age, gender, smoking history, health status, etc.) are selected, and then the eligible nicotine-addicted subjects (hereinafter referred to as subjects) are randomly assigned to an experimental group and a control group to construct a smoking paradigm for neuromodulating the nicotine-addicted subjects in the two groups of the experimental group and the control group. The experimental process of this smoking paradigm usually includes 4 stages: basic training, neurofeedback training, post-training behavioral training, and subsequent follow-up training. Under the guidance of this smoking paradigm, the experimental group conducts neurofeedback training, while the control group does not conduct neurofeedback training. The resting-state electroencephalogram (EEG) signals of the experimental group and the control group are collected respectively during the basic training and post-training behavioral training stages. Among them, the basic training stage is usually used to collect the original state of the subjects before addiction intervention. During the basic training stage, questionnaire assessments and behavioral tasks are performed on the subjects. The questionnaire assessment refers to quantifying the subjective state of the subjects (such as the craving for smoking) through a standardized scale (such as the Tobacco Craving Questionnaire (TCQ)), and the behavioral task (such as the smoking cue response task) is used to observe the objective behavioral responses of the subjects in specific situations (such as in the face of smoking pictures) (such as grasping impulse, choice preference). The neurofeedback training stage is used to guide the subjects to autonomously regulate their brain activities through real-time feedback to reduce the craving for smoking cues. During the neurofeedback training stage, the subjects are successively subjected to a pre-neurofeedback questionnaire assessment (such as TCQ), a pre-neurofeedback task (such as the smoking cue reactivity task, by presenting smoking-related cues (such as cigarette pictures, smoking scenes, etc.), measuring the physiological or psychological responses of the subjects to the cues (such as craving, heart rate changes, etc.), and used to evaluate the cue sensitivity of addictive behaviors), neurofeedback training (or treatment), a post-neurofeedback questionnaire assessment (such as TCQ), and a post-neurofeedback task (such as the smoking cue reactivity task). The post-training behavioral training stage is used to capture the short-term behavioral effects after the intervention. By comparing the data in the basic training stage, it is analyzed whether the neurofeedback causes short-term changes in brain electrical activities and behavioral indicators, and to verify the direct impact of NF training on the behavioral level. During this stage, the subjects are exposed to smoking-related cues (such as cigarette pictures, odors) again, and their behavioral responses (such as grasping impulse, physiological index changes) are observed to evaluate whether the NF training is transformed into an improvement in actual behavioral control ability. The subsequent follow-up training stage is used to evaluate the long-term maintenance of the intervention effect.

[0022] In step S102, each of the collected resting-state EEG signals is preprocessed to obtain the corresponding processed EEG signals.

[0023] In an embodiment of the present invention, each collected resting-state electroencephalogram (EEG) signal is preprocessed. The specific operations of the preprocessing include, but are not limited to, removing useless electrodes, removing noise, removing interference, removing artifacts, segmenting fragments, and interpolating bad channels. Specifically, first, useless electrodes (including horizontal eye movement and vertical eye movement channels) are removed from the resting-state EEG signal. Then, common average referencing is performed on the signal after removing useless electrodes to remove random noise with a zero-mean distribution. After that, low-pass filtering of 1 - 45 Hz is performed on the signal after removing noise to remove interference from irrelevant artifacts such as lower-frequency electrooculogram (EOG) signals and higher-frequency electromyogram (EMG) signals, and notch filtering of 50 Hz is used to remove power frequency interference. Subsequently, an independent component analysis (ICA) matrix is calculated for the signal after removing interference, and blink artifacts and horizontal eye movement artifacts are removed according to the ICA matrix. Finally, in order to ensure the stability of the EEG signal quality and reduce the computational amount, 120 s - 240 s in the middle of the signal is segmented, and bad channels are interpolated. Finally, a clean EEG signal is obtained. For the convenience of description and distinction, the EEG signal obtained after preprocessing is referred to as the processed EEG signal.

[0024] In step S103, electroencephalogram microstate analysis is performed on all the processed EEG signals to obtain a number of population microstate templates and the microstate features of each processed EEG signal.

[0025] In an embodiment of the present invention, electroencephalogram microstate analysis is performed on all the processed EEG signals to reveal the transient and stable electrophysiological state of the brain in the resting state, and a number of population microstate templates and the microstate features of each processed EEG signal are obtained. Among them, the population microstate template is the global optimal microstate template across subjects and treatments. The microstate features include coverage (Cov), duration (Dur), occurrence (Occ), and transition probability (TP). Coverage refers to the proportion of the total duration that a specific microstate remains in the dominant state to the total duration of the microstate sequence. Duration refers to the average duration that a specific microstate remains in the dominant state. Occurrence refers to the number of times a specific microstate continuously appears within one second. Transition probability refers to the probability that a specific microstate transitions to another microstate.

[0026] In a feasible embodiment, electroencephalogram microstate analysis of all the processed EEG signals is achieved through the following steps: (S103.1) Perform spatio-temporal clustering analysis on all the processed EEG signals to generate population microstate templates; In an embodiment of the present invention, spatio-temporal clustering analysis of all processed electroencephalogram signals is achieved through the following steps: (S103.1.1) Calculate the global field power of each processed electroencephalogram signal; In an embodiment of the present invention, the formula is used to calculate the global field power (Global Field Power, GFP) of each processed electroencephalogram signal, where represents the time point of the processed electroencephalogram signal, represents the total number of channels of the processed electroencephalogram signal, represents the ordinal number of the channel, represents the th channel at time voltage value, represents the average voltage of all channels at time, and the GFP peak corresponds to the most stable and spatially significant moment of the electroencephalogram topographic map, which is the key time point for microstate switching.

[0027] (S103.1.2) Cluster the electroencephalogram topographic maps at the GFP peak to obtain several individual microstate templates for each nicotine-addicted subject; (S103.1.3) Cluster the individual microstate templates of all nicotine-addicted subjects to obtain a group microstate template.

[0028] In an embodiment of the present invention, the electroencephalogram topographic maps at the GFP peak moment are extracted to obtain the best signal-to-noise ratio, and the adaptive agglomerative hierarchical clustering algorithm (Adaptive Agglomerative Hierarchical Clustering, AAHC) is used to perform two-step clustering on these electroencephalogram topographic maps to identify the average potential field map: First, perform the first-step microstate clustering on the electroencephalogram topographic maps at the GFP peak of all subjects, and then merge the individual microstate templates obtained from the first-step microstate clustering as the input for the second-step microstate clustering. Finally, the globally optimal microstate template obtained has the characteristics of high robustness across treatments and subjects. In addition, during the clustering process, the polarity of the electroencephalogram signal is ignored, and the number of clusters selected is k = 2:8.

[0029] Specifically, first, the first - step clustering is performed: the electroencephalogram topographies at the GFP peak moments of each subject are spatially clustered using the AAHC algorithm without considering polarity, and the number of clustering templates is defined as k. During the clustering iteration process, the clustering 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 clustering templates. After the first - step spatial clustering, finally, each subject will obtain a set of microstate templates, that is, individual optimal microstate templates. Then, the second - step clustering is performed: the second k - time spatial clustering is performed on all the individual optimal microstate templates obtained in the first - step clustering, and the defined number of clusters is still k. After the second - step spatial clustering, k cross - treatment and cross - subject microstate templates for neurodynamics analysis are obtained. Finally, according to the two indicators of GEV and CV corresponding to each microstate template obtained in the second - step clustering, the number of the final optimal templates and the corresponding optimal microstate templates, that is, the global optimal microstate templates are determined. Among them, GEV reflects the quality of the extracted clustering centroids, that is, to what extent the microstate templates can represent the original EEG signal, and the larger the value, the better. CV is used to measure the similarity and compactness between microstates. The lower the CV value, the lower the similarity between microstates, the more obvious the distinctiveness, and the better the clustering effect. The ratio of GEV to CV is used to evaluate the quality of the microstate templates. When the first - order difference (that is, the difference between adjacent ratios) of this ratio is the largest, it indicates that the corresponding microstate template is the best microstate template.

[0030] As an example, in a specific embodiment, according to different clustering numbers k, the population microstate templates obtained by two - step clustering are as Figure 2 shown in (a) of Figure 2 . It can be seen from (b) and (c) of

[0031] that the maximum first - order GEV / CV difference indicates that 7 is the number of the best microstate templates. Then these 7 templates are respectively labeled as MS1, MS2, MS3, MS4, MS5, MS6, and MS7. These templates can effectively reflect the temporal dynamic change process of the EEG signals of nicotine - addicted patients before and after neurofeedback treatment. (S103.2)Extract features from the post - treatment EEG signal of each subject according to the population microstate template to obtain microstate features.

[0032] In the embodiment of the present invention, the feature extraction of the post - treatment EEG signal of each subject is realized through the following steps: (S103.2.1)Inverse - fit each population microstate template to the post - treatment EEG signal of each subject to obtain the corresponding EEG microstate sequence; (S103.2.2) Smooth the EEG microstate sequence; (S103.2.3) Extract microstate features from the smoothed EEG microstate sequence.

[0033] In the embodiments of the present invention, each population microstate template is back-fitted to each processed EEG signal. Through a top-down back-fitting process, the processed EEG signals are re-represented as a series of dynamic microstate sequences. Based on the criterion of Global Map Dissimilarity (GMD), each sample point in the EEG signal will be assigned to a microstate with high spatial similarity. To eliminate the influence of noise, the back-fitted microstate sequence is smoothed, that is, the terminal microstate periods less than 30 milliseconds in the back-fitted microstate sequence are re-assigned to another microstate. Finally, the corresponding microstate features are extracted to quantify the dynamics of EEG microstates.

[0034] As an example, in a specific embodiment, the population microstate template is back-fitted and 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, TP, 6, TP7, and 7 represents the number of population microstate templates.

[0035] In a feasible embodiment, residual normality test and homoscedasticity test are performed on the eigenvalues of all extracted microstate features. From the test results, it is found that for all features in the residual normality test, there is a slight skewness at both ends of the data. However, considering the large data sample size, the slight skewness can be ignored. In the homoscedasticity test, the scatter plots of the residual values and the fitted values of all features are evenly distributed around 0, so homoscedasticity is satisfied.

[0036] In step S104, a linear mixed effects model is constructed based on the microstate features, and the microstate transition pairs with statistical significance are screened out through two-way interaction analysis of the linear mixed effects model.

[0037] In an embodiment of the present invention, a linear mixed effects model (LME) is constructed based on microstate features, and the time dynamic changes of microstate features before and after neurofeedback training are analyzed through two-way interaction analysis of LME to screen out microstate transition pairs with statistical significance, so as to evaluate the regulatory effect of neurofeedback on the response to smoking cues. When screening microstate transition pairs with statistical significance, specifically, microstate transition pairs with significant interaction effects (such as p<0.05) are screened through FDR correction (False Discovery Rate Correction).

[0038] In a feasible embodiment, the model formula of the linear mixed effects model is , where represents the eigenvalue of the microstate feature, represents the time variable, represents the group variable, represents nicotine-addicted subjects, represents the time-group interaction term, used to control the individual repeated measurement effect.

[0039] In an embodiment of the present invention, based on the microstate features after the residual normality test and homoscedasticity test, an LME including factors of time (i.e., before and after neurofeedback training) and group (i.e., experimental group vs. control group) is constructed, expressed as , where represents the eigenvalue of the microstate feature, represents the time variable, represents the group variable, represents nicotine-addicted subjects, represents the time-group interaction term, used to control the individual repeated measurement effect, that is, to control the correlation caused by "intra-individual repeated measurement", so as to model the interaction of time*group through LME, and at the same time consider the repeated measurement structure (multiple measurements for each subject), which is a more rigorous and statistically efficient method than multiple t-tests.

[0040] In a feasible embodiment, the constructed LME is used to analyze the interaction and main effects of time and group. The results are shown in Table 1 and Figure 3 as shown, and it can be seen from Table 1 and Figure 3 that there is a significant Day:Group interaction effect on the mutual conversion probability between MS1 and MS2 before false positive (FDR) correction.

[0041] Table 1 In another feasible embodiment, to reduce the probability of type I error, false discovery rate (FDR) correction was performed on the static features (Cov, Dur, Occ) and dynamic features (TP) respectively. The corrected results are shown in Table 2. As can be seen from Table 2, only the transition probability from MS2 to MS1 shows a significant interaction after correction, indicating that the neurofeedback treatment makes the time change pattern of the experimental group different from that of the control group, suggesting that this feature difference may be related to the treatment effect.

[0042] Table 2 In yet another feasible embodiment, a simple effect analysis was further performed on the transition pairs with significant interaction effects. Specifically, for the between-group differences, a two-sample t-test was used to analyze whether there were feature differences between the experimental group and the control group before and after neurofeedback training; for the within-group differences, a paired t-test was used to analyze the feature differences obtained by the control group and the experimental group before and after neurofeedback training. Before the test, all feature values were examined to see if they met the prerequisite conditions of the t-test. For feature values that did not meet the prerequisite conditions, non-parametric test methods were used instead. The test results showed that after FDR correction, there were no significant differences in all microstate features between the experimental group and the control group before neurofeedback training. Therefore, excluding the interference of the differences before neurofeedback training, it can be shown that the subsequent treatment effect differences are more likely to be attributed to the neurofeedback treatment itself. After neurofeedback treatment, there were also no significant differences in all features between the experimental group and the control group after correction, which may be due to the interaction between the group and time. The results of the paired t-test for the experimental group before and after neurofeedback treatment are shown in Table 3. As can be seen from Table 3, before and after neurofeedback treatment, the transition probability 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.

[0043] Table 3 In step S105, a brain power analysis was performed on the population microstate templates corresponding to the microstate transition pairs to determine the functional brain network related to 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, and achieve the intervention of nicotine addiction.

[0044] In the embodiments of the present invention, in order to further clarify the physiological functional significance of the population microstate templates, brain power analysis is performed on the population microstate templates corresponding to the microstate transitions to determine the functional brain networks related to neurofeedback training. Based on the activation characteristics of the functional brain networks, nicotine-addicted subjects are guided to actively adjust their brain activity patterns through neurofeedback closed-loop training to achieve the intervention of nicotine addiction.

[0045] In a specific embodiment, for the transition pair MS2 and MS1 with significant interaction effects, brain power analysis is performed using the electroencephalogram (EEG) source localization method. Specifically, the spatio-temporal source estimation (Topographic Electro-physiological State Source-imaging, TESS) algorithm is used to obtain the position information β values of these two templates in the source space. These values reflect the neural activity intensity of the brain in the microstate. Then, one-sample t-tests and false discovery rate (FDR) corrections (correction level p < 0.001) are performed on the β values to obtain the activation information of the two templates at the voxel level. Then, according to the cortical template of Schaefer400 and the subcortical template containing 16 regions of interest (ROIs), the voxels are assigned to each ROI to obtain the activation information of the two templates at the ROI level. Finally, the ROIs are assigned to 8 and 18 typical brain networks to obtain the activation states of the two templates at the network level, that is, the functional brain networks corresponding to these two templates. Finally, according to the functions of different brain networks, the physiological functional significance and their conversion relationships corresponding to the two templates are analyzed. The activation of MS1 and MS2 in 8 and 18 typical brain networks is as Figure 4 shown. As can be seen from Figure 4 (a) of, in the 8 typical brain networks, the activation of MS1 is mainly concentrated in the ventral attention network and the default mode network, which is mainly related to visual information processing, self-related thinking, emotion processing, and introspection. The activation of MS2 is mainly distributed with relatively large weights in the ventral attention network, the default mode network, the dorsal attention network, and the sensorimotor network, indicating that MS2 may be related to vision, sensation, movement, attention maintenance, and spatial orientation. The distribution difference in the activation of MS1 and MS2 may reflect the functional division of labor between emotion processing and cognitive regulation. As can be seen from 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.

[0046] 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.

[0047] Example 2: 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: 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; The EEG signal processing unit 52 is used to pre-process each collected resting-state EEG signal to obtain a corresponding processed EEG signal; The electroencephalogram (EEG) signal analysis unit 53 is configured to perform EEG microstate analysis on all processed EEG signals to obtain a plurality of group microstate templates and the microstate features of each processed EEG signal. The microstate screening unit 54 is configured to construct a linear mixed-effects model based on the microstate features, and screen out the microstate transition pairs with statistical significance through two-way interaction analysis of the linear mixed-effects model. The functional brain determination unit 55 is configured to perform brain source analysis on the group microstate templates corresponding to the microstate transition pairs, determine the functional brain network related to neurofeedback training, and based on the activation characteristics of the functional brain network, guide nicotine-addicted subjects to actively adjust their brain activity patterns through neurofeedback closed-loop training, so as to achieve the intervention of nicotine addiction.

[0048] Preferably, the EEG signal analysis unit 53 includes: The clustering analysis unit is configured to perform spatio-temporal clustering analysis on all processed EEG signals to generate group microstate templates. The feature extraction unit is configured to extract features from each processed EEG signal according to the group microstate templates to obtain microstate features.

[0049] The clustering analysis unit includes: The power calculation unit is configured to calculate the global field power of each processed EEG signal. The first clustering unit is configured to cluster the EEG topographic maps at the global field power peaks to obtain a plurality of individual microstate templates for each nicotine-addicted subject. The second clustering unit is configured to cluster the individual microstate templates of all nicotine-addicted subjects to obtain group microstate templates.

[0050] In the embodiments of the present invention, for the convenience and conciseness of description, only the above-mentioned functional units and modules are used as examples for illustration. In practical applications, the above functions can be allocated to different functional units and modules according to needs, 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. Each unit and module of the device can be implemented by corresponding hardware or software units. The units and modules can be independent software and hardware units, or integrated into a software and hardware unit, which is not used to limit the present invention. In addition, the specific names of the functional units and modules are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the device can refer to the corresponding descriptions in the foregoing method embodiments and will not be repeated here.

[0051] Embodiment III: Figure 6The structure of the computing device provided in the third embodiment of the present invention is shown. For ease of illustration, only the parts related to the embodiments of the present invention are shown.

[0052] The computing device 6 in 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 in the above-mentioned embodiment of the functional brain network determination method for nicotine addiction intervention are implemented, such as Figure 1 the steps S101 to S105 shown. Alternatively, when the processor 60 executes the computer program 62, the functions of each unit in the above-mentioned device embodiments are implemented, such as Figure 5 the functions of the units shown.

[0053] In the embodiment of the present invention, the resting-state electroencephalogram (EEG) signals of nicotine-addicted subjects in the experimental group and the control group are collected respectively under the guidance of the smoking paradigm. The collected resting-state EEG signals are preprocessed, and EEG microstate analysis is performed on all the processed EEG signals obtained, to obtain a number of group microstate templates and the microstate features of each processed EEG signal. Through two-way interaction analysis of the linear mixed effect model constructed according to the microstate features, the microstate transition pairs with statistical significance are screened out. Brain 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, so as to perform nicotine addiction intervention according to the functional brain network, thereby achieving a more refined capture of the subtle changes in the brain function state before and after neurofeedback treatment, and providing more objective and quantitative neural markers for revealing its intervention mechanism and optimizing treatment strategies.

[0054] The computing device in the embodiment of the present invention can be a personal computer. The steps implemented when the processor 60 in the computing device 6 executes the computer program 62 to implement the functional brain network determination method for nicotine addiction intervention can refer to the description of the foregoing method embodiments and will not be elaborated here.

[0055] Embodiment 4: In the embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned embodiment of the functional brain network determination method for nicotine addiction intervention are implemented, for example, Figure 1 the steps S101 to S105 shown. Alternatively, when the computer program is executed by a processor, the functions of each unit in the above-mentioned device embodiments are implemented, such as Figure 5 the functions of the units shown.

[0056] In the embodiments of the present invention, the resting-state electroencephalogram (EEG) signals of nicotine-addicted subjects in the experimental group and the control group are respectively collected under the guidance of a smoking paradigm. The collected resting-state EEG signals are preprocessed, and EEG microstate analysis is performed on all the processed EEG signals to obtain a number of group microstate templates and the microstate features of each processed EEG signal. Through two-way interaction analysis of a linear mixed-effects model constructed based on the microstate features, microstate transition pairs with statistical significance are screened out. Brain source analysis is performed on the group microstate templates corresponding to the microstate transition pairs to determine the functional brain networks related to neurofeedback training, so as to perform nicotine addiction intervention based on the functional brain networks, thereby achieving a more refined capture of the subtle changes in the 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.

[0057] The computer-readable storage medium in the embodiments of the present invention may, for example, be but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection with 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 of the above. In the embodiments of the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0058] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing 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, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0059] Moreover, although the operations are depicted in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the foregoing discussion, these should not be construed as limitations on the scope of the invention. Certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented separately or in any suitable subcombination in multiple embodiments.

Claims

1. A method for determining a functional brain network for nicotine addiction intervention, characterized in that, The method includes the following steps: Collect the resting-state electroencephalogram (EEG) signals of nicotine-addicted subjects in two groups, namely the experimental group and the control group, under the guidance of a pre-constructed smoking paradigm. Among them, each nicotine-addicted subject in the experimental group undergoes neurofeedback training in the smoking paradigm, while each nicotine-addicted subject in the control group does not undergo the neurofeedback training; Preprocess each of the collected resting-state EEG signals to obtain the corresponding processed EEG signals; Perform EEG microstate analysis on all the processed EEG signals to obtain a number of group microstate templates and the microstate features of each of the processed EEG signals; Construct a linear mixed-effects model based on the microstate features, and screen out the microstate transition pairs with statistical significance through two-way interaction analysis of the linear mixed-effects model; Perform brain source analysis on the group microstate templates corresponding to the microstate transition pairs to determine the functional brain networks related to the neurofeedback training, and based on the activation characteristics of the functional brain networks, guide nicotine-addicted subjects to actively adjust their brain activity patterns through neurofeedback closed-loop training to achieve the intervention of nicotine addiction.

2. The method according to claim 1, characterized in that, The step of performing EEG microstate analysis on all the processed EEG signals includes: Perform spatio-temporal clustering analysis on all the processed EEG signals to generate the group microstate templates; Extract features from each of the processed EEG signals according to the group microstate templates to obtain the microstate features.

3. The method according to claim 2, wherein The step of performing spatio-temporal clustering analysis on all the processed EEG signals includes: Calculate the global field power of each of the processed EEG signals; Cluster the EEG topographies at the peak of the global field power to obtain a number of individual microstate templates for each nicotine-addicted subject; Cluster the individual microstate templates of all nicotine-addicted subjects to obtain the group microstate templates.

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 templates includes: Inverse fit each of the group microstate templates to each of the processed EEG signals to obtain the corresponding EEG microstate sequences; Smooth the EEG microstate sequences; Extract the microstate features from the smoothed EEG microstate sequences.

5. The method according to claim 1, characterized in that, The model formula of the linear mixed effects model is , where represents the eigenvalue of the microstate feature, represents the time variable, represents the group variable, represents the nicotine-addicted subjects, represents the time-group interaction term, used to control the individual repeated measurement effect.

6. A functional brain network determination device for nicotine addiction intervention, characterized in that, The device includes: An EEG signal acquisition unit for collecting the resting-state EEG signals of nicotine-addicted subjects in two groups, namely the experimental group and the control group, under the guidance of a pre-constructed smoking paradigm. Among them, each nicotine-addicted subject in the experimental group undergoes neurofeedback training in the smoking paradigm, while each nicotine-addicted subject in the control group does not undergo the neurofeedback training; An EEG signal processing unit for preprocessing each of the collected resting-state EEG signals to obtain the corresponding processed EEG signals; An EEG signal analysis unit for performing EEG microstate analysis on all the processed EEG signals to obtain a number of group microstate templates and the microstate features of each of the processed EEG signals; A microstate screening unit, configured to construct a linear mixed-effects model based on the microstate features, and screen out microstate transition pairs with statistical significance through two-way interaction analysis of the linear mixed-effects model; A functional brain determination unit, configured to perform brain power analysis on the population microstate templates corresponding to the microstate transition pairs, determine the functional brain network related to the neurofeedback training, and based on the activation characteristics of the functional brain network, guide nicotine-addicted subjects to actively adjust the brain activity pattern through neurofeedback closed-loop training, so as to achieve the intervention of nicotine addiction.

7. The device according to claim 6, characterized in that, The electroencephalogram signal analysis unit includes: A clustering analysis unit, configured to perform spatio-temporal clustering analysis on all the processed electroencephalogram signals to generate the population microstate templates; A feature extraction unit, configured to extract features from each of the processed electroencephalogram signals according to the population microstate templates to obtain the microstate features.

8. The device according to claim 7, characterized in that The clustering analysis unit includes: A power calculation unit, configured to calculate the global field power of each of the processed electroencephalogram signals; A first clustering unit, configured to cluster the electroencephalogram topographies at the global field power peaks to obtain several individual microstate templates for each nicotine-addicted subject; A second clustering unit, configured to cluster the individual microstate templates of all the nicotine-addicted subjects to obtain the population microstate templates.

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

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