Method for detecting brain function activation level lateralization based on near-infrared image data

Abstract

The application discloses a brain function activation level lateralization detection method based on near-infrared image data, comprising the following steps: acquiring near-infrared image data and extracting at least one group of brain function activation indexes of left and right brain areas from the near-infrared image data; acquiring optimal solutions of a, b and c calculated according to a lateralization calculation model; comparing the extracted brain function activation indexes with 0, a, b and c respectively according to a preset lateralization detection process, and outputting a lateralization detection result. The application is based on near-infrared image data, analyzes the near-infrared blood oxygen data characteristics of the left brain and the right brain time sequence, accurately identifies the size difference of the function activation levels between the left brain and the right brain, provides help for clinical departments to timely and effectively identify the brain function activation characteristics of mental disease patients, and provides a feasible neurobiological marker for evaluating the treatment effect and tracking the prognosis of patients.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for detecting brain function activation level lateralization based on near-infrared image data. BACKGROUND

[0002] Near-infrared imaging technology is a non-invasive brain function imaging technology, which mainly uses the difference in absorption rate of near-infrared light of different wavelengths (600-900 nm) by oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in brain tissue to detect the hemodynamic activity of the cerebral cortex in real time and directly, and at the same time, indirectly assess the brain neural activity status by combining the neural vascular coupling rule. When using near-infrared imaging technology to measure brain function, a task or paradigm is usually combined to carry out, the purpose is to observe the brain blood flow activity pattern of the subject when performing a specific task, so as to judge whether the task can cause the brain function activation of the subject and the size of the activation level.

[0003] In recent years, many researchers have carried out a large number of researches on neurobiological markers and neuroimaging characteristics of mental diseases by using near-infrared imaging technology, and found that there are significant differences in brain function activation patterns between mental disease patients and healthy controls when performing VFT (Verbal Fluency Task, verbal fluency task). One of the more obvious differences is that some mental disease patients will show inconsistent activation levels between the left and right brains, while this feature called lateralization is less common in healthy controls. Therefore, the lateralization feature has great value for identifying mental disease patients. However, although this feature has been reported in many studies, there is currently no algorithm to detect whether lateralization exists.

[0004] Some researchers have proposed using lateralization index as an indicator to try to study the lateralization feature, but this indicator still has some problems that make it difficult to be applied in practice, these problems include:

[0005] 1. There is no uniform threshold to distinguish between healthy controls and mental disease patients for this indicator, and there are not many cases in previous literature, and there is a lack of research based on large sample data mining methods;

[0006] 2. In some cases, calculating the lateralization index has no practical significance, such as when the activation levels of the left and right brains are both negative, at this time there is no activation in the left and right brains, so there is no activation left or right, and previous methods have not defined this situation;

[0007] 3. The lateralization index may misjudge, because the brain activity has a hemispheric dominance phenomenon, and the slight lateralization of healthy controls is completely normal, but the lateralization caused by disease is different, and there is currently no algorithm to make this judgment. SUMMARY

[0008] To solve the problem of how to accurately and effectively identify the lateralization of brain function activation level, the present application provides a method for detecting the lateralization of brain function activation level based on near-infrared image data. The method is based on near-infrared image data and analyzes the characteristics of near-infrared blood oxygen data time series of left and right brain to accurately identify the size difference of function activation level between left and right brain, which provides help for clinical departments to timely and effectively identify the brain function activation characteristics of patients with mental illness, and also provides a feasible neurobiological marker for evaluating treatment effect and tracking patient prognosis.

[0009] According to an aspect of the present application, a method for detecting the lateralization of brain function activation level based on near-infrared image data is provided, comprising:

[0010] Obtaining near-infrared image data and extracting at least one set of brain function activation indicators of left and right brain regions therefrom;

[0011] Obtaining the optimal solutions of a, b and c calculated according to the following lateralization calculation model:

[0012]

[0013] Wherein a is the minimum activation difference, b is the minimum activation threshold, c is the lateralization index threshold, P(Y=1) is the probability of lateralization of the current data, and β0, β1, β2 and β3 are model parameters;

[0014] Comparing the extracted at least one set of brain function activation indicators of left and right brain regions with 0, a, b and c respectively according to a preset lateralization detection process, and outputting the lateralization detection result.

[0015] Preferably, the at least one set of brain function activation indicators of left and right brain regions includes but is not limited to: task period oxyhemoglobin integral value, oxyhemoglobin beta value, oxyhemoglobin mean value or oxyhemoglobin peak value.

[0016] As a further technical solution, comparing the extracted at least one set of brain function activation indicators of left and right brain regions with 0, a, b and c respectively according to a preset lateralization detection process includes:

[0017] Judging whether the brain function activation indicator of the left brain region is less than 0;

[0018] If the brain function activation indicator of the left brain region is less than 0, judging whether the brain function activation indicator of the right brain region is less than 0, and if so, outputting no lateralization.

[0019] As a further technical solution, when the brain function activation indicator of the left brain region is greater than or equal to 0, it further includes:

[0020] Determine whether the brain function activation index in the left brain region is less than or equal to b. If not, output left lateralization when the brain function activation index in the right brain region is less than 0.

[0021] As a further technical solution, if the brain function activation index of the left brain region is greater than b and the brain function activation index of the right brain region is greater than or equal to 0, or the brain function activation index of the left brain region is less than or equal to b and the brain function activation index of the right brain region is greater than b, then the detection result is given according to the following calculation result of g(T):

[0022] ,

[0023] ,

[0024] Among them, IV 左 Indicators of brain function activation in the left hemisphere, IV 右 This indicates an indicator of brain function activation in the right brain region.

[0025] As a further technical solution, when the brain function activation index in the left brain region is less than or equal to b, it also includes:

[0026] Determine whether the right brain region brain function activation index is less than 0. If not, output is unbiased when the right brain region brain function activation index is less than or equal to b.

[0027] As a further technical solution, when the brain function activation index in the right brain region is less than 0, it also includes:

[0028] Determine whether the difference between the brain function activation index of the left brain region and the brain function activation index of the right brain region is greater than or equal to 'a'. If yes, output left lateralization; otherwise, output no lateralization.

[0029] As a further technical solution, when the brain function activation index in the left brain region is less than 0 and the brain function activation index in the right brain region is greater than or equal to 0, it also includes:

[0030] Determine whether the difference between the brain function activation index of the right brain region and the brain function activation index of the left brain region is greater than or equal to 'a'. If yes, output right lateralization; otherwise, output no lateralization.

[0031] As a further technical solution, the method also includes:

[0032] Based on the constructed objective function and constraints, and using a near-infrared image data sample set, the simulated annealing algorithm is used to solve the lateralization calculation model to obtain the optimal solutions for a, b, and c.

[0033] As a further technical solution, the objective function is constructed as follows:

[0034] ,

[0035] The constraint condition is:

[0036] ,

[0037] Wherein, , respectively represent the number of healthy people and patients, , respectively represent whether the data of healthy people and patients exist lateralization.

[0038] According to an aspect of the present application, a brain function activation level lateralization detection system based on near-infrared image data is provided for implementing the method, comprising:

[0039] A first main module is used for acquiring near-infrared image data and extracting at least one set of brain function activation indicators of left and right brain regions from the near-infrared image data;

[0040] A second main module is used for acquiring the optimal solution of a, b and c calculated according to the following lateralization calculation model:

[0041] ,

[0042] Wherein, a is the minimum activation difference, b is the minimum activation threshold, c is the lateralization index threshold, P(Y=1) is the probability of the current data existing lateralization, and β0, β1, β2 and β3 are all model parameters;

[0043] A third main module is used for comparing the extracted at least one set of brain function activation indicators of the left and right brain regions with 0, a, b and c respectively according to a preset lateralization detection process, and outputting a lateralization detection result.

[0044] Compared with the prior art, the present application has the following beneficial effects:

[0045] The present application analyzes and mines the near-infrared image data of mental illness patients and healthy people, proposes a discrimination method for detecting whether there is lateralization feature, can assist in identifying whether the brain function activation exists lateralization, which is a neural biomarker of mental illness, and then assist the clinician to objectively make a diagnosis, and can also provide an objective basis for the doctor to formulate a subsequent treatment plan. The present application provides a feasible solution to the problem of excessive subjectivity caused by the clinician relying on behavior observation, scale or experience judgment for diagnosis and treatment, which has great potential value and significance for the clinical work of the clinical related department. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0047] Figure 1 The flowchart of the brain function activation level lateralization detection method based on near-infrared image data provided by the embodiments of the present application is shown.

[0048] Figure 2 The judgment logic diagram of the brain function activation level lateralization detection based on near-infrared image data provided by the embodiments of the present application is shown.

[0049] Figure 3 The result diagram after the near-infrared image data analysis and processing of a mental illness patient provided by the embodiments of the present application is shown.

[0050] Figure 4 The result diagram after the near-infrared image data analysis and processing of another mental illness patient provided by the embodiments of the present application is shown.

[0051] Figure 5 The result diagram after the near-infrared image data analysis and processing of a healthy person provided by the embodiments of the present application is shown. DETAILED DESCRIPTION

[0052] The technical solutions of the embodiments of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of the present application.

[0053] The present application is based on large sample norm database research and development, and describes a detection method for brain function activation level lateralization. Please refer to Figure 1 The embodiments of the present application provide a brain function activation level lateralization detection method based on near-infrared image data. First, near-infrared image data is obtained and at least one set of brain function activation indicators of left and right brain regions is extracted from the data. Then, the optimal solutions of a, b and c calculated according to the lateralization calculation model are obtained. Subsequently, the extracted at least one set of brain function activation indicators of the left and right brain regions are compared with 0, a, b and c according to the preset lateralization detection process, and the lateralization detection result is output.

[0054] The brain function activation level lateralization detection method based on near-infrared image data provided by the embodiments of the present application specifically includes the following steps:

[0055] 1. Collecting near-infrared image data of a subject when performing VFT.

[0056] 2. Processing the collected data, following the basic process of near-infrared image data, to extract the concentration changes of oxyhemoglobin (HbO2) in the brain when performing VFT.

[0057] 3. Calculating the task period HbO2 integral value, oxyhemoglobin beta value, oxyhemoglobin mean value or oxyhemoglobin peak value of each brain region. According to previous literature research, patients with mental illness will show abnormal activation function in the left frontal lobe, right frontal lobe, left temporal lobe and right temporal lobe. Therefore, the embodiment of the present application mainly calculates the activation degree of the above four brain regions. The embodiment of the present application takes the task period HbO2 integral value as the brain function activation index for illustration. The integral value is an index for measuring the intensity of cerebral hemodynamic response. By calculating the area under the curve of the change of HbO2 concentration with time during the task period, the size of the increase of HbO2 concentration during the task period is reflected, and then the activation degree of the brain region is reflected. The calculation formula of the integral value (IV) is as follows:

[0058] ,

[0059] Where t is the time point, f(t) is the HbO2 concentration at time t, and a and b represent the start time point and end time point of the task period respectively.

[0060] It should be noted that the calculation of the oxyhemoglobin beta value, the oxyhemoglobin mean value or the oxyhemoglobin peak value can be realized by using the existing technology. The key point of the present application is to output the preset process of lateralization result according to the minimum activation difference (a), the minimum activation threshold (b) and the lateralization index threshold (c) and the brain function activation index, without limiting the selection and calculation of the brain function activation index.

[0061] 4. Determine whether the left and right brain function activation levels of the subject exist lateralization according to the following algorithm. Starting from the principle of near-infrared detection, the embodiment of the present application distinguishes the different situations of positive and negative integral values of left and right brain, and defines the following three parameters for quantitative judgment: minimum activation difference (a), minimum activation threshold (b) and lateralization index threshold (c). The minimum activation difference refers to that even if there is a certain difference between the left and right brain, but it does not reach a certain degree, at this time, there is still no lateralization. The minimum activation threshold refers to that even if the integral value is positive, but it does not reach a certain numerical range, at this time, there is still no activation. The lateralization index threshold refers to that when the condition is met, whether there is lateralization is calculated by the formula g(T), the value needs to exceed the threshold value to be recognized as lateralization. a, b, c are all positive values.

[0062] Taking the task period HbO2 integral value as the brain function activation index as an example, the specific judgment implementation process is as shown in FIG. 1 Figure 2 左 represents the left brain region integral value (i.e. the task period oxygenated hemoglobin integral value of the left brain region), IV 右 represents the right brain region integral value (i.e. the task period oxygenated hemoglobin integral value of the right brain region). The calculation formula of g(T) is as follows:

[0063] ,

[0064] The calculation formula of T is as follows:

[0065] .

[0066] The specific judgment process is as follows:

[0067] Firstly, it is judged whether IV 左 is less than 0, if yes, it is further judged whether IV 右 is less than 0.

[0068] If IV 右 is less than 0, the output is no lateralization.

[0069] If IV 右 is greater than or equal to 0, it is judged whether IV 右 -IV 左 is greater than or equal to a, if yes, the output is right lateralization, if not, the output is no lateralization.

[0070] Then, when IV 左 is greater than or equal to 0, it is further judged whether IV 左 is less than or equal to b.

[0071] If IV 左 is less than or equal to b, it is further judged whether IV 右 is less than 0. If yes, it is further judged whether IV 左 -IV 右 is greater than or equal to a: if yes, the output is left lateralization; if not, the output is no lateralization.

[0072] When IV 左 is less than or equal to b, and IV 右 is greater than or equal to 0, it is judged whether IV 右 is less than or equal to b. If yes, the output is no lateralization; if not, g(T) is calculated, and according to the calculation result, it is judged whether it is left lateralization, right lateralization or no lateralization.

[0073] Further, if IV 左 is greater than b, it is further judged whether IV 右 ​whether less than 0, if yes, output left lateralization; if no, calculate g(T), and determine left lateralization, right lateralization or no lateralization according to the calculation result.

[0074] The optimal solution solving process of the lateralization calculation model described in the embodiment of the application includes the following flow:

[0075] The collected large sample near-infrared image data is divided into a training set and a test set. The training set has 1552 cases, including 762 cases of mental illness patients and 790 cases of healthy people. The test set has 4280 cases, including 2011 cases of mental illness patients and 2269 cases of healthy people. The training set is used to study the image data features, combine the unsupervised learning method to optimize the parameter values, and improve the accuracy of the algorithm. The test set is mainly used to verify the accuracy of the algorithm, determine the generalizability of the algorithm, and evaluate the performance of the algorithm. The training set and the test set are not contacted in the process of model training and parameter tuning, that is, they belong to two batches of relatively independent data, which can be regarded as: the a, b and c calculated from the training set are included in the lateralization calculation model, and then verified in the test set to determine whether it can be generalized to other conditions.

[0076] Specifically, the training set is first learned and arranged, the lateralization features of the brain function activation level in the training set data are integrated, and the three core parameters in the algorithm, that is, the minimum activation difference (a), the minimum activation threshold (b) and the lateralization index threshold (c), are optimized.

[0077] The training process of the lateralization calculation model is as follows:

[0078] Firstly, according to the above algorithm flow, whether a piece of near-infrared image data has lateralization is determined by the values of a, b and c, that is:

[0079] ,

[0080] Wherein, P(Y=1) is the probability of the existence of lateralization of the data, a, b and c are the minimum activation difference, the minimum activation threshold and the lateralization index threshold respectively. The embodiment of the application adopts a logstic regression model to construct the lateralization calculation model, wherein β0, β1, β2 and β3 are parameters of the logstic regression model.

[0081] According to the probability, the classification is as follows:

[0082] ,

[0083] Wherein, That is, it is judged whether the data has lateralization, "1" means that there is lateralization, and "0" means that there is no lateralization.

[0084] Secondly, according to the meta-analysis research literature, the proportion of lateralization in the population of patients with mental illness is more than 30%, while in the healthy population, only less than 5% of people will appear lateralization. Therefore, there are the following constraints:

[0085] (1) , and the value is as large as possible.

[0086] (2) , and the value is as small as possible.

[0087] Finally, according to the literature research and clinical experience, the proportion of lateralization in the population of patients with mental illness is much larger than that in the healthy population, that is, a, b, c need to be optimized to meet the above constraints, and have:

[0088] ,

[0089] According to the objective function and the constraint condition, the simulated annealing algorithm is used to obtain the optimal solution S=(a, b, c). The simulated annealing algorithm starts from a higher initial temperature, accompanied by the continuous decrease of the temperature parameter, and combines the probability of jumping characteristics to find the global optimal solution of the objective function in the solution space, that is, the local optimal solution can be probabilistically jumped out and eventually tend to be global optimal. The specific algorithm implementation process is as follows:

[0090] 1. Initialization, set the initial temperature T, the initial solution state S, and the iteration number of each T value is L;

[0091] 2. Perform steps 3 to 6 for k=1,....,L;

[0092] 3. Generate a new solution S1 according to the current temperature and the predetermined condition;

[0093] 4. Calculate the objective function increment ;

[0094] 5. If , then accept S1 as the new current solution, otherwise accept S1 as the new current solution with a probability ;

[0095] 6. If the termination condition is met, output the current solution as the optimal solution;

[0096] 7. T is reduced, so that T gradually tends to 0, and then go to step 2.

[0097] The simulated annealing algorithm avoids falling into local optimization in the process of finding the solution through the probability of jumping characteristics, so as to find the global optimal solution of the objective function. In the process of generating a new solution S1, the size of will be used to dynamically adjust the generation of appropriate new solutions, that is, when When the value is large, the difference between the new solution and the old solution is large; conversely, when the value is small, the difference is small. This facilitates rapid convergence of the solution and reduces the time consumed.

[0098] As one implementation method, such as Figure 3 The image shows the results of near-infrared imaging data analysis of a patient with mental illness. The brain region is the temporal lobe, the horizontal axis represents time, and the vertical axis represents HbO2 concentration. The calculated integral value for the left temporal lobe is 76.0138, and the integral value for the right temporal lobe is 327.0274. According to the algorithm, the calculation conditions are met. The T-value is -0.6228, which meets the threshold judgment condition, indicating right lateralization.

[0099] As one implementation method, such as Figure 4 The image shows the results of near-infrared imaging data analysis of a patient with mental illness. The brain region is the temporal lobe, the horizontal axis represents time, and the vertical axis represents HbO2 concentration. The calculated integral value for the left temporal lobe is 288.6495, and the integral value for the right temporal lobe is -70.4879. According to the algorithm, this can be directly determined as left lateralization.

[0100] As one implementation method, such as Figure 5 The image shows the results of near-infrared imaging data analysis of a healthy individual. The brain region is the temporal lobe, the horizontal axis represents time, and the vertical axis represents HbO2 concentration. The calculated integral value for the left temporal lobe is 115.7382, and the integral value for the right temporal lobe is 168.4069, which, according to the algorithm, indicates no lateralization.

[0101] Based on the same inventive concept as the foregoing method embodiments, this embodiment of the invention also provides a brain functional activation level lateralization detection system based on near-infrared image data, used to implement the method described in any of the foregoing embodiments.

[0102] The system includes: a first main module for acquiring near-infrared image data and extracting at least one set of brain function activation indicators for the left and right hemispheres; and a second main module for acquiring the optimal solutions for a, b, and c calculated according to the following lateralization calculation model: Where a is the minimum activation difference, b is the minimum activation threshold, c is the lateralization index threshold, P(Y=1) is the probability that the current data has lateralization, and β0, β1, β2 and β3 are model parameters; the third main module is used to compare the extracted brain function activation indicators of at least one set of the left and right brain regions with 0, a, b and c respectively according to the preset lateralization detection process, and output the lateralization detection results.

[0103] The system described in this invention can help identify neurobiomarkers of mental illness, such as lateralization of brain function activation, thereby assisting clinicians in making objective diagnoses and providing objective evidence for doctors to formulate subsequent treatment plans.

[0104] It should be noted that the above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting lateralization of brain functional activation levels based on near-infrared imaging data, characterized in that, include: Acquire near-infrared image data and extract at least one set of brain function activation indicators for the left and right hemispheres from it; Obtain the optimal solutions for a, b, and c calculated using the following lateralization computation model: ， Where a is the minimum activation difference, b is the minimum activation threshold, c is the lateralization index threshold, P(Y=1) is the probability that the current data is lateralized, and β0, β1, β2 and β3 are model parameters. The extracted brain function activation indices of at least one set from the left and right brain regions are compared with 0, a, b, and c respectively according to a preset lateralization detection procedure, and the lateralization detection results are output. Specifically, when the brain function activation index of the left brain region is greater than or equal to 0, if the brain function activation index of the left brain region is greater than b and the brain function activation index of the right brain region is greater than or equal to 0, or if the brain function activation index of the left brain region is less than or equal to b and the brain function activation index of the right brain region is greater than b, then the detection result is given based on the following calculation result of g(T): ， ， Among them, IV 左 Indicators of brain function activation in the left hemisphere, IV 右 This indicates an indicator of brain function activation in the right brain region.

2. The method for detecting lateralization of brain functional activation levels based on near-infrared imaging data according to claim 1, characterized in that, The extracted brain function activation indicators of at least one set of the left and right brain regions are compared with 0, a, b, and c according to a preset lateralization detection procedure, including: Determine whether the brain function activation index in the left brain region is less than 0; When the brain function activation index in the left brain region is less than 0, determine whether the brain function activation index in the right brain region is less than 0. If so, output unbiased.

3. The method for detecting lateralization of brain functional activation levels based on near-infrared imaging data according to claim 2, characterized in that, When the brain function activation index in the left brain region is greater than or equal to 0, it also includes: Determine whether the brain function activation index in the left brain region is less than or equal to b. If not, output left lateralization when the brain function activation index in the right brain region is less than 0.

4. The method for detecting lateralization of brain functional activation levels based on near-infrared imaging data according to claim 3, characterized in that, When the brain function activation index in the left brain region is less than or equal to b, it also includes: Determine whether the right brain region brain function activation index is less than 0. If not, output is unbiased when the right brain region brain function activation index is less than or equal to b.

5. The method for detecting lateralization of brain functional activation levels based on near-infrared imaging data according to claim 4, characterized in that, When the brain function activation index in the right brain region is less than 0, it also includes: Determine whether the difference between the brain function activation index of the left brain region and the brain function activation index of the right brain region is greater than or equal to 'a'. If yes, output left lateralization; otherwise, output no lateralization.

6. The method for detecting lateralization of brain functional activation levels based on near-infrared imaging data according to claim 2, characterized in that, When the brain function activation index in the left brain region is less than 0 and the brain function activation index in the right brain region is greater than or equal to 0, it also includes: Determine whether the difference between the brain function activation index of the right brain region and the brain function activation index of the left brain region is greater than or equal to 'a'. If yes, output right lateralization; otherwise, output no lateralization.

7. The method for detecting lateralization of brain functional activation levels based on near-infrared imaging data according to claim 1, characterized in that, The method further includes: Based on the constructed objective function and constraints, and using a near-infrared image data sample set, the simulated annealing algorithm is used to solve the lateralization calculation model to obtain the optimal solutions for a, b, and c.

8. The method for detecting lateralization of brain functional activation levels based on near-infrared imaging data according to claim 7, characterized in that, The objective function constructed is: ， The constraints are: ， in, , These represent the number of healthy individuals and the number of patients, respectively. , This indicates whether there is lateralization in the data of healthy individuals and patients, respectively.

9. A brain functional activation level lateralization detection system based on near-infrared imaging data, used to implement the method according to any one of claims 1-8, characterized in that, include: The first main module is used to acquire near-infrared image data and extract at least one set of brain function activation indicators for the left and right hemispheres. The second main module is used to obtain the optimal solutions for a, b, and c calculated according to the following lateralization calculation model: ， Where a is the minimum activation difference, b is the minimum activation threshold, c is the lateralization index threshold, P(Y=1) is the probability that the current data is lateralized, and β0, β1, β2 and β3 are model parameters. The third main module is used to compare the extracted brain function activation indicators of at least one set of the left and right brain regions with 0, a, b and c respectively according to the preset lateralization detection process, and output the lateralization detection results.

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