Brain network analysis method, device, equipment and medium for social conformity effect

Abstract

The present invention is applicable to the field of brain science and technology, and provides a brain network analysis method for the social conformity effect, the method comprising: performing magnetic resonance imaging on the brains of several participants when they are performing risk decision-making tasks with different decision-making conditions, obtaining a first brain function image of each participant, preprocessing each first brain function image, performing brain activation analysis on each second brain function image obtained after the preprocessing, obtaining active brain regions significantly activated in the risk decision-making task of each participant, performing a psychophysiological interaction analysis on the second brain function image based on the active brain regions, obtaining a dynamic connection pattern between different brain regions in the individual's brain, so as to reveal the dynamic neural network of the brain in the social conformity effect decision-making situation, thereby realizing a more accurate and comprehensive method to reveal the action mechanism of the social conformity effect at the neural level, and laying a scientific foundation for the development of targeted risk management and behavioral intervention strategies.

Description

Technical Field

[0001] The present invention belongs to the field of brain science technology, and in particular relates to a brain network analysis method, device, equipment and medium for social conformity effect. Background Art

[0002] How humans and non-human animals understand and represent the internal states and experiences of others has always been an important research topic in the field of self-other learning, decision-making, and social interaction. Conformity refers to the behavior of individuals in self-perception, judgment, and cognition that conforms to public opinion or the majority, influenced by the external environment and the behavior of social groups. Recent studies have shown that the social conformity effect has a profound impact on individual beliefs and behaviors, especially in individual decision-making, including risk-related choices. Observing the behavior of others can significantly affect individual decision-making behavior. Therefore, a deep understanding of the impact of the social conformity effect on risk decision-making behavior is crucial for social decision-making and development. At the neuroscience level, how observing the risk behavior of others regulates one's own risk preferences has relevant neural representations. By combining neuroimaging and computational modeling methods, it can be revealed that the change in decision-making behavior is reflected in value-related neural activities. The study found that learning about others' risk preferences interacts with the dorsolateral prefrontal cortex (dlPFC), while responding to the gap between personal and group cognition triggers activity in the dorsomedial prefrontal cortex (dmPFC) and the dorsal anterior cingulate cortex (dACC). Further understanding of the neural mechanisms of the social conformity effect on risk decision-making behavior can reveal the cognitive process and neural mechanisms of decision-making behavior, and provide theoretical guidance and practical significance for practical applications (such as risk management and behavioral intervention).

[0003] Currently, research on the social conformity effect is mainly focused on the behavioral level, while the exploration of neural mechanisms is still in its infancy. Although some studies have attempted to use neuroimaging techniques such as functional Magnetic Resonance Imaging (fMRI) to explore the neurological basis of decision-making behavior, these studies generally lack in-depth analysis of the social conformity effect and rarely involve comparative studies of individuals of different age groups. Therefore, existing technologies still have significant limitations in understanding and explaining the neural mechanisms of the social conformity effect on individual risk decision-making behavior. Summary of the invention

[0004] The purpose of the present invention is to provide a brain network analysis method, device, equipment and medium for the social conformity effect, aiming to solve the problem that the existing technology cannot fully capture the dynamic changes of the brain in the risk decision-making process, and is insufficient in understanding the impact of the social conformity effect on individual risk preferences.

[0005] In one aspect, the present invention provides a brain network analysis method for social conformity effect, the method comprising the following steps:

[0006] Performing magnetic resonance imaging on the brains of several participants while performing risk decision-making tasks under different decision-making conditions, to obtain a brain structural image and a first brain functional image of each participant, wherein the decision-making conditions include an individual decision-making situation and a social conformity effect decision-making situation;

[0007] Based on each of the brain structural images, preprocessing the corresponding first brain functional image to obtain a corresponding second brain functional image;

[0008] Performing brain activation analysis on each of the second brain function images to obtain active brain regions that are significantly activated by each of the participants in the risk decision-making task;

[0009] Based on the active brain areas, a psychophysiological interaction analysis is performed on the second brain functional image to obtain the dynamic connection pattern between different brain areas in the individual's brain, so as to reveal the dynamic neural network of the brain in the social conformity effect decision-making situation.

[0010] Preferably, based on each of the brain structural images, the step of preprocessing the corresponding first brain functional image comprises:

[0011] Remove the data of the first 6 time points in the first brain function image;

[0012] Performing time layer correction on the first brain functional image with the time point removed;

[0013] performing head motion correction on the first brain functional image after time layer correction;

[0014] Registering the first brain functional image after head motion correction with the brain structural image, registering the registered brain structural image to the Montreal standard space, and obtaining structural image standardization parameters;

[0015] According to the structural image standardization parameter, spatially registering the first brain functional image, and performing voxel sampling on the first brain functional image after spatial registration according to a preset voxel size;

[0016] The first brain function image after spatial registration is spatially smoothed using a half-width Gaussian smoothing kernel with a preset kernel size to obtain the second brain function image.

[0017] Preferably, the step of performing brain activation analysis on each of the second brain function images to obtain the active brain regions significantly activated by each of the participants in the risk decision-making task comprises:

[0018] Based on each of the second brain function images and a task design matrix related to the risk decision-making task, constructing a corresponding first generalized linear model for each of the participants;

[0019] Using the least square method to perform linear fitting on each of the first generalized linear models, to obtain the response intensity of the BOLD signal of each voxel in the brain area of ​​each of the participants under different decision conditions;

[0020] Based on the response intensity of each participant, a pre-built full factorial design model is used to perform condition main effect, group main effect and interaction effect analysis to obtain analysis results;

[0021] Based on the analysis results, a single-sample T test, a paired T test and a two-sample T test were performed respectively, and the active brain areas significantly activated by each participant in the risk decision-making task were determined according to the obtained test results.

[0022] Preferably, before the step of using the pre-built full factorial design model to perform conditional main effect, group main effect and interaction effect analysis, the method further comprises:

[0023] The full factorial design model is constructed according to the decision conditions and the sample group identifiers of the participants, wherein the sample group identifiers include a first group identifier and a second group identifier.

[0024] Preferably, based on the active brain regions, the step of performing a psychophysiological interaction analysis on the second brain function image to obtain a dynamic connection pattern between different brain regions in the individual's brain comprises:

[0025] Determining regions of interest based on the active brain regions and ventromedial prefrontal cortex of each of said participants;

[0026] generating a PPI interaction term for each of the participants based on the ROI feature variable of the BOLD signal of the region of interest;

[0027] Based on the PPI interaction term, the ROI characteristic variable and the second brain function image, construct a corresponding second generalized linear model for each participant;

[0028] Performing linear fitting on each of the second generalized linear models to obtain the whole-brain BOLD signal change of each of the participants, so as to reveal the interaction between the corresponding individual participant and the seed point at the whole-brain level through the whole-brain BOLD signal change;

[0029] Performing linear fitting on the third generalized linear model constructed based on the whole-brain BOLD signal change of each participant and the PPI interaction term to estimate the influence of the PPI interaction term on the whole-brain BOLD signal;

[0030] Based on the degree of influence, a one-sample T test and a two-sample T test were performed to obtain the PPI connection results between the region of interest and the whole brain.

[0031] Preferably, the step of determining the region of interest based on the active brain region and the ventromedial prefrontal cortex of each of the participants comprises:

[0032] Determining, according to the active brain area of ​​each participant, the common activation area between the participants corresponding to the first and second group identifiers;

[0033] The regions of interest were determined based on the common activation areas between the groups and the ventromedial prefrontal cortex.

[0034] Preferably, the step of generating the PPI interaction term of each participant based on the ROI feature variable of the BOLD signal of the region of interest comprises:

[0035] According to each of the second brain function images, feature extraction is performed on the BOLD signal of the region of interest to obtain a corresponding ROI feature variable;

[0036] Based on the ROI feature variables, a PPI interaction term for each of the participants is generated.

[0037] In another aspect, the present invention provides a brain network analysis device for social conformity effect, the device comprising:

[0038] A magnetic resonance imaging unit, used to perform magnetic resonance imaging on the brains of a number of participants when they perform risk decision-making tasks under different decision-making conditions, to obtain a brain structural image and a first brain functional image of each participant, wherein the decision-making conditions include an individual decision-making situation and a social conformity effect decision-making situation;

[0039] An image preprocessing unit, configured to preprocess the corresponding first brain function image based on each of the brain structure images to obtain a corresponding second brain function image;

[0040] A brain activation analysis unit, configured to perform brain activation analysis on each of the second brain function images to obtain an active brain region that is significantly activated by each of the participants in the risk decision-making task;

[0041] The interaction analysis unit is used to perform a psychophysiological interaction analysis on the second brain function image based on the active brain area, so as to obtain the dynamic connection pattern between different brain areas in the individual brain, so as to reveal the dynamic neural network of the brain in the social conformity effect decision-making situation.

[0042] On the other hand, the present invention also provides a computing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps described in the above-mentioned brain network analysis method for the social conformity effect are implemented.

[0043] On the other hand, the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps described in the above-mentioned brain network analysis method for social conformity effect.

[0044] The present invention performs magnetic resonance imaging on the brains of several participants when they perform risk decision-making tasks with different decision-making conditions, obtains a first brain function image of each participant, preprocesses each first brain function image, performs brain activation analysis on each second brain function image obtained after the preprocessing, obtains the active brain area significantly activated by each participant in the risk decision-making task, and based on the active brain area, performs a psychophysiological interaction analysis on the second brain function image to obtain the dynamic connection pattern between different brain areas in the individual's brain, so as to reveal the dynamic neural network of the brain in the social conformity effect decision-making situation, thereby realizing a more accurate and comprehensive method to reveal the mechanism of action of the social conformity effect at the neural level, and laying a scientific foundation for the development of targeted risk management and behavioral intervention strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flowchart of the implementation of the brain network analysis method for social conformity effect provided by the first embodiment of the present invention;

[0046] Figure 2 Schematic diagram of the experimental paradigm in the brain network analysis method for social conformity effect provided in the first embodiment of the present invention;

[0047] Figure 3 It is the activation map of each brain region obtained by full factorial design in the brain network analysis method for social conformity effect provided in the first embodiment of the present invention;

[0048] Figure 4 It is the activation map of each brain region obtained by single sample, paired and two-sample T test in the brain network analysis method for social conformity effect provided in the first embodiment of the present invention;

[0049] Figure 5: is a schematic diagram of ROI in the brain network analysis method for social conformity effect provided in the first embodiment of the present invention;

[0050] Figure 6 It is a schematic diagram of the results obtained by PPI analysis in the brain network analysis method for social conformity effect provided in the first embodiment of the present invention;

[0051] Figure 7 is a schematic diagram of the structure of a brain network analysis device for social conformity effect provided by Embodiment 2 of the present invention;

[0052] Figure 8 It is a schematic diagram of the structure of a computing device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] It should be understood that the terms "including" and "having" and any variations thereof in the present invention are intended to cover but not exclude inclusion. For example, a product or device comprising a series of components is not necessarily limited to those components explicitly listed, but may include other components not explicitly listed or inherent to these products or devices.

[0055] In the present invention, the term "and / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0056] Unless otherwise specified, the term "plurality" in the present invention refers to two or more than two, and other quantifiers are similar to them.

[0057] The terms "first", "second", "third", etc. in the present invention are used to distinguish similar or similar objects or entities, and are not necessarily meant to limit a specific order or sequence, unless otherwise noted. It should be understood that the terms used in this way are interchangeable under appropriate circumstances, for example, they can be implemented in an order other than those given in the diagrams or descriptions of the embodiments of the present disclosure.

[0058] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments:

[0059] Embodiment 1:

[0060] Figure 1The implementation process of the brain network analysis method for social conformity effect provided by the first embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0061] In step S101, magnetic resonance imaging is performed on the brains of several participants when they perform risk decision-making tasks under different decision conditions to obtain a brain structural image and a first brain functional image of each participant, wherein the decision conditions include an individual decision-making situation and a social conformity effect decision situation.

[0062] The embodiment of the present invention is applicable to computing devices, such as personal computers, servers, etc. In the embodiment of the present invention, MRI equipment is used to collect brain imaging data of several participants in a controlled experimental environment when performing risk decision-making tasks. These risk decision-making tasks are designed to simulate risk decision-making scenarios in real life. Here, risk decision-making scenarios include individual decision-making scenarios (i.e., individuals make decisions alone to complete risk decision-making tasks, referred to as Solo) and social conformity decision-making scenarios (i.e., individuals make decisions after being influenced by the decisions of others to complete risk decision-making tasks, such as displaying information selected by others, referred to as Info). The brain imaging data includes brain structural images and brain functional images, wherein the brain structural images reflect the anatomical structure of the brain, and the brain functional images reflect the changes in the blood oxygen level dependent (BOLD) signal when the brain performs tasks. For the convenience of description and distinction, the brain functional images acquired by magnetic resonance imaging are referred to as first brain functional images.

[0063] In step S102, based on each brain structure image, the corresponding first brain function image is preprocessed to obtain a corresponding second brain function image.

[0064] In an embodiment of the present invention, based on each brain structure image, the SPM (Statistical Parametric Mapping) toolkit is used to preprocess the corresponding first brain function image to obtain the corresponding second brain function image, wherein the preprocessing of the corresponding first brain function image includes but is not limited to removing unstable time points, time layer correction, head movement correction, spatial alignment and spatial smoothing.

[0065] In a feasible embodiment, the preprocessing of each first brain function image is achieved through the following steps:

[0066] ① Remove the data of the first 6 time points in the first brain function image;

[0067] In an embodiment of the present invention, the data of the first six time points in the first brain function image corresponding to each trial are deleted to remove the initial unstable data caused by factors such as magnetic field instability or participants' incomplete concentration, thereby improving the accuracy of subsequent analysis.

[0068] ② Perform time layer correction on the first brain functional image with the time point removed;

[0069] In the embodiment of the present invention, since magnetic resonance imaging usually adopts multi-layer scanning, there may be differences in the acquisition time between each layer. Here, the 39th layer is used as the reference layer, and other time layers are corrected to align the data of all layers to the same time point, thereby eliminating the time inconsistency caused by inter-layer scanning.

[0070] ③ Perform head motion correction on the first brain function image after time layer correction;

[0071] In an embodiment of the present invention, during the scanning process, a participant's slight head movement may cause the image position to move, thereby affecting the time series analysis of the brain function image. Here, six rigid linear transformation head movement parameters (including three translation parameters and three rotation parameters along the X, Y, and Z axes) are used. By applying two rigid transformations, rotation and translation, the image of each time point in the time series of the first brain function image after time layer correction is spatially aligned with the image of the first time point to ensure the spatial consistency of the images at each time point in the time series. At the same time, if the head movement parameter (such as the translation amount or the rotation angle) corresponding to the image at a certain time point exceeds a preset head movement threshold, the image at the time point is deleted from the time series to ensure the spatial consistency of the remaining images, thereby improving the accuracy and reliability of data analysis. Specifically, the time point images whose maximum translation amount in any direction (X, Y, and Z axes) is greater than 3 mm or whose rotation angle around any axis (X, Y, and Z axes) is greater than 3 degrees are deleted. The six rigid linear transformation head movement parameters (referred to as head movement parameters) are analyzed from the first brain function image.

[0072] ④ Align the first brain functional image after head motion correction with the brain structural image, and align the aligned brain structural image to the Montreal standard space to obtain the standardized parameters of the structural image;

[0073] In an embodiment of the present invention, the first brain functional image after head motion correction is registered with the brain structural image (i.e., the two are spatially aligned) to ensure that the activated area on the functional image can be accurately mapped to the structural image, so that the location of the functional activity can be interpreted based on the anatomical structure of the brain. After that, the registered brain structural image is registered to the Montreal standard space to achieve data alignment between different participants and facilitate group-level analysis. At the same time, in the process of registering the brain structural image to the Montreal (Montreal Neurological Institute, MNI) standard space, a set of conversion parameters (i.e., structural image standardization parameters) will be generated for subsequent functional image standardization.

[0074] ⑤ According to the standardized parameters of the structural image, the first brain functional image is spatially registered, and voxel sampling is performed on the first brain functional image after spatial registration according to the preset voxel size;

[0075] In the embodiment of the present invention, the first brain functional image is also converted to the Montreal standard space by applying the structural image standardization parameters to achieve alignment and analysis of the functional image at the group level, that is, it can be directly compared with the registered structural image and the data of other participants. After that, the first brain functional image after spatial registration is voxel sampled according to the preset voxel size to convert the data into a uniform spatial resolution, which is convenient for subsequent data processing and statistical analysis. Specifically, the voxel sampling of the first brain functional image after spatial registration is 3×3×3mm 3 .

[0076] ⑥ Use a half-width Gaussian smoothing kernel with a preset kernel size to perform spatial smoothing on the first brain functional image after spatial registration to obtain a second brain functional image.

[0077] In the embodiment of the present invention, a 6 mm full width half maximum (FWHM) Gaussian smoothing kernel is used to perform spatial smoothing on the first brain function image after spatial registration to reduce noise and improve the signal-to-noise ratio of data, thereby obtaining a second brain function image.

[0078] The above steps ① to ⑥ are used to preprocess each first brain functional image, thereby improving image quality and data reliability.

[0079] In step S103, brain activation analysis is performed on each second brain function image to obtain the active brain area that is significantly activated in the risk decision-making task of each participant.

[0080] In an embodiment of the present invention, a statistical model is used to perform brain activation analysis on each second brain function image to identify the active brain area (i.e., the significantly activated active brain area) of each participant in the risk decision-making task, especially the brain activation pattern under the condition of social conformity effect.

[0081] In a feasible embodiment, the identification of the active brain regions significantly activated by each participant in the risk decision-making task is achieved through the following steps:

[0082] (1) Based on the second brain function images and the task design matrix related to the risk decision-making task, a corresponding first generalized linear model was constructed for each participant;

[0083] In an embodiment of the present invention, a task design matrix related to the risk decision-making task is convolved with a hemodynamic response function (HRF) to simulate the BOLD signal response of the brain under specific task stimulation to obtain a task function of the BOLD signal. The task function is used as a predictor variable (or independent variable), and the time series of the BOLD signal in the second brain function image is used as a response variable (or dependent variable). A corresponding first generalized linear model (GLM) is constructed for each participant to perform an individual level (Specify 1st-level) analysis, wherein the task design matrix describes the tasks or conditions in the experiment and the time points when they occur.

[0084] (2) The least squares method was used to perform linear fitting on each first generalized linear model to obtain the BOLD signal response intensity of each voxel in the brain area of ​​each participant under different decision conditions;

[0085] In the embodiment of the present invention, based on each first generalized linear model constructed, the BOLD signal of each voxel in the individual brain is linearly fitted using the least squares method to obtain the response intensity of the BOLD signal of each voxel in the brain area of ​​each participant under different decision conditions (i.e., the parameter estimation value ), the response intensity reflects the contribution of risk decision-making tasks under different decision conditions to the BOLD signal.

[0086] (3) Based on the response intensity of each participant, a pre-constructed full factorial design model was used to analyze the condition main effect, group main effect, and interaction effect to obtain the analysis results;

[0087] In an embodiment of the present invention, the response intensity of each participant obtained in the individual level analysis is integrated into the group level (Specify 2nd-level) analysis to evaluate the differences in brain activity between different subject groups (such as different age groups, different disease states, etc.) or between different decision conditions. Here, at the group level, based on the response intensity of each participant, a pre-constructed full factorial design model is used to perform condition main effect, group main effect and interaction effect analysis to obtain analysis results. The analysis results include BOLD signal differences between different decision conditions corresponding to the condition main effect, BOLD signal differences between different sample group identifiers corresponding to the group main effect, and BOLD signal differences under the joint action of the decision condition and the sample group identifier corresponding to the interaction effect, wherein the sample group identifier is the group identification number of the group to which the participant belongs, and the sample group identifier can reflect specific attributes or characteristics of the participant (such as age, gender, disease state, etc.).

[0088] In a feasible embodiment, before using a pre-constructed full factorial design model to perform conditional main effects, group main effects, and interaction effect analysis, a full factorial design model is constructed based on the decision conditions and the sample group identifiers of each participant, wherein the sample group identifiers include a first group identifier and a second group identifier, and the full factorial design model is a 2×2 matrix.

[0089] As an example, in a specific embodiment, the participants can be divided into an old group and a young group according to their ages, and the corresponding sample group identifiers can be set to a first group identifier (older group) and a second group identifier (young group). Among the 39 adults participating in the experiment, 18 were identified as the first group identifier and 21 were identified as the second group identifier.

[0090] In another feasible embodiment, the T value of each factor in the full factorial design model is calculated according to the response intensity of each participant, and p<0.05 is selected as the significance threshold to obtain the full factorial design brain area activation results shown in Table 1 and Figure 3 The activation map of the conditional main effect (Solo and Info) shown in Figure (a) is shown in Figure Figure 3 The activation map of the main effect of group (older and young) shown in Figure (b) is as follows: Figure 3 Activation map for the interaction effect (condition × sample group identity) shown in (c).

[0091] Table 1

[0092]

[0093]

[0094] (4) Based on the analysis results, one-sample T test, paired T test and two-sample T test were performed respectively, and the active brain areas significantly activated in each participant during the risk decision-making task were determined according to the test results.

[0095] In an embodiment of the present invention, based on the specific conditions indicated by these effects in the analysis results, the sample group identification or the response intensity under the interaction, a one-sample T test, a paired T test and a two-sample T test were respectively applied, and according to the results of these T tests, the active brain areas significantly activated in the risk decision-making task of each participant were determined, wherein the one-sample T test was used to obtain the significant activation at the group level under the Solo and Info conditions, the paired T test was used to obtain the activation difference between the Solo and Info conditions, and the two-sample T test was used to obtain the inter-group activation difference between the elderly group and the young group under the Solo and Info conditions, respectively.

[0096] In a feasible embodiment, the response intensity of 39 participants in the elderly group and the young group was subjected to a one-sample T test (FDR correction, p<0.001; uncorrected, p<0.001), and the significant activation of each participant at the group level under the Solo and Info conditions was obtained. A paired T test (uncorrected, p<0.02) and a two-sample T test (FDR correction, p<0.001) were performed to obtain the activation differences between the Solo and Info conditions and the differences between the elderly group and the young group under the Solo and Info conditions, respectively. The results of the brain area activation of each T test are shown in Tables 2 and 3. Figure 4 As shown, Figure 4 The middle (a) figure is the Solo condition activation map in the elderly group. Figure 4 The middle (b) figure is the Info conditional activation map in the elderly group. Figure 4 The middle (c) figure is the Solo condition activation map in the young group. Figure 4 The middle (d) figure is the Info conditional activation map within the young group. Figure 4 The middle (e) figure is the activation map of the Info>Solo condition in the elderly group. Figure 4 The middle (f) figure is the activation map of the Info>Solo condition in the young group. Figure 4 The middle (g) figure shows the activation map of the old group > young group under Solo condition. Figure 4 The middle figure (h) shows the activation map of the old group > the young group under the Info condition.

[0097] Table 2

[0098]

[0099]

[0100] According to Table 2 and Figure 4It can be seen that there are significantly activated brain areas between the Solo and Info conditions, including the right cuneus, left middle occipital gyrus, right precuneus, right middle frontal gyrus, right middle temporal gyrus, right triangular inferior frontal gyrus, right lateral thalamic nucleus, left dorsolateral superior frontal gyrus, left middle temporal gyrus and left postcentral gyrus;

[0101] There were significant activations in the brain regions between the old and young groups, including the left fusiform gyrus, left precentral gyrus, right inferior occipital gyrus, right middle occipital gyrus, left middle occipital gyrus, right inferior frontal gyrus of the operculum, left thalamus, left putamen, right middle temporal gyrus, right precuneus, left supplementary motor area, right superior occipital gyrus, and left supramarginal gyrus.

[0102] The brain regions with interaction effects between condition (Info and Solo) and sample group identity (old and young) included left middle temporal gyrus, right cerebellar area 9, right superior temporal gyrus, right middle frontal gyrus, left temporal pole: superior temporal gyrus, left cingulate cortex, left insula, right fusiform gyrus, right inferior orbital frontal gyrus, left anterior cingulate gyrus, and left orbitofrontal gyrus;

[0103] Under the Solo condition, the brain activations of the old and young groups showed a certain degree of similarity: there were common activations in the left superior parietal gyrus, right occipital lobe, precuneus, right angular gyrus, etc. In addition, compared with the young group, the old group showed significant activations in the right fusiform gyrus, left precentral gyrus, left fusiform gyrus, left orbital inferior frontal gyrus, and left supramarginal gyrus;

[0104] Under the Info condition, the brain activations of the old and young groups showed a certain degree of similarity: there were common activations in the right inferior occipital gyrus, right inferior temporal gyrus, right fusiform gyrus, right lingual gyrus, etc. In addition, compared with the young group, the old group had significant activations in the left cerebellum, left paracentral lobule, right inferior occipital gyrus, left lingual gyrus, and left fusiform gyrus;

[0105] In the elderly group, the Info condition induced stronger activations in the right precuneus, left middle occipital gyrus, right cuneus, right cerebellum, and right inferior temporal gyrus compared with the Solo condition;

[0106] In the young group, the Info condition induced stronger activations than the Solo condition in the left middle occipital gyrus, right temporal pole: middle temporal gyrus, left parahippocampal gyrus, left OFC (orbitofrontal cortex), left precentral gyrus, left middle temporal gyrus, middle frontal gyrus, right inferior parietal angular gyrus, anterior cingulate cortex, and left pars triangularis inferior frontal gyrus.

[0107] Through the above steps (1) to (4), the active brain regions significantly activated in each participant in the risk decision-making task can be identified, and in particular, the significant activation changes of brain regions under the influence of the social conformity effect can be accurately identified, thereby revealing how social influence shapes the neural basis of individual risk decision-making under specific conditions, such as under Solo and Info conditions. In addition, by comparing the brain activation patterns of the elderly group and the young group, the present invention discovered age-related differences in neural mechanisms, which are crucial for understanding how different age groups respond to the social conformity effect. The present invention also determined the significant impact of social information presentation on the individual decision-making process, namely the conditional effect, and demonstrated how social information significantly affects the decision-making process. At the same time, by providing detailed brain region activation information, including MNI coordinates and T values, the present invention helps researchers to deeply understand the mechanism of action of specific brain regions under the social conformity effect. Finally, by constructing a 2×2 full factorial design, the present invention can comprehensively analyze the conditional and sample group identification factors and their interaction effects, providing a more comprehensive statistical analysis framework for the study of the neural mechanism of the social conformity effect.

[0108] In step S104, based on the active brain regions, a psychophysiological interaction analysis is performed on the second brain function image to obtain a dynamic connection pattern between different brain regions in the individual brain, so as to reveal the dynamic neural network of the brain in the social conformity effect decision-making situation.

[0109] In an embodiment of the present invention, based on the active brain areas, a psychophysiological interaction analysis (PPI) is performed on the second brain functional image to detect the direct activation caused by the risk decision-making task and the functional connections between other brain areas and this, so as to obtain the dynamic connection pattern between different brain areas in the individual's brain, so as to reveal the dynamic neural network of the brain in the social conformity effect decision-making situation.

[0110] In a feasible embodiment, the psychological and physiological interaction analysis of the second brain function image is achieved through the following steps:

[0111] (1) Identify regions of interest based on each participant’s active brain regions and ventromedial prefrontal cortex;

[0112] In an embodiment of the present invention, a region of interest (ROI) is determined based on the active brain regions and the ventromedial prefrontal cortex (vmPFC) of each participant.

[0113] In a feasible embodiment, when determining the region of interest based on the active brain area and ventromedial prefrontal cortex of each participant, first determine the inter-group common activation area between the participants corresponding to the first and second group identifiers based on the active brain area of ​​each participant, and then determine the region of interest based on the inter-group common activation area and the ventromedial prefrontal cortex. Specifically, use xjview software to overlap the active brain area of ​​each participant in the elderly group and the active brain area of ​​each participant in the young group, so as to identify the brain area that shows significant activation in both the elderly group and the young group, that is, the inter-group common activation area. Then, in order to select the region of interest related to vmPFC from the inter-group common activation area, here, overlap the vmPFC (including Brodmann areas 10, 11, 24, 25, 32, and 47) with the inter-group common activation area again to select the region of interest, which will be used as the seed point for PPI analysis, wherein Brodmann Area 10 is the anterior prefrontal cortex, located in the anterior part of the superior and middle frontal gyri of the frontal lobe, and is closely related to memory retrieval, higher-level cognitive functions, social behavior, and emotion regulation; Brodmann area 11 is the orbitofrontal region, located on the medial ventral surface of the frontal lobe, and is involved in functions such as emotion processing and reward learning; Brodmann area 24 is the anterior ventral cingulate cortex, which is related to emotion regulation and pain processing; Brodmann area 25 is the subgenual region of the corpus callosum, which is also part of the ventromedial prefrontal cortex and is involved in emotion regulation and reward processing; Brodmann area 32 is the dorsal part of the posterior cingulate cortex, which is related to memory, emotion processing, and self-referential thinking; Brodmann area 47 is the orbital part of the inferior frontal gyrus, which is involved in social behavior and emotion regulation.

[0114] As an example, the selected region of interest is as follows: Figure 5 The left anterior cingulate cortex (x=-10, y=44, z=12) is shown as the center of a sphere with a radius of 5 mm.

[0115] (2) Generate a PPI interaction term for each participant based on the ROI feature variable of the BOLD signal in the region of interest;

[0116] In an embodiment of the present invention, when generating the PPI interaction term of each participant based on the ROI feature variable of the BOLD signal of the region of interest, firstly, feature extraction is performed on the BOLD signal of the region of interest according to each second brain function image to obtain the corresponding ROI feature variable, and then the PPI interaction term of each participant is generated based on the ROI feature variable.

[0117] Specifically, SPM12 is used to extract the BOLD signals of the regions of interest in each second brain functional image. Subsequently, the characteristic variables of these BOLD signals are calculated, including but not limited to the average BOLD signal intensity, signal change rate, and possible other related indicators (such as functional connectivity, coherence, etc.) to obtain ROI characteristic variables. Afterwards, the ROI characteristic variables will be used as physiological items, and the variables related to the risk decision-making task will be used as psychological items. The PPI interaction term is generated by calculating the product of the physiological term and the psychological term. The PPI interaction term reflects the interaction between psychological and physiological factors.

[0118] (3) Based on the PPI interaction term, ROI characteristic variables, and the second brain functional image, a corresponding second generalized linear model was constructed for each participant;

[0119] In this embodiment of the present invention, the BOLD signal in the second brain function image of each participant is used as the dependent variable, the PPI interaction term and ROI characteristic variable corresponding to the participant are used as independent variables, and a corresponding second generalized linear model is constructed for each participant.

[0120] (4) Perform linear fitting on each second generalized linear model to obtain the change in the whole-brain BOLD signal of each participant, so as to reveal the interaction between the corresponding individual participant and the seed point at the whole-brain level through the change in the whole-brain BOLD signal;

[0121] In an embodiment of the present invention, by fitting each second generalized linear model, the average change of the BOLD signal of each participant at the whole-brain level is estimated, and the change of the whole-brain BOLD signal of each participant is obtained. These whole-brain BOLD signal changes are used to analyze the functional connections or interactions related to the seed points of the corresponding individual participants in the whole brain.

[0122] (5) Linear fitting was performed on the third generalized linear model constructed based on the change in the whole-brain BOLD signal of each participant and the PPI interaction term to estimate the influence of the PPI interaction term on the whole-brain BOLD signal;

[0123] In an embodiment of the present invention, the PPI interaction term is used as the independent variable, the change in the whole-brain BOLD signal is used as the dependent variable, and a third generalized linear model is constructed. By fitting the third generalized linear model, the influence of the PPI interaction term on the whole-brain BOLD signal is estimated.

[0124] (6) Based on the degree of influence, one-sample T test and two-sample T test were performed to obtain the PPI connection results between the region of interest and the whole brain.

[0125] In the embodiment of the present invention, at the group level, a single-sample T test (p<0.02, uncorrected) was performed on the beta values ​​(i.e., the degree of influence) of the individual level of 39 subjects in the elderly group and the young group to obtain the PPI connection between the vmPFC and the whole brain of the elderly group and the young group. At the same time, a two-sample T test (p<0.02, uncorrected) was used to obtain the difference in the connection between the vmPFC and the whole brain between the elderly group and the young group. The connection results are shown in Figure 2. Figure 6 As shown in Table 3, Figure 6 The middle (a) figure shows the connection between vmPFC and whole-brain PPI in the elderly group. Figure 6 The middle (b) figure shows the connection between vmPFC and whole-brain PPI in the young group. Figure 6 The middle figure (c) shows the differences in the connectivity between the vmPFC and the whole brain between the old and young groups.

[0126] Table 3

[0127]

[0128] according to Figure 6 As shown in Table 3 , the elderly group had interactions with the vmPFC (region of interest) in the left precentral gyrus, left supplementary motor area, left inferior parietal gyrus, left fusiform gyrus, right inferior occipital gyrus, right middle frontal gyrus, left superior parietal gyrus, left inferior occipital gyrus, and right superior occipital gyrus;

[0129] The young group had interactions with the vmPFC (region of interest) in the left gyrus rectus, left cuneus, right supplementary motor area, left hippocampus, left middle frontal gyrus of the orbital region, and right paracentral lobule;

[0130] Inter-group comparison revealed that the young group had stronger interactions than the old group in the left triangular inferior frontal gyrus, right temporal pole: middle temporal gyrus, left dorsolateral superior frontal gyrus, left lenticular putamen, left transverse temporal gyrus, left insula, and right caudate nucleus.

[0131] The above steps (1) to (6) reveal the dynamic neural network in the decision-making process under social influence by analyzing the functional connection between vmPFC and the whole brain. This network shows unique activation patterns under different conditions. In addition, by comparing the PPI results of the elderly and young groups, differences in cognitive and emotional processing in the decision-making process under social influence were found in different age groups. These differences are crucial for understanding the social influence in complex decision-making processes. It can be seen that PPI analysis not only helps to understand how social information affects the brain network connection and decision-making behavior of individuals in risk decision-making, but also provides clues for potential clinical applications, especially in understanding neurological and psychiatric disorders related to social cognition. In summary, PPI analysis provides a deeper understanding of the neural mechanism of social conformity effect in risk decision-making, and lays a scientific foundation for the development of targeted risk management and behavioral intervention strategies.

[0132] In an embodiment of the present invention, magnetic resonance imaging is performed on the brains of several participants when they perform risk decision-making tasks with different decision-making conditions to obtain a first brain function image of each participant, each first brain function image is preprocessed, and brain activation analysis is performed on each second brain function image obtained after the preprocessing to obtain the active brain area significantly activated by each participant in the risk decision-making task, and based on the active brain area, a psychophysiological interaction analysis is performed on the second brain function image to obtain the dynamic connection pattern between different brain areas in the individual's brain to reveal the dynamic neural network of the brain in the social conformity effect decision-making scenario, thereby realizing a more accurate and comprehensive method to reveal the mechanism of action of the social conformity effect at the neural level, and laying a scientific foundation for the development of targeted risk management and behavioral intervention strategies.

[0133] Embodiment 2:

[0134] Figure 7 The structure of the brain network analysis device for social conformity effect provided by the second embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown, including:

[0135] Magnetic resonance imaging unit 71, used for performing magnetic resonance imaging on the brains of several participants when they perform risk decision-making tasks under different decision-making conditions, to obtain a brain structural image and a first brain functional image of each participant, wherein the decision-making conditions include a single decision-making situation and a social conformity effect decision-making situation;

[0136] An image preprocessing unit 72, configured to preprocess the corresponding first brain function image based on each brain structure image to obtain a corresponding second brain function image;

[0137] A brain activation analysis unit 73, used to perform brain activation analysis on each second brain function image to obtain the active brain area significantly activated by each participant in the risk decision-making task;

[0138] The interaction analysis unit 74 is used to perform a psychophysiological interaction analysis on the second brain function image based on the active brain areas, and obtain the dynamic connection pattern between different brain areas in the individual brain to reveal the dynamic neural network of the brain in the social conformity effect decision-making situation.

[0139] In the embodiment of the present invention, each unit of the brain network analysis device for social conformity effect can be implemented by a corresponding hardware or software unit, and each unit can be an independent software or hardware unit, or can be integrated into a software or hardware unit, which is not intended to limit the present invention. Specifically, the implementation of each unit can refer to the description of the aforementioned embodiment 1, which will not be repeated here.

[0140] Embodiment three:

[0141] Figure 8 The structure of a computing device provided in the third embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown.

[0142] The computing device 8 of the embodiment of the present invention includes a processor 80, a memory 81, and a computer program 82 stored in the memory 81 and executable on the processor 80. When the processor 80 executes the computer program 82, the steps in the above-mentioned brain network analysis method embodiment for social conformity effect are implemented, for example Figure 1 Alternatively, when the processor 80 executes the computer program 82, the functions of each unit in the above-mentioned device embodiments are realized, for example Figure 7 Function of the unit shown.

[0143] In an embodiment of the present invention, magnetic resonance imaging is performed on the brains of several participants when they perform risk decision-making tasks with different decision-making conditions to obtain a first brain function image of each participant, each first brain function image is preprocessed, and brain activation analysis is performed on each second brain function image obtained after the preprocessing to obtain the active brain area significantly activated by each participant in the risk decision-making task, and based on the active brain area, a psychophysiological interaction analysis is performed on the second brain function image to obtain the dynamic connection pattern between different brain areas in the individual's brain to reveal the dynamic neural network of the brain in the social conformity effect decision-making scenario, thereby realizing a more accurate and comprehensive method to reveal the mechanism of action of the social conformity effect at the neural level, and laying a scientific foundation for the development of targeted risk management and behavioral intervention strategies.

[0144] The computing device of the embodiment of the present invention may be a personal computer or a server. The steps implemented when the processor 80 in the computing device 8 executes the computer program 82 to implement the brain network analysis method for the social conformity effect can be referred to the description of the aforementioned method embodiment, which will not be repeated here.

[0145] Embodiment 4:

[0146] In an embodiment of the present invention, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned brain network analysis method embodiment for social conformity effect are implemented, for example, Figure 1 Alternatively, when the computer program is executed by a processor, the functions of each unit in the above-mentioned device embodiments are realized, for example Figure 7 Function of the unit shown.

[0147] In an embodiment of the present invention, magnetic resonance imaging is performed on the brains of several participants when they perform risk decision-making tasks with different decision-making conditions to obtain a first brain function image of each participant, each first brain function image is preprocessed, and brain activation analysis is performed on each second brain function image obtained after the preprocessing to obtain the active brain area significantly activated by each participant in the risk decision-making task, and based on the active brain area, a psychophysiological interaction analysis is performed on the second brain function image to obtain the dynamic connection pattern between different brain areas in the individual's brain to reveal the dynamic neural network of the brain in the social conformity effect decision-making scenario, thereby realizing a more accurate and comprehensive method to reveal the mechanism of action of the social conformity effect at the neural level, and laying a scientific foundation for the development of targeted risk management and behavioral intervention strategies.

[0148] The computer-readable storage medium of the embodiment of the present invention may include any entity or device or recording medium capable of carrying computer program code, for example, ROM / RAM, magnetic disk, optical disk, flash memory and other memories.

[0149] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A brain network analysis method for social conformity effect, characterized in that: The method comprises the following steps: Performing magnetic resonance imaging on the brains of several participants while performing risk decision-making tasks under different decision-making conditions, to obtain a brain structural image and a first brain functional image of each participant, wherein the decision-making conditions include an individual decision-making situation and a social conformity effect decision-making situation; Based on each of the brain structural images, preprocessing the corresponding first brain functional image to obtain a corresponding second brain functional image; Performing brain activation analysis on each of the second brain function images to obtain active brain regions that are significantly activated by each of the participants in the risk decision-making task; Based on the active brain regions, a psychophysiological interaction analysis is performed on the second brain function image to obtain a dynamic connection pattern between different brain regions in the individual brain, so as to reveal the dynamic neural network of the brain in the social conformity effect decision-making situation; The step of performing brain activation analysis on each of the second brain function images to obtain the active brain regions significantly activated by each of the participants in the risk decision-making task includes: Based on each of the second brain function images and a task design matrix related to the risk decision-making task, constructing a corresponding first generalized linear model for each of the participants; Using the least square method to perform linear fitting on each of the first generalized linear models, to obtain the response intensity of the BOLD signal of each voxel in the brain area of ​​each of the participants under different decision conditions; Based on the response intensity of each participant, a pre-built full factorial design model is used to perform condition main effect, group main effect and interaction effect analysis to obtain analysis results; Based on the analysis results, a single-sample T test, a paired T test and a two-sample T test were performed respectively, and the active brain areas significantly activated by each participant in the risk decision-making task were determined according to the obtained test results.

2. The method according to claim 1, characterized in that The step of preprocessing the corresponding first brain function image based on each of the brain structure images comprises: Remove the data of the first 6 time points in the first brain function image; Performing time layer correction on the first brain functional image with the time point removed; performing head motion correction on the first brain functional image after time layer correction; Registering the first brain functional image after head motion correction with the brain structural image, registering the registered brain structural image into the Montreal standard space, and obtaining structural image standardization parameters; According to the structural image standardization parameter, spatially registering the first brain functional image, and performing voxel sampling on the first brain functional image after spatial registration according to a preset voxel size; The first brain function image after spatial registration is spatially smoothed using a half-width Gaussian smoothing kernel with a preset kernel size to obtain the second brain function image.

3. The method according to claim 1, characterized in that Before the step of using the pre-built full factorial design model to perform conditional main effect, group main effect and interaction effect analysis, the method further includes: The full factorial design model is constructed according to the decision conditions and the sample group identifiers of the participants, wherein the sample group identifiers include a first group identifier and a second group identifier.

4. The method according to claim 1, characterized in that Based on the active brain regions, the step of performing a psychophysiological interaction analysis on the second brain function image to obtain a dynamic connection pattern between different brain regions in the individual brain includes: Determining regions of interest based on the active brain regions and ventromedial prefrontal cortex of each of said participants; generating a PPI interaction term for each of the participants based on the ROI feature variable of the BOLD signal of the region of interest; Based on the PPI interaction term, the ROI characteristic variable and the second brain function image, construct a corresponding second generalized linear model for each participant; Performing linear fitting on each of the second generalized linear models to obtain the whole-brain BOLD signal change of each of the participants, so as to reveal the interaction between the corresponding individual participant and the seed point at the whole-brain level through the whole-brain BOLD signal change; Performing linear fitting on the third generalized linear model constructed based on the whole-brain BOLD signal change of each participant and the PPI interaction term to estimate the influence of the PPI interaction term on the whole-brain BOLD signal; Based on the degree of influence, a one-sample T test and a two-sample T test were performed to obtain the PPI connection results between the region of interest and the whole brain.

5. The method according to claim 4, characterized in that The step of determining a region of interest based on the active brain regions and the ventromedial prefrontal cortex of each of the participants comprises: Determining, according to the active brain regions of each participant, the inter-group common activation regions between the participants corresponding to the first and second group identifiers; The regions of interest were determined based on the common activation areas between the groups and the ventromedial prefrontal cortex.

6. The method according to claim 4, characterized in that The step of generating a PPI interaction term for each of the participants based on the ROI feature variable of the BOLD signal of the region of interest comprises: According to each of the second brain function images, feature extraction is performed on the BOLD signal of the region of interest to obtain a corresponding ROI feature variable; Based on the ROI feature variables, a PPI interaction term for each of the participants is generated.

7. A brain network analysis device for social conformity effect, characterized in that: The device comprises: A magnetic resonance imaging unit, used to perform magnetic resonance imaging on the brains of a number of participants when they perform risk decision-making tasks under different decision-making conditions, to obtain a brain structural image and a first brain functional image of each participant, wherein the decision-making conditions include an individual decision-making situation and a social conformity effect decision-making situation; An image preprocessing unit, configured to preprocess the corresponding first brain function image based on each of the brain structure images to obtain a corresponding second brain function image; A brain activation analysis unit is used to perform brain activation analysis on each of the second brain function images to obtain the active brain area that is significantly activated by each of the participants in the risk decision-making task, including: based on each of the second brain function images and the task design matrix related to the risk decision-making task, construct a corresponding first generalized linear model for each of the participants; use the least squares method to perform linear fitting on each of the first generalized linear models to obtain the response intensity of the BOLD signal of each voxel in the brain area of ​​each of the participants under different decision conditions; based on the response intensity of each of the participants, use a pre-constructed full factorial design model to perform condition main effect, group main effect and interaction effect analysis to obtain analysis results; based on the analysis results, perform a single-sample T test, a paired T test and a two-sample T test respectively, and determine the active brain area that is significantly activated by each of the participants in the risk decision-making task according to the obtained test results; The interaction analysis unit is used to perform a psychophysiological interaction analysis on the second brain function image based on the active brain area, so as to obtain the dynamic connection pattern between different brain areas in the individual brain, so as to reveal the dynamic neural network of the brain in the social conformity effect decision-making situation.

8. 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 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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