Auxiliary analysis system based on neural activity synchronicity

Abstract

The application relates to an auxiliary analysis system based on neural activity synchrony, which comprises a cooperative task output module for outputting a two-person cooperative task; a task execution module for adjusting each target object to synchronously participate in the cooperative task in the same time window based on the output two-person cooperative task; a data acquisition module for acquiring brain function signals and behavior data of each target object in the execution process of the two-person cooperative task; a neural activity synchrony calculation module for quantitatively calculating the neural activity synchrony of each target object in the cooperative task process based on the acquired brain function signals and behavior data of each target object, so as to obtain a neural activity synchrony index; and a risk monitoring module for obtaining an auxiliary analysis result based on the neural activity synchrony index. The system can improve the objectivity, ecological validity and clinical application potential of the auxiliary identification of autism.

Description

Technical Field

[0001] This application relates to the field of human-computer interaction technology, and in particular to an auxiliary analysis system based on the synchronization of neural activity. Background Technology

[0002] Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction, limited communication skills, and stereotyped and repetitive behaviors. Onset typically begins in early childhood, and symptoms exhibit significant individual variability and developmental heterogeneity. Current autism assessment methods primarily rely on clinical behavioral scales, parent or teacher questionnaires, and expert observation and assessment. While these methods have some value in practice, they are highly dependent on the assessor's experience, inherently subjective, and require a high degree of cooperation and language proficiency from the child, making it difficult to achieve early, objective, and continuous monitoring of autism risk. Furthermore, behavioral scales cannot directly reflect the state of a child's brain function or characterize underlying neural mechanisms, limiting their application in refined risk assessment and intervention decision support.

[0003] With the development of brain functional imaging technology, researchers have begun to explore non-invasive techniques such as electroencephalography (EEG) and functional near-infrared brain imaging (fNIRS) to objectively measure the brain activity characteristics of children with autism. Among these, functional near-infrared imaging has advantages such as equipment safety, strong resistance to motion artifacts, and child-friendly characteristics. It can measure changes in blood oxygen dynamics in the cerebral cortex under natural interaction or task conditions and has been gradually applied to research on children's cognitive and social functions.

[0004] Although numerous studies in recent years have begun to employ dual-person synchronous data acquisition and analysis techniques, related work has primarily focused on the social cognitive mechanisms behind interpersonal neural synchronization in scenarios such as emotion regulation and parent-child interaction. Existing research generally remains dominated by mechanistic analysis and phenomenological observation, lacking a systematic technical solution that directly serves autism risk identification and stratified assessment, using neural synchronicity as the core indicator, and is geared towards dual-person interaction scenarios. In particular, there is a lack of integrated design for data acquisition adaptation, task paradigm optimization, robust extraction of synchronization features, individual difference correction, and risk discrimination model construction tailored to the characteristics of children's dual-person interaction. Therefore, it is necessary to propose an experimental design more suitable for children's natural social contexts, a feature construction method for quantitative analysis of neural synchronicity, and an optimized assessment framework for autism assistance identification tasks under dual-person interaction conditions, thereby improving the objectivity, ecological validity, and clinical application potential of autism assistance identification. Summary of the Invention

[0005] Therefore, it is necessary to provide an auxiliary analysis system based on neural activity synchronization that can improve the objectivity, ecological validity, and clinical application potential of autism auxiliary identification, addressing the aforementioned technical problems.

[0006] In a first aspect, this application provides an auxiliary analysis system based on the synchronicity of neural activity, the system comprising:

[0007] The collaborative task output module is used to output collaborative tasks for two players.

[0008] The task execution module is used to adjust the participation of each target object in the cooperative task synchronously within the same time window based on the output of the two-person cooperative task;

[0009] The data acquisition module is used to collect brain function signals and behavioral data of each target object during the execution of the two-person cooperative task;

[0010] The neural activity synchronization calculation module is used to quantify the neural activity synchronization of each target object during the cooperative task based on the collected brain function signals and behavioral data of each target object, and obtain a neural activity synchronization index.

[0011] The risk monitoring module is used to obtain auxiliary analysis results based on the neural activity synchronicity index.

[0012] In some optional embodiments, the neural activity synchronization calculation module includes:

[0013] The mapping first-level unit is used to determine the brain regions of interest corresponding to the two-person collaborative task;

[0014] The synchronization calculation unit is used to obtain neural activity index values ​​based on the brain functional signals, behavioral data, and brain regions of interest of each target object related to the two-person cooperative task.

[0015] In some optional embodiments, the synchronization calculation primary unit includes:

[0016] The mapping secondary unit is used to perform cortical mapping based on the brain functional signals of each target object related to the two-person cooperative task, so as to obtain the activation status of each voxel on the cerebral cortex.

[0017] The secondary unit for calculating neural activity synchronization is used to determine the temporal synchronization and / or spatial synchronization of neural activity of each target object under different cooperative modes based on the activation status of voxels in the brain regions of interest of each target object and behavioral data.

[0018] The secondary unit for calculating the neural activity synchronicity index is used to determine the temporal difference synchronization matrix and / or spatial difference synchronization matrix based on the temporal synchronicity and / or spatial synchronicity of neural activity of each target object under different cooperative modes, and to obtain the neural activity synchronicity index based on the temporal difference synchronization matrix and / or spatial difference synchronization matrix.

[0019] In some optional embodiments, the neural activity synchronization calculation secondary unit includes:

[0020] The cooperation mode determination three-level unit is used to determine the cooperation mode based on the behavioral data corresponding to each target object. The cooperation mode includes a baseline mode, an observation mode, and an operation mode.

[0021] A three-level unit for synchronicity calculation is used to determine the temporal synchronicity and / or spatial synchronicity of neural activities in each of the aforementioned cooperative modes.

[0022] In some optional embodiments, the synchronization calculation level 3 unit includes:

[0023] A four-level unit for calculating time synchronization is used to determine the time series of activation of any pair of voxels in the brain regions of interest for each of the target objects, and to determine the covariance of the time series corresponding to each target object. Based on the covariance, the time synchronization of neural activity of each target object is obtained; and / or

[0024] The spatial synchronization calculation four-level unit is used to determine the set of voxels in the brain regions of interest of each target object at time point t, and to determine the covariance of the voxel set corresponding to each target object. Based on the covariance of the voxel set corresponding to each target object, the spatial synchronization of the neural activity of each target object is obtained.

[0025] In some alternative embodiments, the system further includes:

[0026] The reference index determination module is used to adjust the participation of each reference subject without autism in the cooperative task within the same time window based on the output of the two-person cooperative task, and to collect the brain function signals of each reference subject during the execution of the two-person cooperative task, and to obtain neural activity reference indexes based on the brain function signals of each reference subject without autism and the brain regions of interest.

[0027] The risk monitoring module is also used to obtain auxiliary analysis results based on the neural activity reference index and the neural activity index value.

[0028] In some optional embodiments, the target object includes at least one person without autism as a reference object and at least one person whose autism needs to be confirmed as an assessment object; the system further includes:

[0029] The anomaly labeling module is used to compare the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern in the brain region of interest of the subject to be evaluated with the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern in the brain region of interest of the reference subject, to identify abnormal brain regions, and to label the abnormal brain regions.

[0030] In some alternative embodiments, the system further includes:

[0031] The parameter determination module is used to determine the intervention weight corresponding to each abnormal brain region based on the degree of abnormality of the abnormal brain region, and to generate stimulation parameters for regulating the synchronicity state of neural activity based on the intervention weight.

[0032] The stimulation module is used to provide light stimulation to the object to be evaluated based on the stimulation parameters.

[0033] In some optional embodiments, the stimulation module is specifically configured to reduce the stimulation intensity and / or shorten the stimulation duration when the difference between the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern of the subject to be evaluated and the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern of the reference subject in the brain region of interest is less than a threshold; and to maintain the stimulation parameters unchanged or enhance the stimulation parameters when the difference between the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern of the subject to be evaluated and the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern of the reference subject in the brain region of interest is greater than or equal to a threshold.

[0034] In some alternative embodiments, the two-person collaborative task is configured based on cognitive type and degree of collaborative dependence, and the two-person collaborative task includes one or more of the following: collaborative task based on action synchronization, collaborative task based on information sharing, collaborative task based on joint decision-making, and collaborative task based on creative expression.

[0035] The aforementioned auxiliary analysis system based on neural activity synchronization includes: a cooperative task output module for outputting a two-person cooperative task; a task execution module for adjusting the synchronous participation of each target object in the cooperative task within the same time window based on the output two-person cooperative task; a data acquisition module for collecting brain function signals and behavioral data of each target object during the execution of the two-person cooperative task; a neural activity synchronization calculation module for quantifying the neural activity synchronization of each target object during the cooperative task based on the collected brain function signals and behavioral data, obtaining a neural activity synchronization index; and a risk monitoring module for obtaining self-auxiliary analysis results based on the neural activity synchronization index. By using neural activity synchronization as a core quantitative indicator, the accuracy of the auxiliary analysis results for autism can be improved. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a block diagram of an auxiliary analysis system based on neural activity synchronization in one embodiment;

[0038] Figure 2 This is a schematic diagram of a joint drawing paradigm in one embodiment;

[0039] Figure 3 This is a schematic diagram of near-infrared superscanning in one embodiment;

[0040] Figure 4 This is a schematic diagram of the brain region of interest in one embodiment;

[0041] Figure 5 This is a schematic diagram of neural activity synchronization (time) analysis in one embodiment;

[0042] Figure 6 This is a schematic diagram of the spatial analysis of neural activity synchronization in one embodiment;

[0043] Figure 7 This is a schematic diagram illustrating the synchronization regulation of neural activity in one embodiment. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0045] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0046] Combination Figure 1 As shown, this application provides an auxiliary analysis system based on neural activity synchronization. The system includes: a cooperative task output module, a task execution module, a data acquisition module, a neural activity synchronization calculation module, and a risk monitoring module.

[0047] The collaborative task output module is used to output two-person collaborative tasks. This module provides two children with collaborative tasks appropriate to their cognitive development levels to induce neural activity patterns related to social interaction, joint attention, and collaborative processing. Collaborative tasks include, but are not limited to: collaborative building tasks, collaborative puzzle tasks, collaborative creative tasks, joint rhythmic movement tasks, real-world collaborative problem-solving tasks, collaborative drawing tasks, collaborative mathematical cognition tasks, and collaborative singing tasks. Through task rule constraints and interactive process design, the module guides the two children to generate sustained social interaction behaviors while achieving a common goal.

[0048] In some alternative embodiments, the two-person collaborative task is configured based on cognitive type and degree of collaborative dependence, and the two-person collaborative task includes one or more of the following: collaborative task based on action synchronization, collaborative task based on information sharing, collaborative task based on joint decision-making, and collaborative task based on creative expression.

[0049] This application first designs a series of collaborative cognitive tasks for two children to complete together. These tasks are configured according to the type of cognitive processing and the degree of cooperation dependence to induce changes in children's neural activity during social interaction and collaborative goal achievement. The collaborative cognitive tasks cover different forms of cooperation, including but not limited to collaborative tasks based on action synchronization, information sharing, joint decision-making, and creative expression, thus encompassing a variety of social collaboration scenarios.

[0050] The task execution module is used to adjust the synchronous participation of each target object in the cooperative task within the same time window based on the output of the two-person cooperative task.

[0051] Specifically, this task execution module organizes and coordinates two children to participate synchronously in a collaborative task within the same time window, and manages the timing of their task execution. It ensures the synchronicity of the two children at key points such as task start, task phase transition, and task completion, thus providing a unified temporal reference basis for subsequent neural activity synchronization analysis. Figure 2 As shown, a collaborative drawing task is used as an example. In this task, two children need to collaboratively complete a graphic or image with a pre-set theme on a shared drawing medium. The two children first complete a drawing with baseline conditions (e.g., a vortex), which serves as a condition for drawing without collaboration. Then, they draw with collaborative conditions (e.g., a class). One child first draws on the shared drawing board according to the theme (e.g., a teacher on a platform), while the other child observes the drawing on the shared drawing board. The two children then switch roles; the child who previously observed adds to the drawing (e.g., adds drawings of students submitting their work), while the child who previously drew observes the other child's drawing on the shared drawing board. This alternation continues until neither child adds anything further, or one child loses the desire to continue. Through the design and implementation of this two-person collaborative cognitive task, neural activity patterns related to social interaction, joint attention, and collaborative processing can be effectively induced, providing a task basis for subsequent analysis of neural activity synchronicity.

[0052] It is important to note that significant differences exist in the synchronicity of neural activity exhibited during social interactions among children of different types, particularly between children with autism spectrum disorder (ASD) and those without ASD, where the levels and patterns of synchronicity differ markedly. Introducing two children at risk of ASD into a two-person cooperative task can easily lead to increased fluctuations in synchronicity characteristics, thus affecting the stability and interpretability of the test results. To improve the stability and consistency of the test results, in each experiment of this application, one child, confirmed by preliminary screening to have no obvious ASD symptoms, is consistently included as a control group, while the other child participates in the two-person cooperative task as the subject of evaluation. Preliminary screening can be conducted based on existing behavioral assessment results, developmental history records, or standardized screening tools to exclude obvious ASD risk. This approach effectively reduces the uncertainty caused by differences in the pair combinations and improves the stability and reproducibility of neural activity synchronicity test results across different experimental batches and individuals.

[0053] The data acquisition module is used to collect brain function signals and behavioral data of each target subject during the execution of a two-person cooperative task. Specifically, this data acquisition module is used to simultaneously collect brain function signals and related behavioral data of two children during the execution of a two-person cooperative task. Brain function signals include functional near-infrared brain imaging signals, and behavioral data includes task reaction time, operation sequence, and interactive event markers.

[0054] In some alternative embodiments, combined with Figure 3 As shown, during the execution of a two-person cooperative cognitive task, this application employs functional near-infrared brain imaging hyper-scanning (fNIRS) technology to simultaneously measure and collect the neural activities of the two children during the cooperative task, such as... Figure 3 As shown, specifically, two children are each fitted with a near-infrared brain imaging acquisition device, allowing for the simultaneous acquisition of cortical blood oxygenation dynamics signals related to a cooperative task without restricting their natural movements and interactive behaviors. The near-infrared acquisition device covers brain regions related to social interaction, joint attention, and collaborative processing, including the prefrontal cortex and temporoparietal regions, thereby acquiring multi-channel time-series data reflecting changes in neural activity. During task execution, the system performs unified time synchronization control on the near-infrared signal acquisition process for both children, ensuring that their brain function signals are recorded within the same time reference frame. Through hyperscanning, the system achieves synchronous acquisition of dynamic changes in neural activity during the two-person cooperation process, providing foundational data for subsequent calculations of the synchronization of neural activity between the two individuals. Simultaneously, the system stores the stage information, operational events, and interactive behavior markers of the two-person cooperative task in time alignment with the acquired near-infrared signals, ensuring that the neural activity data accurately corresponds to specific cooperative behaviors and task stages. By employing near-infrared hyperscanning technology, this application can simultaneously characterize the brain function activity states of two children in a real two-person collaborative context, providing reliable data support for analyzing the synchronization of their neural activity during social interaction.

[0055] Optionally, in some alternative embodiments, the system may further include: a data preprocessing module, used to preprocess the cortical blood oxygenation dynamics signal acquired by the data acquisition module to obtain the change in hemoglobin concentration, and to standardize the change in hemoglobin concentration to obtain a standardized brain function signal. Specifically, the data preprocessing module is used to preprocess and standardize the acquired brain function signals and behavioral data. Its processing includes, but is not limited to, operations such as signal denoising, artifact correction, time synchronization, signal segmentation, and feature extraction to obtain standardized data input suitable for synchronous computation.

[0056] After the data acquisition module acquires the raw near-infrared ultrasound data of the two children during the two-person cooperative task, the data preprocessing module preprocesses the near-infrared signals of the two children respectively to obtain standardized cerebral blood oxygenation dynamics time series data that can be used for subsequent synchronization calculations, and aligns it with the task event markers in time.

[0057] Specifically, child A is the reference, and child B is the subject to be evaluated. The original signal undergoes preprocessing steps such as optical density conversion, artifact detection and correction, filtering for noise reduction, and drift removal to ultimately obtain the change in hemoglobin concentration, as shown in Formula 1.

[0058] (1)

[0059] The signals from each channel are then standardized to obtain the input signals used for subsequent analysis, as shown in Equations 2 and 3. Represents the standardized child A's first... Signal from each channel, Represents the first child A The change in hemoglobin concentration in each channel. and Represents the first child A The mean and standard deviation of hemoglobin concentration changes in each channel.

[0060] (2)

[0061] (3)

[0062] The neural activity synchronization calculation module is used to quantify the neural activity synchronization of each target object during the cooperative task based on the collected brain function signals and behavioral data of each target object, and obtain the neural activity synchronization index.

[0063] Specifically, the neural activity synchronization calculation module is used to quantify the neural activity synchronicity of two children during a cooperative task based on preprocessed brain functional signals. By analyzing the temporal coordinated changes of the two children at the corresponding brain regions or functional network levels, a neural activity synchronicity index reflecting their degree of social interaction coordination is constructed to characterize their neural coordination level in cooperative tasks.

[0064] The risk monitoring module is used to obtain auxiliary analysis results based on neural activity synchronicity indicators.

[0065] Specifically, the risk monitoring module is used to assess and monitor the autism-related risk status of children based on neural activity synchronicity indicators. By comparing synchronicity characteristics with preset reference models or norms, it outputs assessment results reflecting the degree of risk of autism spectrum disorder, which is used to achieve risk identification and dynamic monitoring.

[0066] The aforementioned auxiliary analysis system based on neural activity synchronization comprises the following modules: a cooperative task output module for outputting a two-person cooperative task; a task execution module for adjusting the synchronous participation of each target subject within the same time window based on the output two-person cooperative task; a data acquisition module for collecting brain function signals and behavioral data of each target subject during the execution of the two-person cooperative task; a neural activity synchronization calculation module for quantifying the neural activity synchronization of each target subject during the cooperative task based on the collected brain function signals and behavioral data, thus obtaining a neural activity synchronization index; and a risk monitoring module for obtaining self-auxiliary analysis results based on the neural activity synchronization index. By using neural activity synchronization as a core quantitative indicator, the accuracy of autism auxiliary analysis results can be improved.

[0067] In some alternative embodiments, the neural activity synchronization computation module includes: a mapping primary unit and a synchronization computation primary unit.

[0068] The mapping first-level unit is used to determine the brain regions of interest corresponding to the two-person collaborative task.

[0069] Combination Figure 4 As shown, the standard brain space is divided into several brain regions using a pre-defined brain region segmentation atlas (Destrieux), among which... Figure 4 Different colors represent different brain regions, and several brain regions of interest have been identified from them, such as in Formula 4. Figure 4 As shown, Figure 4 To distinguish them, only brain regions of interest 1, 2, 3, 4, and 5 are used. Specifically, the brain region of interest includes at least one of the following: dorsolateral prefrontal cortex (DLPFC), superior frontal gyrus (SFG), inferior frontal gyrus (IFG, formed by the trigone and tectal), temporal pole (TP), inferior temporal gyrus (ITG), middle temporal gyrus (MTG), and temporoparietal junction (TPJ). In other embodiments, the brain region of interest may also include other regions, which are not specifically limited here. Formula 4 uses seven regions as an example for illustration:

[0070] (4)

[0071] Based on relevant experience from previous meta-analysis studies, the neural synchronization brain regions corresponding to various two-person cooperative tasks in the cooperative task module are mapped, and the mapping rules are shown in Formulas 5 to 12.

[0072] (5)

[0073] (6)

[0074] (7)

[0075] (8)

[0076] (9)

[0077] (10)

[0078] (11)

[0079] (12)

[0080] The synchronization computation unit is used to obtain neural activity index values ​​based on the brain functional signals, behavioral data, and brain regions of interest of each target object related to the two-person cooperative task.

[0081] The brain function signal is the cortical blood oxygen dynamics signal. Therefore, after identifying the brain region of interest, the neural activity index value can be obtained based on the cortical blood oxygen dynamics signal and behavioral data of each target object related to the two-person cooperative task.

[0082] In some alternative embodiments, the synchronization calculation primary unit includes: a mapping secondary unit, a neural activity synchronization calculation secondary unit, and a neural activity synchronization index calculation secondary unit.

[0083] The mapping secondary unit is used to perform cortical mapping based on the brain functional signals of each target object related to the two-person cooperative task, and to obtain the activation status of each voxel on the cerebral cortex.

[0084] Among them, the brain function signal is the cortical blood oxygen dynamics signal, and then the activation status of each voxel on the cerebral cortex is obtained by preprocessing the cortical blood oxygen dynamics signal.

[0085] Specifically, at the temporal level, cross-subject functional connectivity analysis was used to characterize the synchronicity of neural activity among subjects during a two-person collaborative task. Specifically, brain functional signals were first cortical mapped to obtain the activation status of each voxel in the cerebral cortex, as shown in Equation 13. The first child A Individual signal, For cortical mapping operators, It is a collection of voxels belonging to the cortex.

[0086] (13)

[0087] The secondary unit for neural activity synchronization calculation is used to determine the temporal and / or spatial synchronization of neural activity of each target object under different cooperative modes, based on the activation status of voxels in the brain regions of interest of each target object and behavioral data.

[0088] The secondary unit for calculating the neural activity synchronicity index is used to determine the temporal difference synchronization matrix and / or spatial difference synchronization matrix based on the temporal and / or spatial synchronicity of neural activity of each target object under different cooperative modes, and to obtain the neural activity synchronicity index based on the temporal difference synchronization matrix and / or spatial difference synchronization matrix.

[0089] In some alternative embodiments, the secondary unit for neural activity synchronization computation includes a tertiary unit for cooperation pattern determination and a tertiary unit for synchronization computation.

[0090] The three-level unit for determining cooperation modes is used to determine the cooperation modes based on the behavioral data corresponding to each target object. The cooperation modes include baseline mode, observation mode, and operation mode.

[0091] The synchronization calculation three-level unit is used to determine the temporal synchronization and / or spatial synchronization of neural activity in each cooperative mode.

[0092] In some optional embodiments, the synchronization calculation level 3 unit includes: a time synchronization calculation level 4 unit and / or a spatial synchronization calculation level 4 unit.

[0093] The time synchronization calculation four-level unit is used to determine the time series of activation of any pair of voxels in the brain regions of interest of each target object, and to determine the covariance of the time series corresponding to each target object. Based on the covariance, the time synchronization of neural activity of each target object is obtained.

[0094] The four-level unit for spatial synchronization calculation is used to determine the set of voxels in the brain regions of interest of each target object at time point t, and to determine the covariance of the voxel set corresponding to each target object. Based on the covariance of the voxel set corresponding to each target object, the spatial synchronization of neural activity of each target object is obtained.

[0095] Subsequently, based on a designated brain region (taking joint drawing as an example, the brain region of interest is the dorsolateral prefrontal cortex), any pair of voxels in this brain region of child A and child B are extracted to obtain the time series of these two voxels, and the synchronicity of neural activity is calculated, as shown in formulas 14-16. Indicates within a specified time window Inner Time series of individual elements and Covariance of time series of individual elements This indicates that child A is within a specified time window. Inner The mean of the time series of individual elements. This indicates that child A is within a specified time window. Inner Standard deviation of the time series of individual elements Indicates within a specified time window Inner Children A Time series of individual elements and children's B-type Synchronicity (time) of neural activity in a time series of individual elements. The result after performing Fisher's Z-transform. For example... Figure 5 As shown, in the constructed inter-subject neural activity synchronicity matrix (time), the individual values ​​on the diagonal of the matrix reflect the degree of synchronicity of a specific voxel between two subjects, forming a spatial distribution map used to characterize the neural response of cross-subject synchronization under the task condition in the time dimension.

[0096]

[0097]

[0098]

[0099] in, Let k be the set of time points contained in the k-th time window. This represents the normalization coefficient in the covariance calculation process.

[0100] At the spatial level, cross-subject functional connectivity analysis was used to characterize the synchronicity of neural activity among participants during a two-person collaborative task. Taking collaborative drawing as an example, the brain region of interest was the dorsolateral prefrontal cortex, and the brain regions retrieved at time point were... For children A and B at that time, any set of voxels in that brain region is used to obtain the activation patterns of these two sets of voxels, and the synchronicity of neural activity is calculated as shown in formulas 17-21. Indicates at a point in time At that time, it is a set of voxels belonging to the brain region of interest. Indicates at a point in time The covariance of the two activation modes This indicates that child A was at a certain point in time. hour The mean of individual chromatin activation. This indicates that child A was at a certain point in time. hour The standard deviation of individual activating factors Indicates at a point in time Child A Docitrin activation patterns and children's B The synchronicity (spatial) of neural activity in the activation patterns of individual elements. The result after performing Fisher's Z-transform. For example... Figure 6 As shown, in the constructed inter-subject neural activity synchronicity matrix (space), the individual values ​​on the diagonal of the matrix reflect the degree of synchronization of several voxels in the brain region of interest between two subjects at a specific time point, forming a spatial distribution map used to characterize the neural response of cross-subject synchronization under the task condition in the spatial dimension.

[0101] (17)

[0102] (18)

[0103]

[0104]

[0105]

[0106] In some optional embodiments, the system further includes a reference index determination module, used to regulate the synchronous participation of non-autistic reference subjects in a collaborative task within the same time window based on the output of the two-person collaborative task. During the execution of the two-person collaborative task, the module collects brain function signals from each reference subject and obtains neural activity reference indices based on the brain function signals and brain regions of interest from the non-autistic reference subjects. The baseline determination unit is used to recruit children definitively diagnosed as not having autism spectrum disorder to perform the two-person collaborative task and record their near-infrared signals to construct norms for the synchronicity of neural activity in the two-person collaborative task. The risk monitoring module is also used to obtain auxiliary analysis results based on the neural activity reference indices and the values ​​of the neural activity indices.

[0107] Based on the baseline and cooperation conditions in the two-person cooperation paradigm, this study focuses on the synchronization of neural activity during cooperation, using the baseline conditions as a foundation. Taking the joint drawing paradigm as an example, the baseline conditions characterize the neural activity signals of two children drawing on a given theme (e.g., a vortex) without cooperation. The cooperation conditions characterize the neural activity signals of two children drawing on a given theme (e.g., attending class) at different stages of cooperation, sometimes in a dominant cooperation (drawing) and sometimes in a passive cooperation (observation). A neural activity synchronization matrix is ​​constructed for the three scenarios of interest (baseline, dominant cooperation, and passive cooperation), and the baseline conditions are subtracted from the latter two parts of the matrix, as shown in Equations 22-25. , , The neural activity synchronization matrices (time) represent dominant cooperation, passive cooperation, and baseline conditions, respectively. , , The neural activity synchronization matrices (spaces) represent dominant cooperation, passive cooperation, and baseline conditions, respectively.

[0108]

[0109]

[0110]

[0111]

[0112] After obtaining the differential synchronicity matrices of neural activity between dominant and passive cooperation and baseline conditions, the differential synchronicity results are further aggregated to construct an index for quantifying the level of neural synchronicity related to social interaction during two-person collaboration. Specifically, for brain regions of interest (taking the dorsolateral prefrontal cortex as an example), the differential synchronicity matrices at the temporal and spatial levels are statistically aggregated to obtain the corresponding cooperative-induced neural activity synchronization intensity index, as shown in Equations 26 and 27.

[0113]

[0114]

[0115] To comprehensively characterize the changes in neural activity during two-person cooperation across two dimensions—temporal continuity and spatial consistency of activation patterns—this application further constructs a spatiotemporally coupled cooperation-induced neural synchronization index, as shown in Formula 28, where... It is used to control the respective weights of space and time.

[0116]

[0117] After obtaining the cooperation-induced neural synchronization index, this application normalizes the synchronization level of the child to be assessed based on the distribution of neural activity synchronization during cooperation in normal children, thereby achieving a quantitative assessment of the risk of autism spectrum disorder, as shown in Formulas 29 and 30.

[0118]

[0119] (30)

[0120] In some optional embodiments, the target objects include at least one object without autism as a reference object and at least one object whose autism needs to be confirmed as an evaluation object; the system also includes: an anomaly labeling module, used to compare the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern of the evaluation object in the brain region of interest with the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern of the reference object in the brain region of interest, to identify abnormal brain regions, and to label the abnormal brain regions.

[0121] The anomaly annotation module is used to annotate and record detected abnormal neural activity synchronization states. When the synchronization index deviates from the preset normal range, this module annotates the corresponding task stage, brain region, and individual, providing a basis for the generation of subsequent intervention parameters.

[0122] Specifically, after completing the calculation of neural activity synchronicity based on a two-person cooperative paradigm and the risk assessment of autism spectrum disorder, the system further refines the localization and labeling of abnormal neural activity synchronicity at the brain region level, and implements targeted intervention and regulation based on the labeling results. Specifically, the system uses a pre-defined set of brain regions of interest as the analysis unit, calculating the cooperative-induced neural activity synchronicity index of the child to be assessed in each brain region under cooperative conditions relative to the baseline condition, and comparing this index with the synchronicity distribution of the reference child in the corresponding brain region. When the synchronicity level of a certain brain region is significantly lower than the statistical distribution range of the reference child, the system labels that brain region as a brain region with abnormal neural activity synchronicity. Through this method, it clarifies which functional brain regions related to social interaction, joint attention, or collaborative processing are mainly concentrated with abnormalities, thus providing a spatial localization basis for subsequent targeted interventions.

[0123] In some optional embodiments, the system further includes a parameter determination module and a stimulation module. Wherein:

[0124] The parameter determination module is used to determine the intervention weights corresponding to each abnormal brain region based on the degree of abnormality of the abnormal brain region, and to generate stimulation parameters for regulating the synchronicity of neural activity based on the intervention weights.

[0125] The stimulation module is used to apply light stimulation to the object to be evaluated based on stimulation parameters.

[0126] The parameter determination module generates intervention parameters that match the individual's neural state based on anomaly labeling and risk monitoring results. These intervention parameters include, but are not limited to, stimulus intensity, stimulus frequency, stimulus rhythm, and stimulus sequence, and are used to guide the implementation of subsequent intervention procedures.

[0127] After labeling abnormal brain regions, corresponding strategies for enhancing neural activity synchronization are generated for each region. Specifically, the system assigns appropriate intervention weights to different brain regions based on the degree of abnormality and maps these weights to a photostimulation intervention module to adjust stimulation parameters. Photostimulation can be performed non-invasively using methods such as transcranial photostimulation or near-infrared photostimulation. The stimulation location is mapped to the corresponding area on the scalp based on the localization of the abnormal brain region in standard brain space. By adjusting parameters such as stimulation intensity, rhythm, and duration, the stimulation protocol can be matched to the degree of synchronization deviation of the abnormal brain region, thereby avoiding the use of a uniform, fixed stimulation strategy and improving the targeting and individualization of the intervention.

[0128] In some optional embodiments, the stimulation module is specifically configured to reduce the stimulation intensity and / or shorten the stimulation duration when the difference between the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern of the subject being evaluated and the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern of the reference subject in the brain region of interest is less than a threshold; and to maintain the stimulation parameters unchanged or increase the stimulation parameters when the difference between the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern of the subject being evaluated and the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern of the reference subject in the brain region of interest is greater than or equal to a threshold.

[0129] The stimulation module is used to perform non-invasive photostimulation interventions on children based on intervention parameters to modulate their neural activity synchronicity. By acting on target brain regions related to social interaction and collaborative processing, it promotes the level of neural activity synchronization between two children during cooperative tasks, thereby achieving intervention and improvement of autism-related functional impairments.

[0130] Combination Figure 7As shown, during the implementation of photostimulation intervention, the system continuously monitors neural activity under a two-person cooperative task and updates the neural activity synchronicity index corresponding to the abnormal brain region in real time. Based on the trend of synchronicity changes, the system dynamically adjusts the photostimulation parameters, forming a closed-loop control mechanism with neural activity synchronicity as the feedback signal in the intervention process. When the synchronicity level of the abnormal brain region is detected to gradually increase and approach the normal range of the reference child, the system correspondingly reduces the stimulation intensity or shortens the stimulation duration; when the synchronicity improvement is not obvious or decreases again, the system maintains or moderately increases the stimulation parameters. Through the above closed-loop control method, this application can achieve a gradual enhancement of the synchronicity of neural activities related to social interaction while ensuring safety, avoiding overstimulation or ineffective stimulation. Through precise labeling of abnormal brain regions and directional photostimulation intervention based on synchronicity feedback, this application constructs a complete technical link from "abnormality identification—abnormality localization—directional intervention—feedback control," enabling neural activity abnormalities related to autism spectrum disorder to no longer be limited to the detection and assessment level, but to be further transformed into operable and controllable intervention strategies, providing a technical means with objective neural evidence for the assessment and training of children's social interaction abilities.

[0131] It should be fully understood that the user information involved in this application (including but not limited to user physiological information, user personal information, etc.) is information and data authorized by the user or fully authorized by all parties. The use of user information shall comply with privacy policies and practices that are often considered to meet or exceed industry requirements for maintaining user privacy. The collection, use and processing of related data shall comply with relevant laws, regulations and standards, and provide corresponding operation access points for users to choose to authorize or refuse.

[0132] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0133] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0134] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An auxiliary analysis system based on the synchronization of neural activity, characterized in that, The system includes: The collaborative task output module is used to output collaborative tasks for two players. The task execution module is used to adjust the participation of each target object in the cooperative task synchronously within the same time window based on the output of the two-person cooperative task; The data acquisition module is used to collect brain function signals and behavioral data of each target object during the execution of the two-person cooperative task; The neural activity synchronization calculation module is used to quantify the neural activity synchronization of each target object during the cooperative task based on the collected brain function signals and behavioral data of each target object, and obtain a neural activity synchronization index. The risk monitoring module is used to obtain auxiliary analysis results based on the neural activity synchronicity index.

2. The system according to claim 1, characterized in that, The neural activity synchronization calculation module includes: The mapping first-level unit is used to determine the brain regions of interest corresponding to the two-person collaborative task; The synchronization calculation unit is used to obtain neural activity index values ​​based on the brain functional signals, behavioral data, and brain regions of interest of each target object related to the two-person cooperative task.

3. The system according to claim 2, characterized in that, The first-level unit for synchronization calculation includes: The mapping secondary unit is used to perform cortical mapping based on the brain functional signals of each target object related to the two-person cooperative task, so as to obtain the activation status of each voxel on the cerebral cortex. The secondary unit for calculating neural activity synchronization is used to determine the temporal synchronization and / or spatial synchronization of neural activity of each target object under different cooperative modes based on the activation status of voxels in the brain regions of interest of each target object and behavioral data. The secondary unit for calculating the neural activity synchronicity index is used to determine the temporal difference synchronization matrix and / or spatial difference synchronization matrix based on the temporal synchronicity and / or spatial synchronicity of neural activity of each target object under different cooperative modes, and to obtain the neural activity synchronicity index based on the temporal difference synchronization matrix and / or spatial difference synchronization matrix.

4. The system according to claim 3, characterized in that, The secondary unit for calculating neural activity synchronization includes: The cooperation mode determination three-level unit is used to determine the cooperation mode based on the behavioral data corresponding to each target object. The cooperation mode includes a baseline mode, an observation mode, and an operation mode. A three-level unit for synchronicity calculation is used to determine the temporal synchronicity and / or spatial synchronicity of neural activities in each of the aforementioned cooperative modes.

5. The system according to claim 4, characterized in that, The three-level synchronous calculation unit includes: A four-level unit for calculating time synchronization is used to determine the time series of activation of any pair of voxels in the brain regions of interest for each of the target objects, and to determine the covariance of the time series corresponding to each target object. Based on the covariance, the time synchronization of neural activity of each target object is obtained; and / or The spatial synchronization calculation four-level unit is used to determine the set of voxels in the brain regions of interest of each target object at time point t, and to determine the covariance of the voxel set corresponding to each target object. Based on the covariance of the voxel set corresponding to each target object, the spatial synchronization of the neural activity of each target object is obtained.

6. The system according to any one of claims 2 to 5, characterized in that, The system also includes: The reference index determination module is used to adjust the participation of each reference subject without autism in the cooperative task within the same time window based on the output of the two-person cooperative task, and to collect the brain function signals of each reference subject during the execution of the two-person cooperative task, and to obtain neural activity reference indexes based on the brain function signals of each reference subject without autism and the brain regions of interest. The risk monitoring module is also used to obtain auxiliary analysis results based on the neural activity reference index and the neural activity index value.

7. The system according to claim 6, characterized in that, The target group includes at least one person without autism as a reference and at least one person whose autism needs to be confirmed as an assessment subject; the system also includes: The anomaly labeling module is used to compare the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern in the brain region of interest of the subject to be evaluated with the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern in the brain region of interest of the reference subject, to identify abnormal brain regions, and to label the abnormal brain regions.

8. The system according to claim 7, characterized in that, The system also includes: The parameter determination module is used to determine the intervention weight corresponding to each abnormal brain region based on the degree of abnormality of the abnormal brain region, and to generate stimulation parameters for regulating the synchronicity state of neural activity based on the intervention weight. The stimulation module is used to provide light stimulation to the object to be evaluated based on the stimulation parameters.

9. The system according to claim 8, characterized in that, The stimulation module is specifically configured to reduce the stimulation intensity and / or shorten the stimulation duration when the difference between the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern of the subject under evaluation in the brain region of interest and the neural activity synchronicity index of the reference subject in the brain region of interest is less than a threshold; and to maintain the stimulation parameters unchanged or increase the stimulation parameters when the difference between the neural activity synchronicity index of the non-baseline pattern relative to the baseline pattern of the subject under evaluation in the brain region of interest and the neural activity synchronicity index of the reference subject in the brain region of interest is greater than or equal to a threshold.

10. The system according to any one of claims 1 to 5, characterized in that, The two-person collaborative task is configured based on cognitive type and degree of collaborative dependence. The two-person collaborative task includes one or more of the following: collaborative task based on action synchronization, collaborative task based on information sharing, collaborative task based on joint decision-making, and collaborative task based on creative expression.

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