Attention Prediction Method, Computer Device, and Storage Medium Based on Anterior Cingulate Cortex and Dorsomedial Prefrontal Cortex

Abstract

The present invention discloses an attention prediction method, a computer device, and a storage medium based on the anterior cingulate cortex and the dorsomedial prefrontal cortex. For the first time, the present invention discovers that the two brain atlas features in the anterior cingulate cortex and the prelimbic cortex in the dorsomedial prefrontal cortex can be used for the effective prediction of whether the attention of a subject is concentrated. Based on this, the present invention provides an attention prediction method, a system, a device, and a computer-readable storage medium based on the anterior cingulate cortex and the dorsomedial prefrontal cortex for the field. The present invention provides a reference for attention detection, evaluation, and in-depth study of the physiological mechanism of attention occurrence.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent medicine, and specifically, relates to an attention prediction method, a computer device, and a storage medium based on the anterior cingulate cortex and the dorsomedial prefrontal cortex. Background Art

[0002] Attention is an important cognitive process that is evolutionarily conserved and is the flexible control of limited computational resources. Attention can be divided into two types: one is bottom-up attention, which is purely driven by prominent external stimuli and is also called transient attention or exogenous attention; the other is top-down attention, which is actively triggered based on task goals and is also called sustained attention or endogenous attention.

[0003] In most cases, the capture of bottom-up attention is regulated by top-down attention. Whether in daily life or work and study, we rely on attention to help us discover task-related stimuli from complex and changing sensory environments. Attention plays an important role in the regulation of various cognitive activities of ours. Therefore, a systematic and comprehensive analysis of the neural activities during the occurrence of attention and the development of a new method that can be used for effective attention prediction are of great significance for further research and exploration in the field of cognitive neuroscience. Summary of the Invention

[0004] Since attention is the cornerstone to ensure the effective operation of people in a complex and information-rich world, it is crucial to deeply understand the relevant multimodal neural activities in the whole brain for systematically studying the mechanism behind attention. The present invention combines EEG with the c-Fos staining method, selects the 5-choice serial reaction time task (5-CSRTT), a behavioral paradigm for evaluating the attention of rodents, to analyze the characteristics of the whole-brain neural activities in the state of focused attention. It is expected to provide an attention prediction method, a computer device, and a storage medium based on the anterior cingulate cortex and the dorsomedial prefrontal cortex for this field.

[0005] The above invention object of the present invention is achieved through the following technical solutions:

[0006] The first aspect of the present invention provides an attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex.

[0007] Further, the prediction method includes:

[0008] Obtain the activity data of neurons in layer 5 and layer 6 of the anterior cingulate cortex and the anterior marginal cortex in the prefrontal cortex of the sample to be tested;

[0009] Obtain the energy data of the EEG of the sample to be tested in the α, β, low-γ, and high-γ frequency bands;

[0010] Based on the activity data of neurons in layer 5 and layer 6 of the anterior cingulate cortex and the anterior marginal cortex in the prefrontal cortex and the energy data of the EEG in the α, β, low-γ, and high-γ frequency bands, perform classification prediction to obtain the classification result of whether the sample to be tested is an attention-concentrated sample.

[0011] Furthermore, if the activities of neurons in layer 5 and layer 6 of the anterior cingulate cortex and the anterior marginal cortex in the prefrontal cortex increase significantly, the EEG shows high energy in the α, β, and low-γ frequency bands, and there is no significant change in the energy of the high-γ frequency band, then the classification result that the sample to be tested is an attention-concentrated sample is obtained.

[0012] Furthermore, if the activities of neurons in layer 5 and layer 6 of the anterior cingulate cortex and the anterior marginal cortex in the prefrontal cortex do not change significantly, and the EEG does not change significantly in the α, β, and low-γ frequency bands, then the classification result that the sample to be tested is a non-attention-concentrated sample is obtained.

[0013] Furthermore, the activity data of neurons in layer 5 and layer 6 of the anterior cingulate cortex and the anterior marginal cortex in the prefrontal cortex are obtained by detecting the sample to be tested using the c-Fos staining method;

[0014] If the number and density of c-Fos positive neurons in neurons in layer 5 and layer 6 of the anterior cingulate cortex and the anterior marginal cortex in the prefrontal cortex are both higher compared to brain regions other than the anterior cingulate cortex and the anterior marginal cortex, it indicates that the activities of neurons in layer 5 and layer 6 of the anterior cingulate cortex and the anterior marginal cortex in the prefrontal cortex increase significantly;

[0015] If the number and density of c-Fos positive neurons in neurons in layer 5 and layer 6 of the anterior cingulate cortex and the anterior marginal cortex in the prefrontal cortex do not change significantly compared to brain regions other than the anterior cingulate cortex and the anterior marginal cortex, it indicates that the activities of neurons in layer 5 and layer 6 of the anterior cingulate cortex and the anterior marginal cortex in the prefrontal cortex do not change significantly.

[0016] Furthermore, the energy data of the EEG in the α, β, low-γ, and high-γ frequency bands and the source data of the EEG signal are obtained by detecting the sample to be tested using the EEG measurement method;

[0017] If compared with the non-α, β, low-γ frequency bands, the EEG shows high-energy activities in the α, β, low-γ frequency bands while the energy in the high-γ frequency band has no significant change, it indicates that the EEG shows high energy in the α, β, low-γ frequency bands while the energy in the high-γ frequency band has no significant change;

[0018] If compared with the non-α, β, low-γ frequency bands, the EEG shows no significant change in energy activities in the α, β, low-γ frequency bands, it indicates that the EEG shows no significant change in the α, β, low-γ frequency bands.

[0019] The second aspect of the present invention provides an attention prediction system based on the anterior cingulate cortex and the dorsomedial prefrontal cortex.

[0020] Furthermore, the system includes:

[0021] A data acquisition unit: acquiring the activity data of neurons in the fifth and sixth layers of the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex of the sample to be tested, and acquiring the energy data of the EEG of the sample to be tested in the α, β, low-γ, high-γ frequency bands;

[0022] An analysis and prediction unit: performing classification prediction based on the activity data of neurons in the fifth and sixth layers of the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex and the energy data of the EEG in the α, β, low-γ, high-γ frequency bands to obtain a classification result of whether the sample to be tested is an attention-concentrated sample;

[0023] A result output unit: if the activities of neurons in the fifth and sixth layers of the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex increase significantly, and the EEG shows high energy in the α, β, low-γ frequency bands while the energy in the high-γ frequency band has no significant change, a classification result that the sample to be tested is an attention-concentrated sample is obtained;

[0024] If the activities of neurons in the fifth and sixth layers of the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex have no significant change, and the EEG has no significant change in the α, β, low-γ frequency bands, a classification result that the sample to be tested is a non-attention-concentrated sample is obtained.

[0025] Furthermore, the activity data of neurons in the fifth and sixth layers of the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex are obtained by detecting the sample to be tested using the c-Fos staining method;

[0026] If the number and density of c-Fos positive neurons in the fifth and sixth layers of the anterior cingulate cortex and anterior limbic cortex in the prefrontal cortex are higher compared to brain regions other than the anterior cingulate cortex and anterior limbic cortex, it indicates that the activity of the neurons in the fifth and sixth layers of the anterior cingulate cortex and anterior limbic cortex in the prefrontal cortex is significantly increased;

[0027] If there is no significant change in the number and density of c-Fos positive neurons in the fifth and sixth layers of the anterior cingulate cortex and anterior limbic cortex in the prefrontal cortex compared to brain regions other than the anterior cingulate cortex and anterior limbic cortex, it indicates that there is no significant change in the activity of the neurons in the fifth and sixth layers of the anterior cingulate cortex and anterior limbic cortex in the prefrontal cortex.

[0028] Furthermore, the energy data of the EEG brain waves in the α, β, low-γ, and high-γ frequency bands and the source data of the EEG brain wave signals are obtained by using the EEG measurement method to detect the sample to be measured;

[0029] If the EEG brain waves show high-energy activity in the α, β, and low-γ frequency bands compared to non-α, β, and low-γ frequency bands, and the energy in the high-γ frequency band has no significant change, it indicates that the EEG brain waves show high energy in the α, β, and low-γ frequency bands, while the energy in the high-γ frequency band has no significant change;

[0030] If the EEG brain waves have no significant change in the energy activity in the α, β, and low-γ frequency bands compared to non-α, β, and low-γ frequency bands, it indicates that the EEG brain waves have no significant change in the α, β, and low-γ frequency bands.

[0031] The third aspect of the present invention provides an attention prediction device based on the anterior cingulate cortex and the dorsomedial prefrontal cortex.

[0032] Furthermore, the device includes:

[0033] A memory and a processor, the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, it implements the attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex described in the first aspect of the present invention.

[0034] The fourth aspect of the present invention provides a computer-readable storage medium.

[0035] Furthermore, a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex described in the first aspect of the present invention.

[0036] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows:

[0037] The present invention for the first time discovers that the two brain atlas features of the anterior cingulate cortex and the dorsomedial prefrontal cortex (prelimbic cortex) can be used for the effective prediction of whether the subject's attention is concentrated. Based on this, the present invention provides a method, system, device and computer-readable storage medium for attention prediction based on the anterior cingulate cortex and the dorsomedial prefrontal cortex in the art. The present invention provides a reference for attention detection, evaluation and in-depth study of the physiological mechanism of attention generation, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 Schematic flowchart of an attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex provided by an embodiment of the present invention;

[0040] Figure 2 Schematic diagram of an attention prediction system based on the anterior cingulate cortex and the dorsomedial prefrontal cortex provided by an embodiment of the present invention;

[0041] Figure 3 Schematic diagram of an attention prediction device based on the anterior cingulate cortex and the dorsomedial prefrontal cortex provided by an embodiment of the present invention;

[0042] Figure 4 Result diagram corresponding to c-Fos staining revealing neural activities during the occurrence of attention;

[0043] Figure 5 Result diagram corresponding to the significant up-regulation of the expression of c-Fos in the higher cortical areas of the neocortex after the occurrence of attention;

[0044] Figure 6 Result diagram corresponding to the use of a 16-channel extracranial flexible EEG electrode array in mice performing 5-CSRTT;

[0045] Figure 7 Result diagram corresponding to the significant up-regulation of the expression of c-Fos in the thalamus of mice in the 5-CSRTT group;

[0046] Figure 8 Result diagram corresponding to the significant up-regulation of the expression of c-Fos in the brainstem involved in wakefulness regulation during the occurrence of attention;

[0047] Figure 9 It is the result diagram corresponding to the whole-brain functional connectivity pattern of attention. Specific implementation manners

[0048] To enable those skilled in the art of the present technology to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0049] In some processes described in the specification and claims of the present invention and the above-mentioned drawings, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as S101, S102, etc., are only used to distinguish the different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present invention.

[0051] Figure 1 It is a schematic flowchart of an attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex provided by an embodiment of the present invention. Specifically, the method includes the following steps:

[0052] S101: Obtain the activity data of neurons in the fifth and sixth layers of the anterior cingulate cortex in the prefrontal cortex of the sample to be measured and the anterior marginal cortex in the dorsomedial prefrontal cortex;

[0053] In one embodiment, the activity data of neurons in the fifth and sixth layers of the anterior cingulate cortex and the anterior marginal cortex in the prefrontal cortex are obtained by detecting the sample to be measured using the c-Fos staining method;

[0054] If compared with brain regions other than the anterior cingulate cortex and the anterior marginal cortex, the number and density of c-Fos positive neurons in the neurons in the fifth and sixth layers of the anterior cingulate cortex and the anterior marginal cortex in the prefrontal cortex are both relatively high, it indicates that the activity of the neurons in the fifth and sixth layers of the anterior cingulate cortex and the anterior marginal cortex in the prefrontal cortex increases significantly;

[0055] If compared with the brain regions of the anterior cingulate cortex and the anterior limbic cortex, there are no significant changes in the number and density of c-Fos positive neurons in the fifth and sixth layer neurons of the anterior cingulate cortex and the anterior limbic cortex in the prefrontal cortex, it indicates that there are no significant changes in the activities of the fifth and sixth layer neurons of the anterior cingulate cortex and the anterior limbic cortex in the prefrontal cortex.

[0056] In one embodiment, the c-Fos staining method can provide the neural activity characteristics of the prefrontal cortex region with high spatial resolution.

[0057] In one embodiment, in order to obtain the neural activity information of different brain regions during the occurrence of attention, we first used the 5-CSRTT, a classic paradigm for detecting rodent attention behavior, to train mice according to the standard procedure and then detect their attention ( Figure 4 A), and at the same time, we designed a control detection procedure similar to 5-CSRTT (control) to extract the process of attention ( Figure 4 A-B). 90 minutes after the mice completed the 5-CSRTT and control detection, the whole brain tissues of the mice were taken, and the whole brain tissue sections were immunofluorescently stained using c-Fos, a molecular marker widely used to detect neural activity (covering all brain slices between bregma 2.53 and bregma -5.77) ( Figure 4 ). To strictly control the data quality, in this study, we only selected animals that had undergone sufficient training for staining, and the accuracy rate of the mice in the 5-CSRTT group after testing should be greater than or equal to 90 ( Figure 4 ). Among them, there were 4 mice in the control group and 6 mice in the 5-CSRTT group.

[0058] In one embodiment, after anesthesia with tribromoethanol (200 mg / kg intraperitoneally), the mice were perfused with physiological saline (4°C) through the heart, and then perfused with phosphate buffered saline (PBS, 4°C) containing 4% paraformaldehyde (PFA), and post-fixed in 4% PFA for 4 hours. Then, the brain was removed and first incubated in filtered 20% and then 30% sucrose solutions (dissolved in 0.1M PBS) until it sank, and then embedded in optical tomography and coronally sectioned into 50-μm thick sections using a cryostat at -20°C. The sections were collected in a plate or fixed on a gel-coated glass slide for immunostaining.

[0059] To stain the cell nuclei, the sections were incubated in a 1:5000 DAPI solution (D1306, Invitrogen, USA) for 1 minute or covered with a mounting medium containing 50% glycerol with 1:2000 DAPI.

[0060] For immunohistochemical staining, the selected sections were collected in a cell culture plate and washed three times with PBS for 15 minutes each. Then, the sections were permeabilized three times with PBS containing 0.3% Triton X-100 (PBST) for 10 minutes each. At room temperature (RT), the sections were incubated in a blocking solution (3% BSA in PBST) for 90 minutes, and then incubated with antibodies against c-Fos (1:1000, 226 008, SYSY, Germany) and NeuN (1:1000, 93972, Cell Signaling, USA) at 4 °C for 24 hours. Then, the sections were washed 5-6 times with PBS for 20 minutes each, and then incubated with the secondary antibody at room temperature for 2.5 hours. Alexa 488 goat anti-rabbit antibody (1:500, 111-545-003, Jackson ImmunoResearch, USA) was used for the c-Fos primary antibody, Alexa 647 goat anti-mouse antibody (1:500, ab 150115, AbCam, UK) was used for the NeuN primary antibody. Finally, the sections were mounted on glass slides using an Anti-Fade Fluorescence mounting medium without DAPI (ab 104135, AbCam, UK) or ProLong TM Diamond Antifade mounting medium (P36962, Veritas, USA).

[0061] In one embodiment, fluorescence images of the immunostained sections were obtained using a fully automated digital slide scanning microscope (Axios ScanZ1, ZEISS, Germany), and detailed representative images were obtained using a confocal microscope (A1 RMPSi, Nikon, Japan). Fiji and ZEN blue software were used to output high-resolution images for further analysis.

[0062] In one embodiment, to map c-Fos positive neurons in the whole brain, we selected 84 complete immunostained sections covering each brain sample from 2.53 mm anterior to the bregma to -5.77 mm anterior to the bregma, and statistical analysis was performed every 100 μm ( Figure 4B). The advanced version of the cell counting program in our previous study was used to count c-Fos positive neurons. The program has the following five steps: Step 1 is image preparation. We used ZEN blue to output the images and then used Adobe Photoshop to crop and straighten them. Step 2 requires obtaining the hemisphere pictures from Step 1, registering them with the Allen reference atlas (https: / / scalablebrainatlas.incf.org / mouse / ABA_v3), and then renaming them according to the numbers in the Allen reference atlas. Then, Step 3 is run, where each brain image is matched with the selected Allen reference atlas by performing reference point-based triangular segmentation on the slice images followed by a non-linear affine transformation. In Step 4, a CNN model is used to detect c-Fos positive neurons in the output of Step 2. After checking and correcting the results of Step 4, Step 5 calls the results of Steps 3 and 4 to complete the cell counting, outputting the counts and densities of c-Fos positive neurons in different brain regions. Finally, we extract the data required for hierarchical clustering and perform functional network analysis.

[0063] In one embodiment, for electrophysiological data recording, a 16-channel electroencephalogram (EEG) signal was acquired and recorded using an Apollo neural signal acquisition system (Bio-Signal Technology, Jiangsu, China) through the above-mentioned EEG electrodes. The sampling rate was 2 GHz. For neural data processing, the code compiled in MARTAL 2019a (The MathWorks) was used. The EEG signal was band-pass filtered in the frequency range of 0.1 - 200 Hz to isolate the signal from any potential interference (noise). A 50 Hz notch filter and its resonator were used to remove specific noises. The 16-channel EEG electrode array includes a reference electrode and 16 working electrodes, all of which are subject to the same stimulus interference as the common-mode noise. Therefore, the common-mode interference was rejected. Finally, a combined method based on independent component analysis (ICA) algorithm and manual screening was applied to remove residual noise sources such as electromyogram, eye movement, and head movement artifacts.

[0064] In one embodiment, statistical analysis was performed using LAB and GraphPad Prism (version 8) (GraphPad Software, Inc). To compare data between different groups, we employed two-way analysis of variance (VAR), followed by Tukey's multiple comparison test, while for comparisons between two groups, we used the unpaired Student's t-test. Data with error bars were represented as mean ± scanning electron microscope. In the context of evaluating correlation, the Spearman's rank correlation coefficient (rho) and the associated p-value were calculated and reported. The Spearman rank correlation coefficient represents a non-parametric correlation metric, designed to assess the monotonic relationship between two variables without assuming linear or non-linear. This coefficient is derived from the ranking of the variables and effectively ignores the exact observed values. To evaluate the correlation between c-Fos expression and mean reaction time, we used the Spearman correlation coefficient of c-Fos density.

[0065] In one embodiment, to reveal the activation patterns and potential associations between brain regions during attentional processing, we performed hierarchical clustering and network construction on 37 brain regions that showed significant differences in c-Fos expression compared to the control group. Based on the Spearman correlation coefficients calculated from the c-Fos densities of the 37 brain regions to generate a correlation matrix, we first constructed a dendrogram using the complete-linkage method and then constructed a weighted undirected network using Cytoscape visualization (version 3.10.1) by considering the correlations with Spearman r's and p < 0.05. Network centrality analysis was performed using the CentiScaPe 2.2 plugin of Cytoscape. Using the Spearman correlation coefficient as the weighted edge, degree, betweenness centrality, and eigenvector centrality were calculated to identify network centrality. Degree measures the edge count linked to a node, reflecting its direct influence. Betweenness centrality is determined by the frequency of the shortest paths that include the node, highlighting its bridging function in the network. Eigenvector centrality evaluates the influence of a node based on the importance of its connected nodes, favoring nodes associated with highly connected neighbors. An increase in eigenvector centrality indicates a great influence through these favorable connections. We ranked the values of these three metrics in descending order, resulting in RANK_degree, RANK_betweenness, and RANK_eigenvector, where higher values indicate a higher corresponding RANK. The total centrality score (referred to as S centrality) was calculated by averaging the RANK values of these three metrics.

[0066] In one embodiment, the process of spectral analysis of EEG is as follows: For each trial, short-time Fourier transform (STFT) algorithm is used for EEG spectral analysis, with a 1000 ms sliding window and a Hamming window, which helps with frequency smoothing. Before the cue onset, the power spectral density is calculated in the delay range of -500 to 0 ms with an incremental resolution of 10 ms, and then the results in the trial are averaged. The analyzed spectrum spans from 1 to 100 Hz with a resolution of 0.5 Hz. The representation of each specified frequency band - alpha (8 - 12 Hz), beta (12 - 30 Hz), low gamma (30 - 50 Hz), and high gamma (50 - 80 Hz) - is quantified according to the average power within their respective frequency ranges.

[0067] In one embodiment, the process of EEG source localization analysis is as follows: Source localization is a complex method aimed at tracing surface signals (such as signals captured by electroencephalogram and magnetoencephalogram (MEG)) to their potential sources of neural activity. However, the accuracy of this technique can be affected by several factors, including the thickness of the skull and the distance from the skull to the cortex. In this study, we adopted the strategy of directly placing electrodes on the skull to mitigate the influence of skull thickness. Additionally, by selecting animals of the same age, we ensured a consistent skull-to-cortex distance, thus minimizing the differences between individuals. For source localization analysis, this study utilized Brainstorm (an open-source software available at https: / / neuuerimage.usc.edu / brainstorm), as well as the MRI atlas of C57 mice to jointly register extracranial electrodes with MRI coordinates. The process first identifies anatomical landmarks within the MRI, horizontally aligns the AC-PC line, and generates head, scalp, and skull surfaces with a proposed number of 1922 vertices to achieve an optimal balance between accuracy and computational efficiency. After configuring these parameters in Brainstorm, the software creates triangular face-vertex meshes for each surface and validates their orientation and position critically based on the MRI. After importing the channel file and electrode positions, any misalignments are corrected to ensure that the electrode coordinates are aligned with the MRI-based surface. The accuracy of electrode localization is visually confirmed by comparing with the positions of the eyes and ears.

[0068] To solve the inverse problem of estimating neuronal activation from measurement data, we used minimum-norm estimation (MNE), which is particularly advantageous for analyzing evoked responses characterized by widespread neuronal activation over time. This method provides an inverse solution by estimating the amplitudes of a distributed model that discretizes the source space into multiple equivalent current dipoles located on the cortical surface or within the brain volume (Lee et al., 2013). MNE works by simultaneously estimating the amplitudes of all modeled source locations with the least total energy, based on the lead field matrix obtained from the EEG signals. By selecting a source estimation method, an inverse solution is generated, facilitating the mapping of the estimated sources.

[0069] The experimental results are as Figure 4 shown. First, we globally compared the distribution of c-Fos positive neurons in the whole brain. Compared with the control group, although the distribution pattern of c-Fos positive neurons in the 5-CSRTT group was similar to that of the control group, the number and density of c-Fos positive neurons were both higher, especially in the neocortex, thalamus, and medulla. Among them, the density of c-Fos positive neurons in the neocortex was the highest, followed by the thalamus. In addition, it is worth noting that the expression of c-Fos in the hypothalamus, midbrain, and pons was also significantly upregulated. And there was no significant difference in the number and distribution of c-Fos positive neurons between the left and right hemispheres. The above results suggest that the neocortex and brainstem may be involved in the process of attention to varying degrees, and there is no obvious hemispheric dominance. In view of this, we will next conduct a detailed analysis of c-Fos expression in the above-mentioned brain regions.

[0070] In one embodiment, all experimental and animal care procedures complied with the approval of the Animal Care and Use Committee of the Beijing Institute of Basic Medicine. Mice were housed in the animal care facilities of our institute (SPF-F01-002; SPF Biotechnology, Beijing, China), with a 12-hour light / dark cycle, and had unrestricted access to standard rodent feed (AIN-93; Tropical Animal Feed High-Tech Co) and clean water. Adult male C57BL / 6 mice aged 2 to 4 months were used in the experiments.

[0071] In one embodiment, the neocortex, a structure unique to mammals, is involved in the integration of sensory information and plays an important role in various cognitive functions. By comparing the distribution of c-Fos positive neurons in the two groups, we found that compared with the control group, the number and density of c-Fos positive neurons were significantly upregulated in the high-level visual cortex area involved in visual feature recognition, the prefrontal cortex area that plays an important role in high-level cognitive functions such as decision-making, working memory, and attention, as well as the retrosplenial cortex involved in high-level cognitive functions such as episodic memory, navigation, and planning, and the posterior parietal association area responsible for multisensory information and decision-making functions ( Figure 5 A).

[0072] The experimental results are as Figure 5 shown. The greatest difference in c-Fos expression in the prefrontal cortex was in the classical anterior cingulate cortex involved in attention control, followed by the anterior limbic cortex. Our further research found that although the proportion of c-Fos positive neurons in the dorsal anterior cingulate cortex (ACAd) of the anterior cingulate cortex (ACA) was slightly higher than that in the ventral anterior cingulate cortex (ACAv), the density of c-Fos positive neurons in ACAv was higher than that in ACAd, and c-Fos positive neurons were concentrated in layer 5, followed by L2 / 3( Figure 5 B). In the sensory cortex, the expression level of c-Fos in the VISal / am / l / pm (anterolateral visual area / anterior medial visual area / lateral visual area / posteromedial visual area) of the high-level visual cortex was higher than that in the control group, and the c-Fos expression in the remaining cortices was stronger than that in the control group except for layer 1, and the c-Fos density in L4 was the highest( Figure 5 B). In the association cortex area, the number and density of c-Fos positive neurons in the posterior parietal association area, including two brain regions of VISa and VISrl (anterior area and lateral area), and RSPd (dorsal posterior area) were significantly higher than those in the control group( Figure 5 ), and there was no significant layer enrichment.

[0073] In one embodiment, the high temporal resolution of electroencephalogram makes it the preferred tool for studying attention. To provide a more comprehensive electrophysiological analysis of neural activities in the neocortex during attention tasks, we used a 16-channel extracranial flexible EEG electrode array in mice performing 5-CSRTT( Figure 6 A-B). 5-CSRTT evaluated the attention to each position and the cognitive processing speed in a large number of trials. Correct trials indicated that the mice were concentrated and had the fastest reaction time, while errors and omissions indicated that attention processing and executive control were impaired, showing longer reaction times compared to the correct group( Figure 6 C). We focused the examination of brain waves and attention load on (500 ms before the visual cue appeared). Since attention is related to the high-frequency band of EEG, we focused the analysis on the alpha, beta, low gamma, and high gamma frequency bands. According to the EEG distribution in 2D space, the prefrontal cortex and the posterior area of the spleen showed considerable activities in the alpha, beta, and low gamma frequency bands. The somatosensory cortex responsible for motor control also showed signals in the alpha and beta frequencies. The visual cortex was marked by its gamma band activity( Figure 6D). The above findings are consistent with the c-Fos expression patterns we observed. Moreover, the average EEG signal power in four designated frequency bands was examined in the correct, incorrect, and omission groups. Different performance states led to changes in the spontaneous EEG activity in these frequency bands. In particular, the correct group showed significant alterations in the alpha, beta, and low gamma frequency bands, with power levels significantly exceeding those of the incorrect and omission groups. In contrast, the activity in the high gamma band was not related to the performance of the animals in various cognitive tasks ( Figure 6 E). These results are consistent with our finding that the c-Fos expression in the brains of mice in the 5-CSRTT group was significantly stronger than that in the control group.

[0074] In one embodiment, we optimized the surgical conditions to minimize the impact on the mice. The surgery was performed under complete general anesthesia, using 2-3% isoflurane in oxygen-rich air to ensure the comfort and safety of the animals. Before the surgery, the head was shaved and disinfected with iodine tincture, and then an incision was made along the midline of the scalp to expose the surface of the skull, which was cleaned with 2.5-3.5% hydrogen peroxide, and the periosteum was carefully removed. To ensure a 16-channel EEG electrode array, three guide holes were drilled in specific positions in the skull relative to the anterior prominence to ensure the precise alignment of the electrodes designed to monitor the brain wave signals. The electrode array was made of a flexible polyurethane substrate (Kedou Brain Computer Technology Co, Suzhou, China) with a copper / gold metal layer (50 nm copper, 150 nm gold) and was fixed to the skull using screws penetrating the epidural space without suturing. This method significantly reduced tissue damage while improving the stability and accuracy of the recording, as shown by the electrode placement positions in the accompanying figures. Figure 6 A). After the surgery, the mice were allowed to recover for at least one week before behavioral training.

[0075] In one embodiment, in the first seven days of the behavioral task, the mice were singly housed and diet-controlled to maintain their body weight at 85% of their free weight. Then, the mice were divided into a 5-CSRTT group (finally 6 qualified animals were selected), an EEG group (finally 6 qualified animals) (the above two groups used the 5-CSRTT task), and a control group (finally 4 qualified animals were selected and trained using the control group's task), and then the training described below was carried out.

[0076] Five-Choice Serial Reaction Time Task (5-CSRTT): The 5-CSRTT was conducted in a sound-attenuated ventilated chamber for standardized testing. There was a food trough on one side of the chamber and a touch screen on the other side, which consisted of a horizontal arrangement of five holes on the touch screen to provide light stimuli to the mice. The Operant TaskStudio V2 software (Ohara, Japan) was used to set the control session program and collect data through a computer interface. Briefly, a trial was triggered by the mouse poking its nose towards the feeder with illumination. After that, after the inter-trial interval (ITI) passed, a brief cue light stimulus was presented in one of the five possible holes. The mouse needed to focus on observing the five holes to look for the appearance of the light stimulus and respond by poking its nose at it in the "correct" hole (correct trial) to obtain a food pellet (10 mg dust-free precision pellet; Test Diet, USA) within a limited hold period. If the mouse responded before the stimulus (premature trial), in the "wrong" hole (incorrect trial), or missed it, a 2-s time-out (TO) phase was initiated, marked by the illumination of the house light and no food reward being provided. After obtaining a reward or at the end of the time-out period, the head entering the feeder initiated the subsequent trial.

[0077] The 5-CSRTT training protocol included six-step training levels (learning tasks) defined by specific criteria for each level. The training sessions included a total of 100 trials, and the mice were trained six days a week until they reached the session criteria. When the mice reached the sixth-level criteria, they entered the testing phase. In the testing phase, to increase the attentional load, the ITI was no longer fixed but set to pseudo-random (7 s / 8 s / 9 s), and the flow was set to 1.0 s. The 5-CSRTT task required the mice to distribute their attention to five spatially separated holes and maintain their attention until the stimulus appeared (top-down attention). Attention was evaluated by measuring the accuracy of the response, calculated as the number of correct trials / (the number of correct trials + the number of incorrect trials). In this study, the reaction time (RT, from the onset of the cue to the nose poke response) was used to evaluate the attentional level of the 5-CSRTT mice, which showed a similar accuracy to that of the electroencephalogram mice.

[0078] Control group training protocol: The training protocol of the control group was modified based on the 5-CSRTT training protocol and conducted in the same testing environment. A trial was initiated by the mouse entering the feeder and illuminating the five possible holes. The mouse was required to respond by poking its nose at any hole to obtain a food pellet. After obtaining the food reward, the head entering the feeder initiated the subsequent trial. The mice were trained for six days (30 minutes per day) to fully learn the task. In the control task, the mice did not need to allocate attention to detect and report the location of the upcoming cue stimulus.

[0079] In one embodiment, the expression of c-Fos in the thalamus of mice in the 5-CSRTT group was significantly upregulated, and the corresponding results are as Figure 7 shown. The diencephalon contains two important nuclei, the thalamus and the hypothalamus. The thalamus is a relay station for information processing and is involved in various brain functions including sensory processing, attention, decision-making, and memory. According to the afferent and efferent connections and functions of the thalamus, the thalamus is divided into the sensory-motor thalamic region (sensory-motor cortex related (DORsm) in the ARA) and the multimodal limbic thalamic region (polymodal association cortex related (DORpm) in the ARA). We carefully analyzed the expression of c-Fos in the entire thalamus and found that during the process of attention, c-Fos was mainly enriched in the multimodal limbic thalamic region, including the parafascicular nucleus (PF) of the thalamic intralaminar group, the central lateral nucleus of the thalamus (CL), the paracentral nucleus (PCN), the central medial nucleus of the thalamus (CM), and the paraventricular nucleus of the thalamus (PVT) of the midline group, which play key roles in consciousness and sleep-wake regulation.

[0080] The mediodorsal nucleus of the thalamus (MD), the intermediodorsal nucleus of the thalamus (IMD), the interanterodorsal nucleus of the thalamus (IAD), the anteromedial nucleus (AM), the anteroventral nucleus of the thalamus (AV), the posterior complex of the thalamus (PO), and the lateral posterior nucleus of the thalamus (LP) in the medial part of the thalamus, which are involved in the regulation of higher cognitive functions such as decision-making.

[0081] In addition, the number and density of c-Fos positive neurons in the ventral posteromedial nucleus of the thalamus (VPM), ventral posterolateral nucleus of the thalamus (VPL), ventral medial nucleus of the thalamus (VM), and ventral anterior-lateral complex of the thalamus (VAL) in the sensorimotor thalamic region involved in functions such as somatosensation and goal-driven motor control were also significantly increased compared to the control group, which may be related to the increased local motor behavior of the 5-CSRTT group mice during task execution. In addition, during the process of attention generation, we also found the expression of a small number of neurons in the thalamic reticular nucleus (TRN), but it was not shown here due to its low c-Fos expression level.

[0082] The hypothalamus is connected to the cortex and subcortical structures and is involved in multiple brain functions including energy metabolism, arousal, and cognition. We also conducted a detailed analysis of c-Fos expression in the hypothalamus and found that during the process of attention generation, the expression of c-Fos positive neurons in the zona incerta (ZI) and subthalamic nucleus (STN) was significantly upregulated, which is consistent with the results of two previous studies on the involvement of ZI and STN in the attention process.

[0083] In one embodiment, the results of the significantly upregulated c-Fos expression in the brainstem involved in arousal regulation during the process of attention generation are shown in Figure 8As shown in the figure. The midbrain, pons, and medulla oblongata have direct projections to the cortex and thalamus and are involved in the regulation of functions such as motor control, sleep-wake cycle, and decision-making. We found in the overall results that the expression of c-Fos in the above-mentioned brain regions showed an upward trend during the process of attention. Therefore, we also conducted a detailed analysis of the above three brain regions. We found that compared with the control group, the regions where the expression of c-Fos-positive neurons was significantly upregulated were concentrated in the ascending arousal system, including the midbrain raphe nuclei rich in serotonergic neurons (Ramb Midbrain raphe nuclei), the central linear nucleus raphe (CLI, Centrallinear nucleus raphe), the dorsal nucleus raphe (DR, Dorsal nucleus raphe), and the superior central nucleus raphe (CS, Superior central nucleus raphe), the pedunculopontine nucleus (PPN, Pedunculopontine nucleus) rich in cholinergic neurons, the laterodorsal tegmental nucleus (LDT, Laterodorsal tegmental nucleus), and the parabrachial nucleus (PB, Parabrachial nucleus) of the pons. In addition, the brain regions with upregulated c-Fos expression also included the periaqueductal gray (PAG, Periaqueductal gray), especially the ventral periaqueductal gray, the red nucleus (RN, Rednucleus), the dorsal tegmental nucleus (DTN, Dorsal tegmental nucleus), and the number and density of c-Fos-positive neurons in the pontine central gray (PCG, Pontinecentral gray) were significantly upregulated compared with the control group. It is worth mentioning that the above-mentioned brain regions are all involved in the regulation of sleep-wake cycle to varying degrees.

[0084] In addition, we also found that the interstitial nucleus of Cajal (INC, Interstitial nucleus of Cajal) and the nucleus of Darkschewitsch (ND, Nucleus of Darkschewitsch) involved in eye movement control and head posture, as well as the pontine reticular nucleus (PRNc, Pontine reticular nucleus, caudal part) that regulates spontaneous activity and the spinal nucleus of the trigeminal, oral part (SPVO, Spinal nucleus ofthe trigeminal, oral part) and the gigantocellular reticular nucleus (GRN, Gigantocellular reticularnucleus) that have direct projections to the motor thalamus and are involved in motor regulation showed a significant increase in the number and density of c-Fos-positive neurons compared with the control group.

[0085] In one embodiment, the resulting graph corresponding to the whole-brain functional connectivity pattern of attention is as Figure 9 shown. To further analyze the relationship between the brain regions with upregulated c-Fos expression and attention, we selected the average reaction time (average RT = average correct RT + average error RT + average omission RT) as the parameter to evaluate the attention level, because using only the correct rate alone cannot comprehensively reflect the attention differences in mice. We calculated the correlation coefficients between RT and the upregulated density of c-Fos in different brain regions. The results showed that the density of c-Fos+ in the brain regions of ACA, PL, AM(*), CL(*), VPL(*), VPM(*), STN(*), ZI(*), INC(*), SCs(*), SCm(*) and PB(*) was significantly negatively correlated with RT (the absolute value of r was greater than 0.85), the density of c-Fos+ in ACAd, VISa, VISrl, RSPd, LSv, LSr, GPe, LP, PF, VM, PO, PCN, PVT, PAG, PPN, RN, MRN, CLI, LDT, SPVO, GRN was strongly negatively correlated with RT (the absolute value of r was between 0.6 and 0.85), the density of c-Fos+ in VISal, VISl, VISam, LSc, VAL, IAD, MD, AV, IMD, ND, CS, DTN, PCG was weakly negatively correlated with RT (the absolute value of r was between 0.2 and 0.55), and in addition, the density of c-Fos+ in CM, DR and PRNc was almost not correlated with RT (the absolute value of r was less than 0.2).

[0086] Given that some of the brain regions with upregulated c-Fos all play regulatory roles in the same physiological function, we performed hierarchical clustering on the above brain regions and obtained a total of 5 clusters. Among them, there was no significant correlation between the density of c-Fos-positive neurons and the average reaction time in Cluster 1 and Cluster 2 ( Figure 9)。Groups 1 and 2 include RSPd and AV, IMD, CM, VAR, MD in the thalamus, which are involved in complex cognitive processes such as working memory, attention guidance, and visual turning, as well as the brains in brain regions CS, DTN, PCG, PRNc, which are key participants in voluntary movement and the control of eye and head movements. This suggests that Groups 1 and 2 may be involved in the joint regulation of visually driven behaviors. Group 3 consists of nuclei involved in arousal regulation, including V2, LS, and Gpe, PVT that is positively correlated with the level of wakefulness, PCO, PF, CL in the intralaminar thalamus involved in the recovery and regulation of consciousness after anesthesia, VPL and VPM in the sensory-motor thalamus that play important roles in motor control, and PPN, RN, PB, LGA, and Ramb, which are involved in the encoding of cortical information and the regulation of the sleep-wake cycle in the brain, and the expression of c-Fos neurons in the brain regions of Group 3 is positively correlated with attention. Cluster 4 contains IAD, LP, VM, and GRN. Group 5 consists of the prefrontal cortex including PL, ACA, PTLp, ZI, STN, AM, and PO, which play important roles in goal-driven cognition, decision-making, and attention control, and also includes LDT and SPVO, which are involved in the regulation of arousal. Similar to Group 3, the c-Fos expression level in Group 5 also shows a significant correlation with the attention level. This result implies that the arousal system may play an important regulatory role in shaping attention( Figure 9 )。

[0087] To reveal the potential associations between brain regions during attention processing, we constructed a network using these 37 brain regions and then used network centrality analysis to identify the hubs of the network. The results showed that the thalamus-mediated interactive functional network was mainly composed of Group 3 and Group 5, Group 1 and Group 2 interacted more frequently with Group 3, and Group 4 interacted more frequently with Group 5( Figure 9 )。Network centrality assessment showed that the centrality scores of thalamic CL and VPM were the highest, followed by the thalamic VPL nucleus. To identify the hub of the network, we calculated the ratio of the fold change in c-Fos expression density (5-CSRTT / average c-Fos density in the control group), evaluated the p-value, and comprehensively evaluated the expression density of c-Fos positive neurons. Considering the above evaluation conditions, we believe that CL is more like the hub of this network compared to VPM( Figure 9 )。Given these results and the different functions of the brain regions enriched in the five groups, our data revealed the existence of two main interactive functional networks, which are mainly mediated by the thalamus. One subnetwork mainly recruits the thalamus involved in arousal and motor regulation, as well as members of the ascending arousal system in the brainstem, while the other subnetwork mainly includes the prefrontal cortex, thalamus, and part of the brainstem involved in the regulation of attention control and higher cognitive functions( Figure 9)。These results further indicate that the arousal system may be a key player in space-time.

[0088] In this study, the inventors of the present invention combined EEG with the staining method of c-Fos, selected the 5-hole choice serial reaction time task, a behavioral paradigm for evaluating rodent attention, and used hierarchical clustering and functional network analysis methods to analyze the whole-brain neural activity pattern in the highly attentive state. The results showed that the results of EEG and c-Fos staining showed that in the state of highly concentrated attention, the neural activities in the anterior cingulate cortex (ACA) and prelimbic prefrontal cortex (PL) in the prefrontal cortex were the most significant, and the EEG showed high energy in the α (alpha), β (beta), and low-γ (low gamma) frequency bands, while there was no significant change in the high-γ (high gamma) frequency band. The results of c-Fos staining further revealed that the activities of neurons in layers 5 and 6 of the anterior cingulate cortex and the prelimbic cortex PL increased most significantly in the state of concentrated attention. The above results demonstrate the effectiveness of the attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex provided by the present invention, which not only provides a neural activity map with high spatio-temporal resolution for attention detection, evaluation, and research on the physiological mechanism of attention occurrence, but also provides a reference for in-depth analysis of the neural circuit of attention.

[0089] S102: Obtain the energy data of the EEG of the sample to be tested in the α, β, low-γ, and high-γ frequency bands;

[0090] In one embodiment, the energy data of the EEG of the sample to be tested in the α, β, low-γ, and high-γ frequency bands and the tracing data of the EEG signal are obtained by using the EEG measurement method to detect the sample to be tested;

[0091] If compared with the non-α, β, low-γ frequency bands, the EEG shows high-energy activities in the α, β, low-γ frequency bands, while the energy in the high-γ frequency band has no significant change, it indicates that the EEG shows high energy in the α, β, low-γ frequency bands, while the energy in the high-γ frequency band has no significant change;

[0092] If compared with the non-α, β, low-γ frequency bands, the EEG has no significant change in the energy activities in the α, β, low-γ frequency bands, it indicates that the EEG has no significant change in the α, β, low-γ frequency bands.

[0093] In one embodiment, the brain regions found according to the tracing data of the EEG signal are consistent with the results of the brain regions observed by c-Fos staining, which are collectively reflected in the anterior cingulate cortex and the dorsomedial prefrontal cortex.

[0094] S103: Perform classification prediction based on the activity data of neurons in the anterior cingulate cortex and the fifth and sixth layers of the anterior limbic cortex in the prefrontal cortex and the energy data of the EEG in the α, β, low-γ, and high-γ frequency bands to obtain the classification result of whether the sample to be tested is an attention-concentrated sample.

[0095] If the activities of neurons in the anterior cingulate cortex and the fifth and sixth layers of the anterior limbic cortex in the prefrontal cortex increase significantly, the EEG shows high energy in the α, β, and low-γ frequency bands, and there is no significant change in the high-γ frequency band, then the classification result that the sample to be tested is an attention-concentrated sample is obtained;

[0096] If there is no significant change in the activities of neurons in the anterior cingulate cortex and the fifth and sixth layers of the anterior limbic cortex in the prefrontal cortex, and there is no significant change in the EEG in the α, β, and low-γ frequency bands, then the classification result that the sample to be tested is a non-attention-concentrated sample is obtained.

[0097] Figure 2 It is a schematic diagram of an attention prediction system based on the anterior cingulate cortex and the dorsomedial prefrontal cortex provided by an embodiment of the present invention. Specifically, the system includes:

[0098] Data acquisition unit: Acquire the activity data of neurons in the anterior cingulate cortex and the fifth and sixth layers of the anterior limbic cortex in the dorsomedial prefrontal cortex of the sample to be tested, and acquire the energy data of the EEG of the sample to be tested in the α, β, low-γ, and high-γ frequency bands;

[0099] Analysis and prediction unit: Perform classification prediction based on the activity data of neurons in the anterior cingulate cortex and the fifth and sixth layers of the anterior limbic cortex in the prefrontal cortex and the energy data of the EEG in the α, β, low-γ, and high-γ frequency bands to obtain the classification result of whether the sample to be tested is an attention-concentrated sample;

[0100] Result output unit: If the activities of neurons in the anterior cingulate cortex and the fifth and sixth layers of the anterior limbic cortex in the prefrontal cortex increase significantly, the EEG shows high energy in the α, β, and low-γ frequency bands, and there is no significant change in the high-γ frequency band, then the classification result that the sample to be tested is an attention-concentrated sample is obtained;

[0101] If there is no significant change in the activities of neurons in the anterior cingulate cortex and the fifth and sixth layers of the anterior limbic cortex in the prefrontal cortex, and there is no significant change in the EEG in the α, β, and low-γ frequency bands, then the classification result that the sample to be tested is a non-attention-concentrated sample is obtained.

[0102] In one embodiment, the activity data of neurons in the fifth and sixth layers of the anterior cingulate cortex and the anterior limbic cortex in the prefrontal cortex are obtained by detecting a test sample using the c-Fos staining method;

[0103] If the number and density of c-Fos positive neurons in the neurons of the fifth and sixth layers of the anterior cingulate cortex and the anterior limbic cortex in the prefrontal cortex are higher compared to brain regions other than the anterior cingulate cortex and the anterior limbic cortex, it indicates that the activity of the neurons in the fifth and sixth layers of the anterior cingulate cortex and the anterior limbic cortex in the prefrontal cortex is significantly increased;

[0104] If there is no significant change in the number and density of c-Fos positive neurons in the neurons of the fifth and sixth layers of the anterior cingulate cortex and the anterior limbic cortex in the prefrontal cortex compared to brain regions other than the anterior cingulate cortex and the anterior limbic cortex, it indicates that the activity of the neurons in the fifth and sixth layers of the anterior cingulate cortex and the anterior limbic cortex in the prefrontal cortex has no significant change.

[0105] In one embodiment, the energy data of the EEG brain waves in the α, β, low-γ, and high-γ frequency bands and the tracing data of the EEG brain wave signals are obtained by detecting a test sample using the EEG measurement method;

[0106] If the EEG brain waves show high-energy activity in the α, β, and low-γ frequency bands, while the energy in the high-γ frequency band has no significant change compared to non-α, β, and low-γ frequency bands, it indicates that the EEG brain waves show high energy in the α, β, and low-γ frequency bands, while the energy in the high-γ frequency band has no significant change;

[0107] If the EEG brain waves have no significant change in energy activity in the α, β, and low-γ frequency bands compared to non-α, β, and low-γ frequency bands, it indicates that the EEG brain waves have no significant change in the α, β, and low-γ frequency bands.

[0108] Figure 3 It is a schematic diagram of an attention prediction device based on the anterior cingulate cortex and the dorsomedial prefrontal cortex provided by an embodiment of the present invention. Specifically, the device includes:

[0109] A memory and a processor, where the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex as described above in the present invention is implemented.

[0110] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex as described above in the present invention is implemented.

[0111] The verification results of this verification embodiment show that assigning a fixed weight to the indication can moderately improve the performance of this method compared to the default setting.

[0112] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0113] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms.

[0114] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, in each embodiment of the present invention, the functional units can be integrated into one processing unit, or each unit exists physically alone, or two or more units are integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0116] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0117] Those of ordinary skill in the art can understand that all or part of the steps in the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc.

[0118] The above provides a detailed introduction to a computer device provided by the present invention. For those of ordinary skill in the art, according to the ideas of the embodiments of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. A method for predicting attention based on the anterior cingulate cortex and the dorsomedial prefrontal cortex, characterized in that: The prediction method comprises: Acquire activity data of neurons in the fifth and sixth layers of the anterior cingulate cortex in the prefrontal cortex and the prelimbic cortex in the dorsomedial prefrontal cortex of the sample to be tested; Obtain the energy data of the EEG of the sample to be tested in the α, β, low-γ, and high-γ frequency bands; Based on the activity data of neurons in the fifth and sixth layers of the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex and the energy data of the EEG in the α, β, low-γ, and high-γ frequency bands, classification prediction is performed to obtain a classification result of whether the sample to be tested is an attention-focused sample; If the activity of neurons in the fifth and sixth layers of the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex increases significantly, the EEG shows high energy in the α, β, and low-γ frequency bands, and the energy in the high-γ frequency band does not change significantly, then the classification result of the sample to be tested is obtained as an attention-focused sample; If there is no significant change in the activities of the fifth and sixth layers of neurons in the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex, and there is no significant change in the α, β, and low-γ frequency bands of the EEG, then the classification result of the sample to be tested is obtained as a non-attention sample; The activity data of the neurons in the fifth and sixth layers of the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex are obtained by detecting the sample to be tested using the c-Fos staining method; If the number and density of c-Fos-positive neurons in the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex are higher than those in the control group, it indicates that the activities of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex are significantly increased; If the number and density of c-Fos-positive neurons in the fifth and sixth layers of neurons in the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex do not change significantly compared with the control group, it indicates that the activities of neurons in the fifth and sixth layers of neurons in the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex do not change significantly; The energy data of the EEG brain waves in the α, β, low-γ, and high-γ frequency bands and the traceability data of the EEG brain waves are obtained by detecting the sample to be tested using an EEG measurement method; If the energy activity of the EEG in the α, β, and low-γ frequency bands is higher than that of the error and omission group, and there is no significant change in the energy activity in the high-γ frequency band, it indicates that the EEG presents high energy in the α, β, and low-γ frequency bands, and there is no significant change in the energy in the high-γ frequency band; If the EEG brain power activity in the α, β, and low-γ frequency bands does not have a significant change compared to the error and omission group, it indicates that the EEG brain power does not have a significant change in the α, β, and low-γ frequency bands; The error and omission group refers to a group in which attention processing and executive control are impaired.

2. An attention prediction system based on the anterior cingulate cortex and the dorsomedial prefrontal cortex, characterized in that: The system comprises: Data acquisition unit: obtains the activity data of the fifth and sixth layers of neurons in the anterior cingulate cortex in the prefrontal cortex and the prelimbic cortex in the dorsomedial prefrontal cortex of the sample to be tested, and obtains the energy data of the EEG brain wave in the α, β, low-γ, and high-γ frequency bands of the sample to be tested; Analysis and prediction unit: based on the activity data of the fifth and sixth layers of neurons in the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex and the energy data of the EEG in the α, β, low-γ, and high-γ frequency bands, classification prediction is performed to obtain a classification result of whether the sample to be tested is an attention-focused sample; Result output unit: if the activity of neurons in the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex increases significantly, the EEG brain wave shows high energy in the α, β, and low-γ frequency bands, and the energy in the high-γ frequency band does not change significantly, then the classification result of the sample to be tested is obtained as an attention concentration sample; If there is no significant change in the activities of the fifth and sixth layers of neurons in the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex, and there is no significant change in the α, β, and low-γ frequency bands of the EEG, then the classification result of the sample to be tested is obtained as a non-attention sample; The activity data of the neurons in the fifth and sixth layers of the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex are obtained by detecting the sample to be tested using the c-Fos staining method; If the number and density of c-Fos-positive neurons in the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex are higher than those in the control group, it indicates that the activities of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex are significantly increased; If the number and density of c-Fos-positive neurons in the fifth and sixth layers of neurons in the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex do not change significantly compared with the control group, it indicates that the activities of neurons in the fifth and sixth layers of neurons in the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex do not change significantly; The energy data of the EEG brain waves in the α, β, low-γ, and high-γ frequency bands and the traceability data of the EEG brain waves are obtained by detecting the sample to be tested using an EEG measurement method; If the energy activity of the EEG in the α, β, and low-γ frequency bands is higher than that of the error and omission group, and there is no significant change in the energy activity in the high-γ frequency band, it indicates that the EEG presents high energy in the α, β, and low-γ frequency bands, and there is no significant change in the energy in the high-γ frequency band; If the EEG brain power activity in the α, β, and low-γ frequency bands does not have a significant change compared to the error and omission group, it indicates that the EEG brain power does not have a significant change in the α, β, and low-γ frequency bands; The error and omission group refers to a group in which attention processing and executive control are impaired.

3. An attention prediction device based on the anterior cingulate cortex and the dorsomedial prefrontal cortex, characterized in that: The device comprises: A memory and a processor, wherein the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex described in claim 1 is implemented.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex of claim 1 is implemented.

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