Attention prediction method based on anterior clasp cortex and dorsal medial prefrontal cortex, computer equipment and storage medium

Abstract

The invention discloses an attention prediction method based on anterior cinerary cortex and dorsal-medial prefrontal cortex, a computer device and a storage medium, and finds that two brain pattern map characteristics of anterior edge cortex in the anterior cinerary cortex and the dorsal-medial prefrontal cortex can be used for effectively predicting whether the attention of a subject is concentrated or not for the first time. Based on this, the invention provides an attention prediction method, system and device based on anterior cingulate cortex and dorsal medial prefrontal cortex, and a computer readable storage medium for the field, and provides reference for attention detection and evaluation and in-depth study of physiological mechanisms of attention occurrence.

Description

Technical Field

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

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

[0003] In most cases, bottom-up attention capture is regulated by top-down attention. Whether in daily life or work and study, we rely on attention to help us find task-related stimuli from complex and changing sensory environments. Attention plays an important role in regulating our various cognitive activities. Therefore, it is of great significance to conduct a systematic and comprehensive analysis of neural activities during the process of attention and develop a new method that can be used to effectively predict attention for further research and exploration in the field of cognitive neuroscience. Summary of the invention

[0004] Given that attention is the cornerstone for ensuring that people function effectively in a complex and information-rich world, a deep understanding of the relevant multimodal neural activities throughout the brain is essential for systematically studying the mechanisms behind attention. The present invention combines EEG with the c-Fos staining method, selects the 5-hole selection continuous reaction time task (5-CSRTT), a behavioral paradigm for assessing rodent attention, and analyzes the characteristics of whole-brain neural activity under a state of focused attention. It is intended to provide an attention prediction method, computer device, and storage medium based on the anterior cingulate cortex and dorsomedial prefrontal cortex in the art.

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

[0006] A 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 comprises:

[0008] 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;

[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 the fifth and sixth layers of the anterior cingulate cortex and 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.

[0011] Furthermore, 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 shows high energy in the α, β, and low-γ frequency bands, while the energy in the high-γ frequency band does not change significantly, then the classification result of the sample to be tested is a sample of focused attention.

[0012] Furthermore, if there is no significant change in the activity of neurons in the fifth and sixth layers of the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex, and there is no significant change in the EEG brain waves in the α, β, and low-γ frequency bands, then the classification result of the sample to be tested is a non-attention sample.

[0013] Furthermore, 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 a c-Fos staining method;

[0014] 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 non-anterior cingulate cortex and prelimbic cortex brain regions, it indicates that the activity of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex is significantly increased;

[0015] 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 do not change significantly compared with brain areas other than the anterior cingulate cortex and prelimbic cortex, it indicates that the activity of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex do not change significantly.

[0016] Furthermore, 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 wave signals are obtained by detecting the sample to be tested using an EEG measurement method;

[0017] If the EEG brainwave shows high energy activity in the α, β, and low-γ frequency bands, while the energy activity in the high-γ frequency band does not change significantly, compared with the non-α, β, and low-γ frequency bands, it indicates that the EEG brainwave shows high energy in the α, β, and low-γ frequency bands, while the energy in the high-γ frequency band does not change significantly;

[0018] If the EEG brainwave has no significantly changed 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 brainwave has no significant changes in the α, β, and low-γ frequency bands.

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

[0020] Further, the system comprises:

[0021] 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;

[0022] 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;

[0023] 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;

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

[0025] Furthermore, 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 a 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 prelimbic cortex in the prefrontal cortex are higher than those in the non-anterior cingulate cortex and prelimbic cortex brain regions, it indicates that the activity of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex is significantly increased;

[0027] 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 do not change significantly compared with brain areas other than the anterior cingulate cortex and prelimbic cortex, it indicates that the activity of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex do not change significantly.

[0028] Furthermore, 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 wave signals are obtained by detecting the sample to be tested using an EEG measurement method;

[0029] If the EEG brainwave shows high energy activity in the α, β, and low-γ frequency bands, while the energy activity in the high-γ frequency band does not change significantly, compared with the non-α, β, and low-γ frequency bands, it indicates that the EEG brainwave shows high energy in the α, β, and low-γ frequency bands, while the energy in the high-γ frequency band does not change significantly;

[0030] If the EEG brainwave has no significantly changed 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 brainwave has no significant changes in the α, β, and low-γ frequency bands.

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

[0032] Further, the device comprises:

[0033] 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 the first aspect of the present invention is implemented.

[0034] A 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 the processor, the attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex described in the first aspect of the present invention is implemented.

[0036] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0037] The present invention discovers for the first time that the two brain grain map features of the anterior cingulate cortex and the dorsomedial prefrontal cortex (prelimbic cortex) can be used to effectively predict whether the subject's attention is focused. Based on this, the present invention provides an attention prediction method, system, device and computer-readable storage medium based on the anterior cingulate cortex and the dorsomedial prefrontal cortex in the field. The present invention provides a reference for attention detection, evaluation and in-depth research on the physiological mechanism of attention, 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 drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 A schematic flow chart of an attention prediction method based on anterior cingulate cortex and dorsomedial prefrontal cortex provided in an embodiment of the present invention;

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

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

[0042] Figure 4 This is the result of c-Fos staining to reveal the neural activity during the attention process;

[0043] Figure 5 This is the result diagram corresponding to the significant upregulation of c-Fos expression in the higher cortical areas of the neocortex after attention occurs;

[0044] Figure 6 The results of using a 16-channel extracranial flexible EEG electrode array in mice performing the 5-CSRTT are shown in Figure 2.

[0045] Figure 7 This is the result graph corresponding to the significant upregulation of c-Fos expression in the thalamus of mice in the 5-CSRTT group;

[0046] Figure 8 This is the result diagram corresponding to the significant upregulation of c-Fos expression in the brainstem involved in wakefulness regulation during the attention process;

[0047] Fig. 9 The resulting diagram corresponds to the whole-brain functional connectivity pattern of attention. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the solutions 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 of the processes described in the specification and claims of the present invention and the above-mentioned drawings, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as S101, S102, etc., are only used to distinguish 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 following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

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

[0052] S101: 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;

[0053] In one embodiment, 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 a c-Fos staining method;

[0054] 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 non-anterior cingulate cortex and prelimbic cortex brain regions, it indicates that the activity of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex is significantly increased;

[0055] 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 do not change significantly compared with brain areas other than the anterior cingulate cortex and prelimbic cortex, it indicates that the activity of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex do not change significantly.

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

[0057] In one embodiment, in order to obtain the neural activity information of different brain regions during the process of attention, we first used 5-CSRTT, a classic paradigm for detecting rodent attention behavior, to train mice with standard procedures and then perform attention detection ( Figure 4 A), and we designed a control training procedure similar to 5-CSRTT to extract the attention process ( Figure 4 AB). 90 minutes after the mice finished the 5-CSRTT and control training test, the whole brain tissue of the mice was taken and immunofluorescence staining was performed on the whole brain tissue sections (covering all brain slices between bregma 2.53 and bregma-5.77) using c-Fos, a molecular marker widely used to detect neural activity. Figure 4 ). In order to strictly control the data quality, in this study, we only selected animals that had undergone sufficient training for staining, and the accuracy of the mice in the 5-CSRTT group was required to be greater than or equal to 90% after testing ( 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 intraperitoneal), mice were perfused transcardially with saline (4°C), then 4% paraformaldehyde (PFA) in phosphate buffered saline (PBS, 4°C), and postfixed in 4% PFA for 4 hours. The brain was then removed and incubated first in filtered 20% and then 30% sucrose solution (dissolved in 0.1M PBS) until it sank, then embedded in optical tomography and sectioned coronally into 50 μm thick sections at -20°C using a freezer. The sections were collected in flat plates or mounted on gel-coated slides for immunostaining.

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

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

[0061] In one embodiment, fluorescent images of 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 export high-resolution images for further analysis.

[0062] In one example, to map c-Fos-positive neurons in the whole brain, we selected 84 complete immunostained sections covering each brain sample from 2.53 mm to -5.77 mm of the anterior tectum, and performed statistical analysis every 100 μm ( Figure 4B). The c-Fos-positive neurons were counted using an advanced version of the cell counting program from our previous study. The program has the following five steps: Step 1 is image preparation. We used ZEN blue to output the images, and then used AdobePhotoshop to crop and straighten them. Step 2 requires taking the hemisphere images from step 1 and 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 to match each brain image to the selected Allen reference atlas by performing a reference point-based triangulation segmentation of the slice image followed by a nonlinear 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, an Apollo neural signal acquisition system (Bio-Signal Technology, Jiangsu, China) was used to collect and record 16-channel EEG signals through the above-mentioned EEG electrodes. The sampling rate was 2 GHz. For neural data processing, code compiled in MARTAL 2019a (The MathWorks) was used. The EEG signals were bandpass filtered in the frequency range of 0.1-200 Hz to isolate the signals from any potential interference (noise). A 50 Hz notch filter and its resonator were used to remove specific noise. The 16-channel EEG electrode array includes a reference electrode and 16 working electrodes, all of which are subject to the same stimulation interference as the common mode noise. Therefore, common mode interference is rejected. Finally, a combined method based on the independent component analysis (ICA) algorithm and manual screening was applied to remove residual noise sources such as electromyography, 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 used a two-way analysis of variance (VAR) followed by Tukey's multiple comparison test, and for comparisons between two groups, we used an unpaired Student's t test. Data with error bars are presented as mean ± SEM. In the context of evaluating correlations, Spearman's rank correlation coefficient (rho) and associated p-values ​​were calculated and reported. The Spearman rank correlation coefficient represents a nonparametric correlation indicator designed to assess the monotonic relationship between two variables without assuming linearity or nonlinearity. This coefficient is derived from the ordering of the variables, effectively ignoring the exact observed values. To assess the correlation between c-Fos expression and mean reaction time, we used the Spearman correlation coefficient of c-Fos density.

[0065] In one example, to reveal the activation patterns and potential associations between brain regions during attention processing, we performed hierarchical clustering and network construction for 37 brain regions that showed significant differences in c-Fos expression compared to controls. Based on the generation of correlation matrices by calculating Spearman correlation coefficients from the c-Fos density of the 37 brain regions, we first constructed cluster trees using the complete linkage method and then constructed weighted undirected networks, visualized using Cellscape (version 3.10.1) by considering correlations with Spearman r's and p < 0.05. Network centrality analysis was performed using the CentiScaPe 2.2 plugin for Celloscape. Degree, betweenness centrality, and eigenvector centrality were calculated to identify network centrality using Spearman correlation coefficients as weighted edges. Degree measures the count of edges linked to a node, reflecting its direct influence. Betweenness centrality is determined by the frequency of the shortest path that includes the node, highlighting its bridge function in the network. Eigenvector centrality assesses the influence of a node according to the importance of its connected nodes, favoring nodes associated with highly connected neighbors. An increase in the eigenvector centrality indicates a large influence through these favorable connections. We rank the values ​​of these three metrics in descending order to obtain RANK_degree, RANK_betweenness, and RANK_eigenvector, where higher values ​​indicate higher corresponding RANKs. The overall centrality score (called S-centrality) is calculated by averaging the RANK values ​​of these three metrics.

[0066] In one embodiment, the spectral analysis process of the EEG is as follows: Each trial is subjected to EEG spectrum analysis using a short-term Fourier transform (STFT) algorithm, using a 1000ms sliding window and a Hamming window, which facilitates frequency smoothing. The power spectral density is calculated over the delay range of -500 to 0ms before the cue onset, with an incremental resolution of 10ms, and the results are subsequently averaged across trials. The analyzed spectrum spans 1 to 100Hz with a resolution of 0.5Hz. The representation of each designated frequency band - alpha (8-12Hz), beta (12-30Hz), low gamma (30-50Hz), and high gamma (50-80Hz) - is quantified based on the average power in the respective frequency range.

[0067] In one embodiment, the EEG source localization analysis process is as follows: Source localization is a complex method that aims to trace surface signals (such as those captured by EEG and magnetoencephalography (MEG)) to their underlying neural activity sources. 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 a strategy of placing electrodes directly on the skull to mitigate the effect of skull thickness. In addition, by selecting animals of the same age, we ensured a consistent skull-to-cortex distance, thereby minimizing individual variability. For source localization analysis, this study used Brainstorm (an open source software available at https: / / neuuerimage.usc.edu / brainstorm) and an MRI atlas of C57 mice to co-register the extracranial electrodes with MRI coordinates. The process first identified anatomical landmarks within the MRI, horizontally aligned the AC-PC line, and generated head, scalp, and skull surfaces with a recommended number of 1922 vertices to achieve the best balance between accuracy and computational efficiency. After configuring these parameters in Brainstorm, the software created a triangular face-vertex mesh for each surface and performed the crucial verification of its orientation and position based on the MRI. After importing the channel file and electrode positions, any misalignment was corrected to ensure that the electrode coordinates were aligned with the MRI-based surface. The accuracy of electrode positioning was visually confirmed by comparison with the eye and ear positions.

[0068] To solve the inverse problem of estimating neuronal activation from measured data, we used minimum norm estimation (MNE), which is particularly advantageous for analyzing evoked responses characterized by widespread neuronal activation over time. This approach provides an inverse solution by estimating the amplitude 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 amplitude of all modeled source locations with the least total energy based on the lead field matrix obtained from the EEG signal. By choosing a source estimation method, an inverse solution is generated that facilitates plotting of the estimated sources.

[0069] The experimental results are as follows Figure 4 As shown in the figure, we first 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 higher, especially in the neocortex, thalamus and medulla, among which 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 in 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 hemisphere advantage. 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 experiments and animal care procedures were approved by the Animal Care and Use Committee of the Beijing Institute of Basic Medical Sciences. Mice were housed in the animal care facility of our institute (SPF-F01-002; SPF Biotechnology, Beijing, China) with a 12-h light / dark cycle and free access to standard rodent chow (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, as a unique structure of mammals, participates in the integration of sensory information and plays an important role in a variety of cognitive functions. 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 in the higher visual cortex area involved in visual feature recognition, the prefrontal cortex area that plays an important role in higher cognitive functions such as decision-making, working memory, and attention, and the retrosplenial cortex involved in higher cognitive functions such as episodic memory, navigation, and planning, and the posterior parietal association area responsible for multisensory information and decision-making functions were significantly upregulated ( Figure 5 A).

[0072] The experimental results are as follows Figure 5 As shown in the results, the area with the greatest difference in c-Fos expression in the prefrontal cortex is the anterior cingulate cortex, which is classically involved in attention control, followed by the prelimbic cortex. Our results further revealed that in the anterior cingulate cortex (ACA), although the proportion of c-Fos-positive neurons in ACAd (dorsal anterior cingulate cortex) was slightly higher than that in ACAv (ventral anterior cingulate cortex), the density of c-Fos-positive neurons in ACAv was higher than that in ACAd, and c-Fos-positive neurons were concentrated in the fifth layer, followed by L2 / 3 ( Figure 5 B). In the sensory cortex, the expression of c-Fos in VISal / am / l / pm (anterolateral visual area / anteromedial visual area / lateral visual area / posterior medial visual area) in the higher visual cortex was higher than that in the control group, and except for the first layer, the c-Fos expression in the remaining cortices was stronger than that in the control group, among which L4 had the highest c-Fos density ( Figure 5 B). The number and density of c-Fos-positive neurons in the association cortex, posterior parietal association area, including VISa and VISrl (anterior and lateral areas), and RSPd (dorsal posterior cortical area) were significantly higher than those in the control group ( Figure 5 ), and no significant laminar enrichment.

[0073] In one embodiment, the high temporal resolution of EEG makes it a preferred tool for studying attention. To provide a more comprehensive electrophysiological analysis of neural activity in the neocortex during attention tasks, we used a 16-channel extracranial flexible EEG electrode array ( Figure 6 AB). The 5-CSRTT assesses attention to various locations and cognitive processing speed over a large number of trials. Correct trials indicated that the mice were focused and had the fastest reaction times, 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 our examination of EEG and attentional load on (500ms before the visual cue). Since attention is associated with high-frequency EEG bands, we focused our analysis on the alpha, beta, low-gamma, and high-gamma frequency bands. Based on the EEG distribution in 2D space, the prefrontal cortex and the retrosplenial area showed considerable activity in the alpha, beta, and low-gamma frequency bands. The somatosensory cortex, which is 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 pattern we observed. Moreover, the average EEG signal power in the four specified frequency bands was examined in the correct, incorrect, and omitted groups. Different performance states led to changes in spontaneous EEG activity in these frequency bands. In particular, the correct group showed significant changes in the alpha, beta, and low gamma frequency bands, with power levels significantly exceeding those of the incorrect and omitted groups. In contrast, activity in the high gamma band was not associated with the animals' performance in various cognitive tasks ( Figure 6 E). These results are consistent with our observation that the expression of c-Fos in the brain 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 full general anesthesia with 2-3% isoflurane in oxygen-enriched air to ensure the comfort and safety of the animals. The head was shaved and disinfected using iodine before surgery, and then an incision was made along the midline of the scalp to expose the skull surface, which was cleaned with 2.5-3.5% hydrogen peroxide, and the periosteum was carefully removed. To ensure the 16-channel EEG electrode array, three guide holes were drilled in the skull at specific positions relative to the anterior process to ensure precise alignment of the electrodes designed to monitor EEG 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 that penetrated the epidural space without the need for sutures. This approach significantly reduced tissue damage while improving the stability and accuracy of the recording, as shown in the electrode placement positions shown in the attached figure ( Figure 6 A). After surgery, 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, mice will be raised in a single cage and controlled diet to maintain body weight at 85% of free body weight. Then, mice are divided into a 5-CSRTT group (finally taking 6 qualified animals), an EEG group (finally taking 6 qualified animals) (the above two groups use the 5-CSRTT task) and a control group (finally taking 4 qualified animals, using the task training of the control group), and then trained as described below.

[0076] Five-Choice Serial Reaction Time Task (5-CSRTT): The 5-CSRTT was conducted in a standardized test sound-attenuated ventilated chamber with a feeding trough on one side and a touch screen on the other side, consisting of a horizontal arrangement of five holes on the touch screen to provide light stimulation to mice. Operant TaskStudio V2 software (Ohara, Japan) was used to set up the control session program and collect data through a computer interface. In brief, a trial was triggered by the mouse poking its nose toward the feeder with illumination, after which a brief cue light stimulus was presented in one of the five possible holes after the inter-trial interval (ITI) had elapsed. The mouse was required to focus on the five holes for the appearance of the light stimulus and respond in the “correct” hole (correct trial) by poking it with its nose to receive a food pellet (10 mg dust-free precision pellet; Test Diet, USA) within a limited holding period. If the mouse responded before the stimulus (premature trial), in the “wrong” hole (incorrect trial), or missed, a 2-s time-out (TO) phase was initiated, marked by the illumination of the house light and the non-delivery of food reward. After receiving a reward, or at the end of a time-out period, a head entry into the feeder initiated the subsequent trial.

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

[0078] Control group training protocol: The training protocol for the control group was modified based on the 5-CSRTT training protocol and was 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 into any hole to obtain a food pellet. After obtaining the food reward, the head entered the feeder to initiate 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 were as follows: Figure 7 As 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 a variety of brain functions including sensory processing, attention, decision-making, and memory. Based on the afferent and efferent connections and functions of the thalamus, the thalamus is divided into the sensory-motor thalamic area (sensory-motor cortex related (DORsm) in the ARA) and the 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 area, including the parafascicular nucleus (PF) of the intralaminar group, central lateral nucleus (CL) of the thalamus, paracentral nucleus (PCN), central medial nucleus of the thalamus (CM), and paraventricular nucleus of the thalamus (PVT) of the midline group, which play a key role in the regulation of consciousness and sleep wakefulness.

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

[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, which are involved in somatosensory and goal-driven motor control, were significantly increased compared with the control group, which may be related to the increased local motor behavior of mice in the 5-CSRTT group during the task. In addition, during the process of attention, we also found a small number of neurons in the thalamic reticular nucleus (TRN), which are not shown here due to their low c-Fos expression.

[0082] The hypothalamus is connected to the cortex and subcortical structures and is involved in a variety of 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 attention, 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 earlier studies on the involvement of ZI and STN in the attention process.

[0083] In one embodiment, the expression of c-Fos in the brainstem involved in arousal regulation is significantly upregulated during the attention process, and the corresponding result is shown in the figure below: Figure 8The midbrain, pons and medulla have direct projections to the cortex and thalamus, and are involved in the regulation of motor control, sleep and wakefulness, decision-making and other functions. In the overall results, we found that the expression of c-Fos in the above brain regions showed an upward trend during the attention process. Therefore, we also conducted a detailed analysis of the above three brain regions. We found that compared with the control group, the areas with significantly upregulated expression of c-Fos-positive neurons were concentrated in the ascending arousal system, including the midbrain raphe nuclei (Ramb Midbrain raphe nuclei), centrallinear nucleus raphe (CLI), dorsal nucleus raphe (DR), and superior central nucleus raphe (CS), which are rich in serotonergic neurons, the pedunculopontine nucleus (PPN), the dorsolateral tegmental nucleus (LDT), and the parabrachial nucleus (PB) of the pons, which are rich in acetylcholinergic neurons. In addition, the number and density of c-Fos positive neurons in the periaqueductal gray (PAG), especially the ventral periaqueductal gray, red nucleus (RN), dorsal tegmental nucleus (DTN), and pontine central gray (PCG) were significantly increased compared with the control group. It is worth mentioning that the above brain regions are all involved in the regulation of sleep and wakefulness to varying degrees.

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

[0085] In one embodiment, the result diagram corresponding to the whole-brain functional connectivity pattern of attention is as follows: Fig. 9 As shown. In order to further analyze the relationship between the above-mentioned 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 a parameter for evaluating the level of attention, because the accuracy alone cannot comprehensively reflect the attention differences of mice. We calculated the correlation coefficient between RT and the density of c-Fos upregulation in different brain regions. The results showed that the density of c-Fos+ in the ACA, PL, AM(*), CL(*), VPL(*), VPM(*), STN(*), ZI(*), INC(*), SCs(*), SCm(*) and PB(*) brain regions showed a significant negative correlation with RT (the absolute value of r was greater than 0.85), and the density of c-Fos+ in the ACA, PL, AM(*), CL(*), VPL(*), VPM(*), STN(*), ZI(*), INC(*), SCs(*), SCm(*) and PB(*) brain regions showed a significant negative correlation with RT (the absolute value of r was greater than 0.85), and the density of c-Fos+ in the ACA, VISa, VISrl, RSPd, LSv, LSr, GPe, LP, PF, VM, PO, PCN, PVT, PAG, P The density of c-Fos+ in PN, RN, MRN, CLI, LDT, SPVO, and GRN showed a strong negative correlation 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, and PCG showed a weak negative correlation with RT (the absolute value of r was between 0.2 and 0.55), and the density of c-Fos+ in CM, DR, and PRNc was almost uncorrelated with RT (the absolute value of r was less than 0.2).

[0086] Given that some of the c-Fos-upregulated brain regions all play a regulatory role in the same physiological function, we hierarchically clustered the above brain regions and obtained a total of 5 clusters, among which clusters 1 and 2 showed no significant correlation between the density of c-Fos-positive neurons and the average reaction time ( Fig. 9). Cluster 1 and cluster 2 include AV, IMD, CM, VAR, MD of RSPd and thalamus, which are involved in complex cognitive processes such as working memory, attention guidance and visual steering, as well as CS, DTN, PCG, PRNc brain regions, which are key players in the control of voluntary movements and eye and head movements. This suggests that cluster 1 and cluster 2 may be involved in the joint regulation of visually driven behaviors. Cluster 3 is composed of nuclei involved in the regulation of wakefulness, including V2, LS and Gpe, PVT, which is positively correlated with the degree of wakefulness, PCO, PF, CL of intralaminar thalamus, which are involved in the recovery and regulation of consciousness after anesthesia, VPL and VPM of sensorimotor thalamus, which play an important role in motor control, and PPN, RN, PB, LGA and Ramb, which are involved in the encoding of cerebral cortical information and the regulation of sleep-wake cycles in the brain, and the expression of c-Fos neurons in the brain region of cluster 3 is positively correlated with attention. Cluster 4 contains IAD, LP, VM and GRN. Cluster 5 consisted 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, as well as LDT and SPVO, which are involved in the regulation of arousal. Similar to cluster 3, the c-Fos expression level of cluster 5 also showed a significant correlation with the level of attention. This result suggests that the arousal system may play an important regulatory role in shaping attention ( Fig. 9 ).

[0087] In order 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 determine the hubs of the network. The results showed that the interactive functional network mediated by the thalamus was dominated by clusters 3 and 5, clusters 1 and 2 interacted more frequently with cluster 3, and clusters 4 interacted more frequently with cluster 5 ( Fig. 9 ). The network centrality assessment showed that the thalamic CL and VPM had the highest centrality scores, followed by the thalamic VPL nucleus. To determine the hub of the network, we calculated the ratio of the fold change in c-Fos expression density (average c-Fos density in 5-CSRTT / control group), evaluated the p-value, and performed a comprehensive assessment of the expression density of c-Fos-positive neurons. Based on the above evaluation conditions, we believe that CL is more like the hub of this network than VPM ( Fig. 9 ). Given these results and the different functions of the brain regions enriched in the five clusters, our data revealed the existence of two major interactive functional networks, which are mainly mediated by the thalamus. One subnetwork mainly recruits the thalamus involved in arousal and movement regulation, as well as members of the ascending arousal system located in the brainstem, while the other subnetwork mainly includes the prefrontal cortex, thalamus, and parts of the brainstem involved in the regulation of attention control and higher cognitive functions ( Fig. 9). These results further suggest that the arousal system may be a key player in space and time.

[0088] In this study, the inventors of the present invention combined EEG with the staining method of c-Fos, selected the 5-hole selection continuous reaction time task, a behavioral paradigm for evaluating the attention of rodents, and used hierarchical clustering and functional network analysis methods to analyze the whole brain neural activity pattern under high attention. The results showed that the results of EEG and c-Fos staining showed that under high attention concentration, the neural activities of the anterior cingulate cortex (ACA) and prelimbic prefrontal cortex (PL) in the prefrontal cortex were the most significant, and the EEG brain waves 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 also further revealed that the activity of neurons in the fifth and sixth layers of the anterior cingulate cortex and the prelimbic cortex PL in the state of attention concentration increased most dramatically. The above results demonstrate the effectiveness of the attention prediction method based on the anterior cingulate cortex and dorsomedial prefrontal cortex provided by the present invention, which not only provides a neural activity map with high temporal and spatial resolution for attention detection, evaluation and research on the physiological mechanism of attention, but also provides a reference for in-depth analysis of the neural circuits of attention.

[0089] S102: Obtaining energy data of the EEG brain wave 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 brain wave in the α, β, low-γ, and high-γ frequency bands and the traceability data of the EEG brain wave signal are obtained by detecting the sample to be tested using an EEG measurement method;

[0091] If the EEG brainwave shows high energy activity in the α, β, and low-γ frequency bands, while the energy activity in the high-γ frequency band does not change significantly, compared with the non-α, β, and low-γ frequency bands, it indicates that the EEG brainwave shows high energy in the α, β, and low-γ frequency bands, while the energy in the high-γ frequency band does not change significantly;

[0092] If the EEG brainwave has no significantly changed 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 brainwave has no significant changes in the α, β, and low-γ frequency bands.

[0093] In one embodiment, the brain regions discovered based on the traceability data of the EEG signal are consistent with the brain regions observed by c-Fos staining, and both are present in the anterior cingulate cortex and the dorsomedial prefrontal cortex.

[0094] S103: Based on the activity data of neurons in the fifth and sixth layers of the anterior cingulate cortex and 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.

[0095] 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;

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

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

[0098] 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;

[0099] 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;

[0100] 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;

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

[0102] In one embodiment, 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 a c-Fos staining method;

[0103] 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 non-anterior cingulate cortex and prelimbic cortex brain regions, it indicates that the activity of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex is significantly increased;

[0104] 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 do not change significantly compared with brain areas other than the anterior cingulate cortex and prelimbic cortex, it indicates that the activity of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex do not change significantly.

[0105] In one embodiment, the energy data of the EEG brainwave in the α, β, low-γ, and high-γ frequency bands and the traceability data of the EEG brainwave signal are obtained by detecting the sample to be tested using an EEG measurement method;

[0106] If the EEG brainwave shows high energy activity in the α, β, and low-γ frequency bands, while the energy activity in the high-γ frequency band does not change significantly, compared with the non-α, β, and low-γ frequency bands, it indicates that the EEG brainwave shows high energy in the α, β, and low-γ frequency bands, while the energy in the high-γ frequency band does not change significantly;

[0107] If the EEG brainwave has no significantly changed 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 brainwave has no significant changes in the α, β, and low-γ frequency bands.

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

[0109] 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 of the present invention as described above 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 validation results of this validation example show that assigning intrinsic weights to indications can moderately improve the performance of the method relative 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 aforementioned method embodiments and will not be repeated here.

[0113] In the 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

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

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

[0116] A person 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 related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0117] A person skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing related 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 disk or an optical disk, etc.

[0118] The above is a detailed introduction to a computer device provided by the present invention. For a person skilled in the art, according to the concept of the embodiments of the present invention, there may be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting 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 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.

2. The attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex according to claim 1, characterized in that: 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 shows high energy in the α, β, and low-γ frequency bands, while the energy in the high-γ frequency band does not change significantly, then the classification result of the sample to be tested is a sample of focused attention.

3. The attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex according to claim 1, characterized in that: If there is no significant change in the activity of neurons in the fifth and sixth layers of the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex, and there is no significant change in the EEG brain waves in the α, β, and low-γ frequency bands, then the classification result is obtained that the sample to be tested is a non-attention sample.

4. The attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex according to claim 1, characterized in that: 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 non-anterior cingulate cortex and prelimbic cortex brain regions, it indicates that the activity of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex is significantly increased; 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 do not change significantly compared with brain areas other than the anterior cingulate cortex and prelimbic cortex, it indicates that the activity of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex do not change significantly.

5. The attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex according to claim 1, characterized in that: 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 EEG brainwave shows high energy activity in the α, β, and low-γ frequency bands, while the energy activity in the high-γ frequency band does not change significantly, compared with the non-α, β, and low-γ frequency bands, it indicates that the EEG brainwave shows high energy in the α, β, and low-γ frequency bands, while the energy in the high-γ frequency band does not change significantly; If the EEG brainwave has no significantly changed 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 brainwave has no significant changes in the α, β, and low-γ frequency bands.

6. 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 activity of neurons in the fifth and sixth layers of the anterior cingulate cortex and the prelimbic cortex in the prefrontal cortex, and there is no significant change in the EEG brain waves in the α, β, and low-γ frequency bands, then the classification result is obtained that the sample to be tested is a non-attention sample.

7. The attention prediction system based on the anterior cingulate cortex and the dorsomedial prefrontal cortex according to claim 6, characterized in that: 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 non-anterior cingulate cortex and prelimbic cortex brain regions, it indicates that the activity of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex is significantly increased; 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 do not change significantly compared with brain areas other than the anterior cingulate cortex and prelimbic cortex, it indicates that the activity of the fifth and sixth layers of the anterior cingulate cortex and prelimbic cortex in the prefrontal cortex do not change significantly.

8. The attention prediction system based on the anterior cingulate cortex and the dorsomedial prefrontal cortex according to claim 6, characterized in that: 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 EEG brainwave shows high energy activity in the α, β, and low-γ frequency bands, while the energy activity in the high-γ frequency band does not change significantly, compared with the non-α, β, and low-γ frequency bands, it indicates that the EEG brainwave shows high energy in the α, β, and low-γ frequency bands, while the energy in the high-γ frequency band does not change significantly; If the EEG brainwave has no significantly changed 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 brainwave has no significant changes in the α, β, and low-γ frequency bands.

9. 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 any one of claims 1-5 is implemented.

10. 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 a processor, the attention prediction method based on the anterior cingulate cortex and the dorsomedial prefrontal cortex according to any one of claims 1 to 5 is implemented.

Images