Research Methods and Applications of the Rich Club in the Brain Functional Network of Juvenile Myoclonic Epilepsy

Abstract

The present invention belongs to the field of information processing of medical or health data and images, and particularly relates to a research method and application of the rich club of the brain functional network for juvenile myoclonic epilepsy. By calculating the Pearson correlation coefficient, the brain functional networks of two groups of subjects are constructed, and the rich connections, feeder connections and local connections of the brain networks of the JME patient group and the normal control group are calculated. The differences in the rich-club organizational structures of the brain networks of the two groups of subjects are analyzed to facilitate the discovery of abnormal rich club tissues and further understand the pathophysiological mechanism of JME.

Description

Technical Field

[0001] The present invention belongs to the field of information processing of medical or health data and images, and particularly relates to a research method and application of the rich club of the brain functional network of juvenile myoclonic epilepsy. Background Art

[0002] The human brain is a complex network composed of interconnected regions. The study of the potential network structure of the brain has found that brain function is not solely attributed to the properties of a single region or a single connection, but rather emerges from the network organization of the entire brain. Highly central and interconnected central organizations play a crucial role in the integration process of the brain and jointly form the central backbone of global brain communication. The research on brain diseases is no longer limited to the study of a single brain region, but rather from the perspective of brain networks. Currently, great progress has been made in the study of brain networks using complex network theory. Recent studies have found a rich club phenomenon in brain networks, that is, a group of nodes with a relatively small number but crucial high clustering degrees in the network tend to be more closely connected than nodes with low clustering degrees. The rich club phenomenon shows the impact of diseases on the connections of brain tissues and plays a key role in the study of brain diseases. From the perspective of information integration, rich nodes are located at the center of the brain network topology. The connections between rich nodes are considered to be the center of information integration between different subsystems of the human brain and can integrate multi-sensory information in a collaborative manner. Rich nodes play a core role in the overall architecture of the brain functional network. In terms of information transmission, the rich club forms a highly aggregated brain communication center and makes an important contribution to cross-regional information transmission. Some recent studies have shown that the connectivity of the rich club organization is reduced in schizophrenia; the connectivity of the rich club of the brain network is reduced in patients with attention deficit hyperactivity disorder. Li Kang et al. found that the feeder connections in the rich club organization of patients with migraine are highly aggregated and the rich club organizational structure is abnormal. The relationship between diseases and the rich club has made us have a strong interest in the rich club organization of the JME (juvenile myoclonic epilepsy, JME) brain network and is also a new direction for studying JME diseases.

[0003] Juvenile myoclonic epilepsy (JME) is a common idiopathic generalized epilepsy that mostly occurs in people aged 12 - 18. Its clinical manifestations mainly include: myoclonic seizures, absence seizures, and generalized tonic-clonic seizures. At the same time, a large number of patients also have varying degrees of cognitive and motor executive function disorders. Currently, the study of JME mainly relies on electroencephalogram, but the electroencephalogram of JME patients often does not have typical changes. Therefore, many researchers have begun to focus on the research of the brain network of JME patients to provide new reference basis for the clinical diagnosis of JME. Previous research results on JME show that the caudal region of the right inferior parietal lobule in JME patients may be one of the important brain regions leading to brain function disorders in JME patients, and it may play a crucial role in the connection between brain regions and the transmission of information; the changes in the microstructure connection of JME electroencephalogram may be related to the frontal lobe cognitive and motor function disorders in JME. JME patients have motor function disorders, resulting in changes in the projection fiber structure connecting the cerebral cortex, subcortical regions, and cerebellum.

[0004] Although previous studies on JME have shown that JME seizures are likely to originate from the thalamus-cortex (mainly the motor-related cortex) network. However, whether the connections that play a role in these specific functional networks will affect the rich club organization of the brain network remains to be studied. Therefore, studying the changes in the rich club organization of the brain network in JME patients is of great significance for further understanding the pathophysiological mechanism of JME. Summary of the Invention

[0005] The purpose of the present invention is to provide a research method for the rich club of the brain functional network of juvenile myoclonic epilepsy. By calculating the Pearson correlation coefficient, the brain functional networks of two groups of subjects are constructed, and the rich connections, feeder connections, and local connections of the brain networks of the JME patient group and the normal control group are calculated. The differences in the rich-club organizational structures of the brain networks of the two groups of subjects are analyzed to facilitate the discovery of abnormal rich club tissues and further understand the pathophysiological mechanism of JME.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A research method for the rich club of the brain functional network of juvenile myoclonic epilepsy, comprising the following steps:

[0008] 1 Data acquisition

[0009] Use the GRE-EPI sequence to scan fMRI data;

[0010] 2. Data preprocessing

[0011] Use matlab2013 to run gretnaV3.0 for fMRI data preprocessing;

[0012] 3 Brain network construction

[0013] Using the Pearson correlation coefficient, the brain functional network of the subject is constructed.

[0014] Further, in some preferred embodiments of the present invention, the parameters for scanning fMRI data in step (1) are: repetition time TR is 2000 ms, echo time TE is 30 ms, slice thickness is 4.0 mm, slice gap is 0.40 mm, number of slices is 33, field of view FOV is 240×240 mm2, matrix is 64×64, flip angle FA is 90°, and a total of 200 time points are collected.

[0015] Preferably, the T1-weighted image in step (1) is acquired by 3D MP-RAGE, and the specific parameters are as follows: repetition time TR is 1900 ms, echo time TE is 30 ms, slice thickness is 0.9 mm, field of view FOV is 256 mm×230 mm, matrix is 256×256, and flip angle FA is 90°.

[0016] Further, in some preferred embodiments of the present invention, the data preprocessing steps in step (2) include:

[0017] 2.1. Format conversion of DICOM data: Convert the 2D image in DICOM to a 3D image in NIFTI;

[0018] 2.2. Elimination of time points: Eliminate the data of the first 10 time points in the time series of each subject;

[0019] 2.3. Temporal inter-slice correction: TR = 2 s, and the interleaved scanning method is used for scanning to correct all different scanning time points to the same parameter point;

[0020] 2.4. Head motion correction: Eliminate subjects with translational head motion greater than 1 mm and rotational motion greater than 1 degree to reduce the noise introduced by head motion in the signal;

[0021] 2.5. Spatial normalization: Make the scanned brain consistent with the standard brain template through stretching, compression, and warping;

[0022] 2.6. Registration: Use dartel registration to register the structural image without anatomical information to the template image in the standard anatomical space;

[0023] 2.7. Removal of linear drift: Eliminate the influence of abnormal signal changes caused by the instability of the scanner machine;

[0024] 2.8. Band - pass filtering: The selected range of the band - pass filtering bandwidth is 0.01∽0.08 Hz to exclude the influence of physiological noise signals outside this frequency band.

[0025] Further, in some preferred embodiments of the present invention, the method for constructing the brain network in step (3) includes the following steps:

[0026] 3.1. Using the Pearson correlation coefficient to obtain a correlation coefficient matrix that changes with time;

[0027] 3.2. rich club detection

[0028] The rich club is defined as: a group of nodes in a random network whose connectivity level exceeds the expected connectivity level; First, calculate the degree value k of each node in the functional networks of the JME patient group and the healthy control group;

[0029] 3.3. rich club connection

[0030] The rich club connection refers to three major connections formed according to the selection criteria of "rich nodes" and "non - rich nodes": rich connection, the connection between rich club nodes; feeder connection, the connection between rich club and non - rich club nodes; local connection, the connection between non - rich club nodes.

[0031] Further, in step 3.1, the Pearson correlation coefficient formula is as follows:

[0032]

[0033] where, x n represents the time series of the nth layer of the brain region, represents the mean value of all scanned layers of the brain region, y n and represents the time series and mean value of another brain region. When the correlation coefficient r(x n , y n ) is greater than a given threshold Tm, it is considered that there is a functional connection between x n and y n , that is, there is an edge connection between the two.

[0034] Further, when using the Pearson correlation coefficient to obtain a correlation coefficient matrix that changes with time, first select the threshold Tm to binarize the correlation matrix network. If the connection value between two brain regions is greater than the threshold, it is considered that there is an edge between the brain regions, set to 1, otherwise set to 0.

[0035] Preferably, when selecting the threshold Tm, the average degree of the network should be followed<k>According to the principle that is more than twice the natural logarithm of the network node N, so Tm = 0.3 is selected to construct a binary brain network.

[0036] Furthermore, the rich club detection steps in step 3.2 are as follows:

[0037] 3.2.1. Discard all nodes in the network with degrees less than k;

[0038] 3.2.2. Then calculate the ratio of the connections between the remaining nodes to the total number of possible connections when the network is in a fully connected state;

[0039] 3.2.3. For each k, perform normalization.

[0040] Furthermore, in step 3.2.3, to obtain the normalized rich club coefficient φnorm(k), first obtain the rich club coefficient φ(k). The normalized rich club coefficient is the rich club coefficient φ(k) divided by the average rich club coefficient φrandom(k) of 100 random networks. Its expression is as follows:

[0041]

[0042]

[0043] Among them, the numerator E>k refers to the number of connections between nodes in the network with degrees greater than k, and the denominator is the total number of possible connections when these nodes are set to be fully connected. Among them, N>k is the number of nodes with degrees greater than k; to calculate E>k, first calculate the degree of each node in the network. Using the node degree k as the threshold, continuously adjust the value of k, and remove the nodes in the network with degrees less than or equal to k.

[0044] Preferably, k takes each value from 1 to 89 once.

[0045] Furthermore, to further understand the rich club of the JME patient group and the healthy control group, calculate the average brain network of the healthy control group and the node degrees of 90 brain regions in the average brain network; define the nodes ranked in the top 10% of the 90 nodes in terms of degree value as "rich nodes", and the remaining nodes as "non-rich nodes"; determine that the right middle cingulate gyrus, left middle cingulate gyrus, left supplementary motor area, right inferior temporal gyrus, right superior temporal gyrus, right orbital inferior frontal gyrus, right supplementary motor area, left superior temporal gyrus, left orbital inferior frontal gyrus, these 9 brain regions are defined as "rich nodes", and the remaining 81 brain regions are defined as "non-rich nodes".

[0046] Further, in step 3.3, the rich connection value calculates the sum of the weights of the connections existing between all rich nodes in the brain network; the feeder connection value calculates the sum of the weights of the connections existing between rich nodes and non-rich nodes; the local connection value calculates the sum of the weights of the connections existing between all non-rich nodes.

[0047] Preferably, to ensure the reliability of the control experiment, the same "rich nodes" and "non-rich nodes" are selected for the three types of connection divisions in the healthy control group and the JME patient group.

[0048] The present invention also provides an application of the above research method of the Rich club of the brain functional network of juvenile myoclonic epilepsy in understanding the pathophysiological mechanism of JME.

[0049] The beneficial technical effects of the present invention are as follows: In the present invention, by combining the JME patient data with the rich club, assuming that there are differences in the rich club organization of the JME patient brain network compared with the healthy control group, analyzing and discussing the influence of this difference in the JME disease is of great significance for further understanding the pathophysiological mechanism of JME. Description of the Drawings

[0050] Figure 1 Left middle cingulate gyrus and right middle cingulate gyrus regions

[0051] Figure 2 Trend charts of rich club changes in the healthy control group and JME patients

[0052] Figure 3 Rich club connections in the healthy control group and JME patients

[0053] Figure 4 Relationship between the disease duration of the patient and the rich club connections in JME patients Detailed Embodiments

[0054] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the following further describes the present invention in detail with reference to embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] The present invention constructs the brain functional networks of two groups of subjects by calculating the Pearson correlation coefficient, calculates the rich connections, feeder connections and local connections of the brain networks of the JME patient group and the normal control group, and analyzes the differences in the rich-club organizational structures of the brain networks of the two groups of subjects, so as to facilitate the discovery of abnormal rich club tissues and further understand the pathophysiological mechanism of JME.

[0056] 1. Data source

[0057] Magnetic resonance image data of 27 JME patients with an average disease duration of 4.03 years who visited the Epilepsy Center of the Second Hospital of Lanzhou University were collected and included in this study. Among them, there were 13 males and 14 females, with an average age of 18.8 years. All patients were diagnosed with JME according to the diagnostic criteria issued by the International League Against Epilepsy in 2001. No structural abnormalities were found by routine MRI examination, and electroencephalograms during seizures showed generalized polyspike-and-slow waves or spike-and-slow complex waves of 4-6 Hz. None of them had received formal treatment. In order to evaluate the severity of epilepsy, each patient was required to perform the National Hospital Seizure Severity Scale (NHS3) before MRI scanning. At the same time, through advertisement recruitment, healthy control subjects with acute physical diseases, drug abuse or dependence, a history of head injury resulting in loss of consciousness, and neurological or mental disorders were excluded before scanning. Magnetic resonance image data of 27 normal control subjects were selected, including 12 males and 15 females, with an average age of 19.4 years. None of them had neurological or mental diseases, and no abnormalities were found in the brain by routine magnetic resonance imaging (MRI). Both groups of subjects were right-handed. By using the within-group paired FDR test, there were no significant differences in age, handedness, and gender between the two groups (P>0.05). This study of normal volunteers was approved by the Ethics Committee of the Second Hospital of Lanzhou University. After the experimental protocol was explained before imaging, written informed consent was obtained from each subject or their legal guardian.

[0058] 2. Data acquisition

[0059] Data of all subjects were collected by the same Siemens Verio 3.0T magnetic resonance scanner. During the data collection, all subjects were required to lie flat, keep their heads fixed, close their eyes, block their ears, and try not to engage in specific thinking. Among them, functional magnetic resonance imaging (fMRI) data were collected using a gradient echo-echo planar imaging (GRE-EPI) sequence. The specific parameters were as follows: repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, slice thickness = 4.0 mm, gap = 0.40 mm, number of slices = 33, field of view (FOV) = 240 mm × 240 mm, matrix = 64 × 64, flip angle (FA) = 90°, and a total of 200 time points were collected. T1-weighted images were collected by a three-dimensional magnetization prepared rapid gradient echo sequence (3D MP-RAGE). The specific parameters were as follows: repetition time (TR) = 1900 ms, echo time (TE) = 30 ms, slice thickness = 0.9 mm, field of view (FOV) = 256 mm × 230 mm, matrix = 256 × 256, flip angle (FA) = 90°.

[0060] 3. Data preprocessing

[0061] Use Matlab 2013 under the Windows 10 system to run GRETNA V3.0 (https: / / github.com / sandywang / GRETNA) for preprocessing functional magnetic resonance imaging data. The main steps of preprocessing include: (1) Format conversion of DICOM data: Convert the 2D images in DICOM to 3D images in NIFTI; (2) Eliminate time points: To select data with good stability and improve experimental accuracy, eliminate the data collected when the scanner is in an unstable running state for a short period of time at startup, and eliminate the first 10 time point data of each subject's time series; (3) Temporal interslice correction: TR = 2s, and the interleaved scanning method is used for scanning to correct all different scanning time points to the same parameter point; (4) Head motion correction: Eliminate subjects with translational head motion greater than 1 mm and rotational motion greater than 1 degree to reduce the noise introduced by subject head motion in the signal; (5) Spatial normalization: Make the scanned brain consistent with the standard brain template through stretching, compression, and warping; (6) Registration: Use dartel registration to register the structural image without anatomical information to the template image in the standard anatomical space; (7) Remove linear drift: Eliminate the influence caused by abnormal signal changes due to the instability of the scanner machine. (8) Band-pass filtering: The selected range of the band-pass filter bandwidth is 0.01 - 0.08 Hz to exclude the influence of physiological noise signals outside this frequency band.

[0062] 4. Brain network construction

[0063] To construct a brain functional network, it is necessary to extract the response sequence of each brain region of each subject over time. Using the Pearson correlation coefficient, a correlation coefficient matrix over time is obtained. The Pearson correlation coefficient formula is as follows:

[0064]

[0065] where x n represents the time series of the nth layer of the brain region, represents the mean value of all scanned layers of the brain region, y n and represent the time series and mean value of another brain region. When the correlation coefficient r(x n , y n ) is greater than a given threshold Tm, it is considered that there is a functional connection between x n and y n , that is, there is an edge connection between the two.

[0066] First, select a threshold to binarize the correlation matrix network. If the connection value between two brain regions is greater than the threshold, it is considered that there is an edge between the brain regions, set to 1, otherwise set to 0. The selection of the threshold Tm should follow the network average degree <k>The principle is greater than twice the natural logarithm of the network node N. Because the network density at this value is between 10% and 50%, and there are no isolated nodes, satisfying the connectivity of the network. In this experiment, Tm = 0.3 is selected to construct a binary brain network.

[0067] Rich club detection

[0068] The rich club is defined as: a group of nodes in a random network whose connectivity level exceeds the expected connectivity level. In this experiment, first, the degree value k of each node in the functional networks of the JME patient group and the healthy control group is calculated. The detection steps are as follows: (1) Discard all nodes in the network with a degree less than k; (2) Then calculate the ratio of the connections between the remaining nodes to the total number of possible connections when the network is in a fully connected state. (3) For each k (here the k value ranges from 1 to 89, each value is taken once), normalization is performed. The normalized rich club coefficient

[16] is the rich club coefficient φ(k) divided by the average rich club coefficient of 100 random networks. The expression is as follows:

[0069]

[0070]

[0071] To obtain the normalized rich club coefficient φnorm(k), first, the rich club coefficient φ(k) needs to be obtained, which is calculated from a ratio. Among them, the numerator E>k refers to the number of connections between nodes in the network with a degree greater than k, and the denominator is the total number of possible connections when these nodes are set to be fully connected. Here, N>k is the number of nodes with a degree greater than k. To calculate E>k, the degree of each node in the network needs to be calculated first. Using the node degree k as the threshold, the k value is continuously adjusted to remove the nodes in the network with a degree less than or equal to k. In this experiment, the k value ranges from 1 to 89, each value is taken once, and the corresponding normalized rich club coefficient is observed.

[0072] To further understand the rich club of the JME patient group and the healthy control group, the average brain network of 27 healthy control subjects and the nodal degrees of 90 brain regions in the average brain network will be calculated. According to the previous selection criteria for "rich nodes", the nodes with the top 10% of the degree values among the 90 nodes are defined as "rich nodes", and the remaining nodes are defined as "non-rich nodes". According to the above "rich node" selection criteria, the right middle cingulate gyrus, left middle cingulate gyrus, left supplementary motor area, right inferior temporal gyrus, right superior temporal gyrus, right inferior frontal gyrus, orbital part, right supplementary motor area, left superior temporal gyrus, and left inferior frontal gyrus, orbital part are determined. These 9 brain regions are defined as "rich nodes", and the remaining 81 brain regions are defined as "non-rich nodes". After the node definition is completed, the next step can be carried out. Here, the top 2 brain regions are shown for demonstration. The positions of the left middle cingulate gyrus and the right middle cingulate gyrus in the brain network are shown in Figure 1 。

[0073] rich club connection

[0074] Rich club connection refers to the three major connections formed by dividing according to the selection criteria of "rich nodes" and "non-rich nodes": rich connection, the connection between rich club nodes; feeder connection, the connection between rich club and non-rich club nodes; local connection, the connection between non-rich club nodes. The rich connection value calculates the sum of the weights of the connections existing between all rich nodes in the brain network; the feeder connection value calculates the sum of the weights of the connections existing between rich nodes and non-rich nodes; the local connection value calculates the sum of the weights of the connections existing between all non-rich nodes.

[0075] To ensure the reliability of the control experiment, the same "rich nodes" and "non-rich nodes" are selected for the healthy control group and the JME patient group for the division of the three connections.

[0076] Results

[0077] Through three major steps of rich club detection in this paper, it is found that the normalized rich club coefficients of the brain networks of the two groups are greater than 1 within a certain range. It is proved that the brain functional networks of the healthy control group and JME patients both have the rich club characteristics, and subsequent rich club research can be carried out.

[0078] Comparison of the normalized rich club coefficients of the healthy control group and JME patients is as Figure 2 As shown. The red star solid line represents the changing trend of the normalized rich club coefficient of normal people. The abscissa shows the range of k values from 1 to 89, and the ordinate shows the coefficient values corresponding to each k value on the abscissa. The red star solid line shows the changing trend of the normalized rich club coefficient of normal people when k changes from 1 to 89. Among them, when k is between 1 and 18 and between 70 and 89, its rich club coefficient is less than 1; on the contrary, when k is between 19 and 69, the rich club coefficient is greater than 1. The blue star solid line represents the changing trend of the normalized rich club coefficient of the JME patient group. Among them, when k is between 1 and 13 and between 77 and 89, its rich club coefficient is less than 1; on the contrary, when k changes from 14 to 76, the rich club coefficient is greater than 1. The changing trend of the normalized rich club coefficient of the JME patient group.

[0079] Figure 2 The results show that in the healthy control group, when k takes values in the range from 19 to 69, the normalized rich club coefficient of the normal brain network is greater than 1. In the JME patient group, when k takes values in the range from 14 to 76, the normalized rich club coefficient of the normal brain network is greater than 1; that is, within a certain range in the brain functional networks of both the normal group and the JME patient group, the normalized rich club coefficient is greater than 1, meeting the evaluation criteria for the brain network to have the rich club property, indicating that the brain networks of both have the rich club property. Therefore, the next step of rich club edge connection analysis can be carried out.

[0080] The rich club connections of the healthy control group and JME patients are as Figure 3 shown. Figure 3 In it, the red bar graph represents the rich club connection weight value of the healthy control group, and the blue bar graph represents the rich club connection weight value of the JME patient group.

[0081] Figure 3 a represents the rich connections in the brain networks of both the healthy control group and the JME patient group, which is the total edge connection weight between the "rich nodes" in their brain networks. Figure 3 b represents the local connections in the brain networks of both the healthy control group and the JME patient group, which is the total edge connection weight between the non-"rich nodes" in their brain networks. Figure 3 c represents the feeder connections in the brain networks of both the healthy control group and the JME patient group, which is the total edge connection weight between the "rich nodes" and the non-"rich nodes" in their brain networks.

[0082] The results of rich connectivity showed that there were statistical differences in rich connectivity between the JME patient group and the healthy control group, and the rich connectivity was significantly reduced (p < 0.05, t = -4.6, FDR corrected). The results of feeder connectivity and local connectivity showed that there was no statistical significance between feeder connectivity and local connectivity. This indicates that compared with the healthy control group, the brain connectivity and rich club organization in the JME patient group were affected, and the impact on network connectivity and network topology was concentrated within the rich connectivity module, that is, the "rich nodes" in the central position of the network played a crucial role in the brain network connectivity of JME patients.

[0083] Meanwhile, to better understand the relationship between the rich club topology and the patients, we performed a correlation analysis on the relationship between rich connectivity and the duration of the patients' illness; the results are as follows:

[0084] The relationship between the duration of the patients' illness and rich connectivity is as Figure 4 shown; the abscissa is the duration of illness of JME patients, and the ordinate is the rich connectivity weight value of the patients. The results show that the overall trend of the rich connectivity weight value and the duration of the patients' illness is close to the red dashed line in the figure, which indicates that there is a negative correlation between the duration of the patients' illness and the weight value of rich connectivity, representing that the longer the duration of the patients' illness, the lower the weight value of rich connectivity in the brain network.

[0085] In this invention, the rich club characteristics analysis and rich club connection method of complex networks are used to study the relationship between the damage of the rich club organizational structure of juvenile myoclonic epilepsy patients and JME. It is found that JME selectively disrupts the rich club connections in the brain, potentially inducing damage to the rich club structure and brain network function, resulting in a decline in the communication ability between the brains. It indicates that JME disease is highly likely to be related to abnormal rich club organization. Therefore, for clinical research, studying the rich club organization of the brain is of great significance for understanding the pattern and mechanism of JME lesions.

[0086] From the perspective of brain network topology, the connection results of the rich club organization show that there are no significant statistical differences in the feeder connections and local connections in the rich club organization of JME patients compared with the healthy control group, while there are significant differences in the rich connections compared with the healthy control group. The rich connections in the rich club refer to the connections between "rich nodes" in the brain network. The "rich nodes" in the brain network are located in the network center and affect many structural and functional characteristics of the network, including topology, path efficiency, and load distribution, and play a key role in realizing the reception of whole-brain nerve signals and communication between the brains. Because of the important role played by the "rich nodes" in the interaction between brain network topology and function, the significant reduction of rich connections in the brain network of JME patients will lead to the blockage of the communication information between the nerve signals passing through the "rich nodes" and the functional network in the brain network of JME patients, resulting in a large reduction in the information passing through this main road and transmitted in and out in the brain network, and thus a significant decrease in the communication information of the whole brain of JME patients. This is manifested as a decrease in rich club connectivity, damage to the rich club organizational structure, and abnormal brain network topology. The feeder connections and local connections do not show significant changes because the abnormality of the "rich nodes" has a small impact on the feeder connections and local connections, so the two do not show significant changes. The relationship between the disease duration of the patients and the rich connections shows that there is a negative correlation between the disease duration of the patients and the rich connections. The longer the disease duration of the patients, the lower the weight value of the rich connections in the brain network. This indicates that with the increase of the disease duration, the damage to the rich club organization becomes more serious.

[0087] From the perspective of brain network function, the reduced significance of rich connections indicates that the information communication ability among nine central regions, namely the right middle cingulate gyrus, left middle cingulate gyrus, left supplementary motor area, right inferior temporal gyrus, right superior temporal gyrus, right inferior frontal gyrus, orbital part, right supplementary motor area, left superior temporal gyrus, and left inferior frontal gyrus, orbital part, is damaged. The middle cingulate gyrus and supplementary motor area are involved in motor activities in the brain network, the inferior temporal gyrus is involved in information processing activities in the brain network, the inferior frontal gyrus, orbital part, is involved in executive control activities in the brain network, and the superior temporal gyrus is the writing center and visual language center of the brain network. The reduced rich club connection among these brain regions leads to the influence on the brain network activities of different functions related to the above-mentioned brain regions, and further affects the information transmission between the default control network and the executive control network in the brain network. As a result, patients exhibit physiological phenomena different from those of the healthy control group, such as varying degrees of cognitive and motor execution function disorders, involuntary myoclonic twitches during wakefulness, generalized tonic-clonic seizures, and a small number of absence seizures. These physiological abnormalities are consistent with the abnormal brain network activities corresponding to the damaged brain regions in the central region.

[0088] This phenomenon does not only occur in JME patients. Some studies on the rich club have pointed out that the reduced internal connectivity of the rich club may lead to a higher probability of developing primary progressive multiple sclerosis (PPMS). When Shu et al. studied the brain structural network of SCD patients, they found that the rich, feeder, and local connection strengths of the patients were significantly lower than those of the normal control group. A study divided baseline aMCI into conversion-type aMCI and stable-type aMCI according to whether the patients converted to AD. The study found that the rich club, branch, and local connection strengths of conversion-type aMCI all decreased; while the rich club and local connection strengths of stable-type aMCI decreased, but the branch connection strength remained stable. This is of great significance for the early clinical prediction of whether aMCI will convert to AD. The above studies have proved that the rich club connection in the patient's brain network is indeed significantly different from that of the normal control group, and this abnormality is of great significance for the study of diseases.

[0089] It can be seen that due to the damage of epileptic behavior to the central region, it will lead to abnormal rich club connections in the brain network, which will reduce the integration ability of brain regions with different functions in the network. Therefore, in the present invention, we believe that the rich club connection may have great potential in clinical applications.< / k> < / k>

Claims

1. A research method for the Richclub of the brain functional network of juvenile myoclonic epilepsy, comprising the following steps: Step (1) Data acquisition Scan fMRI data using the GRE-EPI sequence; Step (2) Data preprocessing Run GretnaV3.0 with Matlab2013 for fMRI data preprocessing; Step (3) Brain network construction Construct the brain functional network of the subject using the Pearson correlation coefficient; Brain network construction method in Step (3) Includes the following steps: 3.

1. Use the Pearson correlation coefficient to obtain the correlation coefficient matrix that changes over time; 3.

2. Rich club detection Rich club is defined as: a group of nodes in a random network whose connectivity level exceeds the expected connectivity level; First, calculate the degree value k of each node in the functional networks of the JME patient group and the healthy control group; 3.

3. Rich club connection Rich club connection refers to the three major connections formed by the selection criteria of "rich nodes" and "non-rich nodes": rich connection, the connection between rich club nodes; feeder connection, the connection between rich club and non-rich club nodes; local connection, the connection between non-rich club nodes; The rich club detection steps in Step 3.2 are as follows: 3.2.

1. Discard all nodes in the network with a degree less than k; 3.2.

2. Then calculate the ratio of the connections between the remaining nodes to the total number of possible connections when the network is in a fully connected state; 3.2.

3. For each k, perform normalization; In Step 3.2.3, to obtain the normalized rich club coefficient φnorm(k), first obtain the rich club coefficient φ(k). The normalized rich club coefficient is the rich club coefficient φ(k) divided by the average rich club coefficient of 100 random networks. Its expression is as follows: Among them, the numerator E>k refers to the number of connections between nodes in the network with a degree greater than k, and the denominator is the total number of possible connections when these nodes are set to be fully connected. Among them, N>k is the number of nodes with a degree greater than k; To calculate E>k, first calculate the degree of each node in the network. Using the node degree k as the threshold, continuously adjust the k value, remove the nodes in the network with a degree less than or equal to k, and take each value of k from 1 to 89 once; φrandom(k) refers to the average rich club coefficient of 100 random networks.

2. The research method for the Richclub of the brain functional network of juvenile myoclonic epilepsy according to claim 1, wherein: The parameters for scanning fMRI data in Step (1) are: repetition time TR is 2000ms, echo time TE is 30ms, slice thickness is 4.0mm, slice gap is 0.40mm, number of slices is 33, field of view FOV is 240×240mm2, matrix is 64×64, flip angle FA is 90°, and a total of 200 time points are collected.

3. The research method for the Richclub of the brain functional network of juvenile myoclonic epilepsy according to claim 1, wherein: In step (1), the T1-weighted image is acquired by 3D MP-RAGE, and the specific parameters are as follows: the repetition time TR is 1900 ms, the echo time TE is 30 ms, the slice thickness is 0.9 mm, the field of view FOV is 256 mm×230 mm, the matrix is 256×256, and the flip angle FA is 90°.

4. The research method for the Richclub of the brain functional network of juvenile myoclonic epilepsy according to claim 1, wherein: In step (2), the data preprocessing steps include: 2.

1. Format conversion of DICOM data: Convert the 2D images in DICOM into 3D images in NIFTI; 2.

2. Excluding time points: Exclude the data of the first 10 time points of each subject's time series; 2.

3. Temporal inter-slice correction: TR = 2 s, and the interleaved scanning method is used for scanning to correct all different scanning time points to the same parameter point; 2.

4. Head motion correction: Exclude the subjects with translational head motion greater than 1 mm and rotational motion greater than 1 degree to reduce the noise introduced by the subject's head motion in the signal; 2.

5. Spatial normalization: Make the scanned brain consistent with the standard brain template through stretching, compression, and warping; 2.

6. Registration: Use dartel registration to register the structural image without anatomical information to the template image in the standard anatomical space; 2.

7. Removal of linear drift: Eliminate the influence caused by the abnormal signal changes due to the instability of the scanner; 2.

8. Band-pass filtering: The selected range of the band-pass filtering bandwidth is 0.01∽0.08 Hz to exclude the influence of physiological noise signals outside this frequency band.

5. A research method for the Richclub of the brain functional network of juvenile myoclonic epilepsy according to claim 1, characterized in that: In step 3.1, the Pearson correlation coefficient formula is as follows: Among them, x n represents the time series of the nth layer of the brain region, represents the mean value of all scanned layers of the brain region, y n and represents the time series and mean value of another brain region. When the correlation coefficient r(x n , y n ) is greater than a given threshold Tm, it is considered that there is a functional connection between x n and y n , that is, there is an edge connection between the two; When obtaining the correlation coefficient matrix that changes with time using the Pearson correlation coefficient, first select a threshold Tm to binarize the correlation matrix network. If the connection value between two brain regions is greater than the threshold, it is considered that there is an edge between the brain regions and is set to 1, otherwise it is set to 0; For the selection of the threshold Tm, the principle that the average degree k of the network is greater than twice the natural logarithm of the network nodes N should be followed. Therefore, Tm = 0.3 is selected to construct the binary brain network.

6. A research method for the Richclub of the brain functional network of juvenile myoclonic epilepsy according to claim 1, characterized in that: To further understand the rich club of the JME patient group and the healthy control group, calculate the average brain network of the healthy control group and the node degrees of 90 brain regions in the average brain network; select the nodes with the top 10% of the degree values among the 90 nodes as "rich nodes", and the remaining nodes as "non-rich nodes"; determine that the right middle cingulate gyrus, the left middle cingulate gyrus, the left supplementary motor area, the right inferior temporal gyrus, the right superior temporal gyrus, the right orbital inferior frontal gyrus, the right supplementary motor area, the left superior temporal gyrus, and the left orbital inferior frontal gyrus, these 9 brain regions are defined as "rich nodes", and the remaining 81 brain regions are defined as "non-rich nodes". The rich connection value calculates the sum of the weights of the connections existing between all rich nodes in the brain network; the feeder connection value calculates the sum of the weights of the connections existing between rich nodes and non-rich nodes; the local connection value calculates the sum of the weights of the connections existing between all non-rich nodes; to ensure the reliability of the control experiment, the same "rich nodes" and "non-rich nodes" are selected for the three types of connection division in the healthy control group and the JME patient group.

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