CN113057621A - Dynamic function connection research method for juvenile myoclonic epilepsy and application - Google Patents
Dynamic function connection research method for juvenile myoclonic epilepsy and application Download PDFInfo
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Abstract
The invention provides a dynamic function connection research method for juvenile myoclonic epilepsy and application, and belongs to the field of information processing of medical or health data and images. The method comprises the following steps: the method comprises the steps of firstly, data acquisition; preprocessing data; establishing a dynamic functional connection; and fourthly, analyzing data. According to the invention, the change of the brain function of the JME patient is explored through the dynamic function connection research method constructed by the sliding window method, the time resolution is improved, the short plate of the nuclear magnetic resonance imaging on the time dimension is made up to a certain extent, the function connection matrix is clustered on the basis, the commonalities existing in the correlation relationship of the brain areas of the patient are found out, the basis based on the image analysis is provided for finding the origin of the JME, and the obtained conclusion is consistent with the research of the disease by predecessors, so that the core brain area causing the brain function network disorder of the juvenile myoclonic epilepsy patient can be searched, and the early diagnosis and early treatment of the patient can be realized early.
Description
Technical Field
The invention belongs to the field of information processing of medical or health data and images, and particularly relates to a research on changes of brain functions of teenager myoclonic epilepsy patients by a dynamic function connection research method constructed by a sliding window method.
Background
Juvenile Myoclonic Epilepsy (JME) is a common important idiopathic generalized epilepsy syndrome. The three attack modes appear in different age stages, the myoclonus attack in the awakening period is used as the main characteristic in the initial stage of onset, the myoclonus attack occurs 30 min-1 h after awakening, the consciousness is clear during the attack, the myoclonus twitching is like an electric shock, irregular and arrhythmic, the upper limbs and the shoulders on both sides are mostly involved, and the lower limbs, the trunk or the head are occasionally involved. The attack frequency is few, the attack is slight and short, the attack is mentally clear, the attack expression is easy to ignore by patients and parents, the clinical diagnosis and treatment are easy to miss, and the JME is easy to cause intractable epilepsy if the clinical treatment is unreasonable, so the research on the pathogenesis of the JME is very important. In adults and adolescents with epilepsy, the prevalence rate is ten percent at the highest, the epilepsy has high genetic susceptibility, the onset age is between 8 and 26 years, the high onset period is 14 to 16 years, electroencephalogram (EEG) usually shows bilateral spikes or multiple spikes and wavy discharges in the center of the frontal lobe, conventional imaging examination usually has no pathological changes, but advanced imaging examination (such as nuclear magnetic resonance and PET) finds that the medial prefrontal cortex, the dorsal lateral prefrontal cortex and the thalamus have structural and functional abnormalities, and are accompanied with thalamic prefrontal cortex network dysfunction, and neuropsychological research finds that patients have cognitive defects. Meanwhile, researches prove that the newly diagnosed patients have some cognitive function damage before taking the antiepileptic drugs, which is mainly reflected in the memory attention and executive ability. As the patient is ill in adolescence, the main attack is in the early morning or in a period after waking from a sleep state, the life, the study and the social interaction of the patient are greatly influenced, and the problem of finding out the exact core brain area of the onset of the juvenile myoclonic epilepsy is urgently to be solved.
Functional magnetic resonance imaging (fMRI) has become a key tool for detecting large-scale tissues of the brain. Functional Connectivity (FC), evaluated by correlation of BOLD activity, can identify coherent brain activity in distributed and replicable networks. FC reveals the nature of the resting brain network. Recent studies have found that the dynamic changes hidden in the magnetic resonance data in the resting state lead to dynamic functional connections (dfcs) that are brought into the human field of view.
Therefore, the research tries to explore the dynamic process of the brain functional network disorder of the juvenile myoclonic epilepsy patient by introducing a dynamic functional connectivity analysis method on the basis of the current research, and helps to find the core brain area causing the brain functional network disorder of the juvenile myoclonic epilepsy patient.
Disclosure of Invention
The invention aims to provide a dynamic function connection research method for juvenile myoclonic epilepsy, which researches the change of brain function of a juvenile myoclonic epilepsy patient through a dynamic function connection research method constructed by a sliding window method, and searches a core brain area causing brain function network disorder of the juvenile myoclonic epilepsy patient so as to be beneficial to early discovery, early diagnosis and early treatment of the patient.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a dynamic function connection research method for juvenile myoclonic epilepsy comprises the following steps:
data acquisition
The head of the testee is fixed by a spongy cushion, the fMRI data is collected by scanning through a GRE-EPI sequence, the whole brain is covered by a scanning range, and a base line is parallel to a front-back combined connecting line;
pretreatment of data
Performing data preprocessing on the original fMRI data to improve the signal-to-noise ratio of an image to obtain preprocessed fMRI data;
construction of three-step dynamic functional connection
Constructing dynamic function connection on the preprocessed fMRI data by adopting a sliding time window method;
fourth data analysis
And performing K-means cluster analysis on the dynamic function connection to analyze the difference of the dynamic function connection states of the juvenile myoclonic epileptic brain patient and the healthy person, comparing the difference between groups by using a double-sample T test, and setting a threshold value to screen out a brain area with obvious change.
Further, in some preferred embodiments of the present invention, the parameters for scanning and acquiring fMRI data in the step of first: the scanning repetition time TR is 2000ms, the echo time TE is 30ms, the single-layer thickness is 4.0mm, the interlayer gap is 0.4mm, the number of layers is 33, the FOV is 240mm multiplied by 240mm, the matrix is 64 multiplied by 64, the inversion angle FA is 90 degrees, 200 time points are collected in total, and the scanning time duration is 400 s.
Further, in some preferred embodiments of the present invention, the data preprocessing in the second step specifically includes removing the first 10 time points, performing inter-layer correction, performing cranial movement correction, performing spatial standardization, performing smoothing, removing linear drift, and performing low-frequency filtering.
Further, in some preferred embodiments of the present invention, the sliding time window method specifically selects a window size with a duration of 30 to 60 seconds to determine the amount of data contained in the time window, and then slides the window to the tail end of the whole set of data according to a certain step size to extract data at different time points in the whole time sequence to form more detailed data for subsequent analysis.
Preferably, the brain region template selected by the sliding time window method is a brain region of the hospital automation center 246, which is used as a template to perform spatial segmentation on the brain, and performs time segmentation on 190 time points.
Preferably, the window size is selected to be 50 seconds in duration, a single window includes 25 time points, the step size of backward sliding of each window is 14 seconds and 7 time points, and a pearson correlation coefficient is used to calculate a functional connection matrix in each window, each subject obtains 12 symmetrical functional connection matrices, and each functional connection matrix has 246 × 246 elements reflecting the correlation between two brain regions.
Further, in some preferred embodiments of the present invention, the K value of the K-means cluster analysis is selected from 2 to 10, an L1 distance function is selected as a cluster similarity measurement function, a time point down-sampling method is adopted to obtain a tested data point, a connection strength variance in a single time window is calculated, a single connection variance sequence is obtained, and a maximum value point of the connection variance sequence is obtained, where a time window corresponding to the maximum value point is an initial cluster sample; further carrying out initial clustering on the samples, and repeating the process for 500 times; and clustering all sample points by using the clustering center obtained by the initial clustering to obtain the clustering center and the state division condition in the functional connection state.
Preferably, in a preferred embodiment of the present invention, the K value of the K-means cluster analysis is 5.
The invention also provides application of the dynamic function connection research method for juvenile myoclonic epilepsy in the research of brain network connection abnormal diseases.
The beneficial technical effects of the invention are as follows: compared with the existing analysis method of functional connectivity, the dynamic functional connectivity research method constructed by the sliding window method improves the time resolution, makes up for the short plate of the nuclear magnetic resonance imaging on the time dimension to a certain extent, clusters the functional connectivity matrix on the basis, finds out the commonalities existing in the correlation relationship of the brain areas of the patients, and removes the influence on the experimental results caused by the growth and development difference of individuals, the sex difference and the age difference in data; the basis based on the image analysis is provided for finding the origin of the juvenile myoclonic epilepsy, and the obtained conclusion is consistent with the previous study on the disease, so that the method can help to find the core brain area causing the brain functional network disorder of the juvenile myoclonic epilepsy patient, and is beneficial to early discovery, early diagnosis and early treatment of the patient.
Drawings
FIG. 1 is a graph illustrating a cluster center evaluation curve according to an embodiment of the present invention;
FIG. 2 is a JME patient cluster center function connection matrix in accordance with an embodiment of the present invention;
FIG. 3 is a state-location diagram of the different brain areas of a JME patient and a healthy subject, wherein a is a state-coronal plane diagram, b is a state-sagittal plane diagram, and c is a state-horizontal plane diagram, in accordance with an embodiment of the present invention;
FIG. 4 is a state two-position diagram of the different brain areas of a JME patient and a healthy subject in an embodiment of the present invention, wherein d is a state two-coronal plane diagram, e is a state two-sagittal plane diagram, and f is a state two-horizontal plane diagram;
FIG. 5 is a state three-position diagram of the different brain areas of a JME patient and a healthy subject according to an embodiment of the present invention, wherein g is a state three-coronal plane diagram, h is a state three-sagittal plane diagram, and i is a state three-horizontal plane diagram;
FIG. 6 is a state four-position diagram of the different brain areas of a JME patient and a healthy subject according to an embodiment of the present invention, wherein j is a state four-coronal plane diagram, k is a state four-sagittal plane diagram, and l is a state four-horizontal plane diagram;
fig. 7 is a state five-position diagram of the different brain areas of the JME patient and the healthy subject according to an embodiment of the present invention, wherein m is a state five-coronal plane diagram, n is a state five-sagittal plane diagram, and o is a state five-horizontal plane diagram.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The dynamic process of the brain functional network disorder of the teenager myoclonic epilepsy patient is explored by introducing a dynamic functional connection analysis method, and the core brain area causing the brain functional network disorder of the teenager myoclonic epilepsy patient is helped to be searched.
1. Subject selection
The study adopted data from the affiliated second hospital of Lanzhou university, which includes 44 subjects, including 29 epileptic patients (15 men) with age ranging from 11 to 32, 15 healthy volunteers (8 men) with age ranging from 23 to 62, all subjects were right-handed, had no history of major physical diseases (especially diseases related to brain tissue changes) and unstable physical diseases (such as major head trauma, neurological or psychiatric diseases, etc.). According to the diagnosis released by the international union for epilepsy in 2001, the standard diagnosis of all patients is JME, and no structural abnormality is found by routine MRI examination. All patients received no regular treatment and had 4-6 Hz wide multi-spike slow waves and complex waves in electroencephalogram during onset. Normal volunteers have excluded a history of acute illness, drug dependence, drug abuse, craniocerebral injury leading to loss of consciousness, and the potential for psychiatric disorders prior to scanning.
2. Data acquisition parameters
In the invention, data is collected by a Simens Verio 3.0T MR scanner, and fMRI data is collected by adopting a Gradient Echo-Echo Planar Imaging (abbreviated as GRE-EPI) sequence, wherein the scanning range covers the whole brain, and the baseline is parallel to a front-back combined connecting line.
The testee head is fixed by the foam-rubber cushion for reduce the influence that the testee head action led to the fact to experimental data, require the testee to keep the eye-closing state and clear-headed, whole body to relax, the thought calm at the whole in-process of resting state scanning, resting state scanning parameter is: the scanning repetition time TR is 2000ms, the echo time TE is 30ms, the single-layer thickness is 4.0mm, the interlayer gap is 0.4mm, the number of layers is 33, the FOV is 240mm multiplied by 240mm, the matrix is 64 multiplied by 64, the inversion angle FA is 90 degrees, 200 time points are collected in total, and the scanning time duration is 400 s.
3. Data pre-processing
And preprocessing the original fMRI data to improve the signal-to-noise ratio of the image, wherein the preprocessing process is realized on a DPARSF software platform based on Matlab. The data preprocessing process comprises the following steps: removing the first 10 time points, carrying out interlayer correction processing, head movement correction processing, carrying out space standardization processing, carrying out smoothing processing, removing linear drift processing and carrying out low-frequency filtering processing.
4. Dynamic functional connection
The method comprises the steps of firstly selecting a fixed time window size to determine the data amount contained in the time window, and then sliding the window to the tail end of the whole group of data according to a certain step length (namely a time interval) to extract the data at different time points in the whole time sequence to form more detailed data for later analysis. The selected brain area template is a brain area of a Chinese academy automation station 246, the space of the brain is divided for the template, and the time division is carried out on 190 time points by adopting a sliding time window method. The time resolution is good between 30 and 60 seconds, and the slight change can be accurately reflected.
In the invention, the window size is selected to be 50 seconds in duration (a single window comprises 25 time points), the overlapping rate between two front windows and two rear windows is 0.75 (namely, the step length of backward sliding of each window is 14 seconds, 7 time points), and a functional connection matrix is calculated in each window by using the Pearson correlation coefficient. Each subject obtained 12 symmetrical functional connectivity matrices, each functional connectivity matrix having 246 x 246 elements, each element of the matrix reflecting the correlation between two brain regions. A total of 348 functional connectivity matrices were generated for 29 patients and 180 for 15 healthy volunteers.
5. Cluster analysis
In order to analyze the difference of the dynamic function connection states of the patient and the healthy person, K-means cluster analysis is carried out on the dynamic function connection, and the main flow of the K-means cluster analysis is as follows: (1) selecting some data and defining their initial clustering centers, the central point is located at the same place of vector length between each data; (2) each data point is classified by calculating the distance between the point and the center of each group, and then classifying this point as the group closest to it; (3) based on these classification points, we recalculate the group center by taking the mean of all vectors in the group; (4) repeating these steps for a set of iterations, it is also possible to choose the random initialization set center several times and then choose those that appear to provide the best results for it to run.
Selecting an L1 distance function (Manhattan distance) as a clustering similarity measurement function, and in order to reduce the repetition rate and the calculated amount of the characteristic of a time window, obtaining a tested data point by adopting a time point down-sampling method; calculating the variance of the connection strength in the single time window, obtaining a single connection variance sequence, and further solving a maximum value point of the connection variance sequence, wherein the time window corresponding to the maximum value point is an initial clustering sample; 323 time windows (10.9 ± 1.5 time windows per subject on average) were selected as initial clustering samples in all subjects; further carrying out initial clustering on the samples, and repeating the process for 500 times so as to reduce the action of the local extreme point on a clustering result; and clustering all sample points by using the clustering center obtained by the initial clustering to obtain the clustering center and the state division condition in the functional connection state.
6. Results
6.1 clustering results analysis
In order to evaluate the clustering results when the clustering numbers are different, the invention calculates the clustering results when the K value is 2-10 and the corresponding clustering evaluation index values. Referring to fig. 1, it is finally determined according to different evaluation indexes that all the evaluation indexes can reach a better level when the number of clusters is 5, so that 5 clustering centers are determined and clustered; please refer to the clustering centers of the juvenile myoclonic epilepsy patient shown in fig. 2, fig. 2 clusters juvenile myoclonic epilepsy into 5 states, 5 graphs respectively describe 5 clustering centers after K-means clustering is performed on a correlation matrix formed according to pearson correlation coefficients between each brain region and the rest of the brain regions under a 246 partition template, and the occurrence frequency and the ratio of each clustering center are labeled, and a red-to-blue (from top to bottom) chromatogram in fig. 2 respectively represents a reference value of the functional connection strength between the brain regions from strong to weak.
6.2 comparative analysis
And comparing the difference of the brain area functional connection strength between the juvenile myoclonic epilepsy patient and the healthy testee, comparing the difference between groups by using a double-sample T test, and setting a threshold value to screen out an area with obvious change.
Compared with a healthy subject group, the brain areas with significantly weakened functional connection strength of the juvenile myoclonic epilepsy patient group are as follows: the results of the dorsal island of the right island loop, the dorsal transverse muscle islands of the left and right island loops, the temporal thalamus of the thalamic part, and the caudate tail region of the left basal ganglia are shown in table 1, and the results are shown in table 1, wherein brain regions with weakened functional connection of JME patients compared with normal people and the position information of the brain regions under MNI coordinates are respectively listed, so that the brain regions can be used for combining clinic and positioning the regions.
TABLE 1 JME patients healthier human functional connectivity-weakened brain regions
Compared with the healthy tested group, the functional connection strength of the juvenile myoclonic epilepsy patient group is significantly enhanced by the following areas: the caudate gyrus of the right central occipital cortex, the motor cortex lower limb area of the left lateral central leaflet, the left and right upper limbs and the head and face motor area, and the results are shown in table 2.
TABLE 2 functional connectivity enhancement of brain regions in healthier human JME patients
Please refer to fig. 3-7, which show the position of the differentiated brain area and the connection with other brain areas after the double-sample T test, wherein each round sphere represents the center of one brain area and the real position thereof in the brain, the size of the round sphere represents the importance of the brain area, the red line (thick line) represents the connection enhancement between two brain areas, the blue line (thin line) represents the connection weakening between two brain areas, and the number of lines represents the connection degree between the brain area and other brain areas. The larger brain regions in FIGS. 3-7 are the regions listed in tables 1 and 2.
The above results show that: the functional connection of the left and right island-shaped gyrus dorsoceus of the JME patient is weakened in different degrees, the performance of the island-shaped gyrus dorsoceus is obvious in a plurality of clustering centers, the region has large influence on human action inhibition, and the region obviously weakens the action inhibition. Therefore, the juvenile myoclonic epilepsy patient can externally show involuntary shaking without inducement, and the description of the Liu Yonghong on the clinical performance of the patient is conformed; the caudal tail region of the basal ganglia on the left has stronger function connection weakening and obviously increased connection quantity, the region mainly influences the cognitive activity and emotional activity of people, so that the patient shows obvious cognitive ability weakening, the same influence is also shown in the right temporal thalamus, juvenile myoclonic epilepsy is a disordered electric signal spreading whole brain during the attack of general epilepsy, and the thalamus is also a main region connected with the left hemisphere and the right hemisphere of the brain and can generate large influence on the patient due to the special consideration of the region; for both regions of the bilateral island-shaped retro-dorsal transverse muscle islands showing a clear drop in the multiple cluster centers and a complicated connection to the other brain regions, these regions mainly affect the perception of pain which seems not to be directly linked to epilepsy, but according to both Williams K D and Eisenberger N I it is mentioned that the brain regions activated by people experiencing social rejection are the same as the brain regions activated by physical pain, which means that when they occur in isolation, that pain is really present. Therefore, the physical trauma and the psychological trauma brought to teenagers by the epileptic seizure are those of the same age and even family, and the people are crowded and isolated, so the psychological treatment is also paid attention to when the physical disease is treated. Region of functional connectivity enhancement: the significant enhancement of the functional connection of the left side central leaflet motor cortex lower limb area, the left and right side upper limb, head and face motor areas and the right central occipital cortex caudal lingual gyrus area which are mainly responsible for the movement of the lower limb is shown as the onset clonus (myoclonus attack and tonic clonus attack). The research result of the invention can also provide reference for treating diseases with abnormal brain network connection.
The above description is not intended to limit the present invention, but rather, the present invention is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present invention.
Claims (9)
1. A dynamic function connection research method for juvenile myoclonic epilepsy is characterized by comprising the following steps:
data acquisition
The head of the testee is fixed by a spongy cushion, the fMRI data is collected by scanning through a GRE-EPI sequence, the whole brain is covered by a scanning range, and a base line is parallel to a front-back combined connecting line;
pretreatment of data
Performing data preprocessing on the original fMRI data to improve the signal-to-noise ratio of an image to obtain preprocessed fMRI data;
construction of three-step dynamic functional connection
Constructing dynamic function connection on the preprocessed fMRI data by adopting a sliding time window method;
fourth data analysis
And performing K-means cluster analysis on the dynamic function connection to analyze the difference of the dynamic function connection states of the juvenile myoclonic epileptic brain patient and the healthy person, comparing the difference between groups by using a double-sample T test, and setting a threshold value to screen out a brain area with obvious change.
2. The method for studying dynamic functional connectivity of juvenile myoclonic epilepsy according to claim 1, wherein: the method comprises the following steps of scanning and acquiring fMRI data: the scanning repetition time TR is 2000ms, the echo time TE is 30ms, the single-layer thickness is 4.0mm, the interlayer gap is 0.4mm, the number of layers is 33, the FOV is 240mm multiplied by 240mm, the matrix is 64 multiplied by 64, the inversion angle FA is 90 degrees, 200 time points are collected in total, and the scanning time duration is 400 s.
3. The method for studying dynamic functional connectivity of juvenile myoclonic epilepsy according to claim 1, wherein: the data preprocessing specifically comprises the steps of removing the first 10 time points, conducting interlayer correction processing, conducting head movement correction processing, conducting space standardization processing, conducting smoothing processing, conducting linear drifting processing and conducting low-frequency filtering processing.
4. The method for studying dynamic functional connectivity of juvenile myoclonic epilepsy according to claim 1, wherein: the sliding time window method specifically comprises the steps of selecting the window size with the duration of 30-60 seconds to determine the data volume contained in the time window, and then sliding the window to the tail end of the whole group of data according to a certain step length to extract data at different time points in the whole time sequence to form more detailed data for later analysis.
5. The method of claim 4, wherein the method comprises the steps of: the brain area template selected by the sliding time window method is a brain area template of a Chinese academy automation station 246, and is used for carrying out space segmentation on the brain and carrying out time segmentation on 190 time points.
6. The method of claim 5, wherein the method comprises the steps of: the window size is selected to be 50 seconds in duration, a single window comprises 25 time points, the step length of backward sliding of each window is 14 seconds and 7 time points, a Pearson correlation coefficient is used for calculating a functional connection matrix in each window, each testee obtains 12 symmetrical functional connection matrixes, and each functional connection matrix has 246 x 246 elements which reflect the correlation relation between two brain regions.
7. The method for studying dynamic functional connectivity of juvenile myoclonic epilepsy according to claim 1, wherein: selecting 2-10K values of the K-means cluster analysis, selecting an L1 distance function as a cluster similarity measurement function, obtaining a tested data point by adopting a time point down-sampling method, calculating a connection strength variance in a single time window, obtaining a single connection variance sequence, and further obtaining a maximum value point of the connection variance sequence, wherein the time window corresponding to the maximum value point is an initial cluster sample; further carrying out initial clustering on the samples, and repeating the process for 500 times; and clustering all sample points by using the clustering center obtained by the initial clustering to obtain the clustering center and the state division condition in the functional connection state.
8. The method of claim 7, wherein the method comprises the steps of: and the K value of the K-means cluster analysis is 5.
9. Use of a method according to any one of claims 1 to 8 for studying dynamic functional connectivity in juvenile myoclonic epilepsy in the study of disorders of brain network connectivity.
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