CN113057620A - Effective connection method for coupling relations of different brain areas of juvenile myoclonus epileptic patient - Google Patents
Effective connection method for coupling relations of different brain areas of juvenile myoclonus epileptic patient Download PDFInfo
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Abstract
The present invention relates to the fields of medical imaging, computer imaging and mathematics. The invention utilizes the resting state functional magnetic resonance imaging technology and combines a Glangel causal analysis model to carry out effective connection research on the clonus epilepsy of the adolescents. The results show that of the six glange causal relationships in the juvenile myoclonic epilepsy patient group, both glange causal relationships from the right frontal median to the left hippocampus and from the right frontal median to the left anterior cuneiform exhibit negative activation. The results indicate that, in comparison with the three brain regions, the right prefrontal gyrus may have a greater influence on juvenile myoclonus epilepsy, i.e., the right prefrontal gyrus may be associated with the core brain region.
Description
Technical Field
The invention relates to the field of medical images, computer imaging and mathematics, and finally obtains a brain area with remarkable Glanberg causal relationship through image preprocessing, model construction and analysis of Glanberg causal relationship among interested areas.
Background
Juvenile Myoclonic Epilepsy (JME) is a common type of Epilepsy. In adolescence, the intelligent physique is normal, myoclonic attacks appear mostly after awakening, the upper limbs on both sides are mainly involved, and the patient can fall down when reaching the lower limbs, and occasionally has generalized tonic-clonic attacks. EEG is characterized by bilateral spiny slow waves or spiny slow wave synthesis, the type is good after the patient gets, the onset age is mainly concentrated in 8-22 years, the average onset age is 15 years, the onset of disease has no sex difference, and the initial symptoms are that myoclonus appears soon after waking up or articles held in hands suddenly fall involuntarily soon after getting up. 85% of children with illness have full tonic-clonic attacks in months or years after onset, and 10% -15% of children with absence attacks. Myoclonus of involuntary disorder is a main symptom of myoclonic epilepsy of teenagers, and the attack is characterized by transient, bilaterally symmetrical, synchronous and arrhythmic muscle contraction, which is usually seen in shoulders and arms, and can also appear in lower limbs, trunk or head and is occasionally seen on one side. The myoclonus can be in rapid and continuous attack and even progress to the state of myoclonus persistence. Sometimes the onset of absence precedes the onset of myoclonus and generalized tonic-clonus.
If the JME is not reasonably treated clinically, intractable epilepsy is easy to cause, so the research on the pathogenesis of the JME is very important. In clinical diagnosis, the diagnosis of JME mainly depends on electroencephalogram, but JME patients often do not have typical electroencephalogram changes, so that many researchers start focusing on research work on brain networks of JME patients and try to provide new reference bases for the clinical diagnosis of JME. At present, the resting state functional connection condition between cerebral hemispheres on two sides of a JME patient is observed by adopting a mirror image homotopy connection technology, and the functional connection abnormality between a plurality of brain areas in a basal nucleus-thalamus-cortex loop of the JME patient in the resting state is found; the local consistency of JME bilateral thalamus and motor function related cortical regions was found to be significantly increased by resting state fMRI studies. Although these results clearly indicate that the brain function network abnormality occurs in the JME patient compared with the normal brain function network, we can use these results to diagnose JME more accurately.
The functional connectivity analysis method can obtain the time correlation of activities of different brain regions, but cannot depict the information flow in the brain. In order to more accurately illustrate the coupling relation between the nerve activities of different brain areas, an effective connection method for the coupling relation of different brain areas of a juvenile myoclonus epileptic patient is provided.
Disclosure of Invention
The invention aims to provide an effective connection method for coupling relations of different brain areas of a juvenile myoclonus epileptic patient, so as to solve the problem that the coupling relations of nerve activities of different brain areas cannot be accurately explained in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme: the effective connection method of the coupling relation of different brain areas of the juvenile myoclonus epileptic patient comprises the following steps:
s1, acquiring image data, namely acquiring fMRI image data and 3DT1 image data of a testee;
s2, preprocessing fMRI data, namely preprocessing the fMRI data obtained in the step S2 by adopting DPARSF software based on a matlab platform;
s3, performing VBM analysis, namely preprocessing and statistically analyzing the 3DT1 image obtained in the step S2 by adopting an SPM8 tool box under a matlab platform and a VBM8 tool box based on SPM 8;
s4, selecting an interested region, selecting a brain atlas (AAL) provided by a (Montreal Neurological Institute, MNI) mechanism as a template for dividing and extracting a brain region from the interested region, and selecting the extracted brain region as the interested region;
and S5, performing Grangen cause and effect analysis (GCA) effective connection research by adopting a REST tool box based on SPM8, and selecting coefficient-based Grangen cause and effect analysis (ROI-wise coefficient-base GCA) between the regions of interest.
Preferably, in step S2, the fMRI data is acquired by using a gradient echo-planar echo imaging (GRE-EPI) sequence, where the specific parameters are as follows: the repetition Time (TR) is 2000ms, the echo Time (TE) is 30ms, the layer thickness (slice thickness) is 4.0mm, the layer pitch (gap) is 0.40mm, the number of layers is 33, the field of view (FOV) is 240mm, matrix is 64 mm 64, and the rotation angle (FA) is 90 °, and 200 time points are collected.
Preferably, in step S3, the T1 weighted image is acquired by three-dimensional magnetization preparation fast gradient echo sequence (3D MP-RAGE), with the specific parameters: the repetition Time (TR) is 1900ms, the echo Time (TE) is 30ms, the layer thickness (slice thickness) is 0.9mm, the field of view (FOV) is 256mm 230mm, the matrix is 256mm 256, and the rotation angle (FA) is 90 °.
Further, in step S2, the fMRI data preprocessing specifically includes: format conversion of DICOM data, removal of the first 10 time points, interlayer correction, head movement correction (translation 1mm, rotation 1 degree), spatial standardization, smoothing, removal of linear drift, low-frequency filtering, and removal of covariates.
Further, in step S3, the main steps of the 3DT1 image preprocessing and statistical analysis are as follows:
s4.1, performing time correction and head movement correction on the tested EPI image, and excluding EPI images with head movement larger than 0.5 mm;
s4.2, carrying out space standardization on the corrected image in the step S4.1 to align the image with a standard template so as to eliminate individual difference of the brain;
s4.3, according to the prior template of brain tissues (gray matter, white matter and cerebrospinal fluid), carrying out anatomical segmentation on the standardized image to obtain gray matter, white matter and cerebrospinal fluid;
s4.4, modulating the image segmented in the step S4.3, analyzing the change of the grey matter volume of the brain of the epileptic, and modulating the segmented grey matter image to truly reflect the grey matter volume;
s4.5, smoothing the gray matter image obtained in the step S4.4 by using a Gaussian kernel with a Full Width Half Maximum (FWHM) of 4 mm to improve the signal-to-noise ratio of the image and obey normal distribution;
s4.6, detecting the difference between groups of gray matter volume in brain structures of the JME patient group and the normal control group by adopting double-sample T test.
Further, in step S5, the brain region selects an activated brain region determined by VBM analysis, and a time-series signal after preprocessing is obtained.
Further, in step S5, GCA time series XtAnd YtThe autoregressive model of (a) is:
wherein epsilonit(i ═ 1,2,3,4) represents the prediction error, and p is the model order;
for the selection of the model order, a Bayesian Information Criterion (BIC) is followed:
selecting the model order p as 1;
the relation of the three brain regions of interest is obtained by extracting the time series of the three selected regions of interest and performing an effective connection experiment based on the glange causal analysis.
The results indicate that, in comparison with the three brain regions, the right prefrontal gyrus may have a greater influence on juvenile myoclonus epilepsy, i.e., the right prefrontal gyrus may be associated with the core brain region.
Compared with the prior art, the invention has the beneficial technical effects that: the efficient ligation method employed in the present invention is the glargine causal analysis, which quantifies the use of the available information from one time series to predict the value of a second time series. In the analysis of effective connectivity of brain function, the theoretical basis of glange causal relationship analysis is the glange causal relationship and an autoregressive model of multivariate time series, i.e., MAR model. The granger causal analysis model defines the connections between brain regions and the strength of the connections. The granger causal analysis method is defined as: for two stationary time series and if the current value is predicted by the past value, the effect is better than that when the current value is predicted by the past value used alone, the reason is called the Glanberg reason, and whether the reason is the Glanberg reason can be checked in the same way. The granger causal analysis method, as an analysis method for effective connectivity, can model the interaction pattern between statistically significant different brain regions, extract the time series of two statistically different brain regions, perform modeling, and further analyze the interaction between these statistically significant different brain regions to infer the structural pattern of potential neural connectivity.
Drawings
FIG. 1 is a graphical representation of the difference between groups in gray matter volume of brain in groups of epileptic patients versus normal controls;
FIG. 2 is a graph of the difference in operative connections between a patient group and a normal control group;
FIG. 3 two effective connections for activation suppression;
FIG. 4 activates four active connections facilitated;
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.
1.1. Subject selection
The subjects included 15 (11 men, 4 women, 10-40 years of age, mean age 18.6 years of epilepsy, mean duration of epilepsy 4.03 years) JME patients and 15 (9 men, 6 women, 10-37 years of age, mean age 19.3 years of age) normal volunteers. Both groups of subjects were right handedness. Two groups of patients have no significant difference (P >0.05) in age, handedness and gender, all patients are diagnosed as JME according to the diagnostic standard released by the international antiepileptic union in 2001, structural abnormality is not seen in routine MRI examination, electroencephalogram in a seizure period shows 4-6 Hz extensive multi-spike slow waves or spike slow complex waves, and normal treatment is not received. Normal volunteers were recruited by advertising to exclude prior to scanning acute physical illness, drug abuse or dependence, a history of mental loss due to craniocerebral injury, and neurological or psychiatric disorders. The content and purpose of the study are informed to the subject and signed with an informed consent.
1.2. Image data acquisition
The method comprises the steps of collecting fMRI image data and 3DT1 image data of a human subject, wherein all the data are collected by a Simens Verio3.0T MR scanner, and the fMRI data are collected by a gradient echo-planar echo imaging (GRE-EPI) sequence. The specific parameters are as follows: the repetition Time (TR) is 2000ms, the echo Time (TE) is 30ms, the layer thickness (slice thickness) is 4.0mm, the layer pitch (gap) is 0.40mm, the number of layers is 33, the field of view (FOV) is 240mm, matrix is 64 mm 64, and the rotation angle (FA) is 90 °, and 200 time points are collected. The T1 weighted image is acquired by three-dimensional magnetization preparation fast gradient echo sequence (3D MP-RAGE). The specific parameters are as follows: the repetition Time (TR) is 1900ms, the echo Time (TE) is 30ms, the layer thickness (slice thickness) is 0.9mm, the field of view (FOV) is 256mm 230mm, the matrix is 256mm 256, and the rotation angle (FA) is 90 °. Data acquisition requires that a subject lies down, has a fixed head, closes eyes and is plugged with ears, and does not make any specific thinking as much as possible.
1.3fMRI data preprocessing
All the pretreatment of the fMRI data is carried out by using DPARSF software based on matlab platform, which specifically comprises the following steps: format conversion of DICOM data, removal of the first 10 time points, interlayer correction, head movement correction (translation 1mm, rotation 1 degree), spatial standardization, smoothing, removal of linear drift, low-frequency filtering, and removal of covariates.
1.4VBM analysis
The 3DT1 images were preprocessed and statistically analyzed using SPM8 tool box under matlab platform and VBM8 tool box based on SPM 8. The method mainly comprises the following steps: firstly, carrying out time correction and head movement correction on 30 tested EPI images, and excluding EPI images with head movement larger than 0.5 mm; secondly, carrying out space standardization on the corrected image to enable the corrected image to be aligned with a standard template, and eliminating individual difference of the brain; then according to the prior template of brain tissue (gray matter, white matter, cerebrospinal fluid), dissect and cut the standardized picture, get gray matter, white matter, cerebrospinal fluid; modulating the segmented image, which is used for analyzing the change of the brain gray matter volume of the epileptic, so that the segmented gray matter image is modulated to truly reflect the gray matter volume; then smoothing the obtained gray matter image by using a Gaussian kernel with a Full Width Half Maximum (FWHM) of 4 mm to improve the signal-to-noise ratio of the image and obey normal distribution; and finally, detecting the difference between groups of gray matter volume in brain structures of the JME patient group and the normal control group by adopting a double-sample T test.
1.5 selection of regions of interest
The region of interest was selected as a template for dividing and extracting brain regions by An Automated Laboratory (AAL) provided by the Institute of mental health, MNI, which divides a total of 116 regions, of which the brain is divided into 90 regions and the rest belong to the cerebellum. And selecting an extracted brain region as an interested region, selecting an activated brain region determined by VBM analysis from the brain region, and obtaining a time series signal after preprocessing.
1.6 Glankey causal analysis
A Granger-based causal analysis (GCA) effective connectivity study was performed using a REST toolbox based on SPM8, selecting a coefficient-based Granger causal analysis (ROI-wise coefficient-base GCA) between regions of interest.
In GCA, the autoregressive model of the time series sum is:
where (═ 1,2,3,4) denotes the prediction error, is the model order,
for the selection of the model order, a Bayesian Information Criterion (BIC) is followed:
the model order is chosen to be 1.
The causal relationship of the three brain regions was explored by extracting a time series of selected three regions of interest and performing a validly connected study based on glangen's causal analysis.
Referring to fig. 1, the structural images of the epileptic group and the normal control group were subjected to a double-sample T test, and the structural and functional images were obtained using the same brain spectrogram, so as to ensure one-to-one correspondence between brain regions and functional image brain regions. The results of the difference in gray matter volume between the epileptic group and the normal control group are shown in fig. 1. The color bars ranged from-5 to 5, showing the level of difference between groups, with cold color indicating gray brain regions. The results show that the brain gray matter volume in JME group showed atrophy in brain regions mainly: the right prefrontal gyrus, the left hippocampus, and the brain areas of the left premxietal fascicles are mainly: right prefrontal gyrus, left hippocampus juxtaposus, left anterior cuneial lobe.
The relationship between abnormalities in the effector junctions between brain regions and reactive attacks was examined by glange causal analysis. Since the GCA at the ROI level is divided into two cases, i.e., information input and information output, the above three brain regions generate 6 causal relationships in total. Namely, the right prefrontal gyrus to the lateral gyrus of the left hippocampus, the left hippocampus to the right prefrontal gyrus, the right prefrontal gyrus to the left premolaral lobe, the left premolaral lobe to the right prefrontal gyrus, the left hippocampus to the left premolaral lobe, and the left premolaral lobe to the left hippocampus. The results were obtained by analyzing the glargine causal coefficients comparing the normal control and patient groups and are shown in fig. 2, with two linked conditional glargine causal reductions in the patient group, right prefrontal gyrus (mfg.r) to left hippocampal gyrus (phg.l) and right prefrontal gyrus (mfg.r) to left prenex lobe (pcun.l), so that these two effective links are negatively activated, and with four linked glargine causal values, left perimastic gyrus (phg.l) to left prenex lobe (pcun.l), left prenex sphenoidal lobe (pcun.l) to right prefrontal gyrus (mfg.r), left perimastix (phg.l) to left prenex sphenoidal (pcun.l), left prenex sphenoidal lobe (phg.l), so that these four effective links are positively activated.
Differences between the patient group and the normal control group were displayed by operatively linking them to a brain template, as shown in fig. 3. The yellow nodes in the graph represent the three brain regions of the right prefrontal gyrus, the left hippocampus and the left anterior cuneiform. Blue arrows represent the inhibitory effect of activation of one brain region on another, as shown in fig. 3, and red arrows represent the activation-promoting effect. As shown in fig. 4, it can also be seen that there are 4 links in the patient group with a causal rise in the condition glange, respectively: left hippocampal juxtapose (phg.l) to right prefrontal gyrus (mfg.r), left promiscuous leaf (pcun.l) to right prefrontal gyrus (mfg.r), left hippocampal juxtapose (phg.l) to left promiscuous leaf (pcun.l), left promiscuous leaf (pcun.l) to left hippocampal juxtapose (phg.l). Two links were connected to the reduction in conditional grande causal values for the patient groups relative to the normal control group, respectively: right prefrontal gyrus (mfg.r) to left hippocampus lateral gyrus (phg.l), right prefrontal gyrus (mfg.r) to left prefrontal lobe (pcun.l).
Claims (10)
1. The effective connection method of the coupling relation of different brain areas of the juvenile myoclonus epileptic is characterized in that: the method comprises the following steps:
s1, acquiring image data, namely acquiring fMRI image data and 3DT1 image data of a testee;
s2, preprocessing fMRI data, namely preprocessing the fMRI data obtained in the step S1;
s3, VBM analysis, namely preprocessing and statistically analyzing the 3DT1 image obtained in the step S1;
s4, selecting an interested region, wherein a brain atlas provided by MNI is used as a template for dividing and extracting a brain region, and the extracted brain region is selected as the interested region;
and S5, performing Glanberg causal analysis, wherein effective connection based on the Glanberg causal analysis is performed by adopting a tool box.
2. The method for effectively connecting different brain areas of a juvenile myoclonus epilepsy patient according to claim 1, wherein: and preprocessing the data in the step S2 by adopting DPARSF software based on the matlab platform.
3. The method for effectively connecting different brain areas of a juvenile myoclonus epilepsy patient according to claim 1, wherein: the tool boxes in the step S3 are an SPM8 tool box under the matlab platform and a VBM8 tool box based on the SPM 8; the tool box described in step S5 is a REST tool box based on SPM 8.
4. The method for effectively connecting different brain areas of a juvenile myoclonus epilepsy patient according to claim 1, wherein: in step S2, the fMRI data is acquired by using a gradient echo-planar echo imaging sequence, and the specific parameters are as follows: the repetition time TR is 2000ms, the echo time TE is 30ms, the layer thickness is 4.0mm, the layer pitch gap is 0.40mm, the number of layers is 33, the field of view FOV is 240mm, matrix is 64 x 64, the rotation angle FA is 90 °, and 200 time points are acquired in total.
5. The method for effectively connecting different brain areas of a juvenile myoclonus epilepsy patient according to claim 1, wherein: in step S3, the T1 weighted image is acquired by a three-dimensional magnetization preparation fast gradient echo sequence, and the specific parameters are as follows: the repetition time TR is 1900ms, the echo time TE is 30ms, the layer thickness slice thickness is 0.9mm, the field of view FOV is 256mm by 230mm, matrix is 256 by 256, and the rotation angle FA is 90 °.
6. The method for effectively connecting different brain areas of a juvenile myoclonus epilepsy patient according to claim 1, wherein: in step S3, the fMRI data preprocessing specifically includes: format conversion of DICOM data, removal of the first 10 time points, interlayer correction, cephalodynamic correction, spatial normalization, smoothing, removal of linear drift, low frequency filtering, and removal of covariates.
7. The method for effectively connecting different brain areas of a juvenile myoclonus epilepsy patient according to claim 1, wherein: in step S3, the main steps of the 3DT1 image preprocessing and statistical analysis are as follows:
s3.1, performing time correction and head movement correction on the tested EPI image, and excluding EPI images with head movement larger than 0.5 mm;
s3.2, carrying out space standardization on the corrected image in the step S4.1, aligning the image with a standard template, and eliminating individual difference of the brain;
s3.3, according to the prior template of the brain tissue, carrying out anatomical segmentation on the standardized image to obtain gray matter, white matter and cerebrospinal fluid;
s3.4, modulating the image segmented in the step S4.3, analyzing the change of the grey matter volume of the brain of the epileptic, and modulating the segmented grey matter image to truly reflect the grey matter volume;
s3.5, smoothing the gray matter image obtained in the step S4.4 by using a Gaussian kernel with the half-height width of 4 mm to improve the signal-to-noise ratio of the image and obey normal distribution;
s3.6, detecting the difference between groups of gray matter volume in brain structures of the JME patient group and the normal control group by adopting double-sample T test.
8. The method for effectively connecting different brain areas of a juvenile myoclonus epilepsy patient according to claim 1, wherein: in step S5, the brain region selects an activated brain region determined by VBM analysis, and a time-series signal after preprocessing is obtained.
9. The method for effectively connecting different brain areas of a juvenile myoclonus epilepsy patient according to claim 1, wherein: in step S5, GCA time series XtAnd YtThe autoregressive model of (a) is:
wherein epsilonit(i ═ 1,2,3,4) represents the prediction error, and p is the model order;
for the selection of the model order, a Bayesian information criterion is followed:
the model order is selected to be p-1.
10. Use of a method according to any of claims 1-9 for the effective connection of different brain areas in a juvenile myoclonic epilepsy patient for studying the interaction between different brain areas and the structural configuration of neural connections in a juvenile myoclonic epilepsy patient.
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