CN111415351B - Method for checking measurement effect of multi-object analysis method based on vertex on cortical connection - Google Patents
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Abstract
The invention discloses a method for checking the measurement effect of a multi-object analysis method based on vertexes on cortical junction, which comprises image acquisition, MRI data processing and the like, wherein in the current study, a cortical SC measurement method (FiCD) based on structural connectors is used for comparing full cerebral cortex SC maps among groups, and reliability tests show that the FiCD method has good reproducibility among individuals and between individuals, and in addition, point-by-point statistical analysis proves the feasibility of comparing the FiCD map sets of healthy people and subjects after stroke. Furthermore, spatial comparisons indicate spatial correspondence between FiCD-altered cortical clusters and subcortical lesions of patients, indicating that the FiCD method can accurately identify affected cortical areas. Taken together, these results demonstrate the feasibility of per-vertex packet comparisons of cortical SC maps.
Description
Technical Field
The invention relates to the field of imaging, in particular to a method for testing the measurement effect of a multi-object analysis method based on vertexes on cortical connection.
Background
Current cortical Structure Connectivity (SC) measurements have not been well integrated with vertex-based multi-object statistical analysis. The purpose of this study was to examine the feasibility of performing cortical SC measurements using the group comparative vertex analysis.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a technical scheme for solving the problems.
A method of verifying the effect of vertex-based multi-object analysis on cortical junction measurements, comprising:
image acquisition, scanning all subjects in a common MRI dataset using a 1.5T MR scanner, scanning all subjects using a 3T MR scanner, acquiring 3D-T1WI images using a 3D Fast Field Echo (FFE) sequence, acquiring a reverse recovery (FLAIR) image inversion recovery (TIR) sequence, acquiring diffusion images using a spin echo-echo planar imaging (SEEPI) sequence;
MRI data processing, preprocessing of diffuse image data including motion correction, EPI distortion and eddy current correction, and brain extraction using FSL toolbox; preprocessing the 3D-T1WI image by using Freesurfer software; the method mainly comprises the steps of intensity normalization, brain tissue removal, automatic Talairach transformation, tissue segmentation, subdivision of GM/WM boundary, automatic topology correction and surface deformation after intensity gradient; generating a surface model of a real and GM/WM interface;
FiCD mapping, drawing a FiCD map for each subject;
calculation of FiCD variability, measurement of average regional FiCD values for partial cortical Regions (ROI) based on lobular/regional anatomy, intra-and inter-object variances for each ROI were calculated by the following formula:
wherein I is more than or equal to 1 and less than or equal to I is more than or equal to 10, J is more than or equal to 1 and less than or equal to J is more than or equal to 2; fiCD (FiCD) .j Is the average fiber connection density value of two scans; fiCD (FiCD) i. Is the average fiber connection density value for each subject; fiCD (FiCD) .. Is the average fiber connection density value between scanned objects in a single cortical region; i represents the number of subjects, V w Representing intra-subject variability, V B Referring to inter-subject variability, intra-subject and inter-subject variability for each cortical region is then calculated by the following formula:
calculating FiCD by adding the anisotropic Fraction (FA) values of all AFs;
point-by-point statistical comparison of FiCD mapping between healthy and stroke groups, the peak-to-peak statistical comparison between FiCD groups is a contrast (Qdec) model estimated using a Freesurfer's query design, the two groups are compared on a peak basis, a Generalized Linear Model (GLM) is calculated point-by-point for each hemisphere to analyze cortical FiCD values, smoothing is performed using a 10mm FWHM Gaussian-check cortical map, and cortical areas are significantly unable to be tilted onto semi-inflated cortical surfaces by coverage; with a significance threshold of p <0.001, the average FiCD value of important cortical clusters, the average value of left frontal lobe cortex and the left frontal lobe/average right frontal lobe for each subject were calculatedRatio of FiCD values (to eliminate individual differences) and comparison between the two, the two groups were compared using t-test of two samples, with a significance threshold of p < 0.05; age and total brain volume group differences were analyzed using a two sample t-test and χ was used 2 The sex difference was checked and analyzed, and the significance threshold was p < 0.05.
The invention has the beneficial effects that: in the current study, the structural linker-based cortical SC measurement method (FiCD) was used to compare full brain cortical SC maps between groups, and reliability tests showed that the FiCD method had good in-vivo and inter-individual reproducibility, and in addition, point-by-point statistical analysis confirmed the feasibility of FiCD profile comparison for healthy and post-stroke subjects. Furthermore, spatial comparisons indicate spatial correspondence between FiCD-altered cortical clusters and subcortical lesions of patients, indicating that the FiCD method can accurately identify affected cortical areas. Taken together, these results demonstrate the feasibility of per-vertex packet comparisons of cortical SC maps.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is an overall workflow of FiCD analysis.
Fig. 2 is a FiCD comparison of post-stroke and control groups, and correspondence between lesion distribution and AF of important clusters.
Detailed Description
Image acquisition
All subjects in the common MRI dataset were scanned using a GE signalhdxt 1.5T MR scanner for group comparison, all subjects were scanned using a 3T MR scanner with an eight channel head coil. Acquiring 3D-T1WI images using a 3D Fast Field Echo (FFE) sequence has the following parameters: repetition time/echo time (TR/TE) =6.7/1.6 ms, flip angle=15 °, excitation Number (NEX) =2, field of view (FOV) =220×220mm, vertex size=0.86x0.86x1.4 mm. Fluid attenuation an inversion recovery (FLAIR) image Turbo Inversion Recovery (TIR) sequence was acquired using the following tool, followed by parameters: TR/TE/ti= 11000/200/2800ms, flip angle=90°, nex=1, fov=220×220mm, vertex size=0.31x0.31x3.0 mm. Diffusion images were acquired using a spin echo-echo planar imaging (SEEPI) sequence with the following parameters: TR/te=10900/84.5 ms, flip angle=90°, nex=2, fov=220×220mm, vertex size=0.86x0.86x3 mm.
MRI data processing
Preprocessing of diffuse image data includes motion correction, EPI distortion and eddy current correction, and brain extraction using FSL toolbox. The 3D-T1WI image was pre-processed using the Freesurfer software. The method mainly comprises the steps of intensity normalization, brain tissue removal, automatic Talairach transformation, tissue segmentation, subdivision of GM/WM boundary, automatic topology correction and surface deformation after intensity gradient. This allows the optimal location of the GM/WM and GM/cerebrospinal fluid boundaries to be placed where the maximum change in intensity defines a transition to other tissue classes. These steps generate surface models of the pial and GM/WM interfaces that correspond spatially and can be transformed between each other.
FiCD mapping
A FiCD image was drawn for each subject according to the procedure described above. The FiCD map was smoothed using a 10mm FWHM Gaussian kernel. Freesurfer is used to construct spatial correspondence between brain surfaces and volume space, as well as surface registration between subjects. The ANTs toolbox is used to convert CUs into diffusion space based on rigid registration, while DSI Studio is used for deterministic fiber tracking.
Calculation of FiCD variability
Average region FiCD values were measured for 12 cortical regions of interest (ROIs) based on leaflet/region anatomy (table 1). The intra-and inter-object variances for each ROI were calculated by the following formula:
wherein I is more than or equal to 1 and less than or equal to I is more than or equal to 10, J is more than or equal to 1 and less than or equal to J is more than or equal to 2; fiCD (FiCD) .j Is the average fiber connection density value of two scans; fiCD (FiCD) i. Is the average fiber connection density value for each subject; fiCD (FiCD) .. Is the average fiber connection density value between scanned objects in a single cortical region; i represents the number of subjects, V w Representing intra-subject variability, V B Referring to inter-subject variability, intra-subject and inter-subject variability for each cortical region is then calculated by the following formula:
FiCD (FA weighting) is calculated by adding the anisotropic Fraction (FA) values of all AFs. However, SC may also be quantified by counting the number of fiber streamlines (count streamlines method). To compare the results of two calculation strategies, we analyzed the intra-and inter-object variability based on two methods.
Point-by-point statistical comparison of FiCD mapping between healthy and stroke groups
Inter-group per-vertex statistical comparison
The primary was to conduct a whole cortical analysis as previously done for cortical thickness analysis to determine normalized local cortical FiCD values for post-stroke and healthy controls. The inter-group vertex statistical comparison of FiCD is a query design estimation contrast (Qdec) module using Freesurfer. The two groups are compared on a vertex basis. For each hemisphere, a Generalized Linear Model (GLM) was calculated point by point to analyze cortical FiCD values. The cortical map was smoothed using a 10mm FWHM gaussian check and the cortical region was significantly unable to tilt onto the semi-inflated cortical surface by coverage. A significance threshold of p <0.001 was used (multiple comparisons of 10000 iterations corrected using Monte Carlo Null-Z simulation). The average FiCD value of the important cortical clusters, the average of the left frontal cortex and the ratio of the left frontal/average right frontal FiCD values (to eliminate individual differences) for each subject were calculated and compared between the two, the two groups were compared using the t-test of the two samples (or the Mann-Whitney U test on non-normal distribution data) with a significance threshold of p <0.05 (uncorrected).
Age and total brain volume group differences were analyzed using a two sample t-test and χ was used 2 Sex difference is checked and analyzed, and the significance threshold is p<0.05。
Verification of spatial correspondence between cortical FiCD reduction and subcortical lesion distribution in stroke patients
Infarct foci of individual patients were manually delineated on 3D-T1WI images using the ITK-SNAP 3D segmentation tool, with reference to FLAIR images, agreed upon by experienced radiologists and neurologists. The final segmented mask of lesions was normalized into the template brain space of montreal neurological institute 152 using the ANTs tool. The FiCD-altered cortical clusters are mapped to the MNI152 brain space and generate their normal AF in WM surgery of the normal brain. The segmented mask from normal atrial fibrillation and lesions of the important clusters is overlaid in a common brain space according to the tensor image of the IIT Diffusion Tensor Imaging (DTI) template of the Iinoy Institute of Technology (IIT) to assess its spatial consistency.
To verify the spatial correspondence from subcortical lesions to cortex, lesion segmentation of each subject was used as a seed to generate its normally passing fibers in the normal brain. Finally, spatial consistency was assessed by checking if the FiCD value in the subject was reduced for cortical areas that normally connect to subcortical lesions.
Results
Reliability test of FiCD measurements
The reliability of SC measurements is important for group level analysis; therefore, we first evaluate the reliability of the FiCD method. A common MRI dataset containing MRI data for 10 healthy subjects was used to assess reproducibility. A FiCD map is constructed for each topic. Average region FiCD values of 12 cortical ROIs were measured, these cortical ROIs were classified according to lobular/regional anatomy, and intra-and inter-subject FiCD changes for each ROI were calculated. Average variation of FiCD (FA weighting) within and between subjects among 12 ROIs was 3.51±2.12% and 19.44±4.79%, respectively, positively indicating good reproducibility of the FiCD mapping method. For the simplified counting method, the average intra-and inter-subject differences for the 12 ROIs were 2.92±2.43% and 16.51±4.75%, respectively. The FA weighted method showed lower intra-subject variability for the three ROIs, but higher inter-subject variability for all 12 ROIs, compared to the count reduction method (table 1).
TABLE 1 intra-and inter-subject variability of two FiCD calculation methods across 12 valve/cortical regions
Point-by-point statistical comparison of FiCD mapping between healthy and stroke groups
Next, for group comparison of cortical SCs, measurements were made for 14 post-stroke patients (average age 68.36 ±7.33 y) and 19 healthy control subjects (average age 66.84 ±8.58 y). Demographic and clinical characteristics of the participants are summarized in table 2 below. There was no significant difference in age, sex (all, p >0.05, uncorrected) and total brain volume between the two groups, indicating that the two groups were comparable. The median MMSE score was significantly lower in the post-stroke group than in the control group (23.5 to 29, p <0.001, uncorrected), and seven patients were likely suffering from cognitive impairment (MMSE < 24). 73 lesions were found in 14 post-stroke subjects, of which 39 were located in the left frontal lobe, indicating that in the post-stroke group, left frontal WM was progressively impaired (table 2).
TABLE 2 demographic, clinical and FiCD data for post-stroke and control groups
FiCD maps were plotted for all subjects. Statistical analysis was performed on the vertices to compare cortical FiCD maps between healthy and stroke groups. As shown in fig. 2, the apparent difference in cortical areas between the two groups (uncorrected p-value < 0.05) indicates a decrease or increase in FiCD value in the post-stroke group. Notably, the FiCD value of the left anterior central anterior gyrus was significantly lower (p <0.001, corrected by monte carlo simulation) in the post-stroke group compared to the control group. The average FiCD value extracted from the left central anterior loop also has statistical significance (p <0.001, uncorrected). The average FiCD value and left/right She Bili of left frontal cortex extraction were significantly reduced (p=0.003, uncorrected) in post-stroke patients.
The reduced FiCD cortical areas correspond to subcortical lesion distribution in post-stroke patients
Finally, to address whether FiCD data faithfully reflects stroke-induced cortical connectivity changes, we examined whether spatial distribution between cortical clusters with reduced FiCD values and subcortical lesions of patients was consistent. For this purpose, both the normal AF from the important cluster and the segmentation mask from the lesions of post-stroke patients are mapped into the MNI152 template brain space. Lesions of the patient after stroke were found to be densely distributed in the subcortical/deep area of the left frontal lobe (fig. 2). Notably, AF from the important clusters of inter-vertex group comparisons passes through the central region of the lesion ROI (fig. 2). This observation provides evidence supporting correspondence between subcortical lesions and reduced FiCD cortical areas.
In the current study, the structural linker-based cortical SC measurement method (FiCD) was used to compare full brain cortical SC maps between groups, and reliability tests showed that the FiCD method has good in-vivo and inter-individual reproducibility. In addition, point-by-point statistical analysis confirmed the feasibility of FiCD profile comparison for healthy and post-stroke subjects. Furthermore, spatial comparisons indicate spatial correspondence between FiCD-altered cortical clusters and subcortical lesions of patients, indicating that the FiCD method can accurately identify affected cortical areas. Taken together, these results demonstrate the feasibility of per-vertex packet comparisons of cortical SC maps.
Claims (3)
1. A method for testing the effect of vertex-based multi-object analysis on cortical junction measurement, comprising the steps of,
s1, acquiring images, namely scanning all subjects in a public MRI data set by using a 1.5T MR scanner, scanning all subjects by using a 3T MR scanner, acquiring 3D-T1WI images by using a 3D rapid field echo sequence, acquiring an inversion recovery image inversion recovery sequence, and acquiring diffusion images by using a spin echo-echo planar imaging sequence;
s2, MRI data processing, preprocessing diffusion image data; preprocessing the 3D-T1WI image;
s3, mapping fiber connection density, and drawing a fiber connection density map for each subject;
s4, calculating fiber connection density variability, namely measuring average regional fiber connection density values of partial cortical regions, wherein the regions are dissected according to large leaves/regions, and the intra-object and inter-object variances of each cortical region are calculated by the following formula:
wherein I is more than or equal to 1 and less than or equal to I is more than or equal to 10, J is more than or equal to 1 and less than or equal to J is more than or equal to 2; fiCD (FiCD) .j Is the average fiber connection density value of two scans; fiCD (FiCD) i. Is the average fiber connection density value for each subject; fiCD (FiCD) .. Is the average fiber connection density value between scanned objects in a single cortical region; i represents the number of subjects, V w Representing intra-subject variability, V B Referring to inter-subject variability, intra-subject and inter-subject variability for each cortical region is then calculated by the following formula:
s5, calculating the fiber connection density by adding the anisotropic fraction values of all the connection fibers;
s6, carrying out point-by-point statistical comparison on fiber connection density mapping between a healthy group and a stroke group, comparing the two groups on the basis of vertexes, calculating a general linear model point by point for each hemisphere to analyze cortical fiber connection density values, carrying out smoothing treatment by using a 10mm FWHM Gaussian check cortical map, and obviously failing to incline a cortical region onto a semi-inflated cortical surface through coverage; calculating an average fiber connection density value of an important cortical cluster, an average value of left frontal lobe cortex and a ratio of the average left frontal lobe/average right frontal lobe fiber connection density value for each subject using a significance threshold of p <0.001, and comparing the two groups using a t-test of two samples, the significance threshold being p < 0.05; age and total brain volume group differences were analyzed using a two sample t-test and χ was used 2 The sex difference is checked and analyzed, and the significance threshold value is p < 0.05.
2. The method of examining the effects of a multi-object vertex-based analysis on cortical connection measurements of claim 1, wherein the preprocessing of diffuse image data includes motion correction, EPI distortion, and eddy current correction.
3. The method for verifying the effect of vertex-based multi-object analysis on cortical connection measurements of claim 1, wherein the pre-processing of 3D-T1WI images includes intensity normalization, brain tissue removal, automatic talapiach transformation, tissue segmentation, subdivision of GM/WM boundaries, automatic topology correction and surface deformation after intensity gradients; surface models of the pial and GM/WM interfaces are generated.
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