Method for identifying somatic depression by using brain structure and function abnormality

Abstract

The invention discloses a method for identifying somatic depression by using brain structure and function abnormality, which comprises the following specific steps: selecting depression patients into groups, dividing the depression patients into a somatic depression patient group and a non-somatic depression patient group according to whether the score of a health questionnaire somatic symptom group scale of the patients is greater than or equal to 10, and recruiting a health control group at the same time. The three groups of subjects are subjected to resting state structure and functional magnetic resonance imaging scanning, and the brain grey matter volume and the brain region with significant difference are analyzed through a voxel-based morphological index and functional connection method. Pearson correlation analysis is adopted in SPSS26.0, and the relevance between the gray matter volume and the abnormal function connection of the patients in the SD group and the NSD group and the PHQ-15 scale score is compared. The method has the advantages that the illness state of the depression patient can be found in time, the patient can be treated at the best treatment opportunity, and early recognition of the body symptoms of the depression is beneficial for reducing waste of medical resources and relieving the pain of the patient.

Description

Technical Field

[0001] The present invention relates to the field of neuroscience, and in particular to a method for identifying somatic depression by utilizing abnormal brain structure and function. Background Art

[0002] Somatic depression (SD) is a special subtype of depression. Silverstein et al. defined depression with physical symptoms as somatic depression, and classified the physical symptoms with diagnostic significance into six items: sleep disorders, eating disorders, fatigue, pain, body shape disorders, and unexplained dyspnea. Studies have shown that about 66% of patients with depression have physical symptoms. Previous studies have found that patients with depression who complain of physical symptoms are prone to delay their condition and miss the best time for treatment. In addition, physical symptoms can aggravate the patient's depression, prolong the course of the patient's illness, and are also one of the important factors leading to poor prognosis. Therefore, early identification of physical symptoms of depression is not only conducive to reducing the waste of medical resources, but also to alleviating the pain of patients. In recent years, neuroimaging technology has developed rapidly. Resting-state functional magnetic resonance imaging and structural magnetic resonance imaging have the advantages of safety, non-invasiveness, and repeatability in adults and children, and have been widely used in various diseases. LIU et al. used voxel-based morphometry (VBM) to study the gray matter volume of patients with depression accompanied by gastrointestinal symptoms. The results showed that the gray matter volume of the precentral gyrus, bilateral middle frontal gyrus, and right superior frontal gyrus in these patients was low, and as the degree of depression worsened, the gastrointestinal symptoms became more obvious. Li Wenling et al. explained in the progress of magnetic resonance imaging research on patients with somatization of depression that the somatization symptoms of patients with depression were related to brain structural damage related to pain processing and emotion regulation systems. The functional connection (FC) study of SD patients showed that the FC between the right postcentral gyrus and the right superior temporal gyrus, the insula and the right orbitofrontal cortex, and the amygdala and the insula and superior temporal gyrus was reduced. In summary, previous studies on gray matter volume changes and brain functional activities in SD patients were relatively simple, lacking effective connections between the two, and the functional connection seed points were mostly selected based on relevant literature or experience, and the results were somewhat heterogeneous. This patent aims to analyze the changes in gray matter volume and abnormal functional connectivity in different brain regions in SD patients, and explore their correlation with clinical characteristics using a combination of structural and functional methods. Summary of the invention

[0003] The purpose of the present invention is to analyze patients with somatic depression by comparing the changes in brain gray matter volume and abnormal functional connectivity between patients with somatic depression (SD) and patients with non-somatic depression (NSD), combined with the correlation with clinical characteristics.

[0004] A method for identifying somatic depression using abnormal brain structure and function, wherein the related functions include the function of the supplementary motor area, the reduction of emotion regulation ability, and the disorder of attention and cognitive function. The specific method comprises the following steps:

[0005] 1. The present invention adopts a cross-sectional study, and the research subjects include patients with somatic depression, patients with non-somatic depression and a healthy control group, and collects general demographic and clinical data; patients with depression are selected for inclusion, and divided into a somatic depression group referred to as the SD group and a non-somatic depression group referred to as the NSD group according to whether the patient's health questionnaire somatic symptom group score is ≥10 points. At the same time, physical examination personnel with similar age, gender, and educational background to the patients and no relationship are recruited as a healthy control group referred to as the HC group;

[0006] Collection of general demographic and clinical data: A self-compiled questionnaire was used to collect the name, ethnicity, gender, years of education, age, family history of mental illness, etc. of the research subjects.

[0007] 2. Image Data Acquisition and Image Data Preprocessing

[0008] The VBM method was used to collect resting-state structural and functional magnetic resonance imaging data of the SD group, NSD group and control group using a German Siemens 3.0T according to unified parameters.

[0009] (3) Structural image scanning: The structural image scanning sequence is as follows: pulse repetition time is 1900ms, echo time is 2.48ms, scanning field size is 250mm×250mm, matrix size is 256×256, flip angle is set to 9°, layer thickness is set to 1mm, and 176-layer scanning is completed in about 4 minutes and 18 seconds. Functional image scanning parameters are as follows: pulse repetition time is 3000ms, echo time is 40ms, scanning field size is 240mm×240mm, and has a resolution of 3.75mm×3.75mm, matrix size is 64×64, flip angle is set to 90°, layer thickness is set to 4mm, and 135-layer scanning is completed in 6 minutes and 45 seconds.

[0010] (4) Functional imaging scan. Preprocessing of imaging data: First, check the image quality of each subject's T1 structural image one by one, and eliminate unclear images caused by various reasons. Secondly, the MATLAB 2013b toolbox was used to preprocess the image data. The procedure was as follows: 1) The “New Segment” image segmentation was performed on the original imaging data of each subject using the plug-in CAT12 in the SPM12 software package based on the MATLAB 2013b platform to obtain the T1 image; 2) The T1 image was corrected for time and head motion and a template was created using a high-order nonlinear segmentation registration method; 3) The Montreal Neurological Institute (MNI) standard space was registered with the template using the DARTEL algorithm; 4) The processed image was cut into gray matter, white matter, and cerebrospinal fluid signals to extract the total intracranial volume (TIV) and the gray matter volume of the whole brain; 5) The cut gray matter image was detected to ensure that the image quality was >80%; 6) The gray matter image was smoothed using an 8mm×8mm×8mm smoothing kernel (full width at half maximum), and the gray matter volume differences among the three groups of subjects were further statistically analyzed.

[0011] The image data were processed using RESTPlus of the MATLAB 2013b toolbox, including the following steps: format conversion, discarding the first five time points, time layer correction, head motion correction, excluding data with an average head motion greater than 2 mm or 2°, space standardization, using a Gaussian smoothing kernel of 6 mm × 6 mm × 6 mm full width at half maximum, removing linear trends, and finally performing a low-frequency filter of 0.01-0.08 Hz. According to the results of VBM analysis, the brain regions with significant differences in gray matter volume between the SD group and the NSD group were used as seed points. Seed point extraction: According to the number of the different brain regions in the third edition of the automated anatomical labeling atlas AAL3, the corresponding mask images of interest were extracted using marsbar software. Finally, the association between the region of interest and the time series of other brain regions was calculated using RESTPlus software, and the FC value after Fisher Z transformation was statistically analyzed.

[0012] 3. Statistical Analysis

[0013] Statistical analysis: SPSS 26.0 software was used to analyze general information and clinical data. The basic demographic data and the measurement data of the scale scores were described in a formal manner. The age and education level of the three groups were compared by one-way analysis of variance (ANOVA), and the gender differences were compared by chi-square test. The HAMD-24 scale scores and PHQ-15 scores of the SD group and the NSD group were analyzed by independent sample T test. If P < 0.05, it means that the difference is statistically significant. VBM was analyzed using the SPM12 software package, with age, gender, years of education and TIV as covariates for regression, Alphasim correction, brain region cluster level P < 0.05, voxel level P < 0.001, voxel value K > 111, and gray matter volume of the three groups was compared by ANOVA method, and then post hoc comparisons between groups were performed. The gray matter volume of the brain region with statistically significant differences between the groups was analyzed by Pearson correlation analysis with the scale score, and Bonferroni correction was performed (P < 0.05 / 2). The brain regions with different gray matter volumes between the SD and NSD groups were used as seed points for whole-brain functional connectivity. One-way ANOVA was used to compare the functional connectivity differences between the groups (Alphasim correction, brain region cluster level P<0.05, voxel level P<0.001, voxel value K>5), and then post hoc comparisons between the groups were performed. Pearson correlation analysis was performed between the functional connectivity values ​​of the different brain regions between the SD and NSD groups and the HAMD-24 scale scores and PHQ-15 scores (Bonferroni correction (P<0.05 / 2)), and P<0.05 was considered statistically significant.

[0014] 4. Conclusion: The smaller the gray matter volume of the right middle temporal gyrus, the more severe the patient's depression and the weaker the functional connection with the left supplementary motor area. The supplementary motor area is one of the brain areas responsible for controlling motor execution. Changes in the structure and function of the middle temporal gyrus are related to reduced emotional regulation ability, attention and cognitive dysfunction.

[0015] Furthermore, in step 1, the inclusion criteria for the depression group were: ① meeting the diagnostic criteria for depression in the Mini International Neuropsychiatric Interview (MINI) and ICD-10; ② being of Chinese Han nationality, right-handed, and having normal intelligence; ③ all patients had a HAMD-24 score of ≥21 points; ④ being aged 18-55 years; ⑤ signing the informed consent. Exclusion criteria: ① excluding the presence of other mental illnesses (such as manic / hypomanic episodes) other than depression in the past or currently; ② excluding secondary depressive episodes caused by organic mental illness (such as epilepsy) or other diseases; ③ having organic brain abnormalities such as craniocerebral trauma, cerebrovascular disease or other neurodegenerative diseases; ④ having a history of alcohol dependence or abuse of other psychoactive substances; ⑤ having a history of major physical diseases such as severe heart, liver, renal insufficiency, diabetes, etc.; ⑥ having recently undergone electroconvulsive therapy; ⑦ women who are pregnant, lactating or preparing to become pregnant; ⑧ having scars on the head; ⑨ people who cannot undergo magnetic resonance imaging.

[0016] Furthermore, in step 1, the inclusion criteria of the healthy control group are as follows: ① Chinese Han nationality, 18-55 years old, right-handed, normal intelligence; ② no history of mental illness in the past or at present and negative family history; ③ no history of major physical illness. Exclusion conditions: ① personal or family history of mental illness; ② the presence of other organic physical diseases, such as hypertension, diabetes, etc.; ③ people who cannot undergo MRI scanning; ④ women in pregnancy or lactation; ⑤ people with scars on the head.

[0017] In summary, the increase in gray matter volume of the right middle temporal gyrus in patients with somatic depression is related to the severity of patients' depressive mood and somatic symptoms, and the related supplementary motor area dysfunction may be the neuropathological basis of somatic depression.

[0018] The method provided by the present invention utilizes the observation of gray matter volume changes to judge the degree of depression of patients. Compared with traditional judgment methods, it is more objective and can timely detect the condition of patients with depression and strive to treat patients at the best time for treatment. Early identification of physical symptoms of depression is not only conducive to reducing the waste of medical resources, but also can alleviate the suffering of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is the difference in gray matter volume among the three groups of subjects;

[0020] Figure 2 For post hoc comparison of gray matter volume differences between groups;

[0021] Figure 3 The right MTG was used as the seed point to connect the brain regions with different whole-brain functional connections for the three groups of subjects;

[0022] Figure 4 The brain regions with different functional connectivity in the SD group compared with the NSD group;

[0023] Figure 5 The brain regions with different functional connectivity in the SD group compared with the HC group;

[0024] Figure 6 Pearson correlation analysis between HAMD-24 scale scores and gray matter volume of right middle temporal gyrus in SD group;

[0025] Figure 7 Pearson correlation between PHQ-15 scale score and gray matter volume of right middle temporal gyrus in SD group;

[0026] Figure 8 This was a correlation analysis between the PHQ-15 scale scores and the functional connectivity values ​​between the right middle temporal gyrus and the left supplementary motor area. DETAILED DESCRIPTION

[0027] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments:

[0028] Embodiment 1:

[0029] 1. This invention adopts cross-sectional research, and the research subjects include patients with somatic depression, patients with non-somatic depression and healthy control group, and collects general demographic and clinical data;

[0030] A total of 96 patients with depression were enrolled and divided into a somatic depression group (SD group, n=48) and a non-somatic depression group (NSD group, n=48) according to whether their scores on the Somatic Symptom Cluster Scale of the Health Questionnaire were ≥10 points. At the same time, 54 physical examination personnel with similar age, gender, and educational background to the patients and no relationship with them were recruited as a healthy control group (HC group, n=48).

[0031] Collection of general demographic and clinical data: A self-compiled questionnaire was used to collect the name, ethnicity, gender, years of education, age, family history of mental illness, etc. of the research subjects.

[0032] All the structural and functional images of the subjects met the requirements of statistical analysis, and there were no missing or unqualified images. A total of 150 subjects were included in the analysis. A total of 59 healthy controls were included, of which 5 were excluded due to excessive head movement during MRI scanning. Finally, a total of 54 healthy controls were included in the study. There was no significant difference in age, gender, and years of education among the three groups of subjects (P>0.05), see Table 1.

[0033] Table 1 Analysis of general demographic data and scale scores in the SD group, NSD group and control group

[0034]

[0035] Note: SD: patients with somatic depression, NSD: patients with non-somatic depression, HC: healthy control group, HAMD-24: Hamilton Depression Rating Scale, PHQ-15: Health Questionnaire Somatic Symptom Cluster Scale

[0036] 2. Image Data Collection

[0037] The VBM method was used to collect resting-state structural and functional magnetic resonance imaging data of the SD group, NSD group and control group using a German Siemens 3.0T according to unified parameters.

[0038] The scanning sequence of structural imaging was as follows: pulse repetition time of 1900ms, echo time of 2.48ms, scanning field of view of 250mm×250mm, matrix size of 256×256, flip angle of 9°, slice thickness of 1mm, and 176 slices were scanned in about 4 minutes and 18 seconds. (2) The scanning parameters of functional imaging were as follows: pulse repetition time of 3000ms, echo time of 40ms, scanning field of view of 240mm×240mm, with a resolution of 3.75mm×3.75mm, matrix size of 64×64, flip angle of 90°, slice thickness of 4mm, and 135 slices were scanned in 6 minutes and 45 seconds.

[0039] ANOVA was performed with age, gender, education level and TIV as regression covariates. There were significant differences in the gray matter volume of the right middle temporal gyrus (MTG.R) among the three groups of subjects (AlphaSim correction, voxel level P<0.001, cluster level P<0.05). The results of inter-group post hoc analysis showed that compared with the NSD group, the gray matter volume of the right middle temporal gyrus of the SD group increased; compared with the healthy control group, the right gray matter volume of the NSD group decreased (AlphaSim correction, brain region cluster level P<0.05, voxel level P<0.001). See Table 2 for details. Figure 1 , Figure 2 . Figure 1 The gray matter volume differences among the three groups were compared by one-way ANOVA: A: right middle temporal gyrus, the color scale on the right represents the F value: Alphasim correction, voxel level P<0.001, cluster level P<0.05, voxel value K>111. Figure 2For the post hoc comparison of gray matter volume differences between groups, B: right middle temporal gyrus; the right color scale represents the t value (Alphasim correction, voxel P<0.001, cluster level P<0.05, voxel value K>315); it can be seen that the gray matter volume of the SD group is greater than that of the NSD group; C: right middle temporal gyrus; the right color scale represents the t value (Alphasim correction, voxel P<0.001, cluster level P<0.05, voxel value K>339), it can be seen that the gray matter volume of the SD group is greater than that of the NSD group.

[0040] Table 2 Differences in brain gray matter volume among the three groups of subjects

[0041]

[0042] Note: SD: patients with somatic depression, NSD: patients with non-somatic depression, HC: healthy controls; MTG: middle temporal gyrus; AAL3: automated anatomical atlas, third edition; Peak point: the point with the most significant difference in gray matter volume; MNI: Montreal coordinates; voxel: continuous voxel value

[0043] The imaging data were processed using RESTPlus of the MATLAB 2013b toolbox, including the following steps: format conversion, discarding the first five time points, time layer correction, head motion correction (eliminating data with an average head motion greater than 2 mm or 2°), spatial standardization, using a 6mm×6mm×6mm full width at half maximum Gaussian smoothing kernel, removing linear trends, and finally performing a 0.01-0.08Hz low-frequency filter. According to the results of VBM analysis, the brain regions with significant differences in gray matter volume between the SD group and the NSD group were used as seed points (seed point extraction: according to the number of the different brain regions in the third edition of the automated anatomical labeling atlas AAL3, the corresponding mask images of interest were extracted using marsbar software). Finally, the association between the region of interest and the time series of other brain regions was calculated using RESTPlus software, and the FC value after Fisher Z transformation was statistically analyzed.

[0044] Using the right middle temporal gyrus (MTG.R) of the brain region with different gray matter volumes as the seed point (extracted according to the number of the right middle temporal gyrus in the Automated Anatomical Labeling version 3 (AAL3)), functional connectivity was performed with the whole brain. Brain regions with different functional connectivity in the three groups of subjects: the right middle temporal gyrus was connected to the left orbital frontal cortex, the left supplementary motor area, and the right paracentral lobule (AlphaSim correction, cluster-level P < 0.05 in the brain region, voxel-level P < 0.001, voxel K > 5). Compared with the NSD group, the functional connectivity between the right middle temporal gyrus and the left supplementary motor area in the SD group was weakened (AlphaSim correction, cluster-level P < 0.05 in the brain region, voxel-level P < 0.001, voxel K > 7); compared with the HC group, the functional connectivity of the right MTG in the SD group decreased with the left inferior frontal gyrus of the orbital part, the left supplementary motor area, and the right paracentral lobule (AlphaSim correction, cluster-level P < 0.05 in the brain region, voxel-level P < 0.001, voxel K > 20). See Table 3 for details. Figure 3 , Figure 4 , Figure 5 。 Figure 3 For the brain regions with different whole-brain functional connectivity with the right MTG as the seed point in the three groups of subjects, one-way ANOVA was performed on the brain regions with different whole-brain functional connectivity with the right MTG as the seed point: A left inferior frontal gyrus of the orbital part; B left supplementary motor area; C right paracentral lobule. The right color scale represents the F value: Alphasim correction, voxel P < 0.001, cluster-level P < 0.05 in the brain region, voxel value K > 5. Figure 4 For the brain regions with different functional connectivity in the SD group compared with the NSD group, it can be seen from the figure that the functional connectivity in the SD group < the functional connectivity in the NSD group in the brain regions: A left supplementary motor area; the right color scale represents the t value (Alphasim correction, voxel P < 0.001, cluster-level P < 0.05 in the brain region, voxel value K > 7). Figure 5 For the brain regions with different functional connectivity in the SD group compared with the HC group, where A is the left inferior frontal gyrus of the orbital part; B is the left supplementary motor area; C is the right paracentral lobule; the right color scale represents the t value (Alphasim correction, voxel P < 0.001, cluster-level P < 0.05 in the brain region, voxel value K > 20), it can be obtained that the functional connectivity in the SD group < the functional connectivity in the HC group in the brain regions.

[0045] Table 3 Brain regions with different whole-brain functional connectivity with the right MTG as the seed point in the three groups of subjects

[0046]

[0047] Note: SD: Somatic depression patients, NSD: Non-somatic depression patients, HC: Healthy control group; AAL3: Automated Anatomical Labeling version 3; Peak point: The point with the most significant difference in functional connectivity; MNI: Montreal Neurological Institute space coordinate system; voxel: Continuous voxel value

[0048] 3. Statistical Analysis

[0049] Statistical analysis: SPSS 26.0 software was used to analyze general information and clinical data. The basic demographic data and the quantitative data of the scale scores were described in a formal manner. The age and education level of the three groups were compared by one-way analysis of variance (ANOVA), and the gender differences were compared by chi-square test. The HAMD-24 scale scores and PHQ-15 scores of the SD group and the NSD group were analyzed by independent sample T test. If P < 0.05, the difference was statistically significant. VBM was analyzed using the SPM12 software package, with age, gender, years of education and TIV as covariates (Alphasim correction, brain region cluster level P < 0.05, voxel level P < 0.001, voxel value K > 111), and the gray matter volume of the three groups was compared by ANOVA method, and then post hoc comparisons between the groups were performed. The gray matter volume of the brain region with statistically significant differences between the groups was subjected to Pearson correlation analysis with the scale score, and Bonferroni correction was performed (P < 0.05 / 2). The brain regions with different gray matter volumes between the SD and NSD groups were used as seed points for whole-brain functional connectivity. One-way ANOVA was used to compare the functional connectivity differences between the groups (Alphasim correction, brain region cluster level P<0.05, voxel level P<0.001, voxel value K>5), and then post hoc comparisons between the groups were performed. Pearson correlation analysis was performed between the functional connectivity values ​​of the different brain regions between the SD and NSD groups and the HAMD-24 scale scores and PHQ-15 scores (Bonferroni correction (P<0.05 / 2)), and P<0.05 was considered statistically significant.

[0050] The correlation analysis between gray matter volume and HAMD-24 and PHQ-15 scores in the SD group, and the correlation analysis between gray matter volume difference brain regions in the SD group and the severity of depression and somatic symptoms in patients found that the increase in gray matter volume in the right middle temporal gyrus of SD patients was negatively correlated with depression in the HAMD-24 scale (r=-0.571, P<0.001) and somatic symptoms in the PHQ-15 scale (r=-0,489, P<0.001) (Bonferroni multiple comparison correction, P<0.05 / 2), see Figure 6 HAMD-24 scale score and right mid-question GMV scatter plot, Figure 7 PHQ-15 scale score and right middle temporal gyrus GMV scatter plot.

[0051] Correlation analysis between the functional connectivity difference brain regions in the SD group and the severity of patients' depressive somatic symptoms found that the functional connectivity values ​​between the right middle temporal gyrus and the left supplementary motor area of ​​SD patients were negatively correlated with the somatic symptoms in the PHQ-15 scale (r=-0.532, P<0.001) (Bonferroni multiple comparison correction, P<0.05 / 2), but not with the depressive mood in the HAMD-24 scale (Bonferroni multiple comparison correction, P>0.05 / 2), see Figure 8 PHQ-15 scale score and right middle temporal gyrus GMV scatter plot.

[0052] Conclusion: Compared with the NSD group, the gray matter volume of the right middle temporal gyrus in the SD group was increased, and the functional connectivity between the right middle temporal gyrus and the left supplementary motor area was weakened, which was negatively correlated with the somatic symptoms of depression.

[0053] The present invention obtains the result that the more severe the somatic symptoms of the SD group patients are, the smaller the gray matter volume of the right middle temporal gyrus is, the more severe the depression of the patients is, and the weaker the functional connection with the left supplementary motor area is. The supplementary motor area is one of the brain areas responsible for controlling the execution of movement. The changes in the structure and function of the middle temporal gyrus are related to the reduction of emotion regulation ability, attention and cognitive function disorders. The right middle temporal gyrus and the left supplementary motor area may be connected to a common neural network, such as the cognitive control network and the emotion regulation network, which play a key role in the occurrence and regulation of depression. The functional connection between the right middle temporal gyrus and the left supplementary motor area is weakened, resulting in motor regulation disorders in patients with depression, which then causes the patients to have somatic symptoms. Therefore, by affecting the functional connection of the neural network, the abnormal connection between the right middle temporal gyrus and the left supplementary motor area may be the neuropathological basis for the occurrence and development of somatic depression.

[0054] In addition, based on the increase in gray matter volume in the right middle temporal gyrus, in this embodiment, compared with the healthy control group, the functional connectivity between the right middle temporal gyrus and the right paracentral lobule, the left orbital inferior frontal gyrus and the left supplementary motor area in the SD group patients decreased. The temporal lobe belongs to the limbic system, which can promote emotional empathy and enhance emotional stability. In pain processing, it is mainly related to the transmission of emotional tone to short-term memory. The orbital frontal cortex is the link between sensory integration, visceral response monitoring and mapping, and internal state. The excellent execution ability of the orbital frontal cortex enables it to control and limit inappropriate actions and emotional responses. For example, visceral sensations are misprocessed, which can lead to increased gastrointestinal sensitivity in patients with depression.

[0055] In summary: The abnormal functional connectivity between the right middle temporal gyrus and the left supplementary motor area in the SD group is correlated with clinical somatic symptoms, among which the change in gray matter volume of the right middle temporal gyrus is a potential biological marker for the severity of somatic depression.

[0056] This specific embodiment is merely an explanation of the present invention and is not a limitation of the present invention. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed. However, as long as they are within the scope of the claims of the present invention, they are protected by the patent law.

Claims

1. A method for identifying somatic depression using abnormal brain structure and function, characterized in that: The following steps are involved: First, the study was cross-sectional, and the subjects included patients with somatic depression, patients with non-somatic depression, and healthy controls, and general demographic and clinical data were collected; Patients with depression were selected for enrollment. According to whether the score of the physical symptom cluster scale of the patient's health questionnaire was ≥10 points, they were divided into a somatic depression group (SD group) and a non-somatic depression group (NSD group). At the same time, physical examination personnel with similar age, gender, and educational background to the patients and no relationship were recruited as a healthy control group (HC group).

2. Image Data Acquisition and Image Data Preprocessing The VBM method was used to collect resting-state structural and functional magnetic resonance imaging data of the SD group, NSD group and control group using a German Siemens 3.0T according to unified parameters; (1) Structural image scanning; (2) Functional imaging scan; Preprocessing of image data: First, the image quality of each subject's T1 structural image was checked one by one, and unclear images caused by various reasons were eliminated. Secondly, the image data were preprocessed using the MATLAB 2013b toolbox; RESTPlus of the MATLAB2013b toolbox was used to process the image data; 3. Statistical Analysis Statistical analysis: SPSS 26.0 software was used to analyze general information and clinical data. Form description Basic demographic data and scale scores were normally distributed. One-way ANOVA analysis was used to compare the age and education level of the three groups of subjects, and the chi-square test was used to compare gender differences; 4. Conclusion: The severity of the patient's condition is judged based on the volume of the gray matter in the right middle temporal gyrus. That is, the smaller the gray matter volume in the right middle temporal gyrus, the more severe the patient's depression is, and the weaker the functional connection with the left supplementary motor area. The supplementary motor area is one of the brain areas responsible for controlling motor execution. Changes in the structure and function of the middle temporal gyrus are related to reduced emotion regulation ability, attention and cognitive dysfunction.

2. The method of claim 1, wherein the method comprises: In step 2, the structural image scanning sequence is as follows: the pulse repetition time is 1900 ms, the echo time is 2.48 ms, the scanning field size is 250 mm×250 mm, the matrix size is 256×256, the flip angle is set to 9°, the layer thickness is set to 1 mm, and the 176-layer scan is completed in about 4 minutes and 18 seconds.

3. The method of claim 1, wherein the method comprises: In the step 2, the functional imaging scanning parameters are as follows: the pulse repetition time is 3000ms, the echo time is 40ms, the scanning field of view is 240mm×240mm, and the resolution is 3.75mm×3.75mm, the matrix size is 64×64, the flip angle is set to 90°, the layer thickness is set to 4mm, and the scanning of 135 layers is completed within 6 minutes and 45 seconds.

4. The method of claim 1, wherein the method comprises: The image data were preprocessed using the MATLAB 2013b toolbox, and the procedure was as follows: 1) The "NewSegment" image segmentation was performed on the original imaging data of each subject using the plug-in CAT12 in the SPM12 software package based on the MATLAB 2013b platform to obtain a T1 image; 2) The T1 image was corrected for time and head motion and a template was created using a high-order nonlinear segmentation registration method; 3) The Montreal Neuroscience Institute standard space was registered with the template using the DARTEL algorithm; 4) The processed images are cut into gray matter, white matter, and cerebrospinal fluid signals to extract the total intracranial volume (TIV) and the whole brain gray matter volume; 5) The cut gray matter images were inspected to ensure that the image quality was >80%; 6) The gray matter images were smoothed using an 8 mm × 8 mm × 8 mm smoothing kernel full width at half maximum, and the gray matter volume differences among the three groups of subjects were further statistically analyzed.

5. The method of claim 1, wherein the method comprises: The imaging data were processed using RESTPlus of the MATLAB 2013b toolbox, including the following steps: format conversion, discarding the first five time points, time layer correction, head motion correction, excluding data with an average head motion greater than 2 mm or 2°, standardizing the space, using a Gaussian smoothing kernel of 6 mm×6 mm×6 mm half-height full width, removing linear trends, and finally performing a 0.01-0.08 Hz low-frequency filter. According to the results of VBM analysis, the brain areas with significant differences in gray matter volume between the SD group and the NSD group were used as seed points. Finally, the association between the region of interest and the time series of other brain areas was calculated using RESTPlus software, and the FC value after Fisher Z transformation was statistically analyzed.

6. The method of claim 1, wherein: In the step 2, the seed points are extracted according to the numbers of the differential brain regions in the third edition of the automated anatomical labeling atlas AAL3, and the corresponding mask images of interest are extracted using the Marsbar software.

7. The method of claim 1, wherein the method comprises: In the step 3, the statistical content specifically includes analyzing the HAMD-24 scale scores and PHQ-15 scores of the SD group and the NSD group by independent sample T test. If P<0.05, it means that the difference is statistically significant. VBM is analyzed using SPM12 software package, with age, gender, years of education and TIV as covariates for regression, Alphasim correction, brain region cluster level P<0.05, voxel level P<0.001, voxel value K>111, and gray matter volumes of the three groups are compared by ANOVA method, and then post hoc comparisons between groups are performed. Pearson correlation analysis was performed on the gray matter volume of brain regions with statistically significant differences between the groups and the scale scores, and Bonferroni correction was performed. P<0.05 / 2. The brain regions with different gray matter volumes between the SD group and the NSD group were used as seed points for whole-brain functional connectivity. One-way analysis of variance was used to compare the differences in functional connectivity between the groups, and post hoc comparisons were performed between the groups. Pearson correlation analysis was performed on the functional connectivity values ​​of the different brain regions between the SD group and the NSD group and the HAMD-24 scale scores and PHQ-15 scores. P<0.05 was considered statistically significant.

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