CN113693584B - Method for selecting predicted variables for symptoms of depression, computer device, and storage medium - Google Patents
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Abstract
The invention belongs to the technical field of depression diagnosis, and particularly relates to a method for selecting a predicted variable of depression symptoms, computer equipment and a storage medium. The method of the invention comprises the following steps: investigation is carried out on patients with depression and normal control, and general data, chinese miltonian depression scale scores and resting state functional magnetic resonance imaging scanning data are collected; collecting the data again after the depression patient receives treatment and the clinical symptoms of the individual are relieved; processing the resting state functional magnetic resonance imaging scanning data to obtain a DC brain map; according to the obtained DC brain map, the changes of brain functional activities before and after treatment of a depressive patient are analyzed, and variables which can represent alleviation of depressive symptoms are found. The invention further provides computer equipment for realizing the method. The invention can provide objective support basis for early clinical curative effect prediction of depression, reduce social burden of diseases and has good application prospect.
Description
Technical Field
The invention belongs to the technical field of depression diagnosis, and particularly relates to a method for selecting a predicted variable of depression symptoms, computer equipment and a storage medium.
Background
Depression is the most common depressive disorder, characterized by a marked and persistent fall in mood, the major clinical feature, being the major type of mood disorder. Each episode lasts at least 2 weeks, longer or even years, most cases have a tendency to recur, most of each episode can be alleviated, some can have residual symptoms or be converted to chronic.
The treatment of depression mainly comprises methods of drug treatment, psychological treatment, physical treatment and the like. Early prediction is carried out on the treatment effect in advance after treatment, which is helpful for doctors to select and adjust the treatment strategy, improves the treatment effect and reduces the social burden of diseases.
Resting state functional magnetic resonance imaging (rs-fMRI) is a method used to study the intrinsic functional connections or networks of the brain. In the last decade, studies of depressed patients using resting state functional magnetic resonance imaging have found that abnormalities in some brain regions are important in the mood management and regulation of depression, and that abnormal functional connections in the brain may be regulated by antidepressant therapy.
In the prior art, research on the relationship between depression and brain region abnormalities using resting state functional magnetic resonance imaging has mostly employed seed-based analysis (JAMA psychiatry 70,373-382;Neuropsychopharmacology:official publication of the American College of Neuropsychopharmacology 30,1334-1344.), i.e. a selection of several regions or networks of interest in advance for further research. However, for depression, the neural targets at which abnormalities occur are not known. Moreover, due to the complex etiology of depression, these abnormal neural targets may vary from individual to individual or over the course of treatment. Therefore, it is difficult to accurately correlate the depression with brain region abnormalities by the above-mentioned methods in the prior art, and thus it is impossible to predict the later development of the depression symptoms by means of resting state functional magnetic resonance imaging.
Disclosure of Invention
Aiming at the difficulties in the prior art, the invention provides a method for selecting a predicted variable of symptoms of depression, computer equipment and a storage medium, and aims to early predict the treatment effect and the disease development of a depression patient.
A method of selecting a predicted variable for symptoms of depression, comprising the steps of:
step 1, examining depression patients and normal controls, and collecting general data, chinese miltonian depression scale scores and resting state functional magnetic resonance imaging scanning data; the general materials include age, gender, and educational years;
step 2, after the depression patient receives treatment and the clinical symptoms of the individual are relieved, acquiring the score of the hamilton depression scale and resting state functional magnetic resonance imaging scanning data for the depression patient and the normal control again;
step 3, processing the resting state functional magnetic resonance imaging scanning data acquired in the step 1 and the step 2 to obtain a DC brain map, obtaining a baseline DC brain map through the resting state functional magnetic resonance imaging scanning data acquired in the step 1, and obtaining a follow-up DC brain map through the resting state functional magnetic resonance imaging scanning data acquired in the step 2;
and 4, analyzing the change of the brain function activities before and after treatment of the patient with depression according to the DC brain map obtained in the step 3, and finding out the variable capable of representing the symptom relief of depression.
Preferably, in step 2, the method for confirming the clinical symptom relief of the individual of the depression patient is that the depression patient is evaluated by using a hamilton depression scale after receiving the treatment, and the result that the score of the hamilton depression scale is less than 7 is judged to be the clinical symptom relief of the individual.
Preferably, in step 2, patients with depression are assessed using the hamilton depression scale at week 8, week 24 and week 48, respectively, after receiving treatment.
Preferably, in step 3, the step of obtaining the DC brain map using the resting-state functional magnetic resonance imaging scan data includes:
step 3A, preprocessing resting state functional magnetic resonance imaging scanning data;
step 3B, degree center index analysis: calculating pearson correlation r between blood oxygen dependent signal time sequences of each pair of voxels by using the preprocessed resting state functional magnetic resonance imaging scanning data to obtain a functional connection matrix covering the whole brain;
step 3C, measuring the weight of the functional connection in the functional connection matrix by adopting a threshold method, and converting the functional connection matrix into a binary matrix according to a threshold value of r > 0.25;
step 3D, calculating the connectivity D of each voxel according to the binary matrix obtained in the step 3C;
and 3E, performing Z conversion on the connectivity D of each voxel obtained in the step 3D to obtain a DC brain map.
Preferably, in step 3A, the preprocessing includes: removing at least one of the first 10 time point data, layer time correction, head motion correction estimation, linear trend removal in the signal, and 0.01-0.08Hz low pass filtering;
and/or, in step 3E, the obtained DC brain map is standardized, wherein the standardized method is to carry out 6mm full-width half-height Gaussian smoothing on the DC brain map.
Preferably, the step 4 specifically includes the following steps:
step 4A, counting the DC brain map obtained in the step 3, and taking a brain area with the specific function changed to the time point of the patient suffering from depression as a central node;
step 4B, analyzing the functional connection indexes of the central node and each region of the brain, and analyzing the change of the functional connection indexes in a baseline DC brain map and a follow-up DC brain map of a patient suffering from depression;
step 4C, calculating HAMD (human immunodeficiency virus) percent reduction before and after treatment of the patient suffering from depression, wherein the calculation formula is as follows:
HAMD decrease rate= [ baseline score-follow-up score ]/baseline score x 100%;
wherein the baseline score is a HAMD score obtained by evaluating the hamilton depression scale in step 1, and the follow-up score is a HAMD score obtained by evaluating the hamilton depression scale in step 2;
and 4D, performing linear regression model analysis on the change of the functional connection index obtained in the step 4B and the HAMD reduction rate obtained in the step 4C to obtain the functional connection index related to the HAMD reduction rate, and obtaining a variable capable of representing depression symptom relief.
Preferably, the method for determining brain regions with time-specific functional changes in the patients suffering from depression comprises the following steps:
step 4Aa, counting the DC brain map obtained in the step 3 by adopting a linear model, wherein the linear model takes diagnosis X time as an independent variable and general data acquired in the step 1 as a covariate; in the independent variables, the diagnosis refers to a depression patient or a normal control, and the time point refers to a baseline DC brain map or a follow-up DC brain map;
step 4Ab, performing simple effect analysis on the region where significant interaction is found in the statistical result of step 4Aa, wherein the simple effect analysis comprises: baseline DC map for depression patient vs. visited DC map for depression patient, baseline DC map for normal control vs. visited DC map for normal control, baseline DC map for depression patient vs. normal control, visited DC map for normal control vs. visited DC map for depression patient; baseline DC map of normal control vs the area of the normal control following DC map showing significant functional changes was identified as the area of time-point change specificity; the areas where the baseline DC map vs. the follow-up DC map of the depressed patient showed significant functional changes were identified as brain areas with altered time-specific function of the depressed patient, excluding the areas with altered specificity of the time points.
The invention also provides a computer device for selecting the predicted variables of the symptoms of depression, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the method for selecting the predicted variables of the symptoms of depression.
The present invention also provides a computer-readable storage medium having stored thereon a computer program for implementing the above-described method of selecting a predicted variable for symptoms of depression.
In the present invention, the "predicted depression symptom variable" or "variable" refers to at least one index selected from the functional connection indexes of all central nodes and each region of the brain in the DC brain map, and the selected index has a correlation with the condition of the depression patient. After obtaining the "predicted depression symptom variable" or "variable", a researcher (or doctor) can further analyze the variable in a subsequent resting state functional magnetic resonance imaging test, so as to predict and analyze the subsequent treatment effect and disease development of a depression patient. In the present invention, the term "significant" means that the p-value is less than 0.05 by statistical analysis, for example: by "significant interactions" is meant interactions with p values less than 0.05.
By adopting the technical scheme, the brain can be regarded as a huge and complete network through analysis by using the brain function connection index of centrality (DC), so that researchers (or doctors) can obtain variables capable of predicting the individual depression symptom development condition of a depression patient under the condition that the prior interested area is not selected. In the subsequent diagnosis and treatment process of the depressed patient, the corresponding variables in other detection data (such as later resting state functional magnetic resonance imaging scanning data) of the depressed patient are further analyzed, so that the treatment effect and early prediction of the disease development in the remission stage can be realized. The invention has good application prospect in diagnosis and treatment of depression patients.
It should be apparent that, in light of the foregoing, various modifications, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
The above-described aspects of the present invention will be described in further detail below with reference to specific embodiments in the form of examples. It should not be understood that the scope of the above subject matter of the present invention is limited to the following examples only. All techniques implemented based on the above description of the invention are within the scope of the invention.
Drawings
FIG. 1 is a schematic flow chart of embodiment 1 of the present invention;
FIG. 2 shows the results of the isocentric analysis and the time-point interaction analysis of example 1 of the present invention.
Detailed Description
It should be noted that, in the embodiments, algorithms of steps such as data acquisition, transmission, storage, and processing, which are not specifically described, and hardware structures, circuit connections, and the like, which are not specifically described may be implemented through the disclosure of the prior art.
Example 1
The present embodiment provides a method for selecting a predicted variable for symptoms of depression, and a computer device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method for selecting a predicted variable for symptoms of depression when executing the program.
The flow of the selection method of the predicted variables of the symptoms of depression is shown in fig. 1, and specifically comprises the following steps:
step 1, examining depression patients and normal controls, and collecting general data, chinese miltonian depression scale scores and resting state functional magnetic resonance imaging scanning data; the general materials include age, gender, and educational years;
step 2, after the depression patient receives treatment and the clinical symptoms of the individual are relieved, acquiring the score of the hamilton depression scale and resting state functional magnetic resonance imaging scanning data for the depression patient and the normal control again; the method for confirming the individual clinical symptom relief of the depression patient is to evaluate the treatment of the depression patient by using the hamilton depression scale at the 8 th week, 24 th week and 48 th week, and judge the individual clinical symptom relief when the HAMD score is less than 7.
Step 3, processing the resting state functional magnetic resonance imaging scanning data acquired in the step 1 and the step 2 to obtain a DC brain map, obtaining a baseline DC brain map through the resting state functional magnetic resonance imaging scanning data acquired in the step 1, and obtaining a follow-up DC brain map through the resting state functional magnetic resonance imaging scanning data acquired in the step 2;
the step of obtaining the DC brain map by utilizing resting state functional magnetic resonance imaging scanning data comprises the following steps of:
step 3A, preprocessing resting state functional magnetic resonance imaging scanning data; the pretreatment comprises the following steps: removing at least one of the first 10 time point data, layer time correction, head motion correction estimation, linear trend removal in the signal, and 0.01-0.08Hz low pass filtering;
step 3B, degree center index analysis: calculating pearson correlation r between blood oxygen dependence (blood oxygen level dependent, BOLD) signal time sequences of each pair of voxels by using the preprocessed resting state functional magnetic resonance imaging scanning data to obtain a functional connection matrix covering the whole brain;
the element in the functional connection matrix is r (i, j), and r (i, j) >0.25 indicates that a functional connection exists between voxel i and voxel j.
Step 3C, measuring the weight of the functional connection in the functional connection matrix by adopting a threshold method, and converting the functional connection matrix into a binary matrix according to a threshold value of r > 0.25;
the element in the binary matrix is d ij When r (i, j)>At 0.25, d ij =1; d when r (i, j) is less than or equal to 0.25 ij ＝0。
Step 3D, calculating the connectivity D of each voxel according to the binary matrix obtained in the step 3C;
for any voxel i, the connectivity is the total number of voxels (j) to which there are adjacent functional connections, and the calculation formula of connectivity D is as follows:
D i ＝Σd ij
where j=1, 2, … N, i+.j, N is the total number of voxels.
And 3E, performing Z conversion on the connectivity D of each voxel obtained in the step 3D to obtain a DC brain map, and standardizing the obtained DC brain map, wherein the standardized method is to perform 6mm full-width half-height Gaussian smoothing on the DC brain map.
Step 4, analyzing the change of brain function activities before and after treatment according to the pre-treatment DC brain map and the post-treatment DC brain map, and finding out variables capable of representing symptom relief of depression, wherein the specific steps are as follows:
step 4A, counting the DC brain map obtained in the step 3, and taking a brain area with the specific function changed to the time point of the patient suffering from depression as a central node;
the method for determining the brain area with the time-point specific function change of the depression patient comprises the following steps:
step 4Aa, counting the DC brain map obtained in the step 3 by adopting a linear model in SPM8 software, wherein the linear model takes diagnosis X time as an independent variable and general data acquired in the step 1 as a covariate; in the independent variables, the diagnosis refers to a depression patient or a normal control, and the time point refers to a baseline DC brain map or a follow-up DC brain map;
step 4Ab, performing simple effect analysis on the region where significant interaction is found in the statistical result of step 4Aa, wherein the simple effect analysis comprises: baseline DC map for depression patient vs. visited DC map for depression patient, baseline DC map for normal control vs. visited DC map for normal control, baseline DC map for depression patient vs. normal control, visited DC map for normal control vs. visited DC map for depression patient; baseline DC map of normal control vs the area of the normal control following DC map showing significant functional changes was identified as the area of time-point change specificity; the areas where the baseline DC map vs. the follow-up DC map of the depressed patient showed significant functional changes were identified as brain areas with altered time-specific function of the depressed patient, excluding the areas with altered specificity of the time points.
Step 4B, analyzing the functional connection indexes of the central node and each region of the brain, and analyzing the change of the functional connection indexes in a baseline DC brain map and a follow-up DC brain map of a patient suffering from depression;
step 4C, calculating HAMD (human immunodeficiency virus) percent reduction before and after treatment of the patient suffering from depression, wherein the calculation formula is as follows:
HAMD decrease rate= [ baseline score-follow-up score ]/baseline score x 100%;
wherein the baseline score is a HAMD score obtained by evaluating the hamilton depression scale in step 1, and the follow-up score is a HAMD score obtained by evaluating the hamilton depression scale in step 2;
and 4D, performing linear regression model analysis on the change of the functional connection index obtained in the step 4B and the HAMD reduction rate obtained in the step 4C to obtain the functional connection index related to the HAMD reduction rate, and obtaining a variable capable of representing depression symptom relief. The term "correlated with HAMD reduction" refers to a relationship with a change in a dependent variable (HAMD reduction) in a regression analysis that is statistically significant.
An example of the variable selection of a depression patient by the above method is shown in fig. 2, and fig. 2 is a diagram showing interaction analysis at diagnosis x time in step 4Aa, wherein (a) is left temporal gyrus, (B) is right cerebellar feet, (C) is left lingual gyrus, and (D) is left dorsal medial forehead She Hui. L represents the left hemisphere and R represents the right hemisphere.
Further regression analysis found that DC changes in the D brain region shown in fig. 2, i.e. left dorsal medial forehead She Hui, correlated with the extent of clinical symptom relief in depressed patients (b=3.404, p < 0.001).
According to the embodiment, the invention aims at individuals with depression, and the variables which can accurately predict the treatment effect of depression and the disease development in remission stage can be obtained under the condition that the prior interested region does not need to be selected. After obtaining the variable, a researcher (or doctor) further analyzes the corresponding variable in other detection data (such as later resting state functional magnetic resonance imaging scanning data) of the depression patient, and can predict the later treatment effect and whether the depression can be relieved for the depression patient. The invention can provide objective support basis for early clinical curative effect prediction of depression, reduce social burden of diseases and has good application prospect.
Claims (7)
1. A method of selecting a predicted variable for symptoms of depression, comprising the steps of:
step 1, baseline evaluation: investigation is carried out on patients with depression and normal control, and general data, chinese miltonian depression scale scores and resting state functional magnetic resonance imaging scanning data are collected; the general materials include age, gender, and educational years;
step 2, follow-up evaluation: after the depression patient receives the treatment and the clinical symptoms of the individual are relieved, acquiring the score of the Hamiltonian depression scale and resting state functional magnetic resonance imaging scanning data for the depression patient and the normal control again;
step 3, processing the resting state functional magnetic resonance imaging scanning data acquired in the step 1 and the step 2 to obtain a DC brain map, obtaining a baseline DC brain map through the resting state functional magnetic resonance imaging scanning data acquired in the step 1, and obtaining a follow-up DC brain map through the resting state functional magnetic resonance imaging scanning data acquired in the step 2;
step 4, analyzing the change of brain function activities before and after treatment of the patient with depression according to the DC brain map obtained in the step 3, and finding out variables capable of representing symptom relief of depression;
the step 4 specifically comprises the following steps:
step 4A, counting the DC brain map obtained in the step 3, and taking a brain area with the specific function changed to the time point of the patient suffering from depression as a central node;
step 4B, analyzing the functional connection indexes of the central node and each region of the brain, and analyzing the change of the functional connection indexes in a baseline DC brain map and a follow-up DC brain map of a patient suffering from depression;
step 4C, calculating HAMD (human immunodeficiency virus) percent reduction before and after treatment of the patient suffering from depression, wherein the calculation formula is as follows:
HAMD decrease rate= [ baseline score-follow-up score ]/baseline score x 100%;
wherein the baseline score is a HAMD score obtained by evaluating the hamilton depression scale in step 1, and the follow-up score is a HAMD score obtained by evaluating the hamilton depression scale in step 2;
step 4D, performing linear regression model analysis on the change of the functional connection index obtained in the step 4B and the HAMD reduction rate obtained in the step 4C to obtain the functional connection index related to the HAMD reduction rate, namely obtaining a variable capable of representing depression symptom relief;
the method for determining the brain area with the time-point specific function change of the depression patient comprises the following steps:
step 4Aa, counting the DC brain map obtained in the step 3 by adopting a linear model, wherein the linear model takes diagnosis X time as an independent variable and general data acquired in the step 1 as a covariate; in the independent variables, the diagnosis refers to a depression patient or a normal control, and the time point refers to a baseline DC brain map or a follow-up DC brain map;
step 4Ab, performing simple effect analysis on the region where significant interaction is found in the statistical result of step 4Aa, wherein the simple effect analysis comprises: baseline DC map for depression patient vs. visited DC map for depression patient, baseline DC map for normal control vs. visited DC map for normal control, baseline DC map for depression patient vs. normal control, visited DC map for normal control vs. visited DC map for depression patient; baseline DC map of normal control vs the area of the normal control following DC map showing significant functional changes was identified as the area of time-point change specificity; the areas where the baseline DC map vs. the follow-up DC map of the depressed patient showed significant functional changes were identified as brain areas with altered time-specific function of the depressed patient, excluding the areas with altered specificity of the time points.
2. A method of selecting a predicted depression symptom variable according to claim 1, wherein: in step 2, the method for confirming the individual clinical symptom relief of the depression patient is that the depression patient is evaluated by using a Hamiltonian depression scale after being treated, and the individual clinical symptom relief is judged if the Hamiltonian depression scale score is less than 7.
3. A method of selecting a predicted depression symptom variable according to claim 2, wherein: in step 2, depressed patients were evaluated using the hamilton depression scale at week 8, week 24 and week 48, respectively, after receiving treatment.
4. A method of selecting a predicted depression symptom variable according to claim 1, wherein: in step 3, the step of obtaining a DC brain map using resting state functional magnetic resonance imaging scan data includes:
step 3A, preprocessing resting state functional magnetic resonance imaging scanning data;
step 3B, degree center index analysis: calculating pearson correlation r between blood oxygen dependent signal time sequences of each pair of voxels by using the preprocessed resting state functional magnetic resonance imaging scanning data to obtain a functional connection matrix covering the whole brain;
step 3C, measuring the weight of the functional connection in the functional connection matrix by adopting a threshold method, and converting the functional connection matrix into a binary matrix according to a threshold value of r > 0.25;
step 3D, calculating the connectivity D of each voxel according to the binary matrix obtained in the step 3C;
and 3E, performing Z conversion on the connectivity D of each voxel obtained in the step 3D to obtain a DC brain map.
5. A method of selecting a predicted depression symptom variable in accordance with claim 4, wherein: in step 3A, the preprocessing includes: removing at least one of the first 10 time point data, layer time correction, head motion correction estimation, linear trend removal in the signal, and 0.01-0.08Hz low pass filtering;
and/or, in step 3E, the obtained DC brain map is standardized, wherein the standardized method is to carry out 6mm full-width half-height Gaussian smoothing on the DC brain map.
6. A computer device for the selection of a predicted depression symptom variable, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of selecting a predicted depression symptom variable according to any of claims 1-5 when executing the program.
7. A computer-readable storage medium, characterized by: a computer program stored thereon for implementing the method of selecting a predicted depression symptom variable according to any of claims 1-5.
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