KR20200139562A - Methods for Diagnosing Major Depressive Disorder using Brain MRI Analysis - Google Patents

Abstract

The present invention relates to a method for diagnosing major depressive disorders using brain MRI analysis. When using the method of the present invention, the major depressive disorders can be simply and efficiently diagnosed with only MRI images, thereby being usefully used for diagnosing the major depressive disorders and predicting prognosis.

Description

Methods for Diagnosing Major Depressive Disorder using Brain MRI Analysis

The present invention relates to a method for diagnosing major depressive disorders using brain MRI analysis.

Major depressive disorder (MDD) is a general mental disorder, and patients with depressive disorder are known to suffer from a variety of mood disorders, cognitive deficits, and sleep disorders.

Recently, neurophysiological studies are being conducted focusing on the mechanisms of major depressive disorders. In particular, it is reported that the limbic system with different parts of the cerebrum can affect the pathophysiology of major depressive disorders. (Hussain and Jacobson, 2015; Ibi et al., 2017).

On the other hand, surface-based vertex analysis (SVA) is a method of quantitatively measuring the brain structure by comparing the relative distances between surfaces. SVA is a method that examines the structural shape from different angles and provides a specific crossover of the existing result. As it can provide validation, it is considered a valuable addition to existing structural analysis methods.

However, there are not many cases of applying SVA to brain MRI images of patients with major depressive disorder.

Korean Patent Publication No. 10-2017-0048638

The present inventors have made extensive research efforts to develop a method for diagnosing major depressive disorders using brain MRI images. As a result, the present invention was completed by evaluating the shape of the subcortical nuclei using a vertex analysis method, and finding out that patients with major depressive disorders can be diagnosed therefrom.

Accordingly, an object of the present invention is to provide a method of providing information for diagnosing a major depressive disorder.

Another object of the present invention is to provide a method of providing information for measuring the prognosis of major depressive disorders.

The present inventors have made extensive research efforts to develop a method for diagnosing major depressive disorders using brain MRI images. As a result, the morphology of the subcortical nuclei was evaluated using a vertex analysis method, and it was found that patients with major depressive disorder can be diagnosed from this.

The present invention relates to a method of providing information for diagnosing a major depressive disorder and measuring a prognosis of a depressive disorder.

Hereinafter, the present invention will be described in more detail.

One aspect of the present invention relates to a method of providing information for diagnosing a major depressive disorder, including the following steps.

a) obtaining an MRI image of the subcortical nuclei;

b) performing automated segmentation; And

c) Based on the obtained segmentation, measuring the scalar distances (SD) values of the subcortical nuclei from the average surface.

The method was designed to distinguish between patients with major depressive disorders and normal subjects during surface-based vertex analysis (SVA) using MRI images.

The method may further include the following steps.

d) Comparing the measured scalar distance value with a specific reference value to determine whether the subject has a major depressive disorder.

In the present invention, "major depressive disorder" includes mood disorder or anxiety disorder.

The mood disorder may include, but is not limited to, Depressive Disorders, Bipolar Disorders, and other mood disorders.

The anxiety disorders include Panic Disorders, Phobic Disorders, Obsessive-Compulsive Disorder, Post-Traumatic Stress Disorder, Acute Stress Disorder, Generalized Anxiety Disorder and other anxiety disorders may be included, but are not limited thereto.

Hereinafter, a method of providing information for diagnosing major depressive disorders of the present invention will be described in detail.

a) step

The subcortical nuclei include both caudate nucleus, nucleus accumbens, pallidum, putamen, thalamus, hippocampi, and amygdalae, and/or brain stem ( brainstem), and preferably includes a brain stem, but is not limited thereto.

The MRI can be broadly classified into a T1- or T2-weighted MRI. T1 and T2 mean the spin-lattice relaxation time or the spin-spin relaxation time, respectively, after the nuclear spin is excited in MRI, and produce different contrast effects.

The MRI may be a T1-weighted MRI, but is not limited thereto.

The MRI image may be a coronal T1-weighted 3D MRI image, but is not limited thereto.

b) step

The automated segmentation may be performed by a method in which the FIRST program of the FSL automatically extracts, but is not limited thereto.

Specifically, the automated segmentation is based on FSL's FIRST segmentation algorithm based on the appearance model and the template shape generated from a manually segmented reference image, and a Bayesian probability using a Gaussian assumption approach. It can be carried out by a method that is treated in combination with.

c) step

The average surface refers to the surface (surface) of the average subcortical nucleus 3D MRI image determined in consideration of clinical/statistical meaning.

Specifically, the average surface is relative to the subcortical nuclei of each individual at the same vertex (three-dimensional coordinates) after extracting 3D MRI images of subcortical nuclei from a statistically significant number of subjects and realignment of the image position. It refers to the surface of the 3D MRI image obtained by calculating the average value of the distance.

According to one embodiment of the present invention, the average surface was calculated by surface-based vertex analysis (SVA) using the FIRST tool of FSL.

The scalar distance from the average surface means the inner/outer distance of the target subcortical nucleus (both caudate nucleus, lateral left nucleus, pale nucleus, conch nucleus, thalamus, hippocampus and amygdala, or brain stem) from the average surface, fslmeants), but is not limited thereto.

Specifically, the scalar distance from the average surface is a univariate permutation at vertex points using the "randomize" command of FSL's FIRST program (fslmeants) for the segmentation obtained in step b). It can be done by a method that is calculated by testing ).

d) step

The specific reference value may be determined in consideration of clinical and statistical significance.

Specifically, the specific reference value may be a scalar distance value of a normal (no major depressive disorder) individual.

Accordingly, when the scalar distance value is larger or smaller than the reference value, it may be determined that the subject has tinnitus.

Specifically, when the subcortical nucleus is the (posterior) brain stem, the reference value may be 0.05, and more specifically, 0.063164. In this case, when the scalar distance value is less than the reference value, it may be determined that the subject has a major depressive disorder.

Another aspect of the present invention relates to a method for providing information for measuring the prognosis of a major depressive disorder, including the following steps.

a) obtaining an MRI image of the subcortical nuclei;

b) performing automated segmentation; And

c) Based on the obtained segmentation, measuring the scalar distances (SD) values of the subcortical nuclei from the average surface.

In the information providing method for measuring the prognosis of the major depressive disorder, the overlapping content of the information providing method for diagnosing the major depressive disorder is omitted in consideration of the complexity of the present specification.

The present invention relates to a method for diagnosing major depressive disorders using brain MRI analysis, and when the method of the present invention is used, it is possible to diagnose major depressive disorders simply and efficiently using only MRI images. It can be useful.

1 is a 3D rendering result based on a surface-based vertex analysis (SVA) result according to an embodiment of the present invention (TFCE FEW-corrected p <0.05). A: anterior, P: posterior, L: left, R: right.
FIG. 2 is a diagram comparing whether a brain stem atrophy of a patient with a major depressive disorder is atrophy with a normal person, based on a surface-based vertex analysis result according to an embodiment of the present invention.

Hereinafter, the present invention will be described in more detail through examples. These examples are only for describing the present invention in more detail, and it will be apparent to those of ordinary skill in the art that the scope of the present invention is not limited by these examples according to the gist of the present invention. .

Experimental Example 1. Confirmation of the presence or absence of depressive disorder

The experiment was conducted with a female patient diagnosed with major depressive disorder (MDD) and a healthy person as subjects (subjects) (see Table 1 below).

Characteristic MDD patients (N=20) Healthy person (N=21) P value Average age (range) 42.25 ± 13.65
(18-65) 42.33 ± 10.24
(18-65) 0.982 Education Middle school graduate 11 2 0.003 High school graduate 7 9 University graduate or higher 2 10 Family history has exist 5 0 0.021 none 15 20 HAM-D score 21.72 ± 8.67 0.95 ± 1.23 <0.0001 MDE onset (age) 31.68 ± 13.10 - MDE period (months) 114.22 ± 135.45 - MDE number 7.12 ± 9.12 - Suicide attempts 0.56 ± 0.71 -

The following exclusion criteria were applied:

1) other major psychosis in the past 6 months, including substance use disorders;

2) a major depressive episode (MDE) with psychotic characteristics;

3) systemic diseases;

4) primary neurological disorders (eg Parkinson's disease, stroke and epilepsy);

5) Contraindication for MRI.

The duration of the MDE was assessed using the life-chart methodology in the interview. It was confirmed that all subjects were right-handed (Edinburgh Handedness Inventory, Oldfield, 1971), and a 17-item Hamilton Depression Rating Scale (HDRS) was used to evaluate the severity of depressive symptoms in both groups on the day of the MRI examination (Beck et al. al., 1988; Williams, 1988).

The study protocol was approved by the Kangwon National University Hospital Review Committee, and all subjects submitted written consent before participation.

Experimental Example 2. MRI image acquisition

Coronal 3D T1-weighted turbo field echo MRI images were acquired using a 1.5-T MRI scanner (Gyroscan ACS-NT; Philips Medical Systems, The Netherlands) with the following scanning parameters:

1.3 mm thickness; no gap; 160 slices; scanning time, 10 min 13 sec. without sensitivity encoding (SENSE) acceleration factor (Park et al., 2008); repetition time/echo time, 10/4.3 msec; number of signal averages, 1; matrix, 256Х256 mm; field of view, 22 x 22 cm; and flip angle, 8°. Coronal slices were obtained perpendicular to the long axis of the anterior commissure to the posterior commissure in the midsagittal plane. The final voxel size was 0.86Х0.86Х1.30 mm (xХyХz).

Experimental Example 3. Automated segmentation of subcortical nuclei

With respect to the MRI image of the whole brain obtained in Experimental Example 2, the subcortical structure (caudate nucleus, nucleus accumbens, pallidum), putamen, thalamus, Automated segmentation of hippocampi and amygdala (amygdalae), and brain stem (brainstem) is described in FMRIB Software Library (FSL) ver. 5.9 (fsl.fmrib.ox.ac.uk/fsl) was used.

Automated segmentation of the subcortical nucleus was performed using the FIRST algorithm (Patenaude et al., 2011), a segmentation software included in the FLS package, and the FIRST algorithm used the appearance model and the template shape created from 336 manually segmented reference images. Based, and combined with Bayesian probability using a Gaussian assumption approach.

This automated segmentation process was performed including a two-step affine transformation for the MNI 132 standard space. First, the T1 image was spatially assigned to the MNI 152 template with standard 12-DOF (degrees of freedom). It was performed by spatially enrolling and then by enrolling individual subcortical nuclei (12-DOF spatial enrollment) using an MNI 152 subcortical mask.

The accuracy of the segmentation was confirmed visually, and the brain size of each individual was normalized by measuring the total intracranial cavity volume (TICV).

Experimental Example 4. Surface-based vertex analysis (SVA)

For the segmentation obtained in Experimental Example 3, surface-based vertex analysis (SVA) was performed using the FIRST tool of FSL (ver. 5.9.) (Patenaude et al., 2011).

SVA is a method of calculating the statistical difference between groups in vertex units using a multivariate general linear model.The surface meshes of the subcortical nuclei composed of parameterized deformable surfaces are used for each nucleus. Represents volumetric information.

On the other hand, SVA was processed in the native space, and vertex points from each nucleus were projected onto the surface of the average shape of all subjects. This means projection of each image pixel to a one-to-one corresponding three-dimensional coordinate at the same anatomical position.

The scalar distances of 15 sites of the subcortical nucleus from the mean surface were calculated by testing univariate permutations at vertex points using the "randomize" command of the FSL program.

As can be seen in FIG. 1, local contraction (atrophy) appeared in the cerebellar peduncles located on the lateral wall of the posterior brainstem.

The scalar distance from the average surface of the entire area where atrophy appeared in the posterior brain stem was measured and shown in Table 2 below. As can be seen in Tables 2 and 2, if the scalar distance value of the posterior brain stem is less than the reference value (normal), it can be determined as depression.

group N Mean Standard Deviation
(S. Deviation) Standard mean error
(S. Error Mean) Brain stem normal 21 0.063164 0.4985414 0.1087907 MDD 20 -0.066322 0.3757236 0.0840143

Sintering

According to the above embodiments, it was confirmed that when SVA is performed using MRI images, major depressive disorders can be diagnosed simply and efficiently.

Claims (6)

How to provide information for diagnosing a major depressive disorder, including the following steps:
a) obtaining an MRI image of the subcortical nuclei;
b) performing automated segmentation; And
c) Measuring the scalar distances (SD) values of the subcortical nuclei from the average surface, based on the obtained segmentation. The method of claim 1, wherein the method further comprises the following steps:
d) Comparing the measured scalar distance value with a specific reference value to determine whether the subject has a major depressive disorder. The method of claim 2, wherein the specific reference value is determined in consideration of clinical and statistical significance, and when the scalar distance value is greater or less than the reference value, it is determined that the subject has a major depressive disorder. The method of claim 1, wherein the subcortical nucleus is both caudate nucleus, nucleus accumbens, pallidum, putamen, thalamus, hippocampi, and amygdalae. , And brain stem (brainstem) that comprises one or more selected from the group consisting of. The method of claim 1, wherein the subcortical nucleus comprises a brain stem. The method of claim 1, wherein the MRI is T1-weighted MRI.

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