KR101696061B1 - Methods and apparatus for acquiring image based biomarkers suitable for diagnosis of neurodegenerative diseases and methods for diagnosing neurodegenerative diseases - Google Patents

Abstract

The present invention relates to a method for acquiring image-based biomarkers suitable for diagnosis of degenerative brain diseases, a method for providing information for diagnosis of degenerative brain diseases, a device for predicting degenerative brain diseases, and information for diagnosing degenerative brain diseases And a computer program for an apparatus for predicting degenerative brain disease.

Description

Methods and apparatus for obtaining image biomarkers suitable for the diagnosis of degenerative brain diseases, and methods for diagnosing degenerative brain diseases using the same

The present invention relates to a method for acquiring image-based biomarkers suitable for diagnosis of degenerative brain diseases, a method for providing information for diagnosis of degenerative brain diseases, a device for predicting degenerative brain diseases, and information for diagnosing degenerative brain diseases And a computer program for an apparatus for predicting degenerative brain disease.

Degenerative brain disease refers to a brain disease that involves gradual loss of neuron structure or function or death of neurons. Such degenerative brain diseases include Lou Gehrig's disease, Alzheimer's disease, Parkinson's disease, and the like.

Alzheimer's disease is the most common neurodegenerative brain disease characterized clinically by cognitive dysfunction, including memory, and daily activity disorders resulting from it. Two pathologic features of AD are neurofibrillary tangles composed of Aβ plaque formation and aggregation of tau proteins in amyloid beta (Aβ) peptide aggregation in the brain. Although the aggregation of A? In AD is known to precede tau pathology, the relationship between the two pathologies is not clear to date.

The Aβ plaque continues to accumulate from the associative cortex, including the frontal lobe, the temporal lobe, the parietal lobe, and the posterior cingulate cortices up to the pathologic stasis, but the medial temporal region areas accumulate a small amount even at the end of AD. On the other hand, tau-related pathology begins to accumulate in the medial temporal lobe, which includes the transentorhinal cortex and the entorhinal cortex, the region that continues to accumulate in the greatest amount until the end of AD.

On the other hand, a technique for diagnosing degenerative brain disease is used by confirming structural abnormalities of the brain such as atrophy of the brain and enlargement of the ventricles in degenerative brain diseases using imaging techniques such as magnetic resonance imaging (MRI) or computed tomography have. However, it is not known how to diagnose degenerative brain disease by developing a biomarker that quantitatively reflects the pathological changes and structural changes of degenerative brain diseases at the same time.

Under these circumstances, the present inventors have developed a technique that can be used for the diagnosis and follow-up of degenerative brain diseases by simultaneously reflecting the pathological changes and structural changes of degenerative brain diseases, especially Alzheimer's disease. As a result, amyloid PET (Positron- Emitting Tomography), we compared subjects with AD, mild cognitive impairment (MCI), and normal cognition (NC) by weighting the cortical thickness values obtained from MRI images And that these changes in amyloid beta pathology-enriched cerebral cortical thickness are effectively compatible with the pathological consequences and future changes in the cortical thickness in late AD, suggesting that the technology is applicable to degenerative brain diseases including Alzheimer's disease And the present invention has been completed.

One object of the present invention is

(a) collecting an MR (Magnetic Resonance) image and a PET (Positron-Emitting Tomography) image of the object k ;

)을 산출하고, PET 데이터로부터 퇴행성 뇌질환의 병리 인자에 대한 가중치 (

)를 산출하는 단계; (b) normalizing the cortical thickness value from the collected MR image

), And from the PET data, the weight for the pathologic factor of degenerative brain disease (

);

)에, PET으로부터 얻어진 상기 대상체의 퇴행성 뇌질환의 병리 인자에 대한 가중치 (

)를 곱하여 퇴행성 뇌질환에 대한 병리 인자 값이 가중된 피질 두께 값 (

)을 산출하는 단계를 포함하는, (c) the calculated normalized cortical thickness value (

), A weight for the pathologic factor of the degenerative brain disease of the subject obtained from PET

) And the pathologic factor value for degenerative brain disease is the weighted cortical thickness value (

≪ / RTI >

A method for obtaining an image biomarker suitable for diagnosis of degenerative brain disease or a method for providing information for diagnosis of degenerative brain disease.

Still another object of the present invention is to provide an image processing apparatus, including an image collection unit for collecting an MR image and a PET image;

A cortical thickness analyzer for analyzing the MR image received from the image collection unit to calculate a normalized cortical thickness value;

A degenerative brain disease pathologic factor analyzing unit for analyzing the PET image received from the image collecting unit to calculate a weight for a pathologic factor of degenerative brain disease; And

And a conversion unit for converting the degenerative brain disease pathologic factor value into a weighted cortical thickness value from a weight for a normalized cortical thickness value received from the cortical thickness analyzer and a pathological factor of a degenerative brain disease received from the degenerative brain disease pathology factor analyzer doing,

And to provide a device for predicting degenerative brain diseases.

Another object of the present invention is to provide

A computer program for providing information for diagnosis of degenerative brain disease,

Analyzing each of the MR image of the subject and the PET image of the subject to calculate a weight for the normalized cortical thickness value and the pathological factor value of the degenerative brain disorder; And

And multiplying the weighted values of the calculated normalized cortical thickness values and pathological factor values of the degenerative brain disease by each other to convert the pathologic factor value of the degenerative brain disease into a weighted cortical thickness value.

One aspect of the present invention for achieving the above object

(a) collecting an MR (Magnetic Resonance) image and a PET (Positron-Emitting Tomography) image of the object k ;

)을 산출하고, PET 데이터로부터 퇴행성 뇌질환의 병리 인자에 대한 가중치 (

)를 산출하는 단계; (b) normalizing the cortical thickness value from the collected MR image

), And from the PET data, the weight for the pathologic factor of degenerative brain disease (

);

)에, PET으로부터 얻어진 상기 대상체의 퇴행성 뇌질환의 병리 인자에 대한 가중치 (

)를 곱하여 퇴행성 뇌질환에 대한 병리 인자 값이 가중된 피질 두께 값 (

)을 산출하는 단계를 포함하는, (c) the calculated normalized cortical thickness value (

), A weight for the pathologic factor of the degenerative brain disease of the subject obtained from PET

) And the pathologic factor value for degenerative brain disease is the weighted cortical thickness value (

≪ / RTI >

A method for acquiring image biomarkers suitable for diagnosis of degenerative brain diseases, and a method for providing information or a diagnostic method for diagnosing degenerative brain diseases.

In the step (a), an MR image and a PET image of a target object are collected.

The brain regions can be classified and the surfaces of the inner and outer cortex can be determined from the collected MR images and PET images. This can be done automatically or manually, and the surfaces of the inner and outer cortices can be classified into CLASP (Constrained Laplacian-Based Automated Segmentation with Proximity) algorithms.

On the other hand, the MR image may be a T1-weighted MR image, but is not limited thereto.

), 및 PET 데이터로부터 퇴행성 뇌질환의 병리 인자에 대한 가중치 (

)를 산출하는 단계이다. Wherein the step (b) comprises calculating a normalized cortical thickness value (

), And the weight for the pathologic factor of degenerative brain disease from the PET data (

).

)은 하기 수학식 1에 의해 얻어질 수 있다.From the MRI image of the subject, a normalized cortical thickness value (

) Can be obtained by the following equation (1).

[Equation 1]

here,

는 대상체 (k)의 MR 이미지의 대뇌 피질모델로부터 계산된 일 지점에서의 대뇌 피질두께를,

Shows the cortical thickness at one point calculated from the cerebral cortical model of the MR image of the object ( k )

는 동일 지점에서의 정상 대조군의 평균 피질두께를,

The average cortical thickness of the normal control group at the same point,

는 동일 지점에서의 정상 대조군의 피질두께의 표준편차를 나타냄.

Represents the standard deviation of the cortical thickness of the normal control at the same point.

Here, analysis of cortical thickness can be performed in region-of-interest (ROI) of the classified brain regions.

In addition, the cortical thickness can be defined by measuring the distance between the gray matter cortex and the white matter cortex.

)는 정규화된 퇴행성 뇌질환 병리 인자 값 (

)을 하기 수학식 2로 계산하고, 계산된 정규화된 퇴행성 뇌질환 병리 인자 값을 하기 수학식 3으로 계산하여 도출될 수 있다. Further, the weight of the degenerative brain disease pathologic factor of the subject (

) Is a normalized degenerative brain disease pathologic parameter value

) Is calculated by the following equation (2), and the computed normalized degenerative brain disease pathologic factor value is calculated by the following equation (3).

&Quot; (2) "

here,

는 대상체 k의 PET의 대뇌 피질모델에서의 퇴행성 뇌질환 병리 인자 값,

Is the value of the degenerative brain disease pathologic factor in the cerebral cortex model of PET of the subject k ,

는 동일 지점에서의 정상 대조군의 평균 퇴행성 뇌질환 병리 인자 값을,

The mean degenerative brain disease pathologic factor value of the normal control group at the same spot was calculated as

는 동일 지점에서의 정상 대조군의 퇴행성 뇌질환 병리 인자 값의 표준편차를 나타내고;

Represents the standard deviation of the degenerative brain disease pathologic factor value of the normal control group at the same spot;

&Quot; (3) "

here,

은 전체 PET 데이터의 정규화된 퇴행성 뇌질환 병리 인자 값 중 최소값을 나타냄.

Represents the minimum of the normalized degenerative brain disease pathologic factor values of all PET data.

The analysis of the degenerative brain disease pathologic factor value may be performed at the same site as the area where the cerebral cortical thickness is analyzed, and specifically, the PET image may be matched to the MR image.

As shown in FIG. 1, the analysis of the pathologic factor values is performed on the basis of the average values of the linearly interpolated values along the column line between the vertices of the gray matter cortex defined by the brain cortex model and the vertices of the corresponding white matter cortex Can be calculated. However, it is not limited thereto.

In one embodiment of the present invention, based on the result of analyzing the aggregation of amyloid beta, which is the first pathological change of Alzheimer's disease, based on the value of Alzheimer's disease pathology collected from the PET, The technique is not particularly limited thereto.

In other words, it involves analyzing the results of MRI image analysis including cerebral cortex analysis in addition to amyloid beta (A [beta]) to examine changes in pathologic factor values caused by Alzheimer's disease or Alzheimer's disease.

At this time, pathologic factors such as the pathology causing Alzheimer's disease or the progression of Alzheimer's disease and the like are determined by an imaging method (11C-PiB, 18F-Amyvid, 18F-Flutemetamol, 18F-Florbetaben, 18F (Imaging techniques for evaluation of deposition of amyloid proteins and their aggregates including AV-45, 123I-CQ, 18F-CABS13, and the like) and imaging techniques to evaluate the accumulation of tau protein (11C-PBB3, 18F- Images for evaluation of deposition of tau protein and its aggregates including PBB3, 18F-T807, 18F-T808, 18F-THK-5117, 18F-THK-523, 18F-THK5105, 18F-Fluoroethyl- Astemizole, (R) -11C-PK11195, 18F-PBR111, 18F-FEPPA, 11C-PBR28, 6-18F-Fluoro-PBR28, 11C-PBR06, 18F-PBR06, 11C-DPA-713, 11C-AcAC, 2-18F-fluoroacetate, 11C-AC5216, 11C-MBMP, 11C-DAC, 11C-DAA1106, 11C-vinpocetine, 18F-FEAC, 18F-FEDAC, 18F- A method for evaluating brain inflammation including DAA1106, 11C-DED, 11C-Sch225336, 1- [11C] -AA, 18F-FAA, 11C-Ro 5-4864). Methods for assessing the degree of brain inflammation include assessing through changes in TSPO.

(11C-AMP, 11C-PMP, 11C-BMP), metabotropic glutamate receptors (18F-FPEB for mGluR5 receptors, 11C-ABP688 for the pathological changes associated with the progression of Alzheimer's disease mGluR5 receptors, muscarinic acetylcholine receptors (18F-FP-TZTP for M2 receptors, 11C-Xanomeline for M1 / M4 agonist), monoamine oxidase B ((R) - , 11C-SL25.1188, 11C-DED, and 18F-Fluorodeprenyl) were also used in the PET-based MRI analysis as a basis for the changes in the occurrence and progression of various Alzheimer's disease .

For the PET tracers described above, Holland JP et al (J Labelled Comp Radiopharm. 2014 Apr; 57 (4): 323-31), which discloses to tracers used in PET of Alzheimer's disease, PET tracers in the Zimmer ER et al, which discloses (J Neuroinflammation 2014 Jul 8; 11 :. 120), Okamura , which discloses a Tau PET tracer N et al (Curr Neurol Neurosci Rep 2014 Nov; 14 (11):. 500) The content disclosed in Schweitzer PJ et al (Drug Discov Today. 2010 Nov; 15 (21-22): 933-42), which discloses a PET tracer for a transposable protein (TSPO) It is included in the description of the tracker.

Specifically, the changes of the acetylcholinesterase, the metabolic glutamate receptor, the muscarinic acetylcholine receptor, and the monoamine oxidase can be confirmed by measuring their levels using a PET tracer, Examples of PET tracers include, but are not limited to those described above.

It is also known that the level of acetylcholinesterase is increased around the amyloid plaque and nerve fiber entanglement (NFT).

The metabotropic glutamate receptors are known to be potentiated in degenerative brain diseases including Alzheimer's disease.

Muscarinic acetylcholine receptors are known to have reduced activity in Alzheimer's disease.

On the other hand, monoamine oxidase (MAO) contains both MAO A and MAO B, MAO B is known to have increased expression in plaque-associated cells in the brain of individuals with Alzheimer's disease, and MAO A increase in Alzheimer's disease Is known to be associated with increased neurotoxic metabolism.

This analysis can also be applied to degenerative diseases other than Alzheimer's disease. For example, a PET imaging technique for evaluation of tau protein and its aggregation deposits has been used for frontotemporal dementia (frontotemporal lobar degeneration as a clinical concept), Parkinson's disease, cortical basal ganglia The same equation can be used for MRI structural brain imaging analysis, including cortical thickness analysis of degenerative brain diseases including Corticobasal degeneration (Corticobasal syndrome), Multiple System Atrophy, and Progressive Supranuclear Palsy .

More specifically, the present invention can be used in a method of providing information or a diagnostic method for acquisition of an image biomarker suitable for diagnosis of Alzheimer's disease and diagnosis of Alzheimer's disease.

Specifically,

(a) collecting an MR image and a PET image of the object ( k );

), 및 PET 데이터로부터 알츠하이머병의 병리 인자에 대한 가중치 (

)를 산출하는 단계; 및(b) normalizing the cortical thickness value from the collected MR image

), And the weight for the pathologic factor of Alzheimer's disease from PET data (

); And

)에, PET으로부터 얻어진 상기 대상체의 알츠하이머병 병리 인자에 대한 가중치 (

)를 곱하여 알츠하이머병 병리 인자 값이 가중된 피질 두께 값 (

)을 산출하는 단계를 포함할 수 있다.
(c) the calculated normalized cortical thickness value (

), The weight of the subject obtained from PET on the pathologic factor of Alzheimer's disease (

) To determine the cortical thickness value (< RTI ID = 0.0 >

). ≪ / RTI >

In the above method, the weight for the pathological factor of Alzheimer's disease can be calculated by the above-described equations (2) and (3). When the above equations (2) and (3) are applied to image biomarker acquisition and diagnosis of Alzheimer's disease, the NDD at the stated values means AD.

The Alzheimer ' s disease pathologic factor is as described above, but more specifically may be accumulation of A [beta].

로 표현될 수 있다. 상기 Aβ 축적에 대한 가중치는 정규화된 Aβ 축적 값 (

)을 하기 수학식 4로 계산하고, 계산된 정규화된 Aβ 축적 값을 하기 수학식 5로 계산하여 도출되는 것일 수 있다. The weight for the A [beta] accumulation is

. ≪ / RTI > The weight for the A [beta] accumulation is the normalized A [beta] accumulation value (

) Is calculated by the following equation (4), and the calculated normalized Aβ accumulation value is calculated by the following equation (5).

&Quot; (4) "

here,

는 대상체 (k)의 PET의 대뇌 피질모델에서의 Aβ 축적 값,

Is an Aβ accumulation value in the cerebral cortex model of the PET of the subject ( k )

는 동일 지점에서의 정상 대조군의 평균 Aβ 축적 값을,

The average A [beta] accumulation value of the normal control group at the same point,

는 동일 지점에서의 정상 대조군의 Aβ 축적 값의 표준편차를 나타내고;

Represents the standard deviation of the A [beta] accumulation value of the normal control at the same spot;

&Quot; (5) "

here,

은 전체 PET 데이터의 정규화된 Aβ 축적 값의 최소값을 나타냄.

Represents the minimum value of the normalized A [beta] accumulation value of the entire PET data.

)를 곱하여 알츠하이머병 병리 인자 값이 가중된 피질 두께 값 (

)을 산출하는 단계이다. The step (c) may include adding the weighted value of the subject's Alzheimer's disease pathology factor obtained from PET to the calculated normalized cortical thickness value

) To determine the cortical thickness value (< RTI ID = 0.0 >

).

)을 산출할 수 있다.
Specifically, the weight for the Alzheimer's disease pathologic factor obtained by the above-described method is multiplied by the normalized cortical thickness value, and the Alzheimer's pathologic factor value is added to the weighted cortical thickness value

) Can be calculated.

The method may further comprise the step of imaging the weighted cortical thickness value in addition to the degenerative brain disease pathologic factor value. Through this, it is possible to easily visually confirm the onset, progress, and progress of degenerative brain disease.

In addition, the method may further include the step of comparing the cortical thickness value with the degenerative brain disease pathologic factor value to the normal control to determine the degree of degenerative brain disease or the degree of degenerative brain disease.

In cases where the degenerative brain disease is Alzheimer's disease, the method further comprises comparing the cortical thickness value of the weighted Alzheimer's disease pathology value with the normal control group and / or the MCI group to determine the degree of progression of Alzheimer's disease or Alzheimer's disease May be further included.

)과 비교하여 퇴행성 뇌질환의 진행 정도를 판단하는 단계를 추가적으로 포함할 수 있다. 이를 통하여 퇴행성 뇌질환에 대한 추적 관찰을 수행할 수 있으나, 이에 제한되지 않는다. In addition, the pathologic parameter values of the prior degenerative brain disease of the same individual are the weighted cortical thickness values

) To determine the degree of progression of the degenerative brain disease. This can be followed, but not limited to, degenerative brain disease.

)과 비교하여 알츠하이머병의 진행 정도를 판단하는 단계를 추가적으로 포함할 수 있다. 이를 통하여 알츠하이머병에 대한 추적 관찰을 수행할 수 있으나, 이에 제한되지 않는다.
In addition, pathologic parameter values of advanced Alzheimer ' s disease of the same individual are compared with the weighted cortical thickness values

) To determine the degree of progression of Alzheimer's disease. This allows, but is not limited to, follow-up observations of Alzheimer's disease.

In another aspect of the present invention,

An image collection unit for collecting an MR image and a PET image;

A cortical thickness analyzer for analyzing the MR image received from the image collection unit to calculate a normalized cortical thickness value;

A degenerative brain disease pathologic factor analyzing unit for analyzing the PET image received from the image collecting unit to calculate a weight for a pathologic factor of degenerative brain disease; And

And a conversion unit for converting the degenerative brain disease pathologic factor value into a weighted cortical thickness value from a weight for a normalized cortical thickness value received from the cortical thickness analyzer and a pathological factor of a degenerative brain disease received from the degenerative brain disease pathology factor analyzer doing,

It is a device for predicting degenerative brain disease.

Further, when the degenerative brain disease is Alzheimer's disease,

An image collection unit for collecting an MR image and a PET image;

A cortical thickness analyzer for analyzing the MR image received from the image collection unit to calculate a normalized cortical thickness value;

An Alzheimer's disease pathologic factor analyzing unit for analyzing the PET image received from the image collecting unit to calculate the weight of the pathological factor of Alzheimer's disease; And

A normalized cortical thickness value received from the cortical thickness analyzer and a conversion unit for converting the weighted Alzheimer's disease pathologic factor value from the Alzheimer's disease pathologic factor weight received from the Alzheimer's disease pathologic factor analysis unit into a weighted cortical thickness value.

The apparatus for predicting a degenerative brain disease according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

5, an apparatus for predicting a degenerative brain disease may include an image collection unit 101, a cortical thickness analysis unit 102, a degenerative brain disease pathology factor analysis unit 103, and a conversion unit 104 And further includes a degenerative brain disease determination unit 105 for determining whether or not a degenerative brain disorder has occurred.

The image collection unit 101 may collect an MR image and a PET image of a subject suspected of having degenerative brain disease.

The cortical thickness analyzer 102 measures the cortical thickness of the entire brain area through the brain cortical model construction using the MR images collected from the suspect individuals of the degenerative brain disorder and classifies the area using the defined template of the cortical area The normalized cortical thickness value can be calculated by analyzing the cortical thickness for the classified brain regions.

Here, analysis of cortical thickness can be performed in region-of-interest (ROI) of the classified brain regions.

In addition, the cortical thickness can be defined by measuring the distance between the gray matter cortex and the white matter cortex.

The normalized cortical thickness value may be calculated by Equation (1) as described above. However, the present invention is not limited to those described above.

The degenerative brain disease pathology factor analysis unit 103 can analyze the pathologic factor value of the degenerative brain disease using the PET image collected from the suspect individuals of degenerative brain disease. The pathological factor values of the analyzed degenerative brain diseases can be converted into normalized values, and the degenerative brain disease pathology factor weights can be calculated based thereon.

Here, the analysis of the degenerative brain disease pathologic factor value can be performed at the same point as the cortical thickness analysis using the MR image, and can be performed using linear interpolation between the gray matter cortex and the white matter cortex.

The normalized degenerative brain disease pathology parameter value may be calculated by Equation (2), and the calculated normalized degenerative brain disease pathology parameter value may be calculated by Equation (3) It is not.

The degenerative brain disease pathology factors collected from the PET are as described above.

When the degenerative brain disease is Alzheimer's disease and the pathological factor of the degenerative brain disease is Aβ, the weight for the Aβ accumulation is calculated by substituting the normalized Aβ accumulation value calculated by Equation (4) But the present invention is not limited thereto.

The transforming unit 104 calculates a weighted sum of the normalized cortical thickness values and degenerative brain disease pathologic factor weights collected from the cortical thickness analyzer 102 and the degenerative brain disease pathology factor analyzer 103, Can be converted to a cortical thickness value.

Specifically, the cortical thickness value and the weights may be multiplied by each other to calculate a cortical thickness value that is a weighted value of a degenerative brain disease pathologic factor.

The degenerative brain disease determination unit 105 can determine whether or not the degenerative brain disease is caused or progressed. Specifically, the weighted cortical thickness values of the degenerative brain diseases calculated from the MR images and the PET images of suspected individuals with degenerative brain disease can be judged by comparison with the normal control group.

At this time, the comparison of the value of the normal control group and the derived value can be used to diagnose or track the progress of the degenerative brain disease.

In addition, when the degenerative brain disease is Alzheimer's disease, it is possible to diagnose or track the onset and progress of the Alzheimer's disease and the normal control group and / or the MCI group.

In addition, the device can be applied to a PET-MRI integrated system.

Another aspect of the present invention is a computer program for providing information for diagnosis of degenerative brain diseases. The program may be for the diagnosis of Alzheimer's disease.

In particular, the above-described methods may be implemented in the form of program instructions that may be executed through various computer means. Analyzing the MR image of the subject and the PET image of the subject, respectively, to calculate a normalized cortical thickness value and a weight for a pathologic factor of the degenerative brain disease; And multiplying the calculated normalized cortical thickness value and the weight for the pathologic factor of the degenerative brain disease by each other to convert the pathologic factor value of the degenerative brain disease to the weighted cortical thickness value.

Further, the computer program may be recorded on a recording medium or a computer device.

The method of the present invention can quantitatively show the pathological changes and structural changes of degenerative brain diseases, and can quantify cerebral cortical changes related to the pathological changes of degenerative brain diseases by whole cerebral region and regions, The clinical significance between the pathological changes and the cerebral changes can be directly derived.

1 shows a method of measuring Aβ accumulation by linear interpolation. Specifically, a virtual straight line is defined between the vertices of the gray matter cortex defined by the brain cortex model and the vertices of the corresponding white matter outer face, and divided into five equal intervals. Then, by using the linear interpolation method, the amount of amyloid beta aggregation defined in the nearby voxel was calculated as the value at each of the six points, and the average value at six points was calculated to derive the average amount of amyloid accumulation in the gray matter.
FIG. 2 is a diagram illustrating amyloid beta (Aβ) weighted cortical thickness patterns and de novo cortical thickness patterns between AD (Alzheimer's disease), MCI (mild cognitive impairment) and NC (normal cognition) objects over the entire cortical apex.
Figure 3 compares the amyloid-beta (A [beta]) absorption pattern between AD, MCI and NC subjects.
Figure 4 shows five patterns of A [beta] weighted cortical thickness based on comparison of A [beta] uptake and cortical thickness reduction in each subject group.
(a) reduced A [beta] -independent cortical thickness; (b) Aβ-related cortical thickness reduction: Aβ-related thickness reduction in AD after non-Aβ related thickness reduction; (c) Aβ-related cortical thickness reduction: Aβ-related thickness further reduced in AD after Aβ-related thickness reduction in MCI; (d) Aβ-related cortical thickness reduction: decreased thickness by continuous Aβ in AD after reduction in thickness by early Aβ in MCI; (e) Aβ deposition only.
5 is a schematic diagram of an apparatus for predicting degenerative brain disease according to an embodiment of the present invention.

Hereinafter, the present invention will be described in detail with reference to the following examples. However, the following examples are for illustrative purposes only, and the scope of the present invention is not limited by the following examples.

Example 1: Data collection

A total of 21 AD (Alzheimer's disease), 56 MCI (mild cognitive) patients in the ANDI (Alzheimer's disease Neuroimaging Initiative) database (http://www.loni.ucla.edu/ADNI/) impairment) and 18 NC (normal cognition) subjects. Because of various technical limitations, all ADNI GO Pittsburgh Compound B (PiB) -PET datasets, including the corresponding T1-MR datasets, were employed, with the exception of nine subjects in AD and one subject in NC.

Detailed statistical information of all objects is shown in Table 1 below.

^* : statistically significant difference from NC

^§ : statistically significant difference from AD

^† : statistically significant difference from MCI

Abbreviations: AD, Alzheimer's disease; MCI, mild cognitive impairment; NC, normal controls; ICV, intracranial volume; MMSE, mini-mnetal status examination; CDR, clinical dementia rating; GDS, global deterioration scale; FAQ, functional assessment questionnaire total score; NPI-Q, neuropsychiatric inventory-questionnaire total score; sd, standard deviation; IQR, inter-quartile ratio.

The data set included a high-resolution T1-weighted MR image obtained sagittally using MPRAGE (magnetization prepared rapid gradient echo) with a 0.94 X 0.94 mm pixel size and a 1.2 mm slice thickness. All T1-weighted MR images were obtained using a 1.5T Philips Medical Systems scanner. After intravenous injection of 15 ± 1.5 mCi 11C-PiB, PET scans were taken from the initial 50 min to 70 min to 20 min using several PET scanners consisting of GE Medical system, Siemens / CTI and Siemens ECAT PET scanner. Detailed information is available on the ADNI website (http://www.loni.ucla.edu/ADNI/).

The data set of six AD patients at the Asan Medical Center included a high-resolution T1-weighted MR image obtained at the sagittal using a 3D T1 Turbo Field Echo sequence of 1.0 mm X 1.0 mm pixel size and 1.0 mm slice thickness. All T1-weighted MR images were obtained using a 1.5T Philips Intera Scanner. 16.2 ± 3 mCi 11C-PiB was injected intravenously and the 3D scanning mode of attenuation correction-based computed tomography and Discovery STePET / CT scanner (GE Medical Systems, Milwaukee, Wis., USA) PET scans were obtained for 20 min at 60 min.

Example 2: MR image processing

All MR images are processed using the standard MNI (Montreal Neurological Institute) CIVET pipeline (ver. 1.1.9) (http://wiki.bic.mni.mcgill.ca/index.php/CIVET) to determine cortical thickness Respectively.

First, the inhomogeneous bias fields are estimated from the original MR images using the N3 algorithm to obtain the distribution of the actual intensity. The original MRI photographs were then matched to the normalized stereotaxic space using a linear transformation and the white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), cerebrospinal fluid , CSF) and background.

Using the Constrained Laplacian-Based Automated Segmentation with Proximity (CLASP) algorithm, the surfaces of internal and external cortices were automatically determined.

Finally, the hemispherical cortical surface consisted of 81,920 triangular meshes with the same number of internal and external vertices. To measure the cortical thickness, the Euclidean distance between the inner WM / GM surface and the vertex connected to the outer GM / CSF crossing surface was calculated.

Example 3: Surface-based PET image processing

Each PiB PET image was summed to the corresponding original MR image using cost function mutual information and rigid-body registration. To generate a standard uptake value ratios (SUVR) image, cerebellum GM was segmented into atlas-based nonlinear matching in MR images, and PiB PET images were adjusted using mean values in the cerebellum GM. The PiB value at each vertex of the cortex surface was calculated as an average of the values determined along the column line between the outer GM / CSF and the inner GM / WM vertices, ie, MATLAB (Version 7.14; MathWorks Inc., Cambridge, Mass., USA) The average PiB value of each column line was obtained by subvoxel resampling in five parts using trilinear interpolation. Since PiB is mainly accumulated in GM, GM partial volume estimation map (GMPVE) is obtained from the MR image for the GM part in each voxel. In order to adjust the point spread function in PET, each mPVE is processed with a Gaussian kernel of 8 mm full-width half-maximum (FWHM), and the subpixel resampling is performed in five parts using trilinear interpolation The average mPVE value of the column line was obtained. Each PiB-SUVR value at each vertex was then divided by the corresponding mPVE value.

As a result, a partial volume modified PiB-SUVR value was obtained at each vertex of each cortical surface. Each PiB-SUVR surface map was subjected to DS (diffusion smoothing) with 20 mm FWHM for statistical analysis and then fitted to the surface template using sphere-to-sphere warp surface matching.

Example 3: Calculation of Aβ weighted cortical thickness values

Regularized z-scores at each vertex of cortical thickness and Aβ accumulation were calculated to use each variable regardless of the various parameters obtained from MRI and PET scanners.

The normalized z-score of the i-th vertex of each object k is defined by the following equations (6) and (7): <

&Quot; (6) "

는 대상체 k의 대뇌 피질모델로부터 계산된 i번째 꼭지점에서의 대뇌 피질두께를, 는 동일 꼭지점에서의 정상군의 평균 피질두께를,

는 정상군의 피질두께의 표준편차를 나타낸다.
here,

Is the cortical thickness at the i-th vertex calculated from the cerebral cortex model of the subject k , The average cortical thickness of the normal group at the same vertex,

Represents the standard deviation of the cortical thickness of the normal group.

&Quot; (7) "

는 대상체 k의 i번째 꼭지점에서의 아밀로이드 베타 집적량을,

는 동일 꼭지점에서 정상군의 아밀로이드 베타 집적량을,

는 정상군의 아밀로이드 베타 집적량의 표준 편차를 나타낸다.
here,

Is the amount of amyloid beta aggregation at the i-th vertex of the subject k ,

The amount of amyloid beta aggregation in the normal group at the same vertex,

Indicates the standard deviation of the amount of amyloid beta aggregation in the normal group.

According to the above formula, all normalized z-scores of cortical thickness and Aβ accumulation appear in three forms: positive, negative, and zero.

)을 찾고, 절대값의 역수를 취해주었다. 역수가 된 값은 다시 정규화시킨 i번째 꼭지점에서의 아밀로이드 베타 집적량에 곱해져, 동일한 배율로 변환하였다. To generate the A [beta] weight value, the normalized z-score of A [beta] integration was converted to a positive range. First, with respect to the entire vertices of the entire data, the minimum value of the normalized z-score of the Aβ accumulation

) And took the inverse of the absolute value. The reciprocal values were multiplied by the amyloid beta aggregation at the i-th vertex again normalized and converted to the same magnification.

Specifically, the Aβ weight value of the i-th vertex of each object k including the minimum value of the normalized z-score of Aβ obtained from the entire object was calculated by the following equation (8)

&Quot; (8) "

는 NC의 각 꼭지점에서 Aβ의 평균량보다 Aβ 집적량이 적다는 것을 나타낸다. 만약 대상체의 Aβ 집적량이 NC 그룹의 Aβ 양과 같은 경우, 상기

값은 0으로,

값은 1이다.

값이 1 보다 큰 것은 NC 그룹에서 Aβ 집적량과 비교하여 대상체의 Aβ 집적이 더 많다는 것을 나타낸다. Thus, an A [beta] weight value of less than 1,

Indicates that the Aβ accumulation amount is smaller than the average amount of Aβ at each vertex of the NC. If the Aβ accumulation amount of the subject is the same as that of the NC group,

The value is 0,

The value is 1.

A value greater than 1 indicates that the Aβ accumulation of the subject is greater than the Aβ accumulation in the NC group.

As a result, A [beta] weighted cortical thickness values were calculated to examine cortical thickness patterns associated with amyloid in all three groups. The Aβ weighted cortical thickness values at the i-th vertex of each subject k were calculated using the following formula:

&Quot; (9) "

Example 4 Classification Using Linear Discriminant Analysis

To confirm whether the Aβ weighted cortical thickness value as a new biomarker for AD is inferior to the cortical thickness alone or the classification model using only Aβ absorption, a multivariate classification model using Aβ weighted cortical thickness was constructed and classification accuracy Respectively.

A classifier between three pairs of groups that minimizes within-class variance and performs adjustments to maximize the between-classes variance until an axis is found that separates the groups of data sets as much as possible. Fisher's LDA (linear discriminant analysis) was used to generate.

In order to reduce the feature dimension in each classifier, we have characterized the Aβ weighted cortical thickness value within a predetermined region-of-interest (ROI), which is widely known as the cortical feature of AD. The cortical features are segmented by automated anatomical labeling (AAL) templates within individual cortical surfaces. A volume-based AAL template is originally defined as a single object is matched to a standard surface model by a 2D surface join, and the AAL 78 label is aligned with individual cortical surfaces using Sphere-to-Sphere warp matching. We selected 52 ROIs that were known to represent AD cortical features (superior frontal gyri, dorsolateral, both superior frontal gyrus, orbital part, both middle frontal gyri, both middle frontal gyri, opercular part, both inferior frontal gyri, triangular part, both medial, both superior superior frontal gyrus, medial orbital, both posterior cingulate gyrus, both parahippocampal gyrus, both superior occipital gyrus, both middle occipital gyrus, both inferior occipital gyrus, both inferior occipital gyrus, inferior frontal gyrus, , both fusiform gyrus, both superior parietal gyrus, both inferior parietal, but supramarginal and angular gyrus, both supramarginal gyrus, both angular gyrus, both temporal pole: superior temporal gyrus, both middle temporal gyrus, both temporal pole: middle temporal gyr, and both inferior temporal gyrus).

A leave-one-out cross-validation was performed to evaluate the generated classifiers by measuring classification accuracy from three pairs of groups: AD-NC, AD-MCI, and MCI-NC. One object was assigned as a test set and the remaining objects were used as a training set for the LDA classifier. The cross validation procedure was repeated 5,000 times to avoid selection bias in each classifier.

Example 5: Statistical analysis

The general multivariate linear model of the package was used to compare age, sex, education level, and intra-cranial volume (ICV), as well as the cortical thickness of the entire cortical apex and the proposed Aβ weighted cortical thickness. To solve the multiple comparison problem, the false discovery rate (FDR) correction was performed at 95% confidence interval. In addition, 78 cortical sites derived from the AAL template were used to compare differences between groups in all three groups, controlling for age, sex, education level and ICV using a two-sample t-test Average A c weighted cortical thickness, and average PiB accumulation were calculated. In ROI-based analysis, Bonferroni correction was applied to estimate multiple comparisons. The protocol of the present invention was approved by Asan Medical Center and all data obtained from ADNI were analyzed according to ADNI guidelines.

Experimental Example: Clinical Characteristics of a Subject

(1) between the three groups de novo  Cortical thickness, A? Weighted cortical thickness, and PiB integration pattern

A statistically significant group difference in cortical thickness and Aβ weighted cortical thickness patterns between the three groups (AD, NC, MCI) was obtained while controlling age, sex and ICV (FIG. 2).

Compared to the NC subjects, the superior temporal gyri, right middle and inferior temporal gyri in the AD patient, part of the bilateral cortex, supramarginal gyrus, Some of the cortical thicknesses of the temporal pole, posterior cingulate cortex, and lateral occipital gyr were thinner (Fig. 2a, left column).

Compared with NC subjects, MCI patients are classified as superior parietal cortex, part of the middle frontal cortex, middle temporal gyri, temporal pole, The thickness of the cortex of the posterior cingulate, part of the supramarginal gyri, and inferior parietal gyr was thinner (Fig. 2a, middle column).

There was no significant group difference in cortical thickness between AD and MCI (Fig. 2a, right column).

Age, sex, and ICV controlled Aβ weighted cortical thickness analysis show areas of thinner cortices in AD and MCI subjects compared to NC subjects (FIG. 2b).

In AD patients, the Aβ weighted cortical thickness reduction was more significant in the superior temporal gyr, the middle frontal gyr, the right upper limb, and the right lateral fascia, (Q <0.05, FDR-corrected; Fig. 2b, left column) with a similar pattern of de novo cortical thickness reduction.

In the Aβ weighted cortical thickness analysis, the cortical thicknesses between the MCI and NC subjects were nearly similar (q <0.05, FDR-corrected; Fig. 2b, right column).

Additional atrophy was observed in AD in postcentral gyri, part of the left tongue (lingual gyrus), and part of the superior temporal gyri compared to MCI (q <0.05, FDR- 2b, middle column).

The Aβ accumulation was measured as the standardized uptake value ratio (SUVR) of each vertex of the cortical area between the three groups (FIG. 3).

In comparison with NC, a portion of the central gyri in the AD patient, a temporal pole, a parahippocampal gyr, bilateral entorhinal cortices, and bilateral lateral occipital lobes (p <0.01, FDR-corrected; Fig. 3a).

Similar patterns of PiB accumulation were seen in MCI patients (q <0.01, FDR-corrected; Fig. 3c) in almost all cortical areas except the superior frontal gyr region compared to NC subjects.

Similar to cortical thickness analysis, there were few significant differences in PiB integration between AD and MCI patients (q <0.01, FDR-corrected; Fig. 3b).

(2) ROI-based cortical thickness, A [beta] weighted cortical thickness, and PiB integration pattern

To further investigate the clinical consequences of weighted cortical thickness, cortical sites were grouped based on the relationship between de novo cortical thickness reduction, de novo PiB retention, and Aβ weighted cortical thickness reduction.

Based on the AAL template, five distinct patterns of A [beta] -related cortical thickness were defined as shown in Fig.

First, the cortical thickness pattern is divided into (1) A? -Independent cortical thickness reduction (FIG. 4a); (2) decreased cortical thickness associated with A [beta] (Fig. 4b-d); And (3) only Aβ accumulation (FIG. 4e).

Group (2) was further subdivided into three subgroups based on the time point of development of A [beta] -related cortical thickness reduction. The primary pattern of A [beta] -related cortical thickness reduction indicates areas in which cortical thickness reduction is probably initiated by other causes, and then accumulation of A [beta] in the brain is promoted (Fig. 4b).

The secondary pattern refers to the site where a decrease in A [beta] -related cortical thickness occurs from the MCI stage (Fig. 4C).

The tertiary pattern of A [beta] -related cortical thickness reduction is a decrease in cortical thickness induced by A [beta]. In this pattern, there was no de novo cortical thickness reduction compared to NC at the MCI stage, but there was a decrease in A? Weighted cortical thickness (Fig. 4d).

Aβ weighted cortical thickness reduction, de novo cortical thickness reduction, and amyloid aggregation as measured by PiB-PET are detailed in FIG. 4 and Table 2.

^* : Statistically significant group difference between AD and MCI at Bonferroni corrected p <0.05 or less

^§ : Statistically significant group difference between MCI and normal control (NC) at Bonferroni corrected p <0.05 or less

^† : statistically significant group difference between AD and NC at Bonferroni corrected p <0.05 or less

Abbreviations: AD, Alzheimer's disease; MCI, mild cognitive impairment; NC, normal controls; CTh, cortical thickness; PIB, Pittsburg compound B deposition; Aβ.w.CTh, Aβ-weighted cortical thickness.

(a) decreased A [beta] -independent cortical thickness

There was no difference in PiB absorption between MCI and NC, even between AD and NC at this site. The de novo cortical thickness reduction showed a more significant difference than the Aβ weighted cortical thickness reduction. The site includes the left and right parahippocampal gyri.

(b) Aβ-related cortical thickness reduction: Aβ-related thickness reduction in AD after non-Aβ related thickness reduction in MCI

At this site there was no difference in PiB absorption between MCI and NC. The de novo cortical thickness reduction showed a more significant difference than the Aβ weighted cortical thickness reduction. In AD, both PiB absorption and de novo cortical thickness decreased and Aβ weighted cortical thickness decreased significantly. The site includes the left middle frontal gyrus, the right superior frontal gyrus, and the bilateral parahippocampal gyrus.

(c) Aβ-related cortical thickness reduction: Aβ-related thickness is further reduced in AD after Aβ-related thickness reduction in MCI

The region shows no difference in PiB absorption between MCI and NC. A decrease in Aβ weighted cortical thickness was noted in MCI than in de novo cortical thickness reduction. In AD, both PiB absorption and de novo cortical thickness decreased and Aβ weighted cortical thickness decreased significantly. The site includes the left island, the left upper and middle temporal gyri (left insula), and the left temporal pole.

(d) Aβ-related cortical thickness reduction: a decrease in thickness due to continuous Aβ in AD after early thickness reduction by Aβ in MCI

The site exhibits more PiB absorption in MCI compared to NC. In MCI, Aβ weighted cortical thickness decreased more than de novo cortical thickness reduction. In AD, both PiB absorption and de novo cortical thickness decreased and Aβ weighted cortical thickness decreased significantly. The area showing decreased cortical thickness by Aβ is the left superior frontal gyrus (inner part), the right posterior cingulate gyrus, the left lingual gyrus, the right inferior occipital gyrus, the right fusiform gyrus, the left supramarginal gyrus, the right temporal pole, and the left inferior temporal gyrus.

(e) independent Aβ accumulation with no reduction in cortical thickness

In the fifth group, PiB absorption was more pronounced in MCI and AD compared to NC. There was no significant difference in de novo cortical thickness reduction and Aβ weighted cortical thickness reduction in AD and MCI. These sites, which are independent of A [beta] -related pathology, include the right precentral gyrus, right postcentral gyrus, bilateral calcarine fissure and surrounding cortex, bilateral wedge, and bilateral olfactory olfactory) cortex. The region also includes the right superior frontal gyrus, the bilateral inferior frontal gyrus (orbital and opercular), the left supplementary motor area, and the bilateral gyrus rectus ), Left anterior and bilateral median cingulate and paracingulate gyri, right lingual gyrus, bilateral occipital gyrus, right lower right inferior temporal gyrus, and bilateral precuneus.

(3) Classification analysis

To compute the accuracy of the proposed value by measuring the classification accuracy, a multivariate classifier was constructed by using cortical thickness, Aβ accumulation, and Aβ weighted cortex thickness in 52 cortical regions through LDA treatment. The accuracy, sensitivity, and specificity of the A? Weighted cortical thickness did not detract from those obtained from the conventional classification using cortical thickness or A? Absorption (Table 3).

From the above description, it will be understood by those skilled in the art that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. In this regard, it should be understood that the above-described embodiments are to be considered in all respects as illustrative and not restrictive. The scope of the present invention should be construed as being included in the scope of the present invention without departing from the scope of the present invention as defined by the appended claims.

Claims (28)

(a) collecting an MR (Magnetic Resonance) image and a PET (Positron-Emitting Tomography) image of the object k ;
(b) normalizing the cortical thickness value from the collected MR image

), And from the PET data, the weight for the pathologic factor of degenerative brain disease (

);
(c) the calculated normalized cortical thickness value (

), A weight for the pathologic factor of the degenerative brain disease of the subject obtained from PET

) And the pathologic factor value for degenerative brain disease is the weighted cortical thickness value (

&Lt; / RTI &gt;
A method for obtaining an image biomarker suitable for diagnosis of degenerative brain diseases.
2. The method of claim 1, wherein the normalized cortical thickness value (

) &Lt; / RTI &gt; is calculated by: &lt; EMI ID =
[Equation 1]

here,

Is the cortical thickness at one point calculated from the cerebral cortical model of the MR image of the subject k ,

The average cortical thickness of the normal control group at the same point,

Indicates the standard deviation of the cortical thickness of the normal control group.
The method according to claim 1,
Weight of the pathologic factor of the degenerative brain disease of the subject (

) Is a normalized degenerative brain disease pathologic parameter value

) Is calculated by the following equation (2), and the computed normalized degenerative brain disease pathologic factor value is calculated by the following equation (3): &lt; EMI ID =
&Quot; (2) &quot;

here,

Is the value of the degenerative brain disease pathologic factor in the cerebral cortex model of PET of the subject k ,

The mean degenerative brain disease pathologic factor value of the normal control group at the same spot was calculated as

Represents the standard deviation of the degenerative brain disease pathologic factor value of the normal control group at the same spot;
&Quot; (3) &quot;

here,

Represents the minimum of the normalized degenerative brain disease pathologic factor values of all PET data.
2. The method of claim 1, wherein the degenerative brain disease is selected from the group consisting of Alzheimer's disease, frontotemporal dementia, Parkinson's disease, cortical basal degeneration, multiple system atrophy and progressive supranuclear palsy.
The method according to claim 4, wherein the pathologic factor of Alzheimer's disease is selected from the group consisting of accumulation of amyloid beta (A?), Accumulation of tau protein, degree of brain inflammation, change of acetylcholinesterase, change of metabolic glutamate receptor, A change in the cholinergic receptor, and a change in the monoamine oxidase B.
6. The method of claim 5, wherein the accumulation of amyloid beta is selected from the group consisting of 11C-PiB, 18F-Amibid, 18F-Flutemetamol, 18F-Florbetaben, 18F- CQ, and 18F-CABS13. &Lt; / RTI &gt;
The method according to claim 5, wherein the accumulation of the tau protein is carried out by the following steps: 11C-PBB3, 18F-PBB3, 18F-T807, 18F-T808, 18F-THK-5117, 18F-THK-523, 18F- (18F-Fluoroethyl-Astemizol), and 18F-FDDNP. &Lt; / RTI &gt;
The method according to claim 5, wherein the degree of cerebral inflammation is selected from the group consisting of (R) -11C-PK11195, 18F-PBR111, 18F-FEPPA, 11C-PBR28, 6-18F-fluoro-PBR28, 11C-PBR06, 18F-PBR06, 11C-DAA1106, 11C-DPA-714, 11C-AcAC, 2-18F-fluoroacetate, 11C-AC5216, Using a PET tracer selected from the group consisting of FEAC, 18F-FEDAC, 18F-DAA1106, 11C-DED, 11C-Sch225336, 1- [11C] -AA, 18F-FAA, and 11C-Ro 5-4864 How, one.
6. The method of claim 5, wherein the change in acetylcholinesterase is confirmed using a PET tracer selected from the group consisting of 11C-AMP, 11C-PMP, and 11C-BMP.
6. The method of claim 5, wherein said metabotropic glutamate receptor changes are identified using 18F-FPEB for the mGluR5 receptor or 11C-ABP688 for the mGluR5 receptor as a PET tracer.
6. The method of claim 5, wherein the change in muscarinic acetylcholine receptor is determined using 18F-FP-TZTP for M2 receptor or 11C-Zanomeline for M1 / M4 antagonist as a PET tracer. Way.
6. The method of claim 5, wherein the change of the monoamine oxidase B is selected from the group consisting of (R) - (-) - 11C-L -deprenyl-D2, 11C- SL25.1188, 11C-DED, and 18F- &Lt; / RTI &gt; as a PET tracer selected from the group consisting of &lt; RTI ID = 0.0 &gt;
5. The method of claim 4, wherein the pathologic factor of frontotemporal dementia, Parkinson's disease, cortical basal degeneration, multiple system atrophy and progressive supranuclear palsy is tau protein or aggregate deposition thereof.
5. The method of claim 4, wherein the Alzheimer's pathologic factor is amyloid beta (A [beta] accumulation.
15. The method of claim 14,
The weight for the Aβ accumulation (

) Is the normalized A [beta] accumulation value (

) Is calculated by the following equation (4), and the calculated normalized Aβ accumulation value is calculated by the following equation (5):
&Quot; (4) &quot;

here,

Aβ accumulation value in the cerebral cortex model of PET of the subject k ,

The average A [beta] accumulation value of the normal control group at the same point,

Represents the standard deviation of the A [beta] accumulation value of the normal control at the same spot;
&Quot; (5) &quot;

here,

Represents the minimum value of the normalized A [beta] accumulation value of the entire PET data.
5. The method of claim 4, wherein the method is for follow-up on Alzheimer's disease.
2. The method of claim 1, wherein the method further comprises calculating a weighted cortical thickness value of a pathologic factor value of degenerative brain disease at a region of interest (ROI) of the subject cerebral cortex model.
2. The method of claim 1, wherein the method further comprises imaging a weighted cortical thickness value of a pathologic factor value of the degenerative brain disease.
(a) collecting an MR (Magnetic Resonance) image and a PET (Positron-Emitting Tomography) image of the object k ;
(b) normalizing the cortical thickness value from the collected MR image

), And from the PET data, the weight for the pathologic factor of degenerative brain disease (

);
(c) the calculated normalized cortical thickness value (

), A weight for the pathologic factor of the degenerative brain disease of the subject obtained from PET

) And the pathologic factor value for degenerative brain disease is the weighted cortical thickness value (

&Lt; / RTI &gt;
Methods for providing information for diagnosis of degenerative brain diseases.
An image collection unit for collecting an MR image and a PET image of the object ( k );
A cortical thickness analyzer for analyzing the MR image received from the image collection unit to calculate a normalized cortical thickness value;
A degenerative brain disease pathologic factor analyzing unit for analyzing the PET image received from the image collecting unit to calculate a weight for a pathologic factor of degenerative brain disease; And
And a conversion unit for converting the degenerative brain disease pathologic factor value into a weighted cortical thickness value from a weight for a normalized cortical thickness value received from the cortical thickness analyzer and a pathological factor of a degenerative brain disease received from the degenerative brain disease pathology factor analyzer doing,
Apparatus for predicting degenerative brain disease.
21. The method of claim 20,
Wherein the device is applicable to a PET-MRI integrated system.
21. The method of claim 20,
The normalized cortical thickness value (

) &Lt; / RTI &gt; is calculated by: &lt; EMI ID =
[Equation 1]

here,

Is the cortical thickness at one point calculated from the cerebral cortical model of the MR image of the subject k ,

The average cortical thickness of the normal control group at the same point,

Represents the standard deviation of the cortical thickness of the normal control at the same point.
21. The method according to claim 20, wherein the weight of the pathological factor value for the degenerative brain disease of the subject

) Is a normalized degenerative brain disease pathologic parameter value

) Is calculated by the following equation (2), and the computed normalized degenerative brain disease pathologic factor value is calculated by the following equation (3): &lt; EMI ID =
&Quot; (2) &quot;

here,

Is the value of the degenerative brain disease pathologic factor in the cerebral cortex model of PET of the subject k ,

The mean degenerative brain disease pathologic factor value of the normal control group at the same spot was calculated as

Represents the standard deviation of the degenerative brain disease pathologic factor value of the normal control group at the same spot;
&Quot; (3) &quot;

here,

Represents the minimum of the normalized degenerative brain disease pathologic factor values of all PET data.
21. The device of claim 20, wherein the degenerative brain disease is selected from the group consisting of Alzheimer's disease, frontotemporal dementia, Parkinson's disease, cortical basal degeneration, multiple system atrophy and progressive supranuclear palsy.
25. The device of claim 24, wherein the pathologic factor of Alzheimer &apos; s disease is amyloid beta (A [beta] accumulation.
26. The method of claim 25,
The weight for the Aβ accumulation (

) Is the normalized A [beta] accumulation value (

) Is calculated by the following equation (4), and the calculated normalized Aβ accumulation value is calculated by the following equation (5):
&Quot; (4) &quot;

here,

Aβ accumulation value in the cerebral cortex model of PET of the subject k ,

The average A [beta] accumulation value of the normal control group at the same point,

Represents the standard deviation of the A [beta] accumulation value of the normal control at the same spot;
&Quot; (5) &quot;

here,

Represents the minimum value of the normalized A [beta] accumulation value of the entire PET data.
21. The method of claim 20,
Wherein the device further comprises a degenerative brain disease determination unit for determining degenerative brain disease.
A computer program stored in a medium for providing information for diagnosis of degenerative brain disease,
Analyzing each of the MR image of the subject and the PET image of the subject to calculate a weight for the normalized cortical thickness value and the pathological factor value of the degenerative brain disorder; And
And multiplying the calculated normalized cortical thickness value and the weight for the pathologic factor value of the degenerative brain disease by each other to convert the pathologic factor value of the degenerative brain disease to a weighted cortical thickness value.

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