Diagnostic value of tensor-based morphology for grey matter atrophy in mild cognitive impairment patients

Yunling LONG

doi:10.16016/j.1000-5404.201709202

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Diagnostic value of tensor-based morphology for grey matter atrophy in mild cognitive impairment patients

Clinical Medicine | 更新时间：2026-01-09

- Diagnostic value of tensor-based morphology for grey matter atrophy in mild cognitive impairment patients
- Journal of Third Military Medical University Vol. 39, Issue 24, Pages: 2423-2429(2017)
- 作者机构：
  
  100050 北京，首都医科大学附属北京天坛医院医学工程处
- 作者简介：
- 基金信息：
- DOI：10.16016/j.1000-5404.201709202
  CLC： R445.2; R749; R811
- Received：25 September 2017，
  
  Revised：2017-10-25，
  
  Published：30 December 2017
- 稿件说明：
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Yunling LONG. Diagnostic value of tensor-based morphology for grey matter atrophy in mild cognitive impairment patients[J]. Journal of Third Military Medical University, 2017, 39(24): 2423-2429.
DOI：

Yunling LONG. Diagnostic value of tensor-based morphology for grey matter atrophy in mild cognitive impairment patients[J]. Journal of Third Military Medical University, 2017, 39(24): 2423-2429. DOI： 10.16016/j.1000-5404.201709202.

摘要

目的

探索轻度认知障碍患者的大脑灰质结构萎缩的规律，区分不同轻度认知障碍亚型的灰质结构萎缩模式的异同。

方法

数据来自阿尔茨海默病的神经影像(Alzheimer’s disease neuroimaging initiative，ADNI)数据库，包括34例正常人、22例稳定性轻度认知障碍和20例进展性轻度认知障碍患者60个月纵向高分辨MRI 3D T1W结构像。基于张量的形态学(tensor-based morphology，TBM)测量方法进行数据分析，利用DARTEL配准过程中得到的雅克比矩阵计算出行列式的值和迹，并对得到的特征参数进行组内方差分析，然后采用多重比较的配对

检验，获取相对首次扫描时间点时不同跟踪时间点大脑灰质萎缩的规律及特征。

结果

采用两种参数得到的结果相对一致，3组被试数据均表现为随着时间的推移，大脑灰质萎缩区域逐步扩大，萎缩程度逐步增加，尤其对于颞叶、额叶、海马和扣带回等与认知相关的区域萎缩最为明显。但是在相同时间点，相对于正常组来说，稳定型的轻度认知障碍患者组大脑灰质萎缩程度较为严重，进展型的轻度认知障碍患者组大脑灰质萎缩最为严重。

结论

TBM测量法得到的大脑灰质萎缩差异可以用于区分稳定性、进展性轻度认知障碍以及正常人大脑结构，可指导阿尔茨海默病的早期诊断和相关临床干预。

Abstract

Objective

To investigate the atrophy pattern of grey matter in the patients with mild cognitive impairment (MCI)

and to identify the atrophy differences in the different subtypes of MCI.

Methods

The longitudinal high-resolution 3D T1W images in this study were all downloaded from Alzheimer's disease neuroimaging initiative(ADNI)

including images of 34 healthy individuals

22 patients of stable MCI and 20 ones with progressive MCI during a period of 60 months. Tensor-based morphology (TBM) was adopted for image analysis. The Jacobian matrix was firstly obtained through DARTEL registration

and the determinant value and trace were calculated from the Jacobian matrix. Finally

ANOVA and Paired

test were performed on the new parameters to statistically analyze the characteristics of grey matter atrophy by comparing the MRI images at tracking time points with those at baseline.

Results

The results showed that there was a corresponding atrophy pattern by using the 2 parameters. In the 3 groups

with the time elapsed

the atrophy regions were expanded

especially the brain regions related to cognitive function

including temporal lobe

frontal lobe

hippocampus and cingulate gyrus etc. Meanwhile

at the same tracking time point

the grey matter atrophy in the SMCI patients was more serious

and the atrophy in the PMCI patients was the most serious when compared to the normal control group.

Conclusion

TBM obtained regions and patterns in grey matter atrophy can be used as radiographic markers to distinguish PMCI from SMCI

and the indicators are helpful for the early diagnosis of AD and clinical treatment.

关键词

Keywords

references

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