Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8.

Q4 Medicine
Open Neuroimaging Journal Pub Date : 2017-05-29 eCollection Date: 2017-01-01 DOI:10.2174/1874440001711010032
Fábio S Ferreira, João M S Pereira, João V Duarte, Miguel Castelo-Branco
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引用次数: 2

Abstract

Background: Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods, which allow the study of multiple dependent variables, e.g. different imaging modalities of the same subject.

Objective: Given the widespread use of SPM software in the brain imaging community, the main aim of this work is the implementation of massive multivariate inferential analysis as a toolbox in this software package. applied to the use of T1 and T2 structural data from diabetic patients and controls. This implementation was compared with the traditional ANCOVA in SPM and a similar multivariate GLM toolbox (MRM).

Method: We implemented the new toolbox and tested it by investigating brain alterations on a cohort of twenty-eight type 2 diabetes patients and twenty-six matched healthy controls, using information from both T1 and T2 weighted structural MRI scans, both separately - using standard univariate VBM - and simultaneously, with multivariate analyses.

Results: Univariate VBM replicated predominantly bilateral changes in basal ganglia and insular regions in type 2 diabetes patients. On the other hand, multivariate analyses replicated key findings of univariate results, while also revealing the thalami as additional foci of pathology.

Conclusion: While the presented algorithm must be further optimized, the proposed toolbox is the first implementation of multivariate statistics in SPM8 as a user-friendly toolbox, which shows great potential and is ready to be validated in other clinical cohorts and modalities.

Abstract Image

Abstract Image

Abstract Image

扩展2型糖尿病多变量GLM患者在SPM8实施的推断组分析。
背景:尽管基于体素的形态测量学研究仍然是分析大脑结构的标准,但它们对大量单变量推理方法的依赖是一个限制因素。通过应用多元推理方法可以更好地理解脑病理,这种方法允许研究多个因变量,例如同一受试者的不同成像方式。目的:鉴于SPM软件在脑成像领域的广泛使用,本工作的主要目的是在该软件包中实现大量多变量推理分析作为工具箱。应用于使用来自糖尿病患者和对照组的T1和T2结构数据。该实现与SPM中的传统ANCOVA和类似的多变量GLM工具箱(MRM)进行了比较。方法:我们实施了新的工具箱,并通过调查28名2型糖尿病患者和26名匹配的健康对照者的大脑变化来测试它,使用T1和T2加权结构MRI扫描的信息,分别使用标准的单变量VBM,并同时使用多变量分析。结果:单因素VBM主要复制了2型糖尿病患者基底节区和岛区双侧的变化。另一方面,多变量分析重复了单变量结果的关键发现,同时也揭示了丘脑是病理的额外焦点。结论:虽然所提出的算法还有待进一步优化,但所提出的工具箱是SPM8中首次实现多变量统计的用户友好工具箱,显示出巨大的潜力,并准备在其他临床队列和模式中进行验证。
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来源期刊
Open Neuroimaging Journal
Open Neuroimaging Journal Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
0.70
自引率
0.00%
发文量
3
期刊介绍: The Open Neuroimaging Journal is an Open Access online journal, which publishes research articles, reviews/mini-reviews, and letters in all important areas of brain function, structure and organization including neuroimaging, neuroradiology, analysis methods, functional MRI acquisition and physics, brain mapping, macroscopic level of brain organization, computational modeling and analysis, structure-function and brain-behavior relationships, anatomy and physiology, psychiatric diseases and disorders of the nervous system, use of imaging to the understanding of brain pathology and brain abnormalities, cognition and aging, social neuroscience, sensorimotor processing, communication and learning.
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