RESEARCH ARTICLE
Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8
Fábio S. Ferreira1, #, João M.S. Pereira1, 2, #, João V. Duarte1, 3, Miguel Castelo-Branco1, 2, 3, *
Article Information
Identifiers and Pagination:
Year: 2017Volume: 11
First Page: 32
Last Page: 45
Publisher ID: TONIJ-11-32
DOI: 10.2174/1874440001711010032
Article History:
Received Date: 22/11/2016Revision Received Date: 07/12/2016
Acceptance Date: 20/03/2017
Electronic publication date: 29/05/2017
Collection year: 2017
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
