Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/108296
Title: Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8
Authors: Ferreira, Fábio S. 
Pereira, João 
Duarte, João V. 
Castelo-Branco, Miguel 
Keywords: SPM; VBM; T1; T2; Multivariate GLM; Type 2 diabetes mellitus
Issue Date: 2017
Publisher: Bentham
Project: DoIT – Diamarker, a consortium for the discovery of novel biomarkers in diabetes type 2 
QREN-COMPETE “Genetic susceptibility of multisystemic complications of diabetes type 2: novel biomarkers for diagnosis and monitoring of therapy” 
UID/NEU/04539/2013 COMPETE POCI-01-0145-FEDER- 007440 
Fundo para a Investigação em Saúde (FIS), INFARMED, FIS-2015-01_DIA_20150630-173 
Serial title, monograph or event: Open Neuroimaging Journal
Volume: 11
Issue: 1
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.
URI: https://hdl.handle.net/10316/108296
ISSN: 1874-4400
DOI: 10.2174/1874440001711010032
Rights: openAccess
Appears in Collections:I&D ICNAS - Artigos em Revistas Internacionais
I&D IBILI - Artigos em Revistas Internacionais
FMUC Medicina - Artigos em Revistas Internacionais

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