A non-parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data

Autor(es)

Fujita, Andre ^[1] ; Takahashi, Daniel Y. ^{[2, 3]} ; Patriota, Alexandre G. ^[4] ; Sato, Joao R. ^[5]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Stat, Dept Comp Sci, BR-05508090 Sao Paulo - Brazil

^[2] Princeton Univ, Dept Psychol, Princeton, NJ 08544 - USA

^[3] Princeton Univ, Inst Neurosci, Princeton, NJ 08544 - USA

^[4] Univ Sao Paulo, Inst Math & Stat, Dept Stat, BR-05508090 Sao Paulo - Brazil

^[5] Univ Fed ABC, Ctr Math Computat & Cognit, Santo Andre - Brazil

• Tipo de documento: Artigo Científico
• Fonte: STATISTICS IN MEDICINE; v. 33, n. 28, p. 4949-4962, DEC 10 2014.
• Citações Web of Science: 2

Resumo

Statistical inference of functional magnetic resonance imaging (fMRI) data is an important tool in neuroscience investigation. One major hypothesis in neuroscience is that the presence or not of a psychiatric disorder can be explained by the differences in how neurons cluster in the brain. Therefore, it is of interest to verify whether the properties of the clusters change between groups of patients and controls. The usual method to show group differences in brain imaging is to carry out a voxel-wise univariate analysis for a difference between the mean group responses using an appropriate test and to assemble the resulting `significantly different voxels' into clusters, testing again at cluster level. In this approach, of course, the primary voxel-level test is blind to any cluster structure. Direct assessments of differences between groups at the cluster level seem to be missing in brain imaging. For this reason, we introduce a novel non-parametric statistical test called analysis of cluster structure variability (ANOCVA), which statistically testswhether two ormore populations are equally clustered. The proposedmethod allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering. We illustrate the performance of ANOCVA through simulations and an application to an fMRI dataset composed of children with attention deficit hyperactivity disorder (ADHD) and controls. Results show that there are several differences in the clustering structure of the brain between them. Furthermore, we identify some brain regions previously not described to be involved in the ADHD pathophysiology, generating new hypotheses to be tested. The proposed method is general enough to be applied to other types of datasets, not limited to fMRI, where comparison of clustering structures is of interest. Copyright (C) 2014 John Wiley \& Sons, Ltd. (AU)

• Processo FAPESP: 11/50761-2 - Modelos e métodos de e-Science para ciências da vida e agrárias
• Beneficiário: Roberto Marcondes Cesar Junior
• Linha de fomento: Auxílio à Pesquisa - Temático
• Processo FAPESP: 14/09576-5 - Desenvolvimento de técnicas estatístico-computacionais para construir, modelar e analisar redes biológicas envolvidas em doenças humanas
• Beneficiário: André Fujita
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 12/21788-2 - Modelos de regressão e aplicações
• Beneficiário: Heleno Bolfarine
• Linha de fomento: Auxílio à Pesquisa - Temático
• Processo FAPESP: 13/10498-6 - Aprendizado de máquina em neuroimagem: desenvolvimento de métodos e aplicações clínicas em transtornos psiquiátricos
• Beneficiário: João Ricardo Sato
• Linha de fomento: Auxílio à Pesquisa - Regular