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

Author(s)

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

Affiliation

^[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

• Document type: Journal article
• Source: STATISTICS IN MEDICINE; v. 33, n. 28, p. 4949-4962, DEC 10 2014.
• Web of Science Citations: 2

Abstract

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)

• FAPESP's process: 12/21788-2 - Regression models and applications
• Grantee: Heleno Bolfarine
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
• Grantee: Roberto Marcondes Cesar Junior
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 14/09576-5 - Development of computational-statistical methods to construct, model and analyze biological networks associated with human diseases
• Grantee: André Fujita
• Support Opportunities: Regular Research Grants
• FAPESP's process: 13/10498-6 - Machine learning in neuroimaging: development of methods and clinical applications in psychiatric disorders
• Grantee: João Ricardo Sato
• Support Opportunities: Regular Research Grants