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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Fujita, Andre [1] ; Takahashi, Daniel Y. [2, 3] ; Patriota, Alexandre G. [4] ; Sato, Joao R. [5]
Total Authors: 4
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
Total Affiliations: 5
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: 13/10498-6 - Machine learning in neuroimaging: development of methods and clinical applications in psychiatric disorders
Grantee:João Ricardo Sato
Support type: Regular Research Grants
FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
Grantee:Roberto Marcondes Cesar Junior
Support type: Research Projects - Thematic Grants
FAPESP's process: 12/21788-2 - Regression models and applications
Grantee:Heleno Bolfarine
Support type: 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 type: Regular Research Grants