A Test Statistic in Canonical Correlation Analysis for Categorical Variables

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventionalt-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

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A Novel Test Statistic for Local Canonical Correlation Analysis of fMRI Data

NeuroImage ◽

10.1016/s1053-8119(09)71175-9 ◽

2009 ◽

Vol 47 ◽

pp. S123 ◽

Cited By ~ 1

Author(s):

M Jin ◽

R Nandy ◽

D Cordes

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Fmri Data ◽

Test Statistic

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The Smoothing Artifact of Spatially Constrained Canonical Correlation Analysis in Functional MRI

International Journal of Biomedical Imaging ◽

10.1155/2012/738283 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Dietmar Cordes ◽

Mingwu Jin ◽

Tim Curran ◽

Rajesh Nandy

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Time Course ◽

Memory Task ◽

Motor Task ◽

Test Statistic ◽

Multivariate Statistical ◽

Activation Patterns ◽

Wide Range

A wide range of studies show the capacity of multivariate statistical methods for fMRI to improve mapping of brain activations in a noisy environment. An advanced method uses local canonical correlation analysis (CCA) to encompass a group of neighboring voxels instead of looking at the single voxel time course. The value of a suitable test statistic is used as a measure of activation. It is customary to assign the value to the center voxel; however, this is a choice of convenience and without constraints introduces artifacts, especially in regions of strong localized activation. To compensate for these deficiencies, different spatial constraints in CCA have been introduced to enforce dominance of the center voxel. However, even if the dominance condition for the center voxel is satisfied, constrained CCA can still lead to a smoothing artifact, often called the “bleeding artifact of CCA”, in fMRI activation patterns. In this paper a new method is introduced to measure and correct for the smoothing artifact for constrained CCA methods. It is shown that constrained CCA methods corrected for the smoothing artifact lead to more plausible activation patterns in fMRI as shown using data from a motor task and a memory task.

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ON THE PROPERTIES OF SOME TESTS FOR COMMON STOCHASTIC TRENDS

Econometric Theory ◽

10.1017/s0266466602186038 ◽

2002 ◽

Vol 18 (6) ◽

pp. 1336-1349

Author(s):

Jörg Breitung ◽

Carsten Trenkler

Keyword(s):

Monte Carlo ◽

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Asymptotic Properties ◽

Small Sample ◽

Test Statistics ◽

Test Statistic ◽

Stochastic Trends ◽

Small Sample Properties

We study the asymptotic properties of the tests suggested by Choi and Ahn (1995, Econometric Theory 11, 952–983) in the case of a (nearly) improper normalization of the cointegration vectors. To overcome the size problems in such situations we suggest a test statistic that is based on the eigenvalues of a canonical correlation analysis. Using Monte Carlo simulations, the small sample properties of our test are compared to various other test statistics recently suggested in the literature.

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Analysis of Maximum Expiratory Flow Volume Curves Using Canonical Correlation Analysis

Methods of Information in Medicine ◽

10.1055/s-0038-1635359 ◽

1985 ◽

Vol 24 (02) ◽

pp. 91-100 ◽

Cited By ~ 3

Author(s):

W. van Pelt ◽

Ph. H. Quanjer ◽

M. E. Wise ◽

E. van der Burg ◽

R. van der Lende

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Flow Volume ◽

Non Linear ◽

Maximum Expiratory Flow ◽

Expiratory Flow ◽

Relationship Of ◽

The Relationship ◽

Age And Sex

SummaryAs part of a population study on chronic lung disease in the Netherlands, an investigation is made of the relationship of both age and sex with indices describing the maximum expiratory flow-volume (MEFV) curve. To determine the relationship, non-linear canonical correlation was used as realized in the computer program CANALS, a combination of ordinary canonical correlation analysis (CCA) and non-linear transformations of the variables. This method enhances the generality of the relationship to be found and has the advantage of showing the relative importance of categories or ranges within a variable with respect to that relationship. The above is exemplified by describing the relationship of age and sex with variables concerning respiratory symptoms and smoking habits. The analysis of age and sex with MEFV curve indices shows that non-linear canonical correlation analysis is an efficient tool in analysing size and shape of the MEFV curve and can be used to derive parameters concerning the whole curve.

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