Download LYNGBY Matlab toolbox for functional neuroimaging analysis

Section 1.1
1.1.1
The Purpose of the Lyngby Toolbox
8
Models Available in the Toolbox
Currently, the toolbox includes the following modelling methods (The exact details of the algorithms are covered later on in the manual):
Cross-Correlation Cross-correlation with the paradigm (The activation function, which usually is a square shaped design function). A well-established method suited for estimation
of delay and activation strength from a given paradigm.
FIR filter A general linear regression model of finite length. The time series are modelled as
a convolution between the paradigm and a linear filter of a finite length, on a per voxel
basis.
Exhaustive FIR filter The same as the FIR filter, but using an exhaustive search of all FIR
models up to a specified filter length. The optimal filter is found from generalization
theory.
K-means Clustering A non-linear statistical method for clustering (labelling) the data using
either the raw time series or the cross-correlation with the paradigm. This method is suited
for identification of areas with similar activation and delay. The method is described in
(Bishop, 1995, pp. 187-189) and has been used for fMRI in (Toft et al., 1997; Goutte
et al., 1999).
Grid Search Lange-Zeger model A parameterized convolution model with a grid search of
optimal parameters with zoom built in. The same as above but a non-iterative grid search
scheme for estimating the parameter is used. The model estimates three parameters that
have easy physical interpretation.
Iterative Lange-Zeger model A parameterized convolution model with an iterative scheme
for estimating the model parameters. The model estimates three parameters that have
easy physical interpretation.
The Ardekani t-test A linear transform of the time-series mapped to a subspace controlled
by the paradigm. In this method the activation estimator can be approximated to be
Student t-distributed, hence a statistical measure of the correlation to the paradigm is
found. The method has been described by Babak Ardekani and Iwao Kanno (Ardekani
and Kanno, 1998).
Ardekani F-test A linear transform of the time-series mapped to a subspace controlled by
a finite size Fourier based subspace. In this method the activation estimator can be
approximated to be F-distributed, hence a statistical measure of the energy in the subspace
relative to the energy lying outside of this subspace is found. The method has been
described by Babak Ardekani and Iwao Kanno (Ardekani and Kanno, 1998).
Ardekani F-test with nuisance subspace A variation of the Ardekani F-test, where a nuisance subspace is identified and extracted from the signal. The method is described in
(Ardekani et al., 1999).
Ordinary t-test From a square wave activation function each of the time series is split into
an activation part and a baseline part. In this model the difference in means relative to a
deviation measure is given a statistical interpretation.
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