LYNGBY Matlab toolbox for functional neuroimaging analysis

LYNGBY Matlab toolbox for functional neuroimaging analysis
LYNGBY
Matlab toolbox for
functional neuroimaging analysis
Peter Toft, Finn Årup Nielsen,
Matthew G. Liptrot, and Lars Kai Hansen
IMM — Informatics and Mathematical Modelling
DTU — Technical University of Denmark
Version 1.05 — August 14, 2006
Contents
1 Introduction
1.1 The Purpose of the Lyngby Toolbox . . . . . . . .
1.1.1 Models Available in the Toolbox . . . . . .
1.2 Organisation of this Book . . . . . . . . . . . . . .
1.3 Explanation of the Typographic Conventions . . .
1.4 fMRI Analysis, Matlab and the Lyngby Toolbox .
1.4.1 Definitions and Glossary . . . . . . . . . . .
1.5 Support on the Web and Downloading the Toolbox
1.6 Other Software . . . . . . . . . . . . . . . . . . . .
1.7 The Lyngby Development Team . . . . . . . . . . .
1.7.1 Acknowledgments . . . . . . . . . . . . . .
1.7.2 Contact us . . . . . . . . . . . . . . . . . .
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2 User Manual
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Installation and Getting Started . . . . . . . . . . . . . . . . .
2.2.1 First-time Users . . . . . . . . . . . . . . . . . . . . . .
2.2.2 Using Lyngby On Your Own Data . . . . . . . . . . . .
2.3 Using Lyngby . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1 Starting Lyngby . . . . . . . . . . . . . . . . . . . . . .
2.3.2 Getting Help . . . . . . . . . . . . . . . . . . . . . . . .
2.4 Data Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.1 How Lyngby Reads in Data . . . . . . . . . . . . . . . .
2.4.2 Setting-up the Conversion Files for Non-supported Data
2.4.3 Examples of the Data Conversion Files . . . . . . . . . .
2.5 Using the Lyngby Windows Interface . . . . . . . . . . . . . . .
2.5.1 The Workflow in the Lyngby Toolbox . . . . . . . . . .
2.5.2 A Typical Analysis Session . . . . . . . . . . . . . . . .
2.5.3 Exporting Variables, Printing and Saving Results . . . .
2.6 Using Lyngby from the commandline . . . . . . . . . . . . . . .
2.6.1 Data Loading . . . . . . . . . . . . . . . . . . . . . . . .
2.6.2 Pre-Processing . . . . . . . . . . . . . . . . . . . . . . .
2.6.3 Global Variables . . . . . . . . . . . . . . . . . . . . . .
2.7 Writing Scripts to Run Lyngby Automatically . . . . . . . . . .
2.8 Adding New Algorithms to the Toolbox . . . . . . . . . . . . .
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3
CONTENTS
3 Data Formats, Masking, and Pre-Processing
3.1 Introduction . . . . . . . . . . . . . . . . . . .
3.2 Volume file formats . . . . . . . . . . . . . . .
3.3 Masking . . . . . . . . . . . . . . . . . . . . .
3.4 Preprocessing steps . . . . . . . . . . . . . . .
3.4.1 Centering . . . . . . . . . . . . . . . .
3.4.2 Variance Normalization . . . . . . . .
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4 Analysis — The Modeling Algorithms
4.1 Cross-correlation . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.1 Our implementation . . . . . . . . . . . . . . . . . . .
4.2 FIR filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.1 Estimation of the Activation strength in the FIR filter
4.2.2 Estimation of the delay from the FIR Filter . . . . . .
4.2.3 Our Implementation . . . . . . . . . . . . . . . . . . .
4.2.4 References . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Exhaustive FIR filter . . . . . . . . . . . . . . . . . . . . . . .
4.3.1 Our Implementation . . . . . . . . . . . . . . . . . . .
4.3.2 References . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 The Ardekani t-test . . . . . . . . . . . . . . . . . . . . . . .
4.4.1 Our Implementation . . . . . . . . . . . . . . . . . . .
4.4.2 References . . . . . . . . . . . . . . . . . . . . . . . . .
4.5 The Ardekani F-test . . . . . . . . . . . . . . . . . . . . . . .
4.5.1 Our Implementation . . . . . . . . . . . . . . . . . . .
4.5.2 References . . . . . . . . . . . . . . . . . . . . . . . . .
4.6 K-means Clustering . . . . . . . . . . . . . . . . . . . . . . .
4.6.1 Our implementation . . . . . . . . . . . . . . . . . . .
4.6.2 References . . . . . . . . . . . . . . . . . . . . . . . . .
4.7 Kolmogorov-Smirnov test . . . . . . . . . . . . . . . . . . . .
4.7.1 Our implementation . . . . . . . . . . . . . . . . . . .
4.7.2 References . . . . . . . . . . . . . . . . . . . . . . . . .
4.8 Lange-Zeger . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.8.1 Estimation of the delay from the Lange Zeger model .
4.8.2 Our implementation . . . . . . . . . . . . . . . . . . .
4.9 Neural networks . . . . . . . . . . . . . . . . . . . . . . . . .
4.9.1 Our implementation . . . . . . . . . . . . . . . . . . .
4.9.2 References . . . . . . . . . . . . . . . . . . . . . . . . .
4.10 Neural network regression . . . . . . . . . . . . . . . . . . . .
4.10.1 Our implementation . . . . . . . . . . . . . . . . . . .
4.10.2 References . . . . . . . . . . . . . . . . . . . . . . . . .
4.11 Neural network saliency . . . . . . . . . . . . . . . . . . . . .
4.11.1 Our implementation . . . . . . . . . . . . . . . . . . .
4.11.2 References . . . . . . . . . . . . . . . . . . . . . . . . .
4.12 The SCVA model: Strother Canonical Variate Analysis . . .
4.12.1 Our implementation . . . . . . . . . . . . . . . . . . .
4.12.2 References . . . . . . . . . . . . . . . . . . . . . . . . .
4.13 The SOP model: Strother Orthonormalized PLS . . . . . . .
4.13.1 Our implementation . . . . . . . . . . . . . . . . . . .
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c
°Lars
Kai Hansen et al 1997
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4
CONTENTS
4.13.2 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.14 The Ordinary t-test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5 Post-Processing
5.1 K-means Clustering on the Modelling Results . . . . . . . . . . . . . . . . . . . .
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A Glossary
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B Functions
B.1 Available Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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C Derivations
C.1 Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C.1.1 Symbol table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C.1.2 The errorfunction for the classifier neural network and its derivatives
C.1.3 Numerical stabil computation of softmax . . . . . . . . . . . . . . .
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D Getting Started
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E Example Analysis
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c
°Lars
Kai Hansen et al 1997
List of Figures
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
2.11
2.12
2.13
The main control window in Lyngby. . . . . . . . . . . . . . . . . . . . . . . . . .
The file load window in Lyngby. . . . . . . . . . . . . . . . . . . . . . . . . . . .
The time window in Lyngby. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The volume window in Lyngby, showing three orthogonal views and a 3-dimensional
one. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The External influences window in Lyngby. . . . . . . . . . . . . . . . . . . . . .
The run window in Lyngby. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The paradigm window in Lyngby. . . . . . . . . . . . . . . . . . . . . . . . . . . .
The preprocessing window in Lyngby. . . . . . . . . . . . . . . . . . . . . . . . .
The Neural Network Saliency parameter window in Lyngby. . . . . . . . . . . . .
The Neural Network Saliency results window in Lyngby. . . . . . . . . . . . . . .
The Concept of Layers in the Result Windows. . . . . . . . . . . . . . . . . . . .
Volume result window in mosaic mode . . . . . . . . . . . . . . . . . . . . . . . .
Meta K-means parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3.1
The data matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4.1
4.2
4.3
4.4
4.5
Time-series assignment . . . .
Clustered vectors . . . . . . .
The gamma density kernel . .
The gamma density kernel . .
Comparison of errorfunctions
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List of Tables
2.1
2.2
2.3
2.4
Direction of the axes in Talairach coordinate space.
Architecture Independent Variables . . . . . . . . . .
Global variables . . . . . . . . . . . . . . . . . . . .
Some Examples of the Global GUI variables . . . . .
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3.1
Data formats read by Lyngby. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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A.1 An Explanation of Lyngby Toolbox, fMRI and Related Terms . . . . . . . . . . .
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B.1 Functions Available in the Lyngby Toolbox . . . . . . . . . . . . . . . . . . . . .
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D.1 Installed, Up and Running in Two Minutes! . . . . . . . . . . . . . . . . . . . . .
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E.1 A Simple Example Analysis - How To Use The Lyngby GUI . . . . . . . . . . . .
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Chapter 1
Introduction
$Revision: 1.26 $
$Date: 2002/08/14 16:52:07 $
1.1
The Purpose of the Lyngby Toolbox
Lyngby is a Matlab toolbox for the analysis of functional magnetic resonance imaging (fMRI)
time series. The main purpose of the toolbox is to model four-dimensional fMRI data (i.e. 3D
spatial volume over time) and to derive parameter sets from them that will allow easy interpretation and identification. The toolbox was primarily written for the analysis of experimental data
obtained from controlled multicentre trials - The Human Brain Project “Spatial and Temporal
Patterns in Functional Neuroimaging”, carried out by the International Consortium for Neuroimaging. All of the methods have low-level modelling functions and a graphical user interface
(GUI) interface for easy access to the data and modelling results. It is important to realize
that no single model can grasp all the features of the data. Each of the models have their own
contributions, and the assumptions underlying the models are very different in nature.
The Lyngby toolbox can import data in various different formats, and it is also able to cope
with non-supported formats with very little user-intervention. In addition to the large set of
data modelling strategies, there is also a choice of pre-processing steps and routines, and the
ability to perform post-processing on the modelling results.
The toolbox was developed on Linux and SGI platforms and should work on all platforms
with Matlab version 5.2. Lyngby was previously supported with Matlab 4.2 and some of the
functions will still work with this version. There are still some teething problems with running
the toolbox under MS Windows however, due to some differences in the way Matlab operates
between the Unix and Windows flavours. We are working to overcome these problems, but as
they are due to subtle, undocumented Matlab ’inconsistencies’, it is something of a trail-anderror process. For the time-being, we encourage people to use the toolbox in Matlab under
Linux, where the majority of our development is now carried out. Any advice regarding the
development of the toolbox in Matlab for MS Windows is, of course, welcomed.
In addition to this manual, there are also a few other pieces of documentation to aid use
of the toolbox. A “Getting Started” guide has been written to show the first-time user how
to download, install and run the toolbox as quickly and simply as possible. In addition, an
“Example Analysis” guide demonstrates how Lyngby can be used to analyse one of the sample
datasets, extract some meaningful results, and then interpret them. Both these documents are
included in this manual as appendices.
7
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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Section 1.2
Organisation of this Book
9
The Kolmogorov Smirnov test In the Kolmogorov Smirnov-test each of the time series is
split into an activated part and a baseline part. The maximal distance between the histograms of the two parts is taken as a measure of match. A probabillity measure is also
derived.
Neural Network regression A feed-forward artificial neural network is trained to predict the
time series for each voxel. If the neural network is better at predicting the time series than
a “null”-model for a specific voxel, that voxel can be said to be activated. The neural
network regression is a non-linear generalization of the FIR filter model.
Neural Network Saliency A feed-forward artificial neural network is trained to classify the
scans according to the paradigm. After the training, the neural network is analyzed to
reveal which voxels were the most important in the prediction.
Poisson Filter An fMRI time-series model.
Singular value decomposition (SVD) The same as principal component analysis (PCA).
Independent component analysis (ICA) A multivariate methods to find independent (not
necessarily orthogonal) components with an implementation of the simple MolgedeySchuster algorithm.
Strother CVA Canonical variate analysis.
Strother Orthonormalized PLS The PLS (partial least square) method finds the images
and sequences that explain the majority of the variation between two matrices. The two
matrices in the Strother orthonormalized PLS are the datamatrix and a designmatrix that
is constructed by putting the scans in the same period in each run into the same class.
1.2
Organisation of this Book
This book fulfills several requirements. The first - explaining what the Lyngby toolbox is for,
what it can do, who developed it and other related information - is here in the first chapter. The
second - the “user manual” element - is contained in Chapter 2. It explains how to go about
using the toolbox, including getting your own data into it, and shows you how to do the different
analyses. Consider it as a “How to. . . ” guide. The following three chapters explain the different
stages of using the toolbox in more detail and provide more background information. The first
of these covers the different data formats that Lyngby supports, how data is loaded and stored
in the toolbox, and the pre-processing that is usually done to the data before any analysis is
performed. The second (Chapter 4) looks at the actual data analysis stage and considers each
of the modelling algorithms in detail. The final chapter of this group (Chapter 5) covers postprocessing of the data. This involves the analysis of the results from the (previous) modelling
stage by way of meta-analysis algorithms. Finally, the appendices cover any derivations of
modelling algorithms and other similar details refererred to in the main part of the book.
In summary:
Chapter 1 Introduction to Lyngby – what is it, who wrote it, and what is it for?
Chapter 2 The main user manual – a step-by-step guide to the user interface.
Chapter 3 Details of the preliminary steps – the data formats and the pre-processing stages.
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Section 1.3
Explanation of the Typographic Conventions
10
Chapter 4 Details of the analysis algorithms in the toolbox.
Chapter 5 Details of the post-processing stages (meta-analysis).
Appendix A Glossary of fMRI and Lyngby terms.
Appendix B Detailed list of all the functions and script files within the toolbox.
Appendix C Derivations of the algorithms used in the toolbox.
Appendix D The Getting Started Guide – useful for first-timers.
Appendix E Example Analysis – An step-by-step example of how to to use the toolbox to
analyse fMRI datasets.
1.3
Explanation of the Typographic Conventions
Within this manual, we have tried to improve the readability of the text by using certain
typographic conventions. For instance:
Any reference to Matlab code,
either on the commandline (at the >> prompt)
or within a Matlab script (*.m) file,
will be written in monospace with all
the inherent formatting included.
In addition, commands written at the Matlab commandline will have the Matlab prompt included on the left-hand side, e.g.:
>> help lyngby
Any Lyngby variables or Matlab files mentioned in running text, such as PARADIGM or
data paradigm.m will also be in bold monospace.
Any reference to a particular button in the Lyngby GUI will have a box around it, whilst
Window Titles and Window Menus will be shown in italics.
1.4
fMRI Analysis, Matlab and the Lyngby Toolbox
The Lyngby toolbox was created for the analysis of fMRI images using advanced mathematical and statistical methods in a way that was easily accessible to researchers from all fields.
fMRI is still a relatively new field, and this is reflected in the continually growing number of
analysis routines available. Originally, methods developed for the analysis of PET images were
adapted to work on fMRI data, and some of these are still present in the toolbox. However,
we have also added a considerable number of innovative analysis methods, and are continually
developing more. One of the purposes of the toolbox is to enable researchers to add their own
algorithms with relative ease, without having to worry about handling the data input, image
display, statistics or saving of results. By writing the toolbox in the universally adopted Matlab
package, and allowing the code to be open, we hope that other researchers will be able to add
their own algorithms with ease. We also hope that they will consider submitting to us any they
think would be useful to other people in the field so that we may include them in a subsequent
release of the Lyngby toolbox. Of course, any other suggestions for developments of the toolbox
will also be greatly welcomed.
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Section 1.5
1.4.1
Support on the Web and Downloading the Toolbox
11
Definitions and Glossary
It would probably be useful at this point to define some of the terms used throughout this
book. Although the fMRI field is developing rapidly, some of the terms used are still ambiguous
and those coming into the field for the first time may not appreciate the difference between,
for example, “volume” when it refers to a 2D-slice-with-time vs. a spatial-3D-volume. For
consistency and clarity we will define what we mean by the standard terms, along with some
Lyngby–specific terms. These are all in the Glossary at the back of the book — see Appendix A.
1.5
Support on the Web and Downloading the Toolbox
The Lyngby toolbox has a mailing list where the latest development is announced, and its own
homepage:
http://hendrix.imm.dtu.dk/software/lyngby/
The parts of the Lyngby homepage relating to downloading of software and documentation
is updated automatically every day. The Lyngby toolbox can either be downloaded file by file
(more than 300 files), or you can get the compressed tar file that contains all the files. You can
download the latest version from:
http://hendrix.imm.dtu.dk/software/lyngby/code/lyngby.tar.gz
Note that currently the software and documentation is password protected. Please fill in the
form on the Lyngby homepage or contact the authors regarding access.
We use CVS to control the code revisions, which enables very easy updating and automatic
generation of the web documentation. On the Lyngby homepage it is also possible to see which
files have changed (and view the change logs) over the last 7 days and last 30 days.
The (closed) mailing list [email protected] can be joined by sending an email to
[email protected]
with the only line in the body being:
subscribe lyngby
The mailing list is used for discussions, problems, and announcements.
1.6
Other Software
Other software packages for the analysis of functional neuroimages do also exist, most notably
SPM from Wellcome Department of Cognitive Neurology (Friston et al., 1995b) and Stimulate
developed by John P. Strupp from University of Minnesota Medical School (Strupp, 1996). (Gold
et al., 1998) gives a review of these two together with several other software packages.
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Section 1.7
1.7
12
The Lyngby Development Team
The Lyngby Development Team
Lyngby has been designed and implemented by a modeling group at Informatics and Mathematical Modelling (IMM, formerly called Department of Mathematical Modelling), Technical
University of Denmark (DTU). The head of this group is Lars Kai Hansen. The programming
has been carried out mainly by Finn Årup Nielsen, Peter Toft and Matthew Liptrot as well as
Carsten Helbo. Peter Toft and Carsten Helbo are now involved in other work.
1.7.1
Acknowledgments
Furthermore, Cyril Goutte, Lars Kai Hansen, Ulrik Kjems, and the student Pedro HøjenSørensen have supplied methods and code for the package.
Jan Larsen should also be mentioned for his long-term support and discussions.
Much valuable input has come from the HBP partners, especially Stephen Strother, Nick
Lange, and Babak Ardekani.
This work has been supported by the The Danish Research Councils through the programs
“Functional Activation in the Human Brain” (9502228), “CONNECT” Computational Neural
Network Center (9500762), the US Human Brain Project “Spatial and Temporal Patterns in
Functional Neuroimaging” (P20 MH57180), and the European Union project MAPAWAMO.
1.7.2
Contact us
Besides the mailing list [email protected], we can also be contacted by email directly –
see the list in the table below.
Lars Kai Hansen
Associate Professor
[email protected]
Finn Årup Nielsen
Matthew G. Liptrot
Post Doc
Research Assistent
[email protected]
[email protected]
Peter Toft can be contacted via [email protected], however he is no longer employed at IMM
or officially involved with Lyngby.
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Chapter 2
User Manual
$Revision: 1.30 $
$Date: 2002/08/14 15:54:22 $
2.1
Introduction
This chapter deals with everyday usage of the toolbox. It is a “How do I. . . ?” guide showing, in
a step-by-step manner, how data is loaded in, how a typical analysis would be carried out, what
results can be obtained, and how these results can be viewed and saved. More information on
each of the stages, such as a description of the available data formats and the different modelling
algorithms, can be found in the later chapters.
2.2
2.2.1
Installation and Getting Started
First-time Users
It is recommended that, if you have not yet installed Lyngby, you first read a seperate document
entitled “Getting Started” which takes the user step-by-step through downloading, installing
and running the toolbox. The aim is to have a first-time user able to start experimenting with
real data within a couple of minutes of downloading the Lyngby toolbox. As an additional help,
the Lyngby toolbox also comes with a sample dataset that allows the first-time user to quickly
get started with using Lyngby without first spending time working out how to get their own
data loaded in.
It is highly recommended that first-time users then read the follow-up document entitled
“Example Analysis”. This takes the user through a typical fMRI analysis session, from loading
in the data and ensuring all the pre-processing steps are done, to performing some modelling
of the data, and finally through to analysing the results obtained. This will give the user a
good overview of how Lyngby can help them in the analysis of their own data. Both of these
documents are available on the Lyngby website for seperate download, and are also included in
this book as Appendices D and E, respectively.
2.2.2
Using Lyngby On Your Own Data
If you want to use Lyngby to analyse your own data, there are two main approaches. If the data
is in one of the supported formats (Analyze, Vapet, Stimulate or Raw) then you can simply load
the data directly into the toolbox. However, if you data is in any other format, you will need
13
Section 2.3
Using Lyngby
14
a few simple conversion files to act as format translators. These are very simple to write, and
once stored alongside the data, will allow seamless loading of your data with little or no user
intervention required. These files, the supported data formats and detailed instructions on how
to load data into the toolbox are given later on in this chapter and in the next.
2.3
Using Lyngby
From this point we will assume that the user has followed the “Getting Started” guide and
installed Lyngby correctly. It may also be advantageous for the user to have worked through the
“Example Analysis” guide to see how the Lyngby toolbox can be used to analyse fMRI data.
2.3.1
Starting Lyngby
Lyngby can be run either through the graphical interface or through the Matlab commandline.
The toolbox consists of over 300 Matlab script (*.m) files, which can all be executed from the
Matlab prompt. This allows the advanced user great flexibility and the ability to add and alter
the code as desired, as well as the option of writing batch scripts to run Lyngby automatically,
or on large jobs. However, it can be difficult to remember all the different script names and
their required parameters, so the novice user will find that the graphical interface is far easier
to use. Here the user is prompted for all the required information, removing the need to call
anything from the commandline (once the toolbox is started).
To start the graphical interface, change directory within Matlab to the directory that contains
the data files that are to be processed, and then type:
>> lyngby
If you get an error saying ”??? Undefined function or variable lyngby.” it is probably because you haven’t set up the path to the lyngby functions. To fix this (assuming that
you have placed the lyngby code in the directory /usr/local/lyngby), simply type:
>> path(path, ’/usr/local/lyngby’);
You can put this command in your ~/matlab/startup.m file.
Details on how to use Lyngby via each method are given in Sections 2.5 and 2.6 respectively.
2.3.2
Getting Help
An overview of all the functions in the toolbox can be obtained through the standard matlab
help function:
>> help lyngby
You can also get help for each individual function, for example:
>> help lyngby_fir_main
To distinguish the functions in this toolbox from other Matlab toolboxes all the functions
have the prefix lyngby *. All the functions associated with the graphical interface to lyngby
are called lyngby ui *. Furthermore, functions to each algorithm also have a special prefix.
For example, the FIR filter algorithm uses the files: lyngby fir * and lyngby ui fir *. A
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Section 2.4
Data Setup
15
very useful index of all the toolbox function files and their inter-dependencies can be found on
the Lyngby website.
Within the GUI, there are Help buttons at various stages. Clicking on a Help button will
bring up a single dialog box explaining the purpose of the different windows and the options
within them. In addition, most of the windows have a “Tooltips” option, invoked from the
Options window menu or with the <Ctrl-T> key combination. These Tooltips will pop-up
handy explanations for any button over which the mouse pointer rests temporarily. They are
meant for the new user, although they are set to be off by default as they can become distracting
after a short while.
2.4
Data Setup
This section will deal with getting data into Lyngby. It will cover the file formats that Lyngby
can currently read and will also explain how your own data format, even highly custom or
non-standard formats, can be read in.
2.4.1
How Lyngby Reads in Data
There are two ways to get new data into Lyngby — either by the file formats already supported,
or via a conversion process that will allow any non-supported file format to be read. For the
supported formats, nothing needs to be done outside the GUI in order to read in the data — all
the required information is extracted from the header files and from details entered into the GUI’s
Load New Data window. Non-supported file formats can be read-in by the use of “conversion
files” (data readdata.m and data readinfo.m). These are small matlab files which act as
header and data-reading files for the Lyngby program so that it can understand the data. These
are very simple to write, and detailed examples of how to do this are given later.
For all file formats, there may be external influences on the data that you will also need for
your analysis. For instance: the paradigm and run signals, any previous voxel-masking, or a
time-mask to remove unwanted scans. These can be specified through the GUI, but it may be
easier to save this information as small matlab files that can then be loaded in at the same time
as the data. Examples include data paradigm.m and data run.m.
In addition, it may be easier to save the parameters that specify the data, such as image size
and data location, in a seperate file as well. Then the user does not have to enter them every
time data is to be loaded. This file will work for any data format. This file is called data init.m
Details on how to create all these files are also given later.
2.4.2
Setting-up the Conversion Files for Non-supported Data Formats
It is easiest to explain how to set-up the files that perform the conversion by use of an example.
To do this, we will use the Jezzard Turner Friston (Friston et al., 1994) dataset: Visual stimulation and a single coronal slice. This is one of the sample datasets available from the Lyngby
website.
The data is stored in a filetype that is not recognized by Lyngby, so we have to make our
own functions that read the data: data readinfo.m and data readdata.m, the latter of the
two being the most important.
These files are called from the Load new data... window of the GUI. Several Lyngby
internal functions are called once the data is required to be loaded. The first of these is
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Section 2.4
Data Setup
16
lyngby getdata.m. This then calls lyngby getvolume.m, which, for non-supported data, then
calls the user-created file data readdata.m. This acts as the actual data-reader, returning the
data to the program in the form of a vector. Details about the actual shape and order of the
volume are not specified in this file, though of course they will be required in order to write the
data-reading function within it that will actually return the data.
However, Lyngby itself will not know the shape of the data, the number of
runs/volumes/time-points etc. This information has to be entered into the GUI, or can be
stored in a matlab file (data init.m) to save the user typing in the relevant details each time
the data is loaded.
In summary, then, there is only one file that is absolutely necessary when reading in nonsupported data (data readdata.m), although up to four others may be used. These others are
optional but do make life easier. Any file that is to be supplied by the user will have a filename
stem of data *.m. Their individual purposes are summarized below:data readdata.m The only necessary file required for reading in non-supported file formats, it
contains a few lines of matlab code that actually return the non-supported data as a vector.
It is also where the user will put in any re-organisation of the data in order to get the
byte-read-order correct. The user must use the standard Matlab functions to re-organise
their data to match this ordering. An example of how to do this is given later.
data readinfo.m This file is only required if the user wants to use the facility within Lyngby to
check the headers of their data files. This is accessed by pressing the Try to setup! button
in the Load new data window. It is therefore not an essential requirement for loading in
non-supported data formats, and indeed may become redundant in future versions of the
toolbox.
data paradigm.m This is used to specify the paradigm (the external reference function) which
is used in the actual analysis stage. It can be specified within the GUI, but it is far easier
to keep it within a file that is loaded in automatically. It is simply a 1D vector indicating
“on” and “off” time points, and as such usually only requires a single line of code. The
GUI can be used to make changes to the paradigm function if required.
data run.m This specifies which time-points belong to which run within a given experiment or
session. As for the paradigm file, this too can be specified through the GUI, although
only if the lengths of all the runs are equal. But once again, it is far easier to keep the
information in a file that is loaded automatically. Changes to the function can also be
made from within the GUI if required.
data init.m This file contains all the parameters that the user would normally enter in the
“Load new data” window. As such, it also is not essential, but it makes life easier because
it then saves the user specifying the parameters every time data is loaded. It is simply a file
of variable assignments, specifying such parameters as the size and shape of the volume,
the number of scans and runs, and a TIME MASK variable which can be used to remove
unwanted scans.
Note that the latter three files can be used for all data formats, and it is only the first two
which are specific to custom data.
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Section 2.4
2.4.3
17
Data Setup
Examples of the Data Conversion Files
This section looks at how to write the actual code within the data conversion files. It uses
the Jezzard dataset as a basis for the code examples. This is a single trial, single slice fMRI
experiment, with 64 volumes (i.e. time points or frames), each of size 64-by-64-by-1.
2.4.3.1
The data readdata.m and data readinfo.m Files
The contents of the data readinfo.m file are read when the user wants to pass the header
information contained within the file into the Load Setup window. This is achieved by pressing
the Try to setup! button. An example of the file is shown below:
function
[siz, vdim, name, datatype] = data_readinfo(index);
siz = [64 1 64];
vdim = [0.003 1 0.003]
name = ’jezzard’;
datatype = ’short’
Pressing the Try to setup! button then attempts to locate the file in the current directory
and read the contents. These are then placed in the correct fields of the Load Setup window,
and the relevant status boxes are set to Header. For all the other file formats, pressing the
Try to setup! button ignores any data readinfo.m file, and instead the relevant header files
for the chosen format are attempted to be read and the extracted parameters again passed into
the relevant fields in the Load Setup window, with the associated status boxes also being changed
to Header.
The other function, data readdata, is the function that should return the actual data. The
data has to be flipped because, in this case, the original ordering is not the same as that used
by Lyngby.
The Lyngby toolbox will always assume that the vector data returned from data readdata
is of the following form:V = [P1 (x, y, z), P2 (x, y, z), P3 (x, y, z), . . .]
where:
P refers to an individual data point
(x,y,z) refers to the location in 3D space of the data point
x refers to the sagittal slice (i.e. ear-to-ear/left-right)
y refers to the coronal slice (i.e. back-to-front)
z refers to the transaxial slice (i.e. bottom-to-top)
. . . and so this is the order into which the user must reshape their own data.
The X index changes quickest, followed by the Y index, with the Z index changing the
slowest. Thus, the data cycles through the X’s first, then the Y ’s, and finally through the Z’s.
So, for a 10-by-10-by-10 cube, the data should be in the order:-
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Section 2.4
18
Data Setup
P1 = (x1, y1, z1),
P11 = (x1, y2, z1),
...
P101 = (x1, y10, z1),
P111 = (x1, y1, z2),
...
P991 = (x1, y10, z10),
P2 = (x2, y1, z1),
P12 = (x2, y2, z1),
P3 = (x3, y1, z1),
P13 = (x3, y2, z1),
....
....
, P10 = (x10, y1, z1),
, P20 = (x10, y2, z1),
...
...
P102 = (x2, y10, z1),
P112 = (x2, y1, z2),
P103 = (x3, y10, z1),
P113 = (x3, y1, z2),
....
....
, P110 = (x10, y10, z1),
, P120 = (x10, y1, z2),
...
...
....
, P1000 = (x10, y10, z10).
P992 = (x2, y10, z10), P993 = (x3, y10, z10),
Note that the direction along each of the three orthogonal axes is also important. The
Lyngby toolbox uses the Talairach co-ordinate space as its reference. The origin of this space is
located at the centre of the brain, with the directions of the axes as given in Table 2.1
Left
Back
Bottom
+
Right
Front
Top
Table 2.1: Direction of the axes in Talairach coordinate space.
The Lyngby toolbox will assume that the data is written with the most negative values first,
increasing through the origin, and up to the most positive values last.
For the Jezzard data, the file looks like:
function V = data_readdata(index);
fid = fopen(sprintf(’jezzard0716phot1_unwarp.%03d’, index), ’r’, ’ieee-be’);
if fid==-1
error(’readdata: Could not open file’);
end
fread(fid, 8, ’char’);
V = fread(fid, ’int16’)’;
V = reshape(fliplr(flipud(reshape(V,64,64))),1,64*64);
fclose(fid);
Let’s look at this in more detail.
The first thing to do is open the file with:
fid = fopen(sprintf(’jezzard0716phot1_unwarp.%03d’, index), ’r’, ’ieee-be’);
The fid is the file pointer. Note that the filename is actually the file-stem of the relevant
data files, and that the individual datafile is specified by the index parameter. This index is
passed to the data readdata function when it is called, and so this function will be called for
each “volume” that is to be read in. The number of times that data readdata is called is given
by the difference between the Start and Stop scan indices as specified in the Load Data window
or in the data init.m file. The former takes preference. (Note that some data formats don’t
use a different file for each time-point, or frame, and instead the whole time-series is contained
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Section 2.4
19
Data Setup
within a single file. When this is the case, the File Pattern field, and the Start and Stop indices
are not used.)
The r means that the files are to be opened “read-only”, while the ieee-be specifies that they
are in “big-endian” format. As a rule, files stored on Intel (Pentium) and DEC Alpha machines
are “little-endian” (ieee-le), whilst those stored on Sun and SGI machines are “big-endian”
(ieee-be). Make sure that the correct format is chosen for your architecture.
The next few lines check that the file was actually there and that it could be opened, returning
an error if not:
if fid==-1
error(’readdata: Could not open file’);
end
The next line skips the header in each data file. The Jezzard data files have an 8-byte header:
fread(fid, 8, ’char’);
Next, the data in the file is read into a single vector, V . Remember that this is only one
volume at a time. The size of the individual data points must be specified — a 2-byte signed
integer in this case. It is probably wise to specify the data size in architecture-independent form,
as shown in Table 2.2 (e.g. use “int16” instead of “short”):
V = fread(fid, ’int16’)’;
MATLAB
’char’
’uchar’
’schar’
’int8’
’int16’
’int32’
’int64’
’uint8’
’uint16’
’uint32’
’uint64’
’float32’
’float64’
C or Fortran
’char*1’
’unsigned char’
’signed char’
’integer*1’
’integer*2’
’integer*4’
’integer*8’
’integer*1’
’integer*2’
’integer*4’
’integer*8’
’real*4’
’real*8’
Description
character, 8 bits
unsigned character, 8 bits
signed character, 8 bits
integer, 8 bits.
integer, 16 bits.
integer, 32 bits.
integer, 64 bits
unsigned integer, 8 bits.
unsigned integer, 16 bits.
unsigned integer, 32 bits.
unsigned integer, 64 bits
floating point, 32 bits.
floating point, 64 bits.
Table 2.2: Architecture Independent Variables
We now have a single volume of the full data (i.e. a spatial volume for a given time point)
in the form of a 1D vector. The next bit re-orders this data so that it will appear in the correct
order for Lyngby as specified earlier. This is the most complex bit!
V = reshape(fliplr(flipud(reshape(V,64,64))),1,64*64);
Looking at this function in more detail:
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First of all, the vector V is reshaped into a 2D matrix, 64-by-64. Note that this case is
relatively easy because the Jezzard data is only single-slice, so the volume is 64-by-64-by-1. (For
a multiple-slice dataset, you would have to use the reshape function with an extra parameter.
Type help reshape at the Matlab prompt for more information.)
reshape(V,64,64)
Next, the matrix is flipped about the horizontal axis:
flipud(matrix)
and then about the vertical axis:
fliplr(matrix)
Finally, this re-ordered matrix is put back into the vector V with another reshape command:
V = reshape(matrix, 1, 64*64)
This results in a vector V that has been re-ordered so that the elements appear in the same
order that Lyngby is expecting them, i.e. X, Y , Z, X, Y , Z, X, etc with the Z indices changing
the quickest.
Finally, the file is closed with:
fclose(fid);
2.4.3.2
The data paradigm.m and data run.m files
The data paradigm function for the Jezzard data is:
function P = data_paradigm();
P = [kron(ones(3,1), [zeros(10,1) ; ones(10,1))] ; zeros(4,1)];
Another example of a data paradigm function is:
function P = data_paradigm
P = lyngby_kronadd(zeros(8,1), [ zeros(24,1) ; ones(24,1) ; zeros(24,1) ]);
This function was created for an 8 run study with 72 scans in each run: first are 24 base line
scans, then 24 activation scans, and finally 24 base line scans. Note that as well as using this
compact way of specifying the paradigm it is also valid to type the paradigm as a long vector,
i.e. specify the paradigm in long-hand:
function P = data_paradigm
P = [ 0 0 0 1 1 1 0 0 0 ... 0]’;
The total length of the paradigm vector must be the same as the whole time series, i.e.
before the time mask is applied. This is also true for the function defining the run structure —
data run:
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function R = data_run();
% The Jezzard dataset contains three runs: The first with 24 scans,
% then one with 20, and the last also with 20.
R = [ones(4,1); ones(20,1) ; 2*ones(20,1) ; 3*ones(20,1)];
Another example of a data run.m file is:
function R = data_run
R = lyngby_kronadd((0:7)’, ones(72,1));
Both examples define a staircase function, with one level per run.
Two of the examples use the convenient lyngby kronadd function, which is actually a “kronecker addition”. It works like the kronecker tensor product (the matlab kron function), just
with addition instead of multiplication.
Alternatively, instead of defining functions you can define *.mat files (data paradigm.mat
and data run.mat) with a PARADIGM and RUN variable in them, or you can define an ascii file
(data paradigm.txt and data run.txt).
When you start Lyngby two functions are automatically called: lyngby paradigm and
lyngby run. These will call your data paradigm.* and data run.* function/files and setup
the global variables PARADIGM and RUN.
2.4.3.3
The data init file
A data init function is created to setup some of the global variables. These variables could
also have been setup through the command line. However it is convenient to have them in a file
so you will not need to set them up every time.
For the Jezzard data:
function data_init();
lyngby_global;
NUM_VOXELS
ROI_VOXELS
= [ 64 1 64 ]’;
= [29 46 ; 1 1 ; 14 43];
A list of the general global variables is given in Table 2.3 on page 37, and a list of the
user-interface related global variables is given in Table 2.4 on page 38.
If another program has masked the volumes so that non-brain voxels are already zero, we will
take advantage of this by setting a VOXEL MASK, — reading in the first volume and extracting
only the voxels that are different from zero.
2.5
Using the Lyngby Windows Interface
The Lyngby toolbox is easy to use due to a graphical user interface (GUI) front-end embedding
the numerical routines. The main window that pops-up when starting Lyngby is shown in
figure 2.1.
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Figure 2.1: The main control window in Lyngby.
2.5.1
The Workflow in the Lyngby Toolbox
As seen from figure 2.1, a top to bottom flow is intended The main window is split into sections
(or frames) according to their purpose as follows:
Title frame The name of the Toolbox.
Data This shows the name or location of the current dataset being used.
Data Input Data is loaded into the toolbox here
External Influences This is where the Run and Paradigm variables are loaded in and/or
edited.
Pre-processing and Design Any set-up of the data, including removal of unwanted scans or
the subtraction of the mean across each time-series, can be performed here.
Data Analysis The heart of the toolbox, this allows the data to be analysed by any of the
built-in algorithms and viewing of the subsequent results.
Post-Processing Once a result dataset has been created, further analysis, such as clustering
of the results, can then be performed.
Close The worksheet is saved, and the toolbox closed, from here.
Status Feedback on what process is currently running is shown here, and the toolbox copyright
infomation can also be obtained by pressing the status line.
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Note that we use the following convention for the menus:
• Ok Accept the values currently shown in the display and close the window.
• Apply Accept the values currently shown in the display.
• Cancel Don’t accept the values currently shown in the display (note that pressing Cancel
after Apply will first accept the current values and then close the window).
2.5.2
A Typical Analysis Session
A typical analysis procedure would progress as follows:
2.5.2.1
Data Loading
The first stage of the process is to get some data into the toolbox. This is done by either loading
an existing worksheet from a previous session, or loading new data from scratch. A previously
saved worksheet (usually *.mat) is recalled by pressing the Load Worksheet! button. This
brings up a standard Matlab file chooser, starting from the directory where lyngby was started.
New data is loaded by pressing the Load new data... button. This then displays a window
called Load setup, shown in Fig. 2.2, allowing specification of the number of scans, the file format,
voxel masking etc. . . . To aid this process, there is a Try to setup! button. This attempts to
probe the header files for the chosen file format (or the data readinfo.m file in the case of
the Lyngby format) and place any extracted information about the datafiles into the correct
fields in the Load Setup window. Any fields successfully read are indicated by the change of the
respective status box to Header.
There are several options within the Load Setup window. The majority of the fields are
used to set up the file structure and image size etc. . . , whilst the bottom right frame is used to
specify some external factors, such as specifying a region-of-interest (ROI) and the removal of
any unwanted scans.
2.5.2.2
The Load Window – Data Fields
There are many fields in this window, and the various file formats support a different number
of these fields. Those that are not supported, or are yet to be fully implemented, are greyed-out
and hence cannot be used (note how the accessibility of the fields changes as you change the file
format).
File Format: lyngby can load in several different file formats. Apart from its own native
format, lyngby can also read Vapet, Analyze Stimulate and Raw formats. The lyngby
format requires some additional small conversion files to be written that describe how the
data should be read (examples were given earlier in Subsection 2.4.3 on how to create your
own). Once this has been done for your dataset, the lyngby format then becomes the
easiest to load, as the image parameters have already been specified.
The other formats will require the user to specify the location of the data, the image size,
the size of each data unit, the ordering of the data etc. . . . It is usual for the data to be
split across many individual files, and this is catered for by the File Pattern, Start Index
and No. of Scans choices, which allows the user to specify the common file stem and the
scope of the file index.
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Figure 2.2: The file load window in Lyngby.
Image Dimensions: Specifies the size of the full data volume, in spatial terms i.e. for a
single time-point, before any region-of- interest is selected.
Image Word Size: The size of the individual words in the datafile.
File Byte Ordering: The order of the bytes within the file.
Data Endian: Intel (Pentium) and DEC Alpha usually use big endian, while Sun and SGI
usually use little endian when storing data on disk.
Data Orientation: allows the user to change the orientation of the displayed image. Note
that this refers to the mirroring around each of the three orthogonal axes, and not to the
way the data is stored within the file — that is catered for by the Ordering option.
Greyscale: How the greyscales are mapped to the colourmap.
Voxel Dimensions: Specifies the real-world size of the image voxels, to allow for calibration
etc. . .
Location of Origin Voxel: Allows spatial calibration of the brain.
Plane angle to horizontal: Compensates for non-orthogonal orientation of the slice-planes.
Repeat period: The frame-rate – allows the calculations to be calibrated in seconds instead
of in frames.
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File Offset: The number of bytes to skip in the datafile in order to get past the header
info and into the data. Very useful for loading in raw data without creating any seperate
translation files.
Compression method: The file compression used on the datafiles. The toolbox is capable
of decompressing the files ’on-the-fly’, allowing the data to be kept compressed and thus
saving space. It also allows the data to be read compressed straight from a CDROM.
Extract to dir: The temporary directory used for on-the-fly decompressing of the data as
it is loaded-in.
2.5.2.3
The Load Window – Bottom-Right Frame
This frame controls some of the external factors that affect the loading-in of the data. Other
external influences, such as the Run and Paradigm variables, don’t affect the way the data is
loaded-in and as such are seperated off into a later stage.
Time Mask: Before actually loading the data, a time mask can be specified by pressing
the Edit. . . button. This is convenient if you want to discard transient scans or scans
at the start that behave badly (such as highly saturated T1 scans). The associated global
variable is TIME MASK, and the window is shown in figure 2.3.
Figure 2.3: The time window in Lyngby.
Voxel Mask: This allows the exclusion of specific voxels within the selected region of
interest (ROI) to be loaded. It is usually in the form of a sparse matrix. At the moment,
the front-end interface for this feature is not yet finished, but the supporting code is in
place and as such you may load in a voxel mask from the command prompt. Details of
how to do this are given later.
Volume/ROI: The ROI can be specified by pressing the Volume/ROI Edit... button.
In order to edit the volume, it must first, of course, be loaded in. This requires the
data parameters to be specified first, so once this is done, press the Apply button at the
bottom and this will then unshade the Volume/ROI Edit. . . button. Note that the whole
data matrix is not loaded at this point — only the relevant volume. In this window, a
rectangular ROI can be specified by the user, who can also browse through all the data in
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both time and space. As can be seen in Fig. 2.4, the user has the choice of five different
viewing layouts to examine the data. The first one simply shows a single slice of the data,
with the choice of the three orthogonal directions and the ability to move along each of
these axes. The second choice allows two slices to be shown alongside one another, either
from the same or different viewpoints. There is also the ability for linking the two views if
they are of the same direction, allowing for easy-stepping through the volume. The third
choice is the standard triple orthogonal view. This is also repeated in the fourth option,
alongside a 3-dimensional view of the data. The final option is for view multiple slices
from the same viewpoint, displaying 8 adjacent slices.
Figure 2.4: The volume window in Lyngby, showing three orthogonal views and a 3-dimensional
one.
Once all the loading parameters have been set, press the Apply button to ensure that they
are all stored, and then press the Load Data! button to start the loading-in of all the data.
The Load Setup window is closed and the status bar at the bottom of the main window indicates
the progress of the loading.
2.5.2.4
Data – External Influences
In the main window, there were links to create, edit and load external influences on the data
that affected the way or the amount of data loaded into the toolbox. This section covers the
external influences on the data that are applied once the data is loaded. To access this stage,
press the Create/Edit External Influences. . . button in the main window. This will bring up
the External Data Influences window, as seen in figure 2.5. Most of these external influences are
usually specified in external files first, and then altered here if necessary. Guidelines for writing
these files are given later:
• The run structure can be verified by pressing the Create of edit the Run function. . .
button. Each of the runs is given a unique number so the graph should be a staircase as
shown in figure 2.6. You will only need this structure if you plan to make a pre-processing
step or analysis type that requires run information, e.g. run centering. The global variable
associated with the run structure is called RUN. Like the paradigm, this function can be
altered here, but is usually defined in an external file.
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Figure 2.5: The External influences window in Lyngby.
Figure 2.6: The run window in Lyngby.
• The paradigm can be verified by pressing the Create or edit the Paradigm function. . .
button. The global Matlab variable associated with this window is called PARADIGM. The
GUI allows alteration of the paradigm function that is usually defined in an external file
(data paradigm located at the same place as the data. The Paradigm window is shown
in Fig. 2.7.
2.5.2.5
Data Pre-Processing
Before the actual modelling, the data should be pre-processed. By pressing the Data Setup...
button, the window shown in Fig. 2.8 pops up. This window has a left and right pane which
serve different purposes. The user should do any work on the left pane first, before selecting the
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Figure 2.7: The paradigm window in Lyngby.
relevant options on the right pane and then pressing the Apply.. button.
On the left, the top button is used to remove unwanted scans by applying the TIME MASK
variable, which was previously specified in the Load Setup window, to the PARADIGM and RUN
variables. The lower button removes the vertical offset (“DC component”) from the PARADIGM
variable. This is necessary for those analysis algorithms which require a zero-mean paradigm.
The right pane focuses only on the data. Three of the choices involve the removal of the mean
from the data: over the whole time series, the whole volume and over each run respectively. The
remaining choice normalizes the variance. The relevant choices should be selected by pressing
the toggle buttons, and then these are applied by pressing the Apply... button at the bottom
of the window. This also closes the pre-processing window.
Figure 2.8: The preprocessing window in Lyngby.
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2.5.2.6
Data Analysis and Modelling
29
The next step is to select the actual data modelling algorithm from the Data analysis frame.
The top button in this frame determines the analysis algorithm that will be used. Pressing this
button causes a list to pop-up of all the available algorithms. Simply select the desired one,
and the list will disappear and the button will display the name of the chosen algorithm. The
Original option simply displays the un-analysed data (which is also the input data used by all
the other analysis algorithms). The actions of the associated buttons are then dependent on the
choice of algorithm, but always follow a standard pattern:
• The Parameters button is used to choose the parameters for the selected modelling algorithm, and as such the window that pops up after pressing it will be different for each one.
For the Neural Network Saliency algorithm, the parameter window is shown in Fig. 2.9.
Once the required parameters have been chosen, then click Apply , followed by Close .
Figure 2.9: The Neural Network Saliency parameter window in Lyngby.
• The Calculate! button will then run the algorithm and the progress of the processing is
shown on the status line at the bottom of the main window.
• Finally, pressing the View button shows the result of the analysis.
• The two ! buttons adjacent to the View and Calculate! buttons will display, in
the command window, the actual code/script that will be run when their neighbouring
buttons are pressed. This allows the user to easily keep track of what variables will be
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modified and displayed at each stage.
Note that the popup menu showing the algorithms will have a (+), (-) or a (!) alongside
each one, indicating whether or not the results of that algorithm are available for visualization :– (-) indicates no result, (+) indicates a new result, and (!) indicates that the
parameters have been changed and the algorithm has not yet been run with these new
variables.
The next step is the actual viewing of the modelling results. Whichever modelling or analysis
method is chosen, the same three windows are used to explore the results. For the Neural
Network Saliency algorithm, these result windows are shown in Fig. 2.10.
Figure 2.10: The Neural Network Saliency results window in Lyngby.
Let’s go through the features of these windows in more detail:
• There is always one control window underneath two data-viewing windows, which usually show spatial/voxel information and time series information respectively. The control
window is arranged in layers to reflect the concept of overlapping data layers in the spatial window. This is perhaps illustrated more clearly with the aid of a diagram — see
figure 2.11.
• Clicking on a voxel in the volume window will automatically update the other window to
show the time-series for the chosen voxel, plus the result of any modelling algorithm for
that particular voxel.
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Figure 2.11: The Concept of Layers in the Result Windows.
• The control window can be expanded to show extra layers with the More. . . button.
These allow the addition of a contour overlay, a background underlay, and data-masking
features.
Time Display Layer: The lower layer controls the right window, which usually displays
the time-series of the currently selected voxel. However, some algorithms also produce
histograms, error curves, periodograms etc. . . , either voxel-based or full-volume-based,
which can be selected from the left-most button in this layer and displayed in the right
window.
The Full button controls the scaling of the bottom axis, and so allows ”temporal zooming”.
The next button switches the vertical axis scaling between fixed and automatic.
Adjacent to this, the slider controls the temporal location of the time-series display, and
is used in conjunction with the horizontal zoom button to move through the time-series.
It is disabled when the full time-series is displayed.
The next four buttons apply to all three windows. The Close all button closes all three
windows. The results are still kept, though, and can be accessed again by pressing the
View these results button in the main window. The Save data. . . button displays the
Save Layer, allowing specific volumes or results to be selected for saving. This is covered
again later. The Help button brings up help for all three windows, and the More. . . or
Less. . . button adds/removes the extra layers for masking, background display etc. . .
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Data Layer: This controls the main results of the modelling algorithm. As in the other
three voxel-related layers (Masking, Contour and Background), the result set being used
is chosen from the leftmost button’s popup list. Some algorithms only have a few result
sets, whilst others may have dozens. The beauty of this layering approach is that it allows
you to display one result set, filtered by another, over a background of a third, whilst
overlaying a contour plot of a fourth.
The next button specifies the slice direction for observing the data volume. This is only
really interesting if the data is a 3D spatial volume. The adjacent slider controls the
position of the slice plane within the volume. Note that these also alter the viewing
direction of the other layers as well (it is no use plotting the transversal activation over a
sagittal background!)
Next to this is a button controlling what each voxel represents. There is a choice of the
current slice, as selected by the previous two buttons, the mean of the volume, or the
maximum voxel value, both measured along the current direction.
Next comes a button specifying the colourmap to be used for this layer. The Default
setting uses the default colourmap as set in the file lyngby ui option.m.
Finally, the last button controls whether the colourbar on the spatial plot should be displayed or not.
Masking Layer: This layer creates a binary mask from the selected dataset, and then
applies it to the Data Layer below. Thus the only parts of the result set that are visible
are those that occur where the mask allows. This is useful for viewing a thresholded
activation result over a background image. This masking process is only activated when
the second button, initially labeled None, is switched to some other value.
Next to the first button, which chooses which of the result sets to use to create the mask,
is the button which selects the threshold type. The choice is between ’¿’, ’¿¡’ (absolute) or
’==’ (integer equals). The first is a simple threshold. The second is an absolute threshold
to remove both positive and negative values greater than a certain value, and the third is
used only with a few result sets to pick out a particular level (for instance, in the K-Means
clustering, you may want to extract the results of just the 3rd cluster). The next two
buttons are used to select the threshold level, either via typing it into the edit box, or by
moving the slider.
The final button controls whether the threshold level is an absolute one, or is done on a
percentage bssis within the current slice.
Contour Layer This is the left side of the top layer, and controls the addition of a contour
overlay on top of the current volume view. It is turned on and off via the radio button to
the left. Next to this is the popup button allowing the selection of the result set to use for
drawing the contours. The adjacent button specifies whether the contour is drawn using
the current slice of the chosen result set, or using its mean or maximum values along the
current direction.
Background Layer This is the right side of the top layer, and it controls the ’underlay’ of a
background image. At the moment, this background image must be chosen from the current result set. We are hoping to incorporate the option of loading in a seperate anatomical
background image soon (although this raises several questions regarding registration and
resolution). For the moment though, a very good approximation to an anatomical image
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can be obtained by use of the mean image, which is automatically included in all result
sets.
Once again, the radio button to the left controls the activation of the layer, and the next
button allows the choice of which result set to use as a background. The final button
specifies whether the background is taken as the values in the current slice, or as a mean
or maximum of the volume along the current direction.
Save Layer: This appears on top of the other four layers and has a red background to
highlight it. In addition, the layers upon which the save layer is acting are also coloured
red. The first button controls what is to be saved e.g. the whole volume, the current slice
or the time-series of the current voxel.
The next button specifies the format in which to save the data. The options depend on
what is to be saved. For instance, the volume has a choice of ASCII, Matlab, Analyze,
STD or VAPET. More choices will be added in future, as will more flexible saving options.
These will be located in the next button which is currently unavailable.
The next button allows choice of a compression algorithm. This currently only works on
Unix-type systems, as it employs the unix-compress and gzip routines.
The filename to save the file under can be typed into the next box, and saved in the current
working directory by pressing the adjacent Save here! button. If you want to choose a
different directory, simply use the Save in dir. . . button instead.
Mosaic View: Originally, most fMRI studies were single slice, but as the amount of volume
studies has grown, we have added in a mosaic slice viewer. This allows the display of
mutliple adjacent slices of a volume so the whole result set can be viewed at once. To
turn this on, select the Toggle Mosaic View from the View Options menu in the volume
window. Alternatively, use the Control+M keyboard shortcut when the volume window
is the selected window.
An example of the mosaic view can be seen in figure 2.12.
The mosaic view requires a minimum of 3 slices of the current view plane. In addition, it
won’t work if you are looking at the mean or maximum result values (as this would lead
to multiple copies of the same image). The current slice is used as the first slice in the
mosaic list, and so there must be at least two more slices left after it for the mosaic view
to work. So if you are currently looking at the last slice of a 16 slice volume, move the
slider to the left to select an earlier slice before choosing the mosaic view.
Once selected, the mosaic view will display up to 20 slices from the current volume. You
can move the slice slider as before to move through the volume. The result sets all use the
same colourmap (i.e. the colourmap has been scaled to the maximum and minimum of
the current result set) so individual colourbars are not necessary, and the slices update far
quicker wiith them turned off. A single colourbar for this view will be added very shortly.
You can turn the mosaic view off again with the same menu or keyboard shortcut that
turned it on.
2.5.2.7
Data Post-Processing
After the data analysis, the user can perform some post-processing on the results data. This
post-processing allows the analysis of the previous result sets in a formal way. At the moment,
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Figure 2.12: The volume result window in mosaic mode.
only one post-processing algorithm, Meta K-means clustering, is included in the toolbox, but
more are due to be added later. To use this feature, one first has to have a collection of result
parameters that require analysis. This result dataset is presently acquired by using either the
FIR Filter or the Iterative Lange Zeger routines. Once this has been created, then the postprocessing can be done. The procedure is exactly the same as for the main analysis routines,
with the user setting the initial parameters of the algorithm via the Parameters... button,
shown in Fig. 2.13 , and then starting the calculation by pressing the Calculate! button. The
results are also viewed using the same interface as before, accessed via the View these results!
button.
The Meta K-Means algorithm attempts to cluster, for example, the parameters of the filters
used to model each of the voxels, in effect looking for commonality of filter types instead of
common voxel time-series. In this way, it is a ’higher level’ of analysis, and should provide
another viewpoint on the data.
2.5.3
Exporting Variables, Printing and Saving Results
Generally, the result variables are not available from the commandline as they are global variables. However, it is easy to access them by issuing the command:
>> lyngby_ui_global
You can then list all the variables by doing:
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Figure 2.13: The parameter options for the Meta K-Means post-processing algorithm.
>> whos
This allows you to both see the size of the result variables and save them directly from the
command line. For example:
>> save MyFilename.txt RESULT_LZ -ascii
This will export the Lange-Zeger result to an ASCII-file named MyFilename.txt. Note that
you might want to change to another directory first – you are in the data directory while working
with the Lyngby package.
Alternatively, you can save the entire worksheet by using the Save Worksheet button near
the bottom of the main window. This will bring up a file chooser, allowing you to store all the
data, the variables and all the results obtained into a single file, usually of the form *.mat.
To print a window of the lyngby toolbox in Matlab 5.0, you can use the print function in
the window frame or you can use the ordinary matlab function print:
>> print -deps MyFilename.eps
An alternative is to save the data from the Result View - Control window. To specify which
data to save, click on the Save data. . . button towards the bottom right of the window. This
will expand the window to five layers - the normal four layers and an extra red-framed “Save”
layer. Changing the choices in the first button on the Save layer specifies which layers, and
hence which data, is to be saved. These layers are also highlighted in red. The other buttons in
the Save layer allow the choice of the Save options - the file format, file compression, filename
and file location.
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2.6
Using Lyngby from the commandline
36
Using Lyngby from the commandline
You may prefer to use Lyngby from the command-line instead of using the window-interface. As
the GUI simply calls individual Matlab *.m files, then these can also be called directly from the
command-line. However, if you are working without the GUI you must setup the parameters in
the data init.m file. In addition, it may help to have the run and paradigm files setup as for the
GUI. If your data is a non-supported format, then it may also be easier to put the data-loading
functions into the data readdata.m file rather than having to enter them on the commandline
each time.
2.6.1
Data Loading
• Change to the relevant data directory
• Setup the global variables. This is usually done within data init.m, data paradigm.m,
and data readdata.m
• X = lyngby getdata;
2.6.2
Pre-Processing
Pre-processing has been used with different meanings in neuroimaging. In terms of the Lyngby
toolbox, by pre-processing we mean the step after loading the data, but before the actual dataanalysis.
In the GUI, the setup of the pre-processing is managed by lyngby ui preproc. This is called
from lyngby ui main upon pressing the Data Setup ... button. The function and its associated
window are used to setup the global variables in lyngby prep global. These variables are used
by lyngby ui main in its call to lyngby normalize — the function that does the actual preprocessing. This is normally done by pressing the Apply Pre-Processing and Close button in
the Data Setup window.
The loaded data, held in the global variable X, is passed to the pre-processing step, which then
outputs the global variables XN, X MEAN and X STD. You do not have to use the lyngby normalize
function, and you can set these variable yourself directly from the command-line:
XN = X;
X_MEAN = mean(X);
X_STD = std(X);
2.6.3
Global Variables
The Lyngby toolbox has to use a set of global variables that under normal conditions are hidden
from the user. The user can make the these variable available to the workspace by calling
lyngby global. A list of the Lyngby global variables is given in table 2.3.
Furthermore, the result variables and the data matrix are also global, but are seperate from
the ones shown above. You can make the these variables available to the workspace by calling
lyngby ui global. An example list of these global variables is given in table 2.4.
X and P will be defined once the data have been loaded (normally done upon pressing the
Load Data button). XN and PN will be defined once the pre-processing has been done, and the
RESULT * will first be defined once an analysis has been performed (via the Calculate! button).
The next section gives more details on how to run the analysis algorithms from the commandline.
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Section 2.7
Writing Scripts to Run Lyngby Automatically
Name
DATATYPE
DEFAULT SAVE PATH
DISCRIM TMASK
FILENAME PATH
FILENAME PATTERN
FILENAME STARTINDEX
FILENAME WORKSPACE
FILE READING TYPE
LOGFILENAME
NUM RUNS
NUM SCANS
NUM SUBJECTS
NUM VOXELS
ORDERING
ORIENTATION
ORIGIN
ROI VOXELS
TIME MASK
UI ON
VOXELSIZE
VOXEL MASK
37
Description
Type of data, e.g. ’float’, ’double’, ’short’,
’byte’, ’long’
The default location in the file chooser when saving data
Path to the data file
String with the filename pattern. Example:
’volume%02d’
Start index for use with the FILENAME PATTERN
The name of the workspace loaded in (lyngby workspace.mat is the default)
1=Analyze/Vapet (obsolete), 2=Lyngby (i.e.
custom), 3=Raw, 4=Analyze, 5=Analyze4D,
6=SDT (Stimulate), 7=VAPET4D
File name to write log information into - not
currently used
Number of runs in the data matrix
Number of scans in the data matrix
Number of subjects in data - not used at the
moment
Number of voxels in the datamatrix. 3x1 vector
The file order of the voxels:’xyz’, ’yxz’, etc. . .
How the data is mirrored to display it the correct
way around [’lr’, ’pa’, ’is’]
Centre voxel
Number of voxels in the (rectangular) ROI. 3x1
vector
Mask in time to discard unwanted scans
If UI ON=1 then the program is run from the
GUI interface, if 0 then text based (not up-todate)
Voxelsize
Spatial mask, e.g. to remove non-brain voxels
Table 2.3: Global variables
2.7
Writing Scripts to Run Lyngby Automatically
The functions in Lyngby do not need to be run from the GUI and can easily be run from the
commandline, although the structure and ordering in which everything is done is not as obvious.
This is the advantage of using the GUI. But the commandline approach does have an advantage
also, and that is the ability to write and run “scripts”, equivalent to batch files. This means
you can load your datafiles, perform the pre-processing, analyse and model the data and do any
post-processing, all without any user intervention. Obviously this allows you to set-up a whole
selection of jobs running overnight on different data, using different modelling parameters, and
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Section 2.7
Writing Scripts to Run Lyngby Automatically
Name
X
XN
RUN
PARADIGM
P
R
PN
X MEAN
X STD
DELAY *
RESULT *
STRENGTH *
RESULT FIR
RESULT BAT
RESULT BAF
RESULT LM ASSIGN
RESULT KM CENTER
RESULT CC
RESULT TS
TS PROBMAP
RESULT LZ
KS PROBMAP
RESULT KS
38
Description
The original datamatrix
The preprocessed datamatrix
The original run series
The original paradigm series
The paradigm variable after the time mask has been applied
The run variable after the time mask has been applied
The preprocessed paradigm (zero-mean)
Mean of each scan
Standard deviation of each scan
The results from algorithms that include a measurement
of temporal shift between the paradigm and the voxel’s
response
The main results from the different algorithms. See below
for examples.
The results from algorithms that include a measurement
of the strength of the voxel’s response
The result from the FIR analysis
The result from the Ardekani t-test
The result from the Ardekani f-test
The assignment labels from the K-means analysis
The cluster center in K-means analysis
The result from the Cross-correlation analysis
The result from the ordinary t-test (t-measure)
The result from the ordinary t-test (probability map)
The result from the Lange-Zeger analysis
The result from the Kolmogorov Smirnov test (probability
map)
The result from the Kolmogorov Smirnov test analysis
Table 2.4: Some Examples of the Global GUI variables
then look at the results the next morning.
It is very straightforward to write scripts for Lyngby, although it will be easier once you are
familiar with using the GUI. You will then know the order in which the data is processed and
the range of possible choices (e.g. the different pre-processing options) and algorithms available.
A full list of all the functions within the Lyngby toolbox and their purpose is given in Table B.1
of Appendix B. In addition, you may want to have a look at the Lyngbywebsite, where there is
ahyperlinked index of all the toolbox files, giving details of its purpose and its interdependencies
with the other files.
When using the commandline, you must have a data init.m file to specify the initial data
parameters, as well as the data run.m, data paradigm.m and data readdata.m files. These are
set up in exactly the same way as would be required when using the GUI (see Section 2.4.2 for
details and examples).
For example, for a particular dataset, the initialisation and conversion files would be as
shown below:
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Kai Hansen et al 1997
Section 2.7
Writing Scripts to Run Lyngby Automatically
The data init.m file:
% data_init.m
function data_init
lyngby_global
NUM_VOXELS = [24 12 1];
NUM_SCANS = 384;
FILE_READING_TYPE = 2;
DISCRIM_TMASK = lyngby_index2tmask(...
lyngby_dropedge(TIME_MASK*lyngby_paradigm, 4, 4), ...
length(TIME_MASK*lyngby_paradigm));
The data paradigm.m file:
% data_paradigm.m
function P = data_paradigm
P = kron(ones(8,1), [zeros(12,1) ; ones(24,1) ; zeros(12,1)]);
The data run.m file:
% data_run.m
function R = data_run
R = kron( (1:8)’, ones(48,1));
The data readdata.m file:
% data_readdata.m
function V = data_readdata(index)
fid = fopen(’../data/simfmri.1’, ’r’);
X = fscanf(fid, ’%f’);
fclose(fid);
offset = (index-1)*288;
V = X( (1:288)+offset );
Then the main script file may look something like this:
% session_load
lyngby_init
lyngby_ui_global
% Set up X
data_readx
% Set up design
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Load data
39
Section 2.7
Writing Scripts to Run Lyngby Automatically
P = lyngby_paradigm;
R = lyngby_run;
% session_prep
lyngby_ui_global
Compute data pre-processing
% Preprocessing
lyngby_prep_global
PREP_CENTERING = 1;
PREP_RUNCENTERING = 0;
PREP_IMAGECENTERING = 0;
PREP_NORMALIZATION = 0;
[XN, X_MEAN, X_STD, X_SEQMEAN, X_SEQSTD] = ...
lyngby_normalize(X, ...
’Centering’, PREP_CENTERING, ...
’RunCentering’, PREP_RUNCENTERING, ...
’ImageCentering’, PREP_IMAGECENTERING, ...
’Normalization’, PREP_NORMALIZATION);
PN = P - mean(P);
% session_lz
Compute Lange-Zeger
lyngby_lzit_global
LZ2_THETA1INIT = 10;
LZ2_THETA2INIT = 2;
LZ2_STEPSIZE = 1;
LZ2_MINCHANGE = 1e-4;
LZ2_ITERATIONS = 90;
RESULT_LZIT = lyngby_lzit_main(PN, XN, ...
’Iterations’,LZ2_ITERATIONS,...
’MinChange’,LZ2_MINCHANGE,...
’StepSize’,LZ2_STEPSIZE,...
’Theta1Init’,LZ2_THETA1INIT,...
’Theta2Init’,LZ2_THETA2INIT...
);
RESULT = RESULT_LZIT;
STRENGTH_LZIT = RESULT_LZIT(1,:);
DELAY_LZIT = RESULT_LZIT(2,:) ./ RESULT_LZIT(3,:);
% session_save_lz
Save LZ results
[y, m, d] = datevec(now);
s = sprintf(’%d-%02d-%02d’, y, m, d’);
t = pwd;
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40
Section 2.8
Adding New Algorithms to the Toolbox
41
t = t(length(t));
eval(sprintf(’save RESULT_LZ_BETA_%s_%s.txt STRENGTH_LZIT -ASCII’, s, ...
t));
eval(sprintf(’save RESULT_LZ_DELAY_%s_%s.txt DELAY_LZIT -ASCII’, s, ...
t));
2.8
Adding New Algorithms to the Toolbox
The Lyngby toolbox is meant as a development platform as well as an analysis one, and as such
we encourage you to add your own functions and models. If you have any you think would
benefit other researchers we would be happy consider it for the next release of the toolbox.
The process of adding your own functions is straightforward and you need only follow the
steps outlined below.
1. In lyngby_ui_main.m : Add the new main algorithm ’.m’ file to the list in the header
under
(See also:)
2. Add the variables returned from the new method to the ones in lyngby_ui_global.m often RESULT_{NAME} where {NAME} is the identifier for the new method. Both in the help
text and the global settings.
3. In lyngby_ui_main.m : Add a line under the line
% Globals
with the name lyngby_{NAME}_global
4. Add the file lyngby_{NAME}_global.m where the control variables of the new algorithm
are made global. (See lyngby_fir_global.m as an example). The control variables should
be named {NAME}_{SOMETHING}, where {SOMETHING} describes the variable contents.
5. In lyngby_ui_main.m under the line
% Method Identifiers
add an identifier for the new method am_{NAME} = {NEXT#} where {NEXT#} is the next
available number. (It is ok to insert and shift the remaining.)
6. In lyngby_ui_main.m under the line
% Initialize
add the line
lyngby_{NAME}_init;
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Section 2.8
42
Adding New Algorithms to the Toolbox
7. Add
the
file
lyngby_{NAME}_init.m
where
the
lyngby_{NAME}_global.m are initialized to suitable values.
variables
used
in
8. In lyngby_ui_main.m add a new line under the line
% Analysis Buttons
which should look like
’(-)
{EXPLAINING TEXT}|’,...
Note that the order should match the selection from 5:
9. In lyngby_ui_main.m locate the line
elseif command == 500
and add two lines
elseif AnalysisMethod == am{NAME}
lyngby_log(’{EXPLAINING TEXT} chosen’);
10. Locate the line
elseif command == 501
add two lines
elseif AnalysisMethod == am{NAME}
lyngby_ui_{NAME}_init;
11. Add the file lyngby_ui_{NAME}_init.m. Here a GUI interface to setting the parameters
from 7: is made. Look at the other lyngby_ui_{OTHERNAME}_init.m for examples.
12. In lyngby_ui_main.m locate the line
% The actual analysis
Add a new block for the new algorithm, which maybe look like
elseif AnalysisMethod == am{NAME}
RESULT_{NAME} = lyngby_{NAME}_test(XN, PN, R, ...
’Something’, {NAME}_{SOMETHING});
RESULT = RESULT_{NAME};
XN is the normalized data matrix at this point, PN is the activation function (with mean
subtracted), and R is the run structure.
13. In lyngby_ui_main.m locate the line
elseif command == 503
Add a new block for visualization of the new method
14. Add the file lyngby_{NAME}_test.m which returns the result of the method. The file
lyngby_ui_main.m will turn the RESULT into activation strength, delay etc.
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Chapter 3
Data Formats, Masking, and
Pre-Processing
$Revision: 1.11 $
$Date: 2002/08/14 14:21:24 $
3.1
Introduction
These next three chapters are reference-texts, covering the different stages of data-input, analysis
and post-processing in more detail than in the previous “user manual”.
This chapter deals with the issues of data formats and preparation before the analysis stage.
The next chapter describes the different analysis algorithms available within the toolbox, whilst
the one after it covers the post-processing, or meta-analysis, algorithms.
Each of the modelling algorithms needs to do several processing steps. Some of them are
common and have been pulled into a common pre-processing step. The most common step
is the substraction of the mean for each of the time series. Furthermore it is becoming more
and more clear that the pre-processing steps are very important; examples could be motion
correction and correction for field inhomogeneity. Currently we have chosen to concentrate our
efforts into the modelling of the data, and to only supply basic pre-processing steps and leave
other preprocessing to other packages. However, we are open to contributions.
3.2
Volume file formats
Given that many have designed their own “best” file format, we cannot support all formats, and
currently the toolbox can read fMRI data in the formats show in table 3.1
The Custom option allows Lyngby to read any data format with a little help from the user.
The interface/conversion files that are required to help Lyngby understand the user’s own data
are very simple and straightforward to write and should only take a few minutes to construct.
Full details and examples on how to write these files are given in the previous chapter — see
section 2.4.2.
If your format cannot be read automatically (and you don’t want to use the custom option)
tell us about the format and maybe it can be incorporated in the toolbox soon. Currently we
would like to support AnalyzeSPM (slightly changed Analyze format), the VoxelView format,
and the XPrime format.
43
Section 3.3
44
Masking
Name
Analyze
Analyze 4D
SDT
Vapet
Files
2
2
2
1
Extension
hdr, img
hdr, img
sdt, spr
—
RAW
1
—
Custom
1
—
Usage
Widely used: SPM, AIR and ANALYZE
All the time frames are in a single file
Stimulate format
Used at the Minneapolis Veterans Medical
Center, PET Center
Support for raw binary data with several
options of orientation and type
Support for other data where the user
writes a small interface file that returns the
volume at a given time step
Table 3.1: Data formats read by Lyngby.
3.3
Masking
The core of this toolbox lies in the composition of a datamatrix (named X in our Matlab code),
where a certain row is the volume at that particular time, and a certain column corresponds to
the time series for that particular voxel. This is shown in figure 3.1.
voxel number
time
The data matrix X
Figure 3.1: The data matrix X.
The toolbox has the potential to mask in both time and space, although currently the user
only has a GUI interface with which to specify a box region.
A mask in time, TTIME MASK (the variable TIME MASK in the code) is multiplied from the left of
the full data matrix. And likewise a mask in space, SVOXEL MASK (the variable VOXEL MASK in the
code), is multiplied from the right. Before the VOXEL MASK is applied, a further masking step can
be made: A box region can be specified with the limits defined in the variable ROI VOXELS.
X = TTIME MASK Xf ull SROI VOXELS SVOXEL MASK
(3.1)
We use sparse matrices for the TIME MASK and the VOXEL MASK, so these extra matrices only
require a modest amount of memory. Note that if all the data is to be used then the two masks
can be set to 1.
3.4
3.4.1
Preprocessing steps
Centering
Currently we have support for the following steps for removal of mean in various ways:
• Subtraction of mean from each voxel, i.e., the mean of each time series is forced to zero.
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Section 3.4
Preprocessing steps
45
• Subtraction of mean from each scan, i.e., the mean of each volume at a certain time step
is forced to zero.
• Subtraction of mean from each run, i.e., the mean of each run in each of the time series is
forced to zero. Note that this makes the first substraction meaningless.
3.4.2
Variance Normalization
Algorithms such as neural networks in general use data normalization to unit variance in order
to improve the stability of the algorithms. Note that this option might lead to unreliable results
in other algorithms.
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Chapter 4
Analysis — The Modeling
Algorithms
$Revision: 1.46 $
$Date: 2004/01/21 10:54:40 $
4.1
Cross-correlation
The “Cross correlation” measures the temporal cross correlation between the paradigm and the
measured data.
4.1.1
Our implementation
In the toolbox the paradigm can be cross-correlated with the time series. This produce a crosscorrelation function, i.e. a function that is approximately twice as long. In general the crosscorrelation function is only interesting around lag zero, and the user can decide how many lags
that should be computed and stored. Note that people often only compute the cross-correlation
for lag zero, which can lead to unreliable results in regions with long delays, so we maintain the
temporal information as well.
In the display of the cross-correlation function we have enabled the display of the maximal
value of the cross-correlation function as well as the delay defined as the position of the maximal
value and the energy of the cross-correlation function.
4.2
FIR filter
The finite impulse response (FIR) filter is the same as a regression model or an ARX(0,n) model
(that is an autoregressive model with exogenous input). The voxel denoted by u is regarded
as a linear system with the input x (the paradigm), the filter (transformation function) to be
estimated h, and the output (fMRI data) y which is disturbed by additive noise.
y(u, t) =
L−1
X
h(u, t − τ )x(u, τ ) + εu (t)
(4.1)
τ =0
The algorithm fits the parameters h(τ ) so that the error is the least square solution. This
46
Section 4.2
47
FIR filter
assumes that the noise εu is gaussian distributed. In that case the model ŷ is
ŷ(u, t) =
L−1
X
h(u, t − τ )x(u, τ )
(4.2)
τ =0
or in matrix vector notation for each value of u:
Ŷ = X h
(4.3)
where Ŷ = [ŷ(u, 0), ŷ(u, 1), ..., ŷ(u, N −1)] and X contains the time-shifted values of the paradigm
where each row of the matrix has L samples.
From the assumption that the error is Gaussian, the minimization of the error function
X
Eu =
(ŷ − y)2
(4.4)
u
can be identified directly through the so-called “normal equation”:
XT Y = XT X h ⇒
T
−1
h = (X X)
(4.5)
T
X Y
(4.6)
If the model is too complex — if the model is allowed to use too many filter coefficients —
the model will not only fit to the signal within the data but also to the noise.
In the toolbox two ways of estimating the inverse matrix have been implemented for handling
the often ill-posed inversion problem.
The first method for reducing the model complexity without reducing the lag size is to
introduce regularization. One regularization technique is the ridge regression controlled by a
single parameter κridge . Note that the neural network community calls this weight decay and
that this technique biases the filter coefficients towards zero.
h = (XT X + κridge I)−1 XT Y
(4.7)
where I is the unity matrix.
The other estimation technique computes the singular value decomposition (SVD) of the
matrix to invert (In this case the U and V will be equal, so that cheaper and equivalent
numerical methods can be used):
XT X = UΣVT
(4.8)
hence
(XT X)−1 ≈ VΣ+ UT
where a threshold κSV D controls how many singular values
enter the pseudo inverse.
½ −1
σi,i if σi,i ≥ κSV D and i = j
+
σi,j =
i=
6 j
0
if σi,i < κSV D or
(4.9)
(4.10)
Note that this inversion does not depend on the voxel position u, hence a transformation
matrix T = (XT X)−1 XT can be generated once and then the filter for each value of u can be
generated from the linear transform
hu = T Yu
(4.11)
This type of regularization is the same as principal component regression (Jackson, 1991).
Note that for the FIR filter with a symmetric paradigm (symmetric with respect to rise and
fall flange) the response will also be symmetric. This means that if you have, for example, an
on/off square wave paradigm, then a positive spike at the rise flank will force a negative spike
at the fall flank.
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Section 4.2
4.2.1
48
FIR filter
Estimation of the Activation strength in the FIR filter
Using the estimated FIR filter, an activation strength can be defined as the standard deviation
of the data estimate, normalized by the standard deviation of the paradigm.
s
V (ŷ(u, t))
(4.12)
A(u) =
V (h)
where V is a variance estimator.
4.2.2
Estimation of the delay from the FIR Filter
Dealing with a FIR filter makes it easy to derive several descriptors. One is the group delay,
e.g., (Oppenheim and Schafer, 1989, pp. 205) defined as,
τF IR (ω) = −
∂
arg H(ω) =
∂ω
∂Hr (ω)
∂ω Hi (ω)
i (ω)
− ∂H∂ω
Hr (ω)
2
|H(ω)|
(4.13)
where index r and i denote the real and the imaginary part respectively.
H(ω) =
∂H(ω)
∂ω
N
X
−jωn
h(n)e
n=0
N
X
= −
=
N
X
h(n)(cos(ωn) − j sin(ωn))
(4.14)
N
X
(4.15)
n=0
nh(n)e−jωn =
n=0
nh(n)(− sin(ωn) − j cos(ωn))
n=0
which means that the delay has a frequency dependence, and given that the paradigm functions
often are dominated by the low frequencies, a simple approximation is to neglect the frequency
dependence and only consider the delay for ω = 0;
¯
P
∂H ¯
t h(u, t)
∂ω ¯
τF IR = −
= P
(4.16)
¯
h(u, t)
H(ω) ¯
ω=0
For some combinations of filter coefficients the denominator of equation 4.16 can be very
small which will influence the delay estimate dramatically. This problem can be explained from
the fact that equation 4.16 is based on a low frequency dominance and in case of a very high
noise level the assumption is in general not valid, and the estimate should rather be disregarded.
A simple way to check whether the assumptions is fulfilled is to evaluate
¯X
¯
X
¯
¯
|h(u, t)|
(4.17)
h(u, t)¯ > γ
¯
where γ should at least be larger than 0.5. More details can be found in (Nielsen et al., 1997).
4.2.3
Our Implementation
Currently the user has to choose to parameters depending on whether the inversion type is done
using equation 4.9 or equation 4.7.
• The order of the filter can be varied.
• The regularization parameter
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Section 4.3
49
Exhaustive FIR filter
– For SVD: The lowest level of singular values accepted κSV D .
– For ridge regression: the weight decay parameter κRidge
The order of the filter should depend on the number of scans in the time series and the
magnitude of the regularization. With a too high order the model will mainly fit the noise in
the data, — with a too small order it will not be able to fit the data.
Currently, although a suitable order must be chosen manually, this does not seem to degrade
the results significantly. If an automatic determination of the order is required, then the more
time consuming algorithm described in section 4.3 should be used.
4.2.4
References
The standard linear regression we employ here should be described in most books about time
series analysis. The ridge technique in regression was first applied by (Hoerl and Kennard,
1970b) (Hoerl and Kennard, 1970a). For an annotated bibliography see (Alldredge and Gilb,
1976). Ridge-regresion is a form of Tikhonov regularization for linear models (Tikhonov, 1963)
(Tikhonov and Arsenin, 1977) (Hansen, 1996). Principal component regression is described in
(Jackson, 1991).
The application of the FIR filter on fMRI is described in (Goutte et al., 2000) and shortly
in (Nielsen et al., 1997). Another type of linear time series analysis on fMRI is described in
(Locascio et al., 1997).
4.3
Exhaustive FIR filter
In the FIR filter method (section 4.2) the user has to make the choice about the length of the
filter. In principle the filter length is a parameter that can be varied as a function of the voxel
index. In voxels where the signal is complex and there is a high signal to noise ratio many filter
coefficients may be needed. On the other hand, in voxels where only noise is observed none of
the estimated filter coefficients are stable, and the best stable signal estimator is a constant of
value zero, i.e. zero taps in the filter.
In the Exhaustive FIR filter, the idea is to find the optimal filter length in each voxel. The
optimal model is chosen by using the mean generalization error as a measure based on a training
and test scheme. (this scheme is especially used in artificial neural networks, see section 4.9).
The basic idea is that each run in a time series is considered as an independent time series.
Using resampling without replacement with that prior it is possible create a new dataset that
gives good statistics in the generalization error.
The mean generalization error for a given model is defined as
Egen (u) =
T N
1 XX
(Yi (u, t) − Yestimat,i (u, t))2
N T t=1
(4.18)
i=1
where i denotes the resample index and t is the time.
The new dataset created with the resampling is split into a training and a test set. The ratio
between the size of these two datasets is used in the algorithm as a variable.
The Exhaustive FIR filter uses a varying filter length combined with the SVD regularization.
This means that for each filter length there is a “filter length” of singular values that can enter
the pseudo inverse. So for each filter length there is a model using the largest singular value,
and another model using the two largest singular values etc. The number of models is now given
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Section 4.4
The Ardekani t-test
50
+1)
by 1 + 2 + ... + Maxorder = Maxorder(Maxorder
, which can lead to several hundred different
2
models being tested.
The job is now to calculate the generalization error (test error) which depends of the different models and the split ratio (for splitting the dataset). The model with the smallest mean
generalization error is chosen as the optimal model for the voxel.
When the optimal model for each voxel has been estimated, the activation strength can be
found from equation 4.12. Note also that the number of filter coefficients will vary across the
volume, and this might also be of interest to investigators.
4.3.1
Our Implementation
All exhaustive FIR functions have an infix of efir . The two parameters that can be set are the
“filter length” and the “reshuffle” amount. The reshuffle amount determines how many times
the data set resampling is done.
4.3.2
References
We can not make any direct references in connection with the exhaustive FIR model. Consult
section 4.2.4 for reference in relation to the architecture of the model. Generalization and
train/test set are terminology from the artificial neural network society, and reference to that
can be found in section 4.9.2.
4.4
The Ardekani t-test
In (Ardekani and Kanno, 1998) another type of t-test is described. The method is rather different
than the one described in section 4.14 and it is actually closer to the cross-correlation method.
The basic idea is to project each of the time-series onto a subspace driven by the paradigm
function. The t-statistics are obtained by dividing by the estimate of the standard deviation
found from the part of the energy in the time-series that is not explained by the paradigm.
4.4.1
Our Implementation
Our implementation follows the paper rather closely and as suggested in the paper we have
implemented a delay option, so that the paradigm can be delayed a number of samples compared
to the time-series in order to adapt to a possible delay in the data.
4.4.2
References
Babak Ardekani’s t-test is described in (Ardekani and Kanno, 1998).
4.5
The Ardekani F-test
In (Ardekani and Kanno, 1998) an F-test has been presented to identify activated regions. The
strategy has some common ideas with the Ardekani t-test (described in the same article).
The basic idea is to project each of the time-series onto a subspace driven by a truncated
Fourier expansion of the data. The power of the data in the subspace driven by the expansion is
divided by the power of the data that lies outside of the Fourier subspace. Thus an F-statistic
is obtained without using requiring any knowledge of the paradigm.
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Section 4.6
51
K-means Clustering
In (Ardekani et al., 1999) the F-test is extended with the modeling of a nuisance subspace:
A subspace orthogonal to the subspace spanned by the Fourier expansion is found and this
subspace is then extracted from the original signal. The dimension of the subspace is found by
using Akaike’s information criterion.
4.5.1
Our Implementation
Functions belonging to the (Barbak) Ardekani F-test have the infix baf , and function for the
Ardekani F-test with nuisance have the infix baf2 . Our implementation follows the paper
rather closely. It is also intended to implement alternatives to the Fourier expansion.
4.5.2
References
Babak Ardekani’s F-test is described in (Ardekani and Kanno, 1998). The F-test with nuisance
parameters extension is described in (Ardekani et al., 1999).
4.6
K-means Clustering
The K-means algorithm is a simple non-parametric clustering method.
The idea behind K-means clustering here is to classify the individual voxel in the volume
with respect to their time series. This is indicated in figure 4.1. After the clustering the
individual clusters may be analyzed further. The method is currently sensitive to the initial
cluster positions and the algorithm converges fast.
Figure 4.1: The time-series are each assigned to the cluster center with the best match.
In the K-means algorithm, K cluster centers have to be chosen (The dimension of the space
is equal to the size of the time series length). Then the distance from each voxel to each cluster
center is calculated. Each voxel is then assigned to the cluster which is the minimum distance
away, as shown in figure 4.2; lastly (before iterating) the new cluster center is calculated as the
mean of all the voxels assigned to that cluster.
(0)
1. Initialize K cluster centers Ck of same dimensionality as the time series, iteration i = 0.
(i)
2. Assign each data vector xj to the cluster with the nearest center Ck . Currently we use
(i)
the ordinary Euclidean distance metric kCk − xj k.
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Section 4.6
52
K-means Clustering
Data vectors are assigned
closest cluster center
Figure 4.2: In a high dimensional space the data vectors are clustered to the nearest cluster
center.
(i+1)
3. Set new cluster centers Ck
to the center of gravity of each cluster:
(i+1)
Ck
= E{xj }x
(i)
j ∈Ck
(4.19)
This formula can also be modified to use the median and/or to include an inertia term.
4. Goto step 2 until convergence.
4.6.1
Our implementation
Instead of using the time series directly as the input to the K-means algorithm, we have made it
possible to use the cross-correlation of the fMRI time series and the paradigm. This has a major
impact of the results of K-means algorithm, which will make it far easier to find the activated
voxels (Toft et al., 1997).
When clustering on the cross-correlation function or the raw time series, the final clusters
centers will be affected by the amplitude level and a possible lag. For instance, if two spatially
separated regions have similar response strength but different delays, then they will be clustered
into two different clusters according to their delay values.
The toolbox enables clustering using K-means or K-median (K-mediod). The range of the
variables can be “standardized” (normalized) to unit variance or standardized according to the
min-max-range. The cluster centers can be initialized randomly (In (Toft et al., 1997) it has
been found that this strategy works rather well for the cross-correlation clustering) or according
to the correlation with the paradigm function. The variable to cluster on can be set to the raw
time series or the cross-correlation function.
It is implemented in the lyngby km main matlab function.
4.6.2
References
The K-means clustering algorithm was first described in (MacQueen, 1967). Other “non-neuroimaging” descriptions of this algorithm are available in (Ripley, 1996, section 9.3), (Sonka et al.,
1993, section 7.2.4) or (Hartigan and Wong, 1979). Our approach in connection with functional
neuroimaging is published in (Goutte et al., 1999) and described shortly in (Goutte et al., 1998;
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Kai Hansen et al 1997
Section 4.7
53
Kolmogorov-Smirnov test
Toft et al., 1997). In (Liptrot et al., 2003) it was applied to extract the time-activity curve from
dynamic PET scans in connection with the 5HT2A -receptor ligand.
Different kinds of clustering methods have been applied in functional neuroimaging. Some
of the references are collected at http://www.inrialpes.fr/is2/people/goutte/fmriclusterefs.html
4.7
Kolmogorov-Smirnov test
Most standard text books in statistics show the Kolmogorov Smirnov test, where the maximal
difference between two histograms is used as a measure of match between the two signals. Hence
a very small difference indicates that the two signals are nearly identical. Note that the method
only uses the histograms, hence the temporal placement is not used here.
4.7.1
Our implementation
As mentioned in section 1.1.1, each of the time series is split into an activated part and a baseline
part, determined from the
paradigm function. In order to compensate somewhat for the hemodynamic response time,
we have made it possible to drop a number of samples after each transition from the baseline to
an activated state and vice versa.
The Kolmogorov Smirnov test requires the histograms, hence some sorting of signal values is
needed. This implies that the function might be slow if the fMRI data has many time elements.
4.7.2
References
A critique of the Kolmogorov-Smrnov test used for fMRI has been made in (Aguirre et al., 1998).
4.8
Lange-Zeger
The Lange-Zeger model (Lange and Zeger, 1997) fits a three parameter gamma density function
hLZ as the convolution filter:
hLZ (u, t) = β(u)θ2,u (θ2,u t)θ1,u −1 exp(−θ2,u t)/Γ(θ1,u )
(4.20)
The gamma density function is convolved with the paradigm to give the voxel value.
X
y(u, t) =
h(u, t − τ )x(u, τ )
(4.21)
τ
Note that the Lange-Zeger kernel is normalized so that the β is the power amplitude of
the hemodynamic response. The dependence on the two parameters can be seen from figures 4.3 and 4.4.
4.8.1
Estimation of the delay from the Lange Zeger model
The Lange Zeger model is in principle a constrained version of the FIR model, hence a delay
can be estimated just as it was in subsection 4.2.2.
In the Lange Zeger case, the spectrum has a rather simple expression
HLZ (ω) =
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Kai Hansen et al 1997
µ
jω
1+
θ2,u
¶−θ1,u
(4.22)
Section 4.8
54
Lange-Zeger
The convolution kernel for θ2=1 and a set of values of θ1
0.5
θ = 1.5
0.45
1
0.4
θ = 2.0
0.35
1
θ = 2.5
0.3
1
θ1 = 3.0
θ = 3.5
1
θ = 4.0
0.25
1
0.2
0.15
0.1
0.05
0
0
1
2
3
4
5
time t
6
7
8
9
10
Figure 4.3: The gamma density kernel as a function of the time t for β = 1 and θ2 = 1 for a set
of θ1 .
The convolution kernel for θ =5 and a set of values of θ
1
2
0.8
θ2 = 4.0
0.7
θ2 = 3.5
0.6
θ2 = 3.0
0.5
θ2 = 2.5
0.4
θ = 2.0
2
θ2 = 1.5
0.3
θ2 = 1.0
0.2
θ2 = 0.5
0.1
0
0
1
2
3
4
5
time t
6
7
8
9
10
Figure 4.4: The gamma density kernel as a function of the time t for β = 1 and θ1 = 5 for a set
of θ2 .
hence the delay for very low frequencies (ω = 0) ends up in a very simple form
τLZ (u) =
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Kai Hansen et al 1997
θ1,u
θ2,u
(4.23)
Section 4.9
4.8.2
55
Neural networks
Our implementation
Compared to the original algorithm, our algorithm does not perform any noise estimates as
suggested in (Lange and Zeger, 1997). Furthermore we have experienced that the iterative way
of estimating the model parameters suggested in (Lange and Zeger, 1997) is often unstable,
which cannot be explained with the lack of a better noise model. In certain situations the noise
makes the iterative algorithm diverge, resulting in extreme values of the model parameters.
In our implementation, the original iterative algorithm is available (though currently not in
the GUI) as well as a version where the (θ1 , θ2 ) space is grid searched and the value of β is
determined directly in every grid point. Furthermore is it possible in the grid search version to
use regularization on the value of β penalizing the extreme values.
It should also be mentioned that the Lange-Zeger kernel shown in equation 4.20 is a continuous function of time t, which needs to be sampled in time as indicated in equation 4.21.
This implies that the number of samples taken from equation 4.20 should at least include the
maximum point of the kernel, which is found at
t = arg max hLZ =
4.9
θ1,u − 1
θ2,u
(4.24)
Neural networks
By neural networks we mean artificial neural network. They have little to do with biologic
neural network, — we are not trying to simulate the biological neural circuit. The name “neural
network” has arisen because the first versions of these mathematical models was highly inspired
by the biological neural circuits. We use neural networks solely as general purpose non-linear
statistical models. In some sense the neural network is a non-linear generalization of the linear
model (see the FIR model in section 4.2).
The main type of neural network we employ is the two-layer feed-forward neural network. It
consists of two layers of weights (the neural network name for the model parameters) and two
(or three) layers of “neurons” (or units). The first layer of neurons is not usually counted as a
layer: It is the input to the neural network. The second layer is the hidden layer. The neurons in
this layer have an activation function, and it is necessary for the non-linearity of neural network
that this activation function is non-linear. The final layer is the output layer. These will also
have an activation function. This might be linear or non-linear.
With x as the input, y as the output, with v as the first layer of weights (the input-to-hidden
weights) and w as the second (the hidden-to-output weights) and with i, h, o and p as the indices
for the input, hidden and output neurons, and the examples, respectively, we get the following
neural network function:
Ãn
Ãn
!
!
i
h
X
X
p
yop = go
who gh
vih xi + vh0 + wo0
(4.25)
h
go
gh
i
Here,
and
are the activation functions and vh0 and wo0 are the biases.
The activation function is usually of the sigmoidal type and we use the hyperbolic tangent.
In connection with classification the output activation function is this hyperbolic tangent to get
a restricted output that can be interpreted as a probability. In connection with regression the
output activation function is linear.
The neural network is optimized to predict to a target, i.e. to make the (numerical) difference
between a desired target t and the computed output of the neural network y as small as possible.
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Section 4.9
56
Neural networks
As a measure for the discrepancy (how well we have optimized the network) we can use
different kinds of cost functions. In regression we usually strive for uniformly minimum variance,
thus we can use the mean square error for the measure:
Eq =
np no
X
X
p
(tpo − yop )2
(4.26)
o
In the case of two-class classification a more appropriate measure is the two-class (cross)entropy error:
Ee =
np no ·
X 1
X
o
p
2
(1 +
tpo ) ln
1
1 + tpo
1 − tpo
p
+
(1
−
t
)
ln
o
1 + yop 2
1 − yop
¸
(4.27)
The cross-entropic errorfunction of equation 4.27 requires the target to be t = 1 or t = −1.
3
Square
Entropic
2.5
Errorfunction
2
1.5
1
0.5
0
−1
−0.8
−0.6
−0.4
−0.2
0
0.2
Neural network output
0.4
0.6
0.8
1
Figure 4.5: Comparison of the square and the cross-entropic errorfunction. The target is t = 1.
The cross-entropic errorfunction is penalizing large deviation from the target value much more
than the square errorfunction.
Another type of errorfunction is the cross-entropy for multiple classes (Bishop, 1995). We
use a so-called “c − 1” variation where the number of output neurons in the neural network is
one less than the number of classes (Andersen et al., 1997).
Ã
! n −1
nX
o −1
o
X
Ec = ln 1 +
exp(φo ) −
to φo
(4.28)
o
o
This errorfunction is developed for a neural network with neural network function as follows.
The hidden layer neurons have a hyperbolic tangent as the activation function:
Ãn
!
i
X
hph = tanh
vhi xpi + vh0
(4.29)
h
c
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Kai Hansen et al 1997
Section 4.10
57
Neural network regression
The second layer has linear activation functions:
φo =
nh
X
woh hph + wh0
(4.30)
h
To be able to interprete the output as a probability we use a softmax layer:
(
Pnoexp(φo )
, if o < no
exp(φ 0 )+1
yo =
o0 P o
no
1 − o0 yo0 , if o = no
(4.31)
The neural network is optimized by adjusting the weights in an iterative scheme, either with
gradient or with Hessian based methods, see, e.g., (Ripley, 1996, appendix A.5). The term
“back-propagation” is usually used to denote gradient-based optimization.
4.9.1
Our implementation
All neural network functions have the prefix lyngby nn. In the present implementation there
are two types of neural networks: A regression type with least square fitting and a classification
type with a cross entropic cost function suitable for binomial distributions. The functions in
connection with the regression neural network have the prefix lyngby nn q where the “q” stands
for quadratic. The second group of functions has the prefix lyngby nn e where the “e” is for
entropic. Functions that are common do only have the prefix lyngby nn.
4.9.2
References
There are a number of good books for that introduce neural networks, e.g., Chris Bishop’s Neural
Networks for Pattern Recognition (Bishop, 1995) and Brian Ripley’s Pattern Recognition and
Neural Networks (Ripley, 1996). Others are (Haykin, 1994) and (Hertz et al., 1991).
The two-layer feed-forward neural network is described in several papers from our department, — the first important one being (Svarer et al., 1993a), followed by (Hintz-Madsen et al.,
1995) (Hintz-Madsen et al., 1996a) (Hintz-Madsen et al., 1996b) and (Svarer et al., 1993b).
4.10
Neural network regression
The “Neural network regression” is a neural network generalization of the FIR model. For the
input to the neural network we use the paradigm, and the weights are adjusted to target the
output to the fMRI time series signal. One neural network is trained for every voxel.
The ability of the neural network to predict the fMRI time series is used
as a measure for the strength of the signal in a voxel: If this signal can be predicted from
the paradigm signal then the voxel is activated.
4.10.1
Our implementation
The functions that belong to the neural network regression analysis method have the prefix
lyngby nnr.
The optimization of a single neural network is usually a tedious affair and when one neural
network has to be optimized for every single voxel, the computational effort of this analysis
method is enormous.
The neural network regression analysis is currently under implementation, and it does not
provide any means for assuring that the neural network is generalizing well so it is presently
dangerous to use the neural network regression.
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Kai Hansen et al 1997
Section 4.11
4.10.2
58
Neural network saliency
References
To our knowledge using neural networks in connection with fMRI time series regression is not
described anywhere, yet. A first application was in a comparison between models (Lange et al.,
1996).
4.11
Neural network saliency
Contrary to the neural network regression analysis (section 4.10) the “Neural Network Saliency”
does not analyze the fMRI signal as time series. Rather, the neural network treats each scan as
an individual object that is used in a classification or regression, and the saliency map estimates
each voxel’s importance for this prediction.
Each scan is — after an SVD-projection or other linear transformation — used as the input
to the neural network. The state of the paradigm corresponding to the scan is used as the
target. The SVD-projection is usually necessary because the weight optimization problem will
be highly ill-posed if the number of parameters is not heavily reduced.
After the neural network has been optimized for best generalization the actual saliency
computation is performed. The saliency resembles the saliency from OBD (Le Cun et al., 1990),
but where the OBD-saliency is for the weights the saliency in connection with the saliency
map is for the variables, i.e. each individual voxel. The saliency describes the change in the
generalization error when a voxel is deleted. The method to derive the change in generalization
error is based on a Taylor expansion of the neural network costfunction and regarding the effect
of the small perturbation when deleting a weight. There are several levels of approximation
from the generalization error to the actual estimate.
Let the matrix B denote the linear transformation we apply to the voxel datamatrix X to
project it onto a subspace containing the data described by the subspace datamatrix X̃:
X̃ = XB
(4.32)
The vectors x̃p from the subspace matrix X̃ are used as input to a two-layer feed-forward neural
network, see section 4.9. When the neural network is fully optimized to best generalization, the
full costfunction from the voxel to the output of the neural network is Taylor-expanded with
respect to the components in the linear transformation B. For an “entropic neural network” the
first order derivative and the diagonal approximation of the second order derivative yield :
∂Ce
∂bil
∂ 2 Ce
∂b2il
= −
np nh
X
X
p
h
³
¡ ¢2 ´
(tp − y p ) woh 1 − hph
vhi xpl
"n
#2
np ³
h
´ X
³
´
X
¡
¢
¡ p ¢2
2
=
1 − (yop )2
woh 1 − hph
xl
vhi
p
(4.33)
(4.34)
h
The symbols are: i = 1..ni is indicing over inputs to the neural network, i.e., the output of the
linear transformation and l = 1..nl indices over voxels. The rest of is the same as the symbols
of section 4.9. Entering the derivatives in the Taylor expansion yields:
δCe,l ≈
np nh ni
X
XX
p
h
i
³
¡ ¢2 ´
(tp − y p ) woh 1 − hph
vhi bil xpl
"n
#2
np ni ³
h
´ X
³
¡ ¢2
¡ p ¢2 ´
1 XX
p 2
1 − (y )
woh 1 − hh
vhi b2il xpl
+
2 p
i
c
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Kai Hansen et al 1997
h
(4.35)
Section 4.12
59
The SCVA model: Strother Canonical Variate Analysis
Similar calculations can be performed for the costfunction to the “quadratic neural network”
Cq . Here the derivatives become:
∂Cq
∂bil
∂ 2 Cq
∂b2il
= −2
np no nh
XX
X
o
p
np
= 2
no
XX
o
p
h
"n
h
X
³
¡ ¢2 ´
(tpo − yop ) woh 1 − hph
vhi xpl
(4.36)
´
(4.37)
³
woh 1 −
h
¡
¢2
hph
vhi
#2
¡
xpl
¢2
Substituting these derivatives into the Taylor expanded saliency yields:
δCq,l ≈ 2
np no nh ni
XXX
X
p
o
h
np
+
ni
no X
XX
p
4.11.1
o
i
i
³
¡ ¢2 ´
vhi bil xpl
(tpo − yop ) woh 1 − hph
"n
h
X
h
³
¡ p ¢2 ´
vhi
woh 1 − hh
#2
(4.38)
¡ ¢2
b2il xpl
Our implementation
The functions that are specific for the neural network saliency all have the infix nns . The
optimization of the neural network is performed with the functions calls nn .
4.11.2
References
The first description of the neural network saliency was in Mørch (Mørch et al., 1995) and
(Mørch et al., 1996). It has also been the subject of three master’s thesis within our department, (Lundsager and Kristensen, 1996), (Mørch and Thomsen, 1994) and (Nielsen, 1996). The
method is still under development, i.e. the mathematical form of the saliency is not yet fully
justified.
4.12
The SCVA model: Strother Canonical Variate Analysis
The SCVA model is directly related to the SOP model (section 4.13: It uses the same designmatrix, — equation 4.51). Using the same terminology as Mardia (Mardia et al., 1979) the analysis
method should not be called canonical variate analysis but canonical correlation analysis.
CVA together with SVD on a datamatrix with degenerate rank is ambiguous: The canonical
correlation coefficients will all be one, thus the canonical correlation vectors will have no unique
orientations. A technique to cope with this problem is canonical ridge, which is a variation of
ridge regression within canonical variate analysis. With the ridge regression parameters on the
highest level, the canonical ridge will become PLS (see section 4.13).
The ordinary canonical correlation analysis / canonical variate analysis is the singular value
decomposition of a (normalized) cross-correlation matrix:
³
´−1/2
³
´−1/2
GT G
GT X∗ X∗ T X∗
= UΛVT
(4.39)
Here X∗ is the SVD projection (see also the equivalent equation 4.53):
X∗ = XV∗T = U∗ Λ∗
c
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Kai Hansen et al 1997
(4.40)
Section 4.12
60
The SCVA model: Strother Canonical Variate Analysis
The equality sign only holds if there are more variables than objects (more voxels than
scans).
V∗ is found through a normal singular value decomposition:
X = U∗ Λ∗ V∗T
(4.41)
Ordinary canonical variate analysis can be extended to canonical ridge. Canonical ridge is
usually written as:
³
GT G + kG I
´−1/2
³
´−1/2
GT X∗ X∗T X∗ + kX I
= UΛVT
(4.42)
A different way to write the canonical ridge equation is by using another form of scaling:
³
´−1/2
³
´−1/2
T
∗T ∗
T ∗
(1 − kG ) G G + kG I
G X (1 − kX ) X X + kX I
= UΛVT
(4.43)
This has the advantage that the ridge parameters kG and kX are morphing between ordinary
CVA and PLS via orthonormalized PLS.
ordinary CVA
ordinary PLS
orthonormalized PLS
kX
0
1
0
kG
0
1
1
Equation 4.40 can be used to eliminate X∗ :
³
´−1/2
T
(1 − kG ) G G + kG I
GT
∗
X
³
∗T
∗
´−1/2
(1 − kX ) X X + kX I
³
´−1/2
U∗ Λ∗ (1 − kX ) (U∗ Λ∗ )T U∗ Λ∗ + kX I
´−1/2
³
U∗ Λ∗ (1 − kX ) Λ∗T U∗T U∗ Λ∗ + kX I
³
´−1/2
U∗ Λ∗ (1 − kX ) Λ∗ T Λ∗ + kX I
(4.44)
(4.45)
(4.46)
(4.47)
If kX = 0, as in ordinary CVA, we will get a simpler equation:
³
´−1/2
(1 − kG ) GT G + kG I
GT
´−1/2
³
U∗ Λ∗ Λ∗T Λ∗
U∗ Λ∗ Λ∗ −1
∗
U
(4.48)
(4.49)
(4.50)
U∗ is an orthonormal matrix: it is a matrix containing eigenvectors — eigensequences — of
the original datamatrix. Such a matrix does not contain any principal direction. All directions
are equally strong, i.e., the eigenvalues have the same magnitude. Multiplying with another
orthonormalized matrix (e.g., the designmatrix part of equation 4.50) will not bring up any new
principal direction. When there are no principal directions in the matrix, the eigenvectors —
eigenimages — cannot be said to represent anything but an arbitrary direction.
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Section 4.13
4.12.1
61
The SOP model: Strother Orthonormalized PLS
Our implementation
The main function that implements the SCVA model is called lyngby scva main. Before the
datamatrix is singular value decomposed it is doublecentered. The actual canonical variate
analysis function is called lyngby cva.
The number of components (singular values) maintained in the final singular value decomposition can be varied by the user. Usually only three or less components are significant. The
function will only return eigenvectors with eigenvalues that are different from “zero”, i.e. larger
than a tolerance set to account for numerical round-off errors.
In the present implementation it is only possible to do the analysis on a run basis, — not
within run (e.g. if you have a repeated stimulus function in a run), and not with longer periods
than a run. The run specification is used to make the designmatrix.
4.12.2
References
SVD and CVA is usually explained in most multivariate analysis textbooks, e.g., Mardia (Mardia et al., 1979). An short overview of multivariate analyses is available in (Worsley et al.,
1997) or (Worsley, 1997). The special canonical ridge technique is described shortly in (Mardia
et al., 1979) which reference the original article (Vinod, 1976). Furthermore, the canonical ridge
analysis has a special case in PLS (see section 4.13).
The SCVA model with the special designmatrix (which we here call the Strother’s designmatrix) was used in (Strother et al., 1996). CVA has also been used in (Friston et al., 1995a)
(Fletcher et al., 1996) and (Van Horn et al., 1996).
4.13
The SOP model: Strother Orthonormalized PLS
The SOP model consists of a preliminary SVD on the datamatrix, a orthonormalization of a
designmatrix, followed by a partial least square analysis (PLS) between the SVD’ed datamatrix
and the orthonormalized designmatrix. The outcome of this analysis is eigenimages, eigenvalues
and the corresponding eigensequences are represent a dependency between the datamatrix and
the designmatrix.
The designmatrix is constructed in a special way as in (Strother et al., 1996) (where it was
used in connection with CVA — canonical variate analysis): All the scans that correspond to a
specific period in a run are given their own class, e.g. the designmatrix of a tiny study consisting
of 2 runs, each with four scans, will have the following structure:






G=





1
0
0
0
1
0
0
0
0
1
0
0
0
1
0
0
0
0
1
0
0
0
1
0
0
0
0
1
0
0
0
1












(4.51)
It is of course important that the stimulus function (the paradigm) is the same for all runs.
We use partial least square, not as in introduced by McIntosh (McIntosh et al., 1996),
but in the orthonormalized version as suggested by Worsley (Worsley et al., 1997) where the
designmatrix is made invariant to linear transformations:
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Section 4.14
62
The Ordinary t-test
(GT G)−1/2 GT X∗ = UΛVT
(4.52)
Here X∗ is the SVD projection:
X∗ = XV∗T
(4.53)
X = U∗ Λ∗ V∗ T
(4.54)
And V∗ is the eigenimage of X:
The eigensequence U and the eigenimage of V of equation 4.52 are the interesting parts.
These are sorted according to descending eigenvalues. The first eigenimage and eigensequence
will (usually) have a direct relationship with the stimulus function.
4.13.1
Our implementation
The main function that implements the SOP model is called lyngby sop main. Before the
datamatrix is singular value decomposed it is doublecentered.
The number of components (singular values) maintained in the final singular value decomposition can be varied by the user. Usually only three or less components are significant. The
function will only return eigenvectors with eigenvalues that are different from “zero”, i.e. larger
than a tolerance set to account for numerical round-off errors.
In the present implementation it is only possible to do the analysis on a run basis, — not
within a run (e.g. if you have a repeated stimulus function in a run), and not with longer periods
than a run. The run specification is used to make the designmatrix.
4.13.2
References
SVD and CVA is usually explained in most multivariate analysis textbooks, e.g. Mardia (Mardia
et al., 1979). For PLS, and orthonormalized PLS see the compact explanation in (Worsley et al.,
1997).
The SOP model — orthonormalized PLS combined with Strother’s designmatrix — is not
described anywhere else than here.
4.14
The Ordinary t-test
A common test in functional brain modeling is the t-test. For a given voxel the fMRI signal is
split into two sections: the activated part and a baseline part. The difference in means of the
two parts divided by a measure of the standard deviation for the two parts can be modeled by
the Student t-distribution. Hence a t-value (and/or a probability measure) for the two parts
being identical can be derived.
If a Gaussian assumption is made about the noise and it is independent and identically distributed then a P -value can be derived. The noise from fMRI BOLD is usually not independent
due to the hemodynamic response function and unmodelled confounds (autocorrelation in the
noise).
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Chapter 5
Post-Processing
$Revision: 1.8 $
$Date: 2002/08/14 12:44:33 $
This chapter covers the post-processing, or meta-analysis, stage within the toolbox. This is
where the results of the previous analysis stage are then themselves analysed in an effort to gain
more insight and robustness into the location of stimulations within functional images.
Currently, only one post processing algorithm has been implemented within the toolbox,
although more are in the pipeline. Any suggestions for expanding this section will also be
greatly received.
5.1
K-means Clustering on the Modelling Results
As described in section 4.6, the K-means clustering algorithm can be used to cluster from the
time series or the cross-correlation function. It can also be taken a step further and be used as
a post-processing tool.
Here, the result of the modelling method is fed into the K-means algorithm, regardless of
the number of parameters generated. For the Kolmogorov Smirnov test the clustering could be
made solely on the maximum test size, but with the FIR, for example, the whole filter could be
fed to the clustering algorithm. The K-means will then cluster the similar results into the same
bins, hence a label is generated for each of the voxels.
Note that this strategy can be questioned. If, for instance, a measure of activation and a
delay measure are generated for each voxel and fed into the post-processing K-means clustering
algorithms (“Meta K-Means”), then the magnitudes of the two parameters are very different,
which naturally will bias the results. In our implementation, the Matlab matrix fed into Kmeans is called RESULT – this is generated by each of the modelling algorithms. If rescaling of
the rows of the RESULT matrix is required before this clustering, e.g. multiplication of the first
row by two, then, from the Matlab commandline, simply type:
>> global RESULT
>> RESULT(1,:) = 2*RESULT(1,:);
and then run the Meta K-means from the GUI as normal.
63
Appendix A
Glossary
$Revision: 1.3 $
$Date: 2002/08/14 13:20:17 $
Table A.1: An Explanation of Lyngby Toolbox, fMRI and Related Terms
64
65
Analysis
The core stage of the investigation of functional images within the
toolbox, usually involving statistical algorithms acting upon a time
series of volumes. It comes directly after the pre-processing stage,
and before the post-processing one.
Axial slice
See Transversal slice
Coronal slice
A slice taken through a spatial volume. For the example of a person,
this would be like looking at them from the front. As such, the slice
index goes from posterior-anterior, or back-to-front.
Experiment
A term used to describe the entire set from which results are derived. An experiment can consist of several trials over different
days, each of which may have several runs.
Frame
The spatial data (which could be 2D or 3D) available at each timepoint. Sometimes this is referred to as a scan or volume
A portion of a window in the Lyngby toolbox that contains buttons
that are related to each other. The main Lyngby window consists
of a separate frame for each of the processing stages.
Layers
A term used in image processing to describe individual images put
together to form a single composite image viewed on common axes.
Part of each image will be transparent, allowing the images underneath to show through. It is rather akin to the building-up of a
cartoon frame from different “cells” - a character or item is drawn
on clear plastic. The majority of the layer is transparent allowing
anything underneath to show through. However, individual items
are not transparent and will obscure those directly underneath.
Pane
A portion of a window in the Lyngby toolbox that contains buttons
that are related to each other. The Load New Data. . . window has
three panes for the data parameters, external influences and the
window control buttons.
Paradigm
The “activation” signal, usually binary, that indicates when the
subject in a functional imaging experiment is performing some task.
Post-processing
The stage that comes after the analysis of the data. It usually consists of some form of analysis of the results sets, such as clustering
of all the result parameters.
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66
Pre-processing
The stage after the data has been loaded into the toolbox, but
before the data analysis is started. It is an initialization stage,
used to get the data into the correct form for analysis by the usual
algorithms. It usually involves some form of mean-removal, normalisation and/or removal of any unwanted scans.
Run
The part of a time series of functional images where a certain task
is performed. A run usually consists of several cycles of a paradigm.
A single experiment can consist of several runs.
Sagittal slice
A slice taken through a spatial volume. For the example of a person,
this would be like looking at them from the side. As such, the
slice index goes from left-to-right. Derived from the Latin word for
“arrow”, due to the direction of the cutting plane indicated by the
angle of an arrow striking a person from the front.
Slice
A spatial volume one voxel thick i.e. a 2-D image. Usually refers to
an entire time-series (e.g. trial or run), and as such can be thought
of as a volume with time as the third dimension.
Transversal slice
A slice taken through a spatial volume. For the example of a person,
this would be like looking at them from above. As such, the slice
index goes from inferior-superior, or bottom-to-top.
Trial (as in “single trial”)
An experiment where only one task is performed, usually repeated
several times.
Volume
A spatial volume of slices, usually spatially adjacent, with a time
reference. It can be thought of as a 4-D set, with three spatial
dimensions and one time dimension.
Voxel
An element of a spatial 3-D volume, usually with a time reference.
It can be thought of as a 1-D time signal.
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Appendix B
Functions
$Revision: 1.3 $
$Date: 2002/08/14 13:32:23 $
B.1
Available Functions
This appendix provides a list of all the functions that are available within the Lyngby package.
This is meant for users who wish to write scripts to automatically process their data. Users of
the GUI will not normally need to access these functions directly.
Help on each function can be obtained from the Matlab command line by typing:
>> help <functionname>
For instance, to obtain help on the “lyngby getdata” function:
>> help lyngby_getdata
This will then return the following:
lyngby_getdata
- Returns the masked datamatrix
function X = lyngby_getdata(ROI, voxelMask, timeMask);
Input:
ROI
voxelMask
timeMask
Output: X
(Optional) Region of Interests, 3x2 matrix
(Optional) Sparse masking matrix
(Optional) Sparse masking matrix
The datamatrix
This function loads the datamatrix. The voxel is in the
horizontale direction (as columns) and the time is in the
vertical direction (as rows).
To save memory a ’Region of Interest’ can be specified,
and further a voxel or/and a time mask can be specified.
If the input arguments are missing TIME_MASK, VOXEL_MASK and
ROI_VOXELS are used.
67
Section B.1
Available Functions
68
Which volumes is loaded is determined by global variables and
through calls to consecutive calls lyngby_getvolume (for each
volume).
See also LYNGBY, LYNGBY_GETVOLUME, LYNGBY_UI_LOADFILE.
$Id: function-appendix.tex,v 1.3 2002/08/14 13:32:23 fnielsen Exp $
The last line is the concurrent revision system (CVS) text. The first number indicates the
revision number and the date is the date of the last revision.
You can also use the helpwin Matlab command:
>> helpwin(’lyngby_getdata’)
A new help window should popup, and you can click between related functions listed at the
button next to “See also”.
The following table lists all the functions available. Not that the list is not complete. Some
will never need to be called directly as they will be called from other functions. All the functions
with a “lyngby ui ” stem are concerned with the GUI controls and as such are unlikely to be
called from a script.
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Section B.1
69
Available Functions
Table B.1: Functions Available in the Lyngby Toolbox
Function Name
Purpose
General Lyngby Functions
lyngby
lyngby circle
lyngby classerr
lyngby corrlin
lyngby cumperiodo
lyngby cva
lyngby design
lyngby dropedge
lyngby filter
lyngby frame
lyngby global
lyngby histeq
lyngby image
lyngby init
lyngby kronadd
lyngby log
lyngby meangeo
lyngby meanhl
lyngby mod
lyngby msvd main
lyngby opls
lyngby plotmatch
lyngby start, Calls lyngby ui main
Draw a circle
Classification error
Lin’s concordance correlation coefficient for reproducibility
Normalized cumulated periodogram
Canonical variate analysis (Canonical Ridge)
n/a
Drop scans at the paradigm shifts
function for averaging volumes
Test function
File defining global variables
Perform histogram equalization
Plot a slice
Initialize the global variables
Kronecker add
Log
Geometric mean
Hodges-Lehmann estimator
Modulus function
Perform meta svd comparison of selected models.
Orthonormalized PLS
Plot paradigm, data and model
Statistical Distributions
lyngby cdf bin
lyngby cdf chi2
lyngby cdf gauss
lyngby cdf t
lyngby cdf wilrank
lyngby fs cdf
lyngby fs invcdf
lyngby idf chi2
lyngby idf t
lyngby pdf bin
lyngby pdf gauss
lyngby pdf poisson
Binomial distribution function
X2̂ cumulated distribution function
Gaussian (normal) distribution function
Student t distribution
“Wilcoxon sign rank” distribution
Returns the cumulative F disttribution
Returns the inverse of the cumulative F dist.
Invers X2̂ distribution function
Inverted Student t distribution
Binomial probability density function
Gaussian (normal) density function
Poisson probability density function
Data Loading and Writing
lyngby filefind
Find the position of a string in a file
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lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
70
Available Functions
read header
read volume
readanahdr
readanavol
readvahdr4
readvapethdr
readvapetvol
readvavol4
readxpihdr
readxpivol
vmask2index
write ana
write sdt
write vapet
Data Formatting and
Information
lyngby full2mask
lyngby getdata
lyngby getinfo
lyngby getslice
lyngby getvolume
lyngby index2tmask
lyngby index2vmask
lyngby mask2full
lyngby normalize
lyngby normalize
lyngby numscan
lyngby paradigm
lyngby prep global
lyngby prep init
lyngby roi
lyngby roi2vmask
lyngby run
lyngby runinfo
lyngby sliceindex
lyngby swaporder
lyngby talaimg
lyngby tmask2index
lyngby tmp files
lyngby tmp getseq
lyngby tmp getvol
lyngby tmp init
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Reads header information of a volume file
Reads a volume from a recognized file
Reads an ANALYZE header
Reads an ANALYZE img file
Reads the header information in a 4 dimensional VAPET file
Reads the header information in a VAPET file
Reads a VAPET volume from a file
Reads a 4 dimensional VAPET structure from a file
Read header from EC flexible format
Read data from EC flexible (Xprime) format
Return Indices from voxel mask
Write a volume to ANALYZE files
Write a volume/datamatrix to SDT/SPR file
Write a volume to VAPET file
Convert indices from Full volume to ROI volume
Returns the masked datamatrix
Get volume info from a file
Extracts a slice from a volume
Get a volume from a file
Return time mask from indices
Return voxel from indices
Convert indices from ROI to Full volume
Normalize in vertical direction of a datamatrix
Normalize (Preprocess) a datamatrix
Returns the masked number of scans volume
Returns the paradigm
Global variables for preprocessing
Initialize global variables for preprocessing
Returns the begin and end index of the volume
Transform ROI definition to masking matrix
Returns the run specification
Number of runs and scans within runs
Returns indices for a specified slice
Swap the xyz order in a volume
Returns Talairach brain plot in the style of SPM
Return time mask from indices
Prints which temporary files was opened by lyngby
Get sequence from temporary file of data matrix
Get volume in temporary file for all voxels
Initialize temp file for datamatrix (Read/Write)
Section B.1
71
Available Functions
lyngby tmp putseq
lyngby tmp putvol
Store a sequence in a temporary file of data matrix
Store volume in temporary file for all voxels
Matrix Operations
lyngby std
lyngby test wilrank
Take the standard deviation of a matrix
Wilcoxon sign rank test
Statistics Tests
lyngby ts ftest
lyngby ts global
lyngby ts init
lyngby ts main
lyngby ts pttest
lyngby ts ttest
lyngby ts uttest
lyngby ui ts init
lyngby ui view ts
lyngby uis ts
lyngby uis ts v
F-test for two samples.
Global variables for TS analysis method
Initialize globals for t-test analysis method
Whole volume Student’s t-test for equal variances.
Student’s paired t-test.
Two sample Student t test, equal variances.
Student’s t-test for unequal variances.
User interface for initialization of t-test
t-test viewing function
Script executed for t-test analysis
Script for t-test view
General User Interface
Functions
lyngby ui credit
lyngby ui global
lyngby ui loadfile
lyngby ui loadsetup
lyngby ui main
lyngby ui message
lyngby ui movie
lyngby ui option
lyngby ui para draw
lyngby ui paradigm
lyngby ui preproc
lyngby ui resinfo
lyngby ui run
lyngby ui run no
lyngby ui saveresult
lyngby ui time
lyngby ui time be
lyngby ui view
lyngby ui view dfl
lyngby ui viewvol
lyngby ui volume
Display a credit
Defines global for the user interface
Opens a window for specifying file parameters.
Opens a window for specifying file parameters.
Main function for the GUI for lyngby
Display a message
User interface for movie
Option/setup for user interface
User interface for paradigm viewing (and specification)
User interface for paradigm viewing (and specification)
UI for setting up preprocessing parameters
Defines global for the user interface
User interface for run viewing (and specification)
User interface for time mask specification
Opens a window for specifying file parameters.
User interface for time mask specification
User interface for time mask specification
User interface for viewing results
Default viewing function
View full volume timeserie and specify ROI and voxelmask
View full volume timeserie and specify mask
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Section B.1
72
Available Functions
lyngby uis none v
Script for ’original’ view
Analysis Functions and
Modelling Algorithms
Ardekani F-Test
lyngby baf global
lyngby baf init
lyngby baf test
lyngby ui baf init
lyngby ui view baf
lyngby uis baf
lyngby uis baf v
Global variables for Ardekani f-test analysis method
Initialize globals for Ardekani F-test
Barbak Ardekani’s F-test
Ardekani’s F-test, GUI for init. of param.
Viewing BAF filter results
Script executed for Ardekani’s F-test
Script for Ardekani F-test view
Ardekani F-Test with
Nuisance Subspace
lyngby baf2 global
lyngby baf2 init
lyngby baf2 main
lyngby baf2 test
lyngby ui baf2 init
lyngby ui view baf2
lyngby uis baf2
lyngby uis baf2 v
Global variables for Ardekani nuisance signal f-test
Initialize globals for Ardekani F-test with nuisance
Barbak Ardekani’s F-test with nuisance estimation
Barbak Ardekani’s F-test with nuisance estimation
Ardekani’s nuisance F-test, init. of param.
Viewing function for Ardekani’s nuisance F
Script executed for Ardekani’s nuisance F
Script for Arkani’s F-test with nuisance view
Ardekani T-Test
lyngby bat global
lyngby bat init
lyngby bat test
lyngby ui bat init
lyngby ui view bat
lyngby uis bat
lyngby uis bat v
Global variables for Ardekani t-test analysis method
Initialize globals for Ardekani t-test
t-test using the Ardekani t-method
User interface for init. of Ardekani’s t-test
Viewing Ardekani t-test results
Script executed for Ardekani’s t-test
Script for Ardekani’t t-test view
Cross-correlation
lyngby cc global
lyngby xcorr
lyngby ui cc init
lyngby ui cc view
lyngby ui view cc
lyngby uis cc
lyngby uis cc v
Global variables for cross-correlation analysis method
Cross correlation
User interface for init. of cross-correlation
User interface for viewing Cross-correlation results
Viewing Cross-correlation results
Script executed for cross-correlation anal.
Script for cross-correlation view
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73
Available Functions
Exhaustive FIR
lyngby efir global
lyngby efir init
lyngby efir main
lyngby ui efir init
lyngby ui view efir
lyngby uis efir
lyngby uis efir v
lyngby plotfilter
Global variables for Exhaustive FIR analysis method
Initialize global variables for EFIR analysis
Main exhautive FIR function
UI for initialization of EFIR model
Viewing EFIR filter results
Script executed at the EFIR analysis
Script for ’exhaustive FIR’ viewing
Plot FIR response
FIR
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
Convolve the FIR kernel with the input
FIR residual signal (Quadratic error)
Global variables for FIR analysis method
Initialize global variables for FIR analysis
Regularized FIR filter, main function
Returns the masscenter of the FIR kernel
Plot paradigm, data and FIR model
User interface for FIR filter analysis
Viewing FIR filter results
Script executed at the FIR analysis
Script for FIR view
Plot FIR response
fir convolve
fir error
fir global
fir init
fir main
fir masscent
fir plot
ui fir init
ui view fir
uis fir
uis fir v
plotfilter
Gaussian Mixture Models
lyngby gmm error
lyngby gmm fit
lyngby gmm plot
Gaussian mixture model, error
Gaussian mixture model, fitting
Plot gaussian mixture model
K-Means
lyngby km error
lyngby km global
lyngby km init
lyngby km main
lyngby km plot
lyngby ui km init
lyngby ui view km
lyngby uis km
lyngby uis km v
K-means, error
K-means, Global variables declarations
K-means, Global variables initialization
Main function for K-means clustering
K-means plot
GUI for K-means parameter initialization
Viewing K-means filter results
Script executed for K-means analysis
Script for K-means view
Kolmogorov-Smirnov
lyngby ks global
lyngby ks init
Global variables for KS analysis method
Initialize globals for KS-test analysis
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lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
74
Available Functions
ks main
ks prob
ks test
ui ks init
ui view ks
uis ks
uis ks v
Kolmogorov Smirnov test
Kolmogorov-Smirnov probability
Kolmogorov-Smirnov test of two arrays.
KS, GUI for initialization of parameters
Kolmogorov-Smirnov viewing function
Script executed for Kolmogorov-Smirnoff
Script for Kolmogorov-Smirnov view
Lange-Zeger
lyngby lz error
lyngby lz lambda
lyngby lz masscent
lyngby lz plot
lyngby lz plottheta
Lange-Zeger residual signal (Quadratic error)
The Lange-Zeger hemodynamic response function
Returns the masscenter of the Lange Zeger Kernel
Plot Paradigm, data and Lange-Zeger model
Plot Lange-Zeger theta1 against theta2
Lange-Zeger Grid Search
lyngby lzgs global
lyngby lzgs init
lyngby lzgs main
lyngby lzgs search
lyngby ui lzgs init
lyngby ui view lzgs
lyngby uis lzgs
lyngby uis lzgs v
Global variables for LZ Grid Search analysis
Initialize global variables for LZGS
Lange-Zeger model with grid search
Lange Zeger main algorithm for grid search
Lange-Zeger GUI init of parameters
Viewing Lange Zeger results
Script executed for Lange-Zeger Grid Search
Script for Lange-Zeger gridsearch view
Lange-Zeger, Iterative
lyngby lzit algo
lyngby lzit beta
lyngby lzit conv
lyngby lzit cost
lyngby lzit ftlamb
lyngby lzit global
lyngby lzit init
lyngby lzit main
lyngby lzit noise
lyngby lzit pickf
lyngby lzit thopt
lyngby ui lzit init
lyngby ui view lzit
lyngby uis lzit
lyngby uis lzit v
Lange-Zeger, optimization of model
Lange-Zeger, Estimate beta parameter
Lange-Zeger, Convolve kernel with the input
The cost function used in the Lange-Zeger model
Lange-Zeger, Fourier transformed of lambda
Global variables for iterative LZ
Initialize global variables for iterative LZ
Main function for Lange-Zeger estimation
Noise estimate in the Lange-Zeger model
Pick frequencies out from the data matrix
Lange-Zeger, Optimize theta parameters
GUI for iterative Lange-Zeger initialization
Viewing Lange Zeger results
Script executed for Lange-Zeger, Iterative
Script for Lange-Zeger ’iterative’ view
Meta K-Means
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75
Available Functions
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
mkm global
mkm init
ui mkm init
ui view mkm
uis mkm
uis mkm v
Meta K-means, Global variables declaration
Meta K-means, Global variables initialization
GUI for Meta K-means initialization
Viewing of meta K-means results
Script executed for Meta K-means analysis
Script for Meta K-means viewing
Neural
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
Network
nn cddevds
nn cddewda
nn cddewds
nn cddru
nn cdev
nn cdew
nn cdru
nn cerror
nn cforward
nn cmain
nn cost
nn cpruneobd
nn csoftline
nn csoftmax
nn ctrain
nn eddevds
nn eddewd
nn eddru
nn edev
nn edew
nn edru
nn eehl
nn eemedian
nn eerror
nn eforward
nn emain
nn epruneobd
nn esoftline
nn esoftline
nn etarget
nn etrain
nn initvw
nn plotnet
nn pruneobd
nn qddeus
2nd order, entropic, input, diag. sym.
Classifier neural network, output, diag 2nd
Classifier neural network, output, diag 2nd
2nd order der. of regularization for weights
Classifier neural network, input 1st der.
Classifier neural network, output 1st der.
Neural network, regularization, 1st der.
Classifier neural network error
Classifier neural network forward
Main functions for classifier neural network
Cost function = error + regularization
Classifier neural network, OBD pruning
Classifier neural network soft linesearch
Softmax for neural network classifier
Classifier neural network training
2nd order, entropic, input, diag. sym.
Entropic neural network, output, diag 2nd
2nd order der. of regularization for weights
Entropic neural network, input 1st der.
Entropic neural network, output, 1st der.
First order derivative, Regularization
Neural network entropic error (Hodge-Lehmann)
Neural network entropic error (median)
Calculate (cross-)entropic error
Entropic neural network, feed-forward
Main function for Entropic Neural network
Pruning by Optimal Brain Damage (entropic)
Soft linesearch with the entropic cost function
Soft linesearch with the quadratic costfunction
Normalize target for entropic neural network
Entropic neural network, training
Initialize weights in neural network
Neural network, plot the network weights
Pruning by Optimal Brain Damage
2nd der., quad., all weights, gauss approx.
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Section B.1
76
Available Functions
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
nn
nn
nn
nn
nn
nn
nn
nn
nn
nn
nn
nn
nn
nn
nn
nn
nn
nn
qddevds
qddevs
qddewds
qddews
qddru
qdev
qdew
qdru
qerror
qforward
qmain
qpruneobd
qtrain
reg2reg
setsplit
tanh
u2vw
vw2u
Neural
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
Network Regression
nnr global
nnr init
nnr main
ui nnr init
ui view nnr
uis nnr
uis nnr v
Global variables for NNR analysis method
Initialize global variables for NNR analysis
Main function for Neural network Regression
User interface for NNR method
Viewing NNR filter results
Script executed for Neural network regression
Script for neural network regression view
Neural
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
Network Saliency
nns esalmap
nns global
nns init
nns main
ui nns init
ui view nns
uis nns
uis nns v
Saliency map with entropic cost function
Global variables for NNS analysis
Initialize global variables for NNS analysis
Main function for Neural network saliency
GUI for Neural network saliency
Viewing of NNS results
Script executed for Neural network saliency
Script for neural network saliency view
PCA Analysis
lyngby pca eqtest
lyngby pca main
lyngby pcafilte
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2nd der., quadratic, input, diag gauss.
2nd der., quadratic, input, gauss approx.
2nd order der, quadratic, output, diag gauss
2nd. order der., quadratic, output, gauss approx.
2nd order der. of regularization for weights
Quadratic neural network, input, 1st der.
First order derivative, quadratic, Output weights
Quadratic neural network, 1st der. reg.
Quadratic error
Neural network forward with linear output function
Main function for quadratic neural network
Pruning by Optimal Brain Damage (quadratic)
Quadratic neural network, training
Standardize regularization
Split the data into training and validation set
Faster hyperbolic tangent
Vectorize neural network weights
Vectorize neural network weights
PCA test for equal eigenvalues
Main PCA analysis
Reduce the dimension or filter a matrix with PCA filtering
Section B.1
77
Available Functions
Poisson
lyngby pois error
lyngby pois forward
lyngby pois forwn
lyngby pois global
lyngby pois init
lyngby pois main
lyngby pois toptim
lyngby ui pois init
lyngby ui view pois
lyngby uis pois
lyngby uis pois v
Poisson residual signal (Quadratic error)
Possion kernel forward (prediction)
Possion kernel forward (prediction)
Poisson filter, Global variables
Poisson filter, Initialize global variables
Main function for Possion filter
Possion kernel optimization
GUI, Poisson filter, initialization
Viewing poisson filter results
Script executed for Poisson filter analysis
Script for Poisson filter view
SCVA
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
scva global
scva init
scva main
ui scva init
ui view scva
uis scva
uis scva v
sdesign
set and
set diff
set unique
Global variables for SCVA analysis
Initialize global variables for SCVA analysis
Strother Canonical variate analysis
GUI for init of Strother CVA
Viewing function for Strother CVA
Script executed for SCVA analysis
Script for SCVA view
Strother design matrix
set intersection: S1 / S2
set difference: S1 S2
set thining: all elements different
SOP
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
lyngby
sop global
sop init
sop main
sopressvd
ui sop init
ui view sop
uis sop
uis sop v
splitonnoff
Global variables for SOP analysis
Initialize global variables for SOP analysis
Strother OPLS
SOP with SVD on the residual
GUI of init. of SOP model
Viewing for Strother OPLS
Script executed for SOP analysis
Script for SOP view
Function to find the indices of the on/off scans.
SVD
lyngby
lyngby
lyngby
lyngby
lyngby
svd
svd global
svd init
svdfilter
ui svd init
Singular value decomposition
Global variables for SVD analysis
Initialize global variables for SVD analysis
Filtering by SVD
GUI for initialization of SVD model
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Section B.1
78
Available Functions
lyngby ui view svd
lyngby uis svd
lyngby uis svd v
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Viewing for Strother OPLS
Script executed for SVD analysis
Script for SVD view
Appendix C
Derivations
$Revision: 1.5 $
$Date: 2002/08/14 13:17:05 $
C.1
C.1.1
Neural Network
Symbol table
δ Kronecker’s delta
φ Output of the neural network before the softmax is applied
Ec Errorfunction for the classifier neural network
h Index for hidden units
i Index for input units
no Number of outputs
np Number of patterns, i.e., examples or scans
o Index for output unit
p Index for pattern
p(. . .) Probability
T Training set
t Target output for the neural network
u Weights (parameters) in the neural network
v Input layer weights (first layer weights)
w Output layer weights (second layer weights)
x Input for the neural network
y (Predicted) Output of the neural network (after the softmax is applied)
79
Section C.1
80
Neural Network
z The hidden unit value after the activation function has been applied
In the following the index for pattern has been omitted, so that what should have been
written as xpi , zhp , φpo , yop and tpo (or in an equivalent notation) is written as xi , zh , φo , yo and to .
C.1.2
The errorfunction for the classifier neural network and its derivatives
The errorfunction for the classifier neural network is developed from the conditional probability:
p(T |u) =
np
Y
p
p(y|x, u) =
np no
Y
Y
p
yoto
(C.1)
o
The error function is constructed as the normalized negative log-likelihood of the parameters
u and with the two-layer feedforward neural network with an added softmax layer and a 1-of(c − 1) coding scheme — or rather 1-of-(no − 1) coding scheme — we get:
1
ln[p(T |u)]
np
np
no
1 X Y
yoto
= −
ln
np
o
Ec = −
(C.2)
(C.3)
np no
1 XX
to ln yo
np
o
! n −1
Ã
µ
¶#
np "
nX
o
o −1
X
X
1
exp (φo )
to ln Pno
= −
yo +
tno ln 1 −
np
o0 exp (φo0 ) + 1
o
o
= −
=
np
= −
"
"
1 X
to ln 1 −
np
nX
o −1
o
Pno
o0
#
exp (φo )
+
exp (φo0 ) + 1
nX
o −1
(C.4)
(C.5)
(C.6)
to φo
o
−
nX
o −1
o
to ln
"n
o
X
o0
##
exp(φo0 ) + 1 (C.7)
"n
##
¸ nX
·
np "
nX
o −1
o −1
o
X
1
1 X
+
to ln Pno
to φo −
= −
to ln
exp(φo0 ) + 1 (C.8)
np
o exp (φo ) + 1
o
o
o0
"n −1
# n −1
#
np " no
o
o
X
X
1 X X
to ln
exp(φo0 ) + 1 −
to φo
(C.9)
=
np
0
o
o
o
#
# n −1
np " "no −1
o
X
X
X
1
(C.10)
ln
exp(φo ) + 1 −
to φo
=
np
o
o
In order to optimize the neural network with gradient or newton methods the derivatives
have to be found. The first order derivative of the classifier neural network errorfunction with
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Kai Hansen et al 1997
Section C.1
81
Neural Network
respect to the weights is:
∂Ec
∂u
=
=
=
=
##
# n −1
"
np " "no −1
o
X
X
∂
1 X
to φo
exp(φo ) + 1 −
ln
∂u np
o
o
#
np "
nX
nX
o −1
o −1
1 X
φo
∂φo
1
−
to
exp(φo )
P
np
∂u
∂u
1 + on0o −1 exp(φo0 ) o
o
"
#
np no −1
nX
o −1
1 X X
φo
exp(φo )
∂φo
to
−
Pno −1
np
∂u
exp(φo0 ) ∂u
o0
o 1+
o
np no −1
1 XX
∂φo
(yo − to )
np
∂u
o
(C.11)
(C.12)
(C.13)
(C.14)
The second order derivative of the classifier neural network errorfunction with respect to the
weight is:
"
#
np no −1
∂ 2 Ec
∂φo
∂
1 XX
(yo − to )
(C.15)
=
∂u2 ∂u1
∂u2 np
∂u1
o
¸
np no −1 ·
∂φo ∂yo
∂ 2 φo
1 XX
+
(C.16)
(yo − to )
=
np
∂u2 ∂u1 ∂u1 ∂u2
o
For the first order derivative we only need to compute the derivative for the two ordinary
layer (∂φ/∂u) — not the softmax layer. However, for the second order derivative we need to
have the derivative through the softmax:
"
#
∂yo
∂
exp(φo )
=
(C.17)
Pno −1
∂u
∂u
exp(φo0 ) + 1
o0
=
nX
o −1
∂φo
∂φo0
exp(φo )
exp(φo0 )
+ exp(φo )
Pno −1
³P
´2
∂u
no −1
exp(φo0 ) + 1 ∂u
o0
exp(φ 00 ) + 1
o0
00
o
= yo
=
o0
=
o
nX
o −1
∂φo0
∂φo
exp φo exp(φo0 )
+
³P
´2
∂u
∂u
no −1
exp(φo00 ) + 1
o0
o00
nX
o −1
nX
o −1
o0
δoo0 yo0
(C.18)
nX
o −1
∂φo0
∂φ0o
yo yo0
+
∂u
∂u
0
(C.19)
(C.20)
o
yo0 (δoo0 − yo )
∂φo0
∂u
(C.21)
We can now use equation C.21 in C.16 arriving at:
#
np no −1 "
nX
o −1
∂ 2 φo
∂ 2 Ec
1 XX
∂φo0 ∂φo
(yo − to )
=
+
yo0 (δoo0 − yo )
∂u2 ∂u1
np
∂u2 ∂u1
∂u2 ∂u1
0
o
(C.22)
o
The partial derivatives for the φ output are the same as for the quadratic neural network.
For the output weights w we have:
∂φo
∂woh
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Kai Hansen et al 1997
= zh
(C.23)
Section C.1
82
Neural Network
Combining equations C.14 and C.23 makes the derivative for the errorfunction to:
∂Ec
∂woh
=
np
1 X
(yo − to ) zh
np
(C.24)
For the input weights v we have:
∂φo
∂vhi
∂Ec
∂vhi
=
¡
¢
= woh 1 − zh2 xi
np
¡
¢
1 X
(yo − to ) woh 1 − zh2 xi
np
(C.25)
(C.26)
The second order derivative — the Hessian — is more complex, and it can unfortunately/luckily be approximated in a number of ways: using only the Gauss-Newton term and
diagonalizing it. The derivative for the output weights is relatively simple:
∂ 2 φo1 o2
∂wo1 h1 ∂wo2 h2
∂ 2 Ec
∂wo1 z1 ∂wo2 h2
=
=
= zh1 zh2
np
1 X
[δo1 o2 (yo − to ) zh1 zh2 + yo1 (δo1 o2 − yo2 )zh1 zh2 ]
np
np
1 X
[(−yo1 yo2 + 2δo1 o2 yo − δo1 o2 to ) zh1 zh2 ]
np
(C.27)
(C.28)
(C.29)
The diagonal approximation is
∂ 2 Ec
2
∂woh
np
¢ ¤
1 X £¡ 2
−yo + 2yo − to zh2
np
=
For the input weights we have:
∂2φ
∂vh2 i2 ∂vh1 i1
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Kai Hansen et al 1997
¡
¢ ¤
∂ £
woh1 1 − (zh1 )2 xi1
∂vh2 i2
¡
¢
= −woh1 xi1 2zh1 1 − zh21 δh1 h2 xi2
¡
¢
= −woh δh1 h2 xi1 xi2 2zh 1 − zh2
=
(C.30)
(C.31)
(C.32)
(C.33)
Section C.1
83
Neural Network
The derivative for the errorfunction is:
np no "
¡
¢
1 XX
∂ 2 Ec
(yo − to )(−woh )δh1 h2 xi1 xi2 2zh 1 − zh2
=
∂vh1 i1 ∂vh2 i2
np
o
=
+
no
X
yo0 (δoo0
o0
=
=
+
o0
=
=
¢
¢
¡
¡
− yo ) wo0 h2 1 − zh22 xi2 woh1 1 − zh21 xi1
"
np
no
X
¡
¢
1 X
xi1 xi2
(yo − to )(−woh )δh1 h2 2zh 1 − zh2
np
o
no
X
yo0 (δoo0
(C.34)
¡
¢
¡
¢
− yo ) wo0 h2 1 − zh22 woh1 1 − zh21
(C.35)
(C.36)
#
"
np
no
no X
X
¢
¡
1 X
2δoo0 δh1 h2 (yo − to )(−woh )zh 1 − zh2
xi1 xi2
np
o
o0
#
¡
¡
¢
¢
+yo0 (δoo0 − yo ) wo0 h2 1 − zh22 woh1 1 − zh21
With diagonal approximation the derivative becomes:
"
np
no
no X
¡
¢
∂ 2 Ec
1 X 2X
2δoo0 (yo − to )(−woh )zh 1 − zh2
=
xi
2
np
∂vhi
o
o0
#
¢
¡
2
=
+yo0 (δoo0 − yo ) wo0 h woh 1 − zh2
C.1.3
#
(C.37)
(C.38)
(C.39)
(C.40)
(C.41)
Numerical stabil computation of softmax
The softmax function has an exponential divided by an exponential which makes it prone to
congest the range of the data type and divide +Inf with +Inf. For 64 bit double data type this
happens a bit over 700:
>> log(realmax)
ans =
709.7827
To cope with this problem the softmax function can be reformulated:
Pno
o
exp(φo )
exp(φo + k)
exp(φo )
exp(k)
= Pno
= Pno
0
exp(k)
exp(φo0 ) + 1
exp(φ
exp(φ
)
+
1
o
o0 + k) + exp(k)
o
o
(C.42)
And by setting k = − max(φ) we can make sure that the exponential in the numerator does not
saturate against +Inf, — and it does not matter that the denominator saturate against -Inf
for computer with IEEE arithmetic since exp(-Inf) yields 0.
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Appendix D
Getting Started
$Revision: 1.3 $
$Date: 2002/08/14 13:03:16 $
Table D.1: Installed, Up and Running in Two Minutes!
Keyboard/mouse action
Explanation
Installation
Click on the link to the toolbox file (lyngby.tar.gz). Choose
a suitable place to save the file. Save the sample dataset
(sampledata.tar.gz) in the same way.
Download the lyngby Toolbox and
the sample dataset from the website at: http://hendrix.imm.dtu.dk/software/lyngby/.
> cp lyngby.tar.gz /usr/local/
Copy the file to a suitable location,
such as /usr/local, where a program
directory will be created.
> gunzip lyngby.tar.gz
Uncompress the file.
> tar -xvf lyngby.tar
Unpack the file, which automatically
creates a ”lyngby” directory.
> rm lyngby.tar.gz
Remove the original distribution file
to save space.
> cp sampledata.tar.gz /usr/local/lyngby
Copy the sample dataset to the lyngby directory.
> gunzip sampledata.tar.gz
Uncompress the sample dataset.
> tar -xvf sampledata.tar
Unpack the sample dataset, automatically creating a ”sampledata”
directory in the process.
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85
> rm sampledata.tar.gz
Remove the original distribution file
to save space.
Setup
> emacs startup.m
Add the line:
path(path, ’/usr/local/lyngby’);
Edit your Matlab ”startup.m” file to
inform Matlab where it can find the
lyngby executable files.
Starting lyngby
> matlab
Start Matlab as normal
>> cd /usr/local/lyngby/sampledata
Within Matlab, change to the directory containing the sample data.
>> lyngby
Start lyngby by simply typing lyngby from the command prompt. The
lyngby main window should pop-up.
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Appendix E
Example Analysis
$Revision: 1.2 $
$Date: 2002/08/14 13:08:46 $
How To Load and Analyse Data Using the Sample Dataset.
Table E.1: A Simple Example Analysis - How To Use The Lyngby GUI
Keyboard/mouse action
Explanation
Press Load new data. . . . A new window titled Load data will pop-up.
First, we need to load the sample dataset.
The data parameters should all be set automatically, as lyngby will have read-in a conversion file specifying them.
Press the Apply button at the bottom of the
new window
The values of the GUI will be written to
global variables needed for the loading.
Press the Load Data! button. The window
will close.
The data will now be read in from the files. In
the main window it should display “Finished
loading data!” at the bottom.
Press the Create/Edit External Influences...
button bringing up a new window
This is were you, e.g., can specify a stimulus
function.
Press Setup design
The first pre-processing step is to mask
out those unwanted timepoints from the
paradigm and run structures.
Press Process design
Next, the mean is removed from the paradigm
structure (i.e. the paradigm signal is transformed from 0, 1, 0, 1, 0, 1. . . to -1/2, +1/2,
-1/2, +1/2, -1/2, +1/2. . . ) as required by
some analysis algorithms.
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87
Press Close . The window should disappear
Press the Data setup. . . button in the main
window. A new window titled Data Setup
will pop-up.
The next stage is to perform any preprocessing of the data.
If not already highlighted, select the first
options (“normalization”) on the left-hand
pane, and press “Setup design and run”. A
new window (“Pre-Processing”) will appear.
The next stage is to select the pre-processing
methods that will be applied to the data.
Select the first and third option and press the
Apply Pre-Processing and Close button.
The Pre-Proceesing window will close, and
the status of the pre-processing will be shown
on the status bar within the main window.
Press Done! in the Data setup window.
This will close the window
Click on the top button of the Data Analysis frame, initally labeled Original. From the
pop-up list, select the FIR Filter algorithm.
Next, the actual analysis of the data can take
place. As an example, we will do a FIR Filter
analysis of the paradigm and the fMRI data.
Press the Parameters. . . button to bring up
a new window. Once you have finished examining the settings, press the Close button to
shut the window.
The initial parameters for the FIR Filter can
be set within a dedicated window. The Parameters window will be different for each
algorithm, reflecting the different variables
used in each case. Note that each algorithm
will have a sensible set of defaults within its
Parameters window. For this example, we
can use default settings.
Press the Calculate button to start the calculation.
The calculation is then started, with feedback
given on the status line within the main window. Note that once the calculation is finished, the symbol adjacent to the algorithm
name changes to a “+”, indicating that results for this algorithm are ready to be examined. A “-” indicates that no calculation
has been performed, whilst a “!” indicates
that the parameters have been changed, but
not yet used in a calculation.
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88
Press the View these results button. A set
of three windows, labelled Control, Volume
and Time will pop-up.
Once the calculations have finished (as shown
in the status line), then the results can be
viewed.
Click on different voxels in the Volume window. Note how the Time window displays
the relevant time-series.
The control window initially shows two “layers”. The lower one controls the right-hand
time-based window, while the upper one is
used to control the left-hand volume-based
window. The Time window shows the timeseries for whichever voxel is selected in the
Volume window. The current voxel location
is shown above the time-series in the form
[x,y,z]. Note how the data in the top-left of
the image [x = 32-34, y = 1, z = 34] correlates better with the paradigm than that at
the bottom-right [x = 14-20, z = 40-45]. Note
also how the FIR model (red-line) matches
the data far more closely in these highlyactivated regions.
Click on Sum of Coefficients in the Data
Layer, and pick Activation Strength from the
pop-up list.
For the FIR filter, there are several results
for both the volume and time windows. The
Activation Strength shows better contrast between the activated and non-activated regions.
Click on Time in the Time Layer, selecting the Histogram of Data option. The Time
window will change from displaying a timeseries to showing a histogram of the greylevels
for the selected voxel.
The time display can also display other types
of data that are voxel-dependent. For instance, the FIR filter algorithm generates a
range of results displaying histograms of the
greyscales of a voxel through the time-series.
Click on different voxels and note that the
general shape of the histogram is different for
the activated and non-activated areas.
The different areas in the volume have different greyscale distributions. Note that
the distribution in activated areas has a bimodal distribution, reflecting the two distinct grey-levels that occur during the activated and non-activated states, whilst the
distribution of non-activated areas is generally monomodal.
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Click on the More. . . button to expand the
control window to display an additional two
rows. The upper row contains two new visual layers, Contour and Background, whilst
the Masking Layer underneath is an intermediate processing layer that affects the data
layer below it. See Fig. 2.11 for a graphical
explanation.
The toolbox can also display concurrent spatial information by the use of overlays - layers
that sit above or below the data.
In the Masking Layer, click on the second
button from the left (showing None ), and
select the “>” option.
Now we can mask the Data layer by using the
Masking layer above. This creates a mask
from a given dataset and then applies it to
the dataset in the Data layer.
Click on the Sum of Coefficients button
in the Masking layer and select Activation
Strength from the pop-up list.
We will choose to define the mask using the
Activation Strength dataset, although any of
the others could be used to give different
masks.
Using the slider control in the Masking layer,
adjust the threshold to around 0.96. You can
use the edit box immediately to the left to
type in 0.96 if you prefer.
We can now adjust the size of the mask by
adjusting the thresholding limit. This can
be done by specifying either an absolute or
fractile value. We’ll use the latter, which is
the default.
In the Background Layer, click on the first
button to the right of the Background label. This turns the layer on. Now click the
Sum of Coefficients button and select the
Mean (data) dataset to display the anatomical data.
It would be more useful if we could now see
this thresholded data overlaid onto the original data.
Click on the button immediately to the right
of the Contour label to activate the contour
layer. Then click on the Sum of Coefficients
button and select the Activation strength
dataset from the pop-up list.
To see how far this threshold is from the rest
of the data, we can now overlay a contour
layer of the full Activation strength dataset.
Note how you can now see that if you decreased the mask threshold, then the peak at
[x = 31, y = 1, z = 27] will be the next to
show up. The contour layer can also be used
to compare different variables from the same
result set. This allows a comparison of methods for highlighting the activated regions.
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In the main window, below the Analysis
pane, is the Post-processing section. MetaK-means should already be selected. Click
on the Parameters button to bring up the
options dialog box. Make sure that the Parameter Variables Set is set to the FIR filter results. The other initial settings should
should already be sensible. Click Close once
you have finished.
Next, we can cluster the actual parameters
of the FIR filter - a Meta K-means clustering
of the results. This enables us to look for
patterns in the result dataset.
Click on the Calculate button.
The post-processing analysis can then be
started, with feedback on the progress given
on the status line.
Once the calculation is finished, click on the
View results button to bring up the tripleset of viewing windows.
The results are viewed in the same way as the
main analysis results.
Click on the Time button in the Time layer
and select the Cluster mean seq. from the
pop-up list. The Time window will now show
an additional red line representing how the
mean of the cluster that the selected voxel is
a member of varies with time.
The data can then be analysed using the same
techniques as for the main anaylsis results.
The Volume window shows the voxels in their
correct locations, but with the colour indicating their cluster membership. Note how
the voxels near the upper left of the image
[around x = 31, y = 1, z = 34] are members
of the same cluster, and that the mean of this
cluster most closely follows the time series.
In the main window, click on the
Save Worksheet button in the seventh
pane. The entire workspace is then saved
into the file lyngby workspace.mat, located
in the directory from which lyngby was
started.
Once the results have been analysed, you will
probably want to save them. In Matlab, you
are able to save the entire variable and result set into a single file which can then be
reloaded at a later date without having to respecify all the data loading parameters.
Click on the Save Data. . . button to bring
up the standard four layers plus the extra
Save layer
Alternatively, you may want to save a subset
of the results. The toolbox has a new save
layer which can be used for this purpose.
Click on the first button on the Save layer
and select the Sequence option. On the second button, select the Matlab Binary option.
Type in a filename into the edit box and then
press the Save here! .
As an example, we will save the timesequence for the current voxel. Other options
allow you to save the present slice, or the entire volume.
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Click on the Exit! button and the lyngby
main window will disappear.
Once you have finished using the GUI, just
close it. All the variables will still be accessible from the command line.
On the command-line, type:
To make the results and variables visible
to the command-line you will need to make
them all global. You can also do this whilst
the GUI is still open.
>> lyngby_ui_global
In the command-line, type:
>> whos
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Kai Hansen et al 1997
This will then display all the variables as used
in the calculations. You can now access the
individual results and variables.
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