Combining Anatomical Images with Estimates of Brain Activity Extracted from Electrographic Data

175 Pages·2003·5.09 MB·English

University of Patras Interdepartmental Program of
School of Medicine Postgraduate Studies in

Biomedical Engineering
National Technical University of Athens
Department of Mechanical Engineering

National Technical University of Athens
Department of Electrical and Computer Engineering

Combining Anatomical Images with Estimates of Brain Activity
Extracted from Electrographic Data:
Methodology and Applications

Doctorate Thesis

University of Patras Alexandra Badea
School of Medicine MSc in Medical Physics
Department of Physiology and
Department of Medical Physics
26500 Patras
Professor George K. Kostopoulos

-Patras 2003-
Examination Committee:

Professor George K. Kostopoulos
Professor Nicolas Pallikarakis
Professor Ilias Kouvelas
Professor Ioannis Varakis
Associate Professor Panagiotis Dimopoulos
Associate Professor Anastasios Bezerianos
Lecturer Constantinos Papatheodoropoulos

Advisory Committee:

Professor George K. Kostopoulos
Doctor Andreas A. Ioannides
Professor Nicolas Pallikarakis
Εξεταστική Επιτροπή

Καθηγητής Γεώργιος Κ. Κωστόπουλος
Καθηγητής Νικόλαος Παλλικαράκης
Καθηγητής Ηλίας Κούβελας
Καθηγητής Ιωάννης Βαράκης
Αναπληρωτής Καθηγητής Παναγιώτης ∆ηµόπουλος
Αναπληρωτλης Καθηγητής Αναστάσιος Μπεζεριάνος
Λέκτορας Κωνσταντίνος Παπαθεωδορόπουλος

Συµβουλευτική Επιτροπή

Καθηγητής Γεώργιος Κ. Κωστόπουλος
∆ρ. Αντρέας Α. Ιωαννίδης
Καθηγητής Νικόλαος Παλλικαράκης
Acknowledgment

I am very grateful for the support of the Greek State Scholarships foundation
(IKY). This made possible my graduate studies in Greece and from here all the
experiences described below and many others.
I would like to thank all those who have taught me lessons during these years I have
been a graduate student.
To Professor George Kostopoulos for being an excellent teacher, for teaching among
others what an elegant character and honesty in research are, for being a lot more
to me than a PhD student supervisor.
To Doctor Andreas A. Ioannides for his close supervision, for giving himself example
of what hard work means, for being a never-ending source of ideas.
I am thankful for the chance to work in their laboratories and for their attempt to
create a common workplace, uniting via internet, phone calls and mainly common goals
people on opposite faces of the Earth.
To the members of the Physiology Department for the wonderful atmosphere and
openness to collaborating with people coming with different backgrounds and from
different countries. It has been great to be part of this large family.
To Professor Charles F. Starmer for teaching me about perseverance and marathon
running, about passing through life in a highly interactive, involved way.
To Professors Nicholas Pallikarakis and Basil Proimos for their involvement in the
graduate course in Medical Physics and Biomedical Engineering, an international
experience they host and nurture in Greece for quite a few years.
To my colleague, collaborator and friend Ovidiu Zainea for many discussions, for his
part in our collaborative work, for his care in planning the EEG experiments, and for
his contribution in keeping the programs, the PCs and the local network working.
Thanks to all the people who have used the software described in this thesis and
contributed comments, ideas or criticism related to it (Andreas Ioannides, George
Kostopoulos, Vahe Poghosyan, Marc Schellens, Cristian Badea, Maria Stavrinou,
Milton Ioannides and others).
Thanks to the people who wrote code, made it publicly available, and/or simply
discussed it and taught many other people, including myself, how to write code in
IDL.
Thanks to all the patient subjects who provided data for the studies.
Thanks to Cristian and Andi for teaching me what happiness is!

This thesis is dedicated to my family, without which nothing would have been
possible.
Table of Contents

Chapter 1. General Introduction 1
1.1 Motivation and goals 1
1.2 Plan of the thesis 3
Chapter 2. Imaging brain structure and function 5
2.1 Structural brain imaging and the use of MRI 7
2.1.1 History of MRI 7
2.1.2 Physical bases and principles of MRI 7
2.1.3 Clinical and research applications 12
2.2 Functional imaging based on MEG 13
2.2.1 History of MEG 14
2.2.2 Biophysical bases of MEG and EEG generation 14
2.2.3 EEG versus MEG 17
2.2.4 The experimental system 19
2.2.5 Inverse Problem 22
2.2.6 MEG:Clinical and research application 25
2.2.7 Coregistration of MRI and MEG 26
2.3 Conclusion 27
Chapter 3. Brain Segmentation 28
3.1 Introduction 28
3.1.1 Definitions related to image segmentation 28
3.1.2 Applications of Segmentation 29
3.1.3 The problem of brain segmentation 32
3.1.4 Mathematical morphology for image analysis 35
3.1.5 White matter – gray matter separation 38
i
3.2 A mathematical morphology based method for brain
segmentation 40
3.3 A modified fuzzy c means method for white matter-gray
matter separation 44
3.4 Geometrical, differential properties of the cortex 47
3.5 Results 51
3.1.6 Whole Brain Segmentation 51
3.6.1 Gray matter-white matter separation with bias field compensation 52
3.6 Discussion and Conclusion 53
Chapter 4. Brain Structure Segmentation 56
4.1 Introduction 56
4.1.1 Motivation for segmentation 56
4.1.2 Selected structures of interest 58
4.1.3 Methods for subcortical structure segmentation 63
4.1.4 Background on active contours segmentation 64
4.2 Methods 66
4.2.1 Manual and snake based segmentation 67
4.2.2 Hippocampus Segmentation 69
4.2.3 Amygdala Segmentation 71
4.2.4 Central sulcus segmentation 73
4.2.5 Thalamus segmentation 75
4.2.6 Brain stem segmentation 77
4.3 Results 78
4.4 Discussion and conclusion 80

ii
Chapter 5. Visualization of surface activation 83
5.1 Introduction 83
5.2 Methods 85
5.2.1 Extracting the structural information 86
5.2.2 Computing the activation maps 87
5.2.3 3 D Visualization 88
5.2.4 Slice views 90
5.2.5 The VISIO software features 90
5.3 Results 92
5.3.1 Qualitative Evaluation of Segmentation 93
5.3.2 Surface activation visualization 94
5.4 Discussion 99
Chapter 6. Applications in Neurophysiology 101
6.1. Introduction 101
6.1.1. The somatosensory system 102
6.1.2. Background on the early somatosensory evoked potentials/fields 105
6.2. Methods 107
6.3. Results 108
6.3.1. Electrical stimulation of nerves in the limbs of normal subjects 108
6.3.2. Electrical stimulation of the limbs for a paraplegic subject 115
6.3.3. The primary visual cortex - a combined fMRI and MEG analysis 117
6.3.4. Use of anatomical constraints for EEG dipole localization. Application to
central sulcus 118
iii
6.4. Discussion 121
Chapter 7. A Software Tool for Interactive Determination of
the Plane of Cut through the Rat Brain 123
7.1. Introduction 124
7.2. Methods and materials 124
7.2.1. Reconstructing the rat brain (structures) based on atlas images 124
7.2.2. Search protocol 126
7.3. Results 127
7.4. Practical solution 128
7.5. Discussion 129
7.6. Conclusions 130
Chapter 8. General Discussion 131
8.1 Programs design 132
8.2 Contributions of SAV to understanding brain function,
complementing and integrating the relevant techniques 135
8.2.1 Source localization and extent 135
8.2.2 Source separation 135
8.2.3 Spatial resolution of electrophysiological techniques 136
8.2.4 Comparison of multimodal data 136
8.3 Applications 136
8.4 Outlook 137
Chapter 9. Conclusion 140
Publications i
Abbreviation List iii
References vi
iv
Abstract

Advances in hardware and software have made possible the reconstruction of brain
activity from non-invasive MEG and EEG data over a large part of the human brain. The
appreciation of the information content in the data is enhanced when relevant anatomical
detail is available for visualization. Different neuroscientific questions give rise to
different requirements for optimal combination of the information from functional and
anatomical data. Much of the software available today deals with scalar measures of
activity, e.g. changes in hemodynamic demand. The brain activity reconstructed from
MEG and EEG incorporates scalar but also vectorial information, which can be presented
in juxtaposition with relevant anatomical detail from MRI. Furthermore the direction of
the current density vector is expected to be related to the local cortical surface.
To address these problems we introduce an object-oriented software tool dedicated to the
visualization of spatio-temporal brain activity which allows the interplay of geometry and
vector properties of the current density directly in the representations.
The software (SAV) provides modules dedicated to: a) segmentation of the cerebrum
and/or b) subcortical or extra cortical structures and ultimately c) visualization of scalar
and vector fields in the background of the anatomy of the segmented surfaces. The
software succeeds to: a) bring forth the timing of activations and their relationships to the
cortical surface topography; b) allow the user to study the functional data in easy-to-
control view settings and hence; c) navigate through large data sets by focusing on
predefined anatomical structures.
We examine the use of detailed anatomical knowledge in functional studies and derive
quantitative properties of the segmented structures.
Additionally we investigate the applicability of quantitative imaging techniques to
planning an electrophysiological experiment on rat brain slices. We developed software to
visualize selected structures within the rat brain and a procedure to derive an optimal
sectioning plane, which would preserve as much as possible the afferent connections to the
selected structure.