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Removal of the Cardiac Cycle Artefact and Subsequent Coupling Analysis between Cortex and Basal Ganglia

Simultaneous Magnetoencephalographic and Intracranial Local Field Potential Recordings in Patients with Movement Disorders

Undergoing Deep Brain Stimulation

vorgelegt von Antje Bock aus Eutin

von der Fakultät IV – Elektrotechnik und Informatik der Technischen Universität Berlin

zur Erlangung des akademischen Grades

Doktor der Naturwissenschaften -Dr. rer. nat.-

genehmigte Dissertation

Promotionsausschuss: Vorsitzender: Prof. Dr. Klaus-Robert Müller Gutachter: Prof. Dr. rer. nat. Klaus Obermayer Gutachter: Prof. Dr.-Ing. Hermann Hinrichs Gutachter: Dr. rer. nat. Tilmann Sander-Thömmes Tag der wissenschaftlichen Aussprache: 01. November 2013

Berlin 2014 D83

Removal of the Cardiac Cycle Artefact and

Subsequent Coupling Analysis between Cortex

and Basal Ganglia

Simultaneous Magnetoencephalographic and Intracranial

Local Field Potential Recordings in Patients with Movement

Disorders Undergoing Deep Brain Stimulation

Dissertation

submitted in partial fulfilment of the requirements for the

degree of

doctor rerum naturalium (Dr. rer. nat.)

in the field of Neuroscience

at Charité - Universitätsmedizin Berlin

in cooperation with Physikalisch-Technische Bundesanstalt Berlin

awarded by

Technische Universität Berlin

by

Antje Bock

born in Eutin, Germany

Berlin

2013

"Wer es unternimmt, auf dem Gebiet der Wahrheit und der Erkenntnis als Autorität

aufzutreten, scheitert am Gelächter der Götter."

Albert Einstein, 1953

ii

Abstract

Simultaneous magnetoencephalography (MEG) and intracranial local field potential recor-

dings in patients with severe movement disorders undergoing deep brain stimulation (DBS)

treatment are a promising tool both for clinical application and basic research. Recordings can

be accomplished during the time interval (two to five days) between electrode insertion and

its connection to the subcutaneous pulse generator while electrodes are externalised. Thusly,

interactions between the DBS targets and cortical areas can be uncovered to understand

physiological and pathophysiological loops. DBS target points are the subthalamic nucleus

or the caudal zona incerta for Parkinson’s disease patients and the internal globus pallidus

for dystonic patients. Coupling measures such as coherence (coh) and the imaginary part

of coherency (icohy) have been applied. However, at lower frequencies below 10 Hz strong

cardiac cycle artefacts (CCAs) are observed in the MEG signals around the area of the burr

holes in the left hemisphere, where both disposable stainless steel electrodes wires leave the

skull. The CCA refers to the remanent magnetic field of those wires underneath the MEG

sensors, which are moved by local pulsations of the blood vessels.

The present thesis essentially aims at accurately identifying the extent of this artefact and

providing and comparing three different methods of its removal from the MEG sensor space: (i)

Applying principal component analysis and subsequent signal space projection (SSP) method

to the CCA in time space (tCCA); (ii) applying independent component analysis; and (iii)

applying SSP to the CCA in frequency space (fCCA). Subsequently, solely the ispilateral coh

and icohy between the target points located within the basal ganglia and cortical areas of

the right hemisphere, which shows less artefacts, were calculated once more. Based on the

a priori assumption that the artefact mainly covers coh and icohy below 10 Hz, preferable

removal characteristics would be a strong suppression below 10 Hz while preserving or yet un-

cover coupling above 10 Hz. In particular after applying the tCCA removal method, typical

topographic dipolar and bipolar patterns in the α (8 to 12 Hz) and β (13 to 30 Hz) fre-

quency ranges are well preserved, whereas artefactual patterns below 10 Hz are significantly

suppressed. Results of disease-specific coupling differences are mostly in line with previous

findings.

Consequently, over and above the technical feasibility of this highly challenging set-up,

especially the tCCA removal method is an appropriate tool and clears the way for more

precise and more extensive coupling analyses using similar data sets.

iii

Kurzdarstellung

Die gleichzeitige Erfassung von magnetoenzephalographischen (MEG) Signalen und intrakra-

nialen lokalen Feldpotentialen bei Patienten mit schweren Bewegungsstörungen, die sich einer

Operation zur tiefen Hirnstimulation (THS) unterzogen haben, bietet sowohl in der klini-

schen Anwendung als auch in der Grundlagenforschung vielversprechende Möglichkeiten. Die

Ableitungen können innerhalb des Zeitintervalls zwischen der Elektrodenimplantation und

ihrer anschließenden Konnexion mit dem subkutanen Stimulationsgerät (zwei bis fünf Tage)

erfolgen, während die Elektroden externalisiert sind. Auf diese Weise können Interaktionen

zwischen den THS-Zielstrukturen und kortikalen Arealen aufgedeckt werden, um physiologi-

sche und pathophysiologische Schleifen nachzuvollziehen. Die THS-Zielstruktur bei an Morbus

Parkinson erkrankten Patienten ist der Nucleus subthalamicus oder die kaudale Zona incerta

und der Globus pallidus internus bei Dystonie-Patienten. Kopplungsanalysen, wie zum Bei-

spiel die Kohärenz (coh; coherence) und der Imaginärteil des normalisierten Kreuzleistungs-

spektrums (icohy; coherency) wurden angewendet. Unterhalb von 10 Hz treten jedoch starke

kardiozyklische Artefakte (CCA; cardiac cycle artefact) in den MEG-Signalen im Bereich der

Trajekte in der linken Hemisphäre auf, dort wo beide Einweg-Elektrodenkabel aus Edelstahl

aus dem Schädel austreten. Das Artefakt verweist auf das remanente magnetische Feld derje-

nigen Kabel unterhalb der MEG-Sensoren, die durch lokale Pulsationen der Blutgefäße bewegt

werden.

Die vorliegende Arbeit zielt im Wesentlichen darauf ab, das Ausmaß dieses Artefaktes zu

identifizieren und drei unterschiedliche Methoden der Bereinigung desselben aus dem MEG-

Sensorraum aufzuzeigen und zu vergleichen: (i) Anwendung der Hauptkomponentenanalyse

und anschließende Signalvektorraumbegrenzung des CCA im Zeitraum (tCCA); (ii) Anwen-

dung der Unabhängigkeitsanalyse; und (iii) Anwendung der Signalvektorraumbegrenzung auf

das CCA im Frequenzraum (fCCA). Im Anschluss daran wurden dann ausschließlich die ip-

silateralen coh und icohy zwischen den Zielpunkten in den Basalganglien und den kortikalen

Arealen der rechten Hemisphäre von Neuem berechnet, da diese weitaus weniger Artefakte

aufwies. Basierend auf der a priori Annahme, dass das Artefakt sich hauptsächlich auf coh und

icohy unterhalb von 10 Hz auswirkt, wären die erwünschten Eigenschaften der entsprechen-

den Beseitigungsmethode ein starke Unterdrückung unterhalb von 10 Hz mit gleichzeitiger

Erhaltung oder gar Aufdeckung von Kopplungen oberhalb von 10 Hz. Insbesondere nach

Anwendung der tCCA-Methode bleiben typische di- und bipolare Frequenzmuster im α- (8

bis 12 Hz) und β-Frequenzbereich (13 bis 30 Hz) erhalten, wohingegen artefaktische Muster

iv

unterhalb von 10 Hz signifikant supprimiert werden. Ergebnisse der krankheitsspezifischen

Kopplungsunterschiede stehen größtenteils im Einklang mit vorangegangenen Studienergeb-

nissen.

Folglich erweist sich neben der erfolgreichen technischen Durchführbarkeit des herausfor-

dernden Versuchsaufbaus insbesondere die tCCA-Bereinigungsmethode als ein geeignetes Werk-

zeug und bereitet den Weg für weitere genauere und ausgiebigere Kopplungsanalysen mit

entsprechenden Datensets.

v

Statement of Originality

This thesis contains no material that has been accepted for the award of any other degree or

diploma in any other university. To the best of my knowledge, this thesis contains no material

previously published or written by another person, except where reference is made in the text.

............................................................

Antje Bock

vi

Acknowledgements

First and foremost I would like to sincerely thank Dr. rer. nat. Tilmann Sander-Thömmes

(Berlin) for his expertise, guidance and support throughout the whole duration of the work

for this thesis. He constantly encouraged me with his patient and trustful nature and helped

me to overcome all obstacles.

Thanks to Prof. Dr. Andrea A. Kühn (Berlin) to provide me the opportunity for this project.

This thesis would not have been possible without the members of the Motor Neuroscience Re-

search Group and nurses from the Neurology and Neurosurgery wards of the Charité Berlin,

Campus Virchow. Special thanks for their assistance with the recordings and their constant

help and advice.

Great appreciation to Christine Huchzermeyer, PhD, (Berlin), Heriberto Zavala-Fernandez,

PhD, (Warsaw), and Dr. Julius Hübl (Berlin) for patiently revising this thesis.

Valuable and helpful comments have been offered by Dr. rer. nat. Klaus Obermayer and the

members of his Neural Information Processing Group (Berlin), Eduardo Martínez-Montes,

PhD, (Havana), and Jan Bock (Göttingen).

Thank you to Dipl.-Des. Lydija Kühr (Berlin) for wonderful graphics.

I owe my sincere gratitude to all patients who participated in this study.

The work for this thesis was financially supported by a fellowship from Dr. Robert Leven und

Dr. Maria Leven-Nievelstein-Stiftung.

This work is dedicated to my beloved parents, who would have been the proudest if they could

still be here with me today.

vii

Contents

List of Figures xi

List of Tables xiii

1 Introduction 1

2 Background 3

2.1 The Motor Functions of the Basal Ganglia . . . . . . . . . . . . . . . . . . . . 3

2.2 Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2.1 The Basal Ganglia-Thalamocortical Circuitry in Parkinson’s Disease . 4

2.2.2 Effects of L-DOPA Therapy . . . . . . . . . . . . . . . . . . . . . . . . 5

2.3 Dystonia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3.1 Classification of Dystonia . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3.2 The Basal Ganglia-Thalamocortical Circuitry in Dystonia . . . . . . . 8

2.4 Deep Brain Stimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.5 Magnetoencephalography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.5.1 Neural Basis for the Generation of Magnetic Fields . . . . . . . . . . . 10

2.5.2 Detection of Neuromagnetic Fields . . . . . . . . . . . . . . . . . . . . 15

2.6 Local Field Potentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.7 Electromyography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.8 Electrooculography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3 Current Status of Research 20

4 Methods 23

4.1 Patients and Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.2 Recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

viii

4.3 Unified Parkinson’s Disease Rating Scale . . . . . . . . . . . . . . . . . . . . . 28

4.4 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.4.1 Head Positioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.4.2 Re-referencing, Rejection of Oversaturated Magnetic Channels, and Fil-

tering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.4.3 Mean Total Spectral Power of Magnetic Channels . . . . . . . . . . . . 32

4.5 Coherence and Coherency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.6 Treatment and Removal of the Cardiac Cycle Artefact . . . . . . . . . . . . . 33

4.6.1 Principal Component Analysis and Subsequent Signal Space Projection

to Remove the Cardiac Cycle Artefact in Time Domain . . . . . . . . 33

4.6.2 Removal of the Artefact Components found by Independent Component

Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.6.3 Principal Component Analyis and Subsequent Signal Space Projection

to Remove the Cardiac Cycle Artefact in Frequency Domain . . . . . . 40

4.6.4 Local Field Potential Analysis in Consideration of Cardiac Pulsations 42

4.7 Time-Shift Principal Component Analysis to Validate Coherence . . . . . . . 42

4.8 Thresholded Coherence to Test for Significance . . . . . . . . . . . . . . . . . 45

4.9 Group Averages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5 Results 48

5.1 Results of Data Before Artefact Removal . . . . . . . . . . . . . . . . . . . . . 48

5.1.1 Mean Total Spectral Power of Magnetic Channels . . . . . . . . . . . . 48

5.1.2 Coherence and Imaginary Part of Coherency . . . . . . . . . . . . . . . 49

5.2 Results of Data after Cardiac Cycle Artefact Removal in Time Domain . . . . 53

5.2.1 Principal Component Analysis of the Cardiac Cycle Artefact in Time

Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

ix

5.2.2 Mean Total Spectral Power of Magnetic Channels after Cardiac Cycle

Artefact Removal by Signal Space Projection in Time Domain . . . . . 53

5.2.3 Coherence and Imaginary Part of Coherency after Cardiac Cycle Arte-

fact Removal by Signal Space Projection in Time Domain . . . . . . . 54

5.3 Results of Data after Removing the Artefact Components found by Independent

Component Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

5.3.1 Mean Total Spectral Power of Magnetic Channels after Independent

Component Removal . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

5.3.2 Coherence and Imaginary Part of Coherency after Independent Com-

ponent Removal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.4 Results of Data after Cardiac Cycle Artefact Removal in Frequency Domain . 66

5.4.1 Coherence and Imaginary Part of Coherency after Cardiac Cycle Arte-

fact Removal by Signal Space Projection in Frequency Domain . . . . 66

5.5 Results of Electrooculographic Data of Two Patients . . . . . . . . . . . . . . 69

5.6 Results of Local Field Potential Analysis Averaged Time-Locked to the R Peak 69

5.7 Group Results of Time-Shift Principal Component Analysis . . . . . . . . . . 70

5.8 Results of Thresholded Coherence and Imaginary Part of Coherency . . . . . 73

5.9 Results of Group Averages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

5.9.1 Results of Mean Coherence and Imaginary Part of Coherency Compar-

isons between before and after Artefact Removal . . . . . . . . . . . . 79

5.9.2 Results of Disease-specific Mean Coherence and Imaginary Part of Co-

herency Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

6 Discussion and Conclusion 88

6.1 Evaluation of Artefact Removal . . . . . . . . . . . . . . . . . . . . . . . . . . 88

6.2 Pathophysiological Coupling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

6.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

6.4 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

x

References 97

A Appendix 110

A.1 Unified Parkinson’s Disease Rating Scale (UPDRS) . . . . . . . . . . . . . . . 110

List of Figures

1 Flowchart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Basal Ganglia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

3 Pathways Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

4 Pathways Dystonia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

5 Deep Brain Stimulation System . . . . . . . . . . . . . . . . . . . . . . . . . . 9

6 Electrical Circuit of the Membrane . . . . . . . . . . . . . . . . . . . . . . . . 11

7 Magnetic Field of a Current Dipole . . . . . . . . . . . . . . . . . . . . . . . . 13

8 Origin of the MEG Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

9 Dipolar Topographic Field Map . . . . . . . . . . . . . . . . . . . . . . . . . . 14

10 Flux-to-voltage Transfer Function . . . . . . . . . . . . . . . . . . . . . . . . . 17

11 SQUID Magnetometer System . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

12 Axial First-Order Gradiometer . . . . . . . . . . . . . . . . . . . . . . . . . . 18

13 MRI Sections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

14 MEG Measuring Facility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

15 Example Deep Brain Electrode Recording . . . . . . . . . . . . . . . . . . . . 27

16 Fiducials of all Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

17 Head Positioning Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

18 Examples of MEG Channel Removal . . . . . . . . . . . . . . . . . . . . . . . 32

19 Electrocardiographic QRS Complex . . . . . . . . . . . . . . . . . . . . . . . . 34

20 Averaged Cardiac Cycle Artefact . . . . . . . . . . . . . . . . . . . . . . . . . 36

xi

21 Partially Removed Averaged Cardiac Cycle Artefact . . . . . . . . . . . . . . 36

22 Eigenvalues of Case 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

23 Principal Components of Case 4 . . . . . . . . . . . . . . . . . . . . . . . . . . 38

24 Coherence and Imaginary Part of Coherency between MEG and ECG of Case 11 41

25 Example for Imaginary Part of Coherency of Case 15 before and after TSPCA 45

26 Topoplot of Imaginary Part of Coherency of Case 16 . . . . . . . . . . . . . . 47

27 Examples of Mean Total Spectral Power of Magnetic Channels . . . . . . . . 48

28 Topoplots of Coherence and Imaginary Part of Coherency for Case 1 . . . . . 50



31 Eigenvalues of All Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

32 Examples of Mean Total Spectral Power of Magnetic Channels after tCCA SSP 54

33 Topoplots of Coherence for Case 1 before and after tCCA SSP . . . . . . . . . 56

34 Topoplots of Coherence for Case 8 before and after tCCA SSP . . . . . . . . . 57

35 Topoplots of Coherence for Case 13 before and after tCCA SSP . . . . . . . . 58

36 Topoplots of Coherence for Case 24 before and after tCCA SSP . . . . . . . . 59

37 Examples of Mean Total Spectral Power of Magnetic Channels after ICA Com-

ponent Removal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

38 Topoplots of Coherence for Case 5 before and after ICA Component Removal 63



41 Topoplots of Coherence for Case 6 before and after fCCA SSP . . . . . . . . . 67

42 Topoplots of Imaginary Part of Coherency for Case 4 before and after fCCA SSP 68

43 Electrooculography of Case 24 . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

44 R Peak Averaged LFP Data of Patient 4, 7, 14, and 22 . . . . . . . . . . . . . 70

xii

45 TSPCA Group Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

46 Topoplots of Thresholded Imaginary Part of Coherency for Case 9 . . . . . . 74

47 Topoplots of Thresholded Coherence for Case 24 . . . . . . . . . . . . . . . . 75


49 Topoplots of Thresholded Coherence for Case 10 . . . . . . . . . . . . . . . . 77


51 Group Coherence Averages for Dystonia Patients . . . . . . . . . . . . . . . . 81

52 Group Imaginary Part of Coherency Averages for Dystonia Patients . . . . . . 81

53 Group Coherence Averages for PD Patients (STN, OFF) . . . . . . . . . . . . 82

54 Group Imaginary Part of Coherency Averages for PD Patients (STN, OFF) . 82

55 Group Coherence Averages for PD Patients (STN, ON) . . . . . . . . . . . . . 83

56 Group Imaginary Part of Coherency Averages for PD Patients (STN, ON) . . 83

57 Group Imaginary Part of Coherency Averages for PD Patients STN vs. ZI . . 84

58 Disease-Specific Coherence Differences . . . . . . . . . . . . . . . . . . . . . . 86

59 Disease-Specific Imaginary Part of Coherency Differences . . . . . . . . . . . . 87

List of Tables

1 Focal Dystonias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Intra- and Extracellular Ion Concentrations . . . . . . . . . . . . . . . . . . . 11

3 Patients’ Clinical Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4 Motor UPDRS Subscores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

xiii

Abbreviations

aCCA averaged cardiac cycle artefact

AC-PC anterior commissure-posterior commissure

ANOVA one-way analysis of variance

AP action potential

AR artefact removal

Avgrh average of right hemisphere

BG basal ganglia

CA cardiac artefact

CCA cardiac cycle artefact

cZI caudal zona incerta

DA dopamine

DBS deep brain stimulation

ECG electrocardiography

EMG electromyography

EOG electrooculography

ERD event-related desynchronisation

ERF event-related field

ERS event-related synchronisation

fCCA frequency domain cardiac cycle artefact

GABA gamma-Aminobutyric acid

GPe external globus pallidus

GPi internal globus pallidus

ICA independent component analysis

L-DOPA L-3,4-dihydroxyphenylalanine

LFP local field potential

LPA left preauricular point

MEG magnetoencephalography

M membrane

NAcc nucleus accumbens

N-PLS multiway partial least-squares

xiv

PARAFAC parallel factor analysis

PCA principal component analysis

PD Parkinson’s disease

PLS partial least-squares

PSP postsynaptic potential

RPA right preauricular point

SNc substantia nigra pars compacta

SNr substantia nigra pars reticulata

SOBI second order blind identification

SQUID superconducting quantum interference device

SSP signal space projection

STN subthalamic nucleus

tCCA time domain cardiac cycle artefact

THS tiefe Hirnstimulation

TSPCA time-shift principal component analysis

UPDRS unified Parkinson’s disease rating scale

ZI zona incerta

xv

Symbols, Units, and Constants

a area

arctanh hyperbolic arctangent function

A mixing matrix

A matrix containing sorted eigenvectors

[A] ampere

B magnetic field

C capacitance

C concentration

Cl chloride

coh coherence

cohy coherency

d diameter

e electronic charge (e = 1.602 · 10−19 As)

E electric field

E equilibrium potential

f frequency

g conductance

h Planck’s constant (h = 6.62606896 · 10−34 Js)

He helium

[Hz] hertz

I current

icohy imaginary part of coherency

[J] joule

K potassium

[K] kelvin

kB Boltzmann’s constant (kB = 1.3806504 · 10−23 J)

l length

[l] litre

L inductance

M(f) Fourier transform of a magnetoencephalographic signal

xvi

M(t) magnetoencephalographic signal in the time domain

M mutual inductance

[m] metre

[mol] mol

µ0 magnetic permeability

n dimension

N quantity

Na sodium

Nb niobium

P permeability

Φ magnetic flux

Φ0 magnetic flux quantum (Φ0 = h/2e = 2.067833636 · 10−15 Tm2)

q(t) noise signal

Q charge dipole

q electric charge

R resistance

r point or distance

S cross-spectrum

s(f) Fourier transform of an LFP signal

s(t) LFP signal

s(t) independent components

[s] second

Si silicium

σ conductivity

τ delay

t time

T temperature

[T] tesla

V voltage

[V] volt

X data set

xvii

1 Introduction

Deep brain stimulation (DBS) as a treatment for severe movement disorders such as

Parkinson’s disease, dystonia, and essential tremor has been rapidly become established

over the past years. DBS offers the unique opportunity to directly record local field

potentials (LFP) from deep brain structures otherwise inaccessible in awake humans.

In order to uncover interactions between the DBS targets and cortical areas and to

understand the physiological and pathophysiological loops, these intracranial recor-

dings can be used along with simultaneous magnetoencephalography (MEG). MEG

can access a broad cortical area without actually touching the head and the patient’s

fresh surgical wounds. Due to clinical constraints and a technically challenging set-up,

simultaneous MEG and LFP recordings are only accomplished by a very few number

of MEG centres worldwide notwithstanding the tremendous scientific potential.

However, MEG recordings in DBS patients are hampered by severe metal artefacts

around the burr holes where externalisation wires leave the skull. Artefacts affect

coupling measures by inducing spurious coherence patterns in low frequency ranges

between 0 and 10 Hz. The aim of this work was now to accurately identify the extent

of this so-called cardiac cycle artefact (CCA) and introduce and compare different

methods of its removal from the MEG sensor space. Subsequently, coherence and the

imaginary part of the coherency could be used to focus on coupling between cortex

and basal ganglia in dystonia and Parkinson’s disease patients. To mimic a more

physiological state, Parkinson’s disease patients have been additionally recorded under

dopaminergic treatment.

This thesis opens by giving detailed background information of all areas being relevant

within the scope of this work in Chapter 2. Along with anatomical informations, the

human basal ganglia (BG) are explained in the context of their motor functions and

the BG-thalamocortical circuitry. Dystonia and Parkinson’s disease are presented not

only by describing their clinical manifestations but also by explaining the causes and

subsequent disturbances of the motor loops. The main focus of the section on MEG

is the induction and detection of neuromagnetic fields. Chapter 3 reviews the current

status of research including typical frequency bands found in the target areas of DBS

patients regarding to the disease entity, and MEG studies showing entrainment of BG

1

and cortical structures. The key chapter of this thesis is Chapter 4 describing all

pertinent methods, beginning with details about the patients and the recording set-up

and procedure. The core section comprises the data analysis including spectral power,

coherence and coherency and focuses on methods to remove the artefact. Figure 1

shows a flowchart representing the steps of data processing: After bandpass filtering,

mean spectral power as well as coupling measures such as coherence and imaginary part

of coherency are calculated. Afterwards, three different methods of artefact removal

are applied. The first one removes the CCA in time domain (tCCA), the second

one applies independent component analysis (ICA) and the third method operates

in frequency domain (fCCA). Validation of observed coherence patterns occurs by

applying time-shift principal component analysis (TSPCA) and permutation testing

identifies significant coherence patterns. Results can be compared in terms of group

averages (boxes highlighted in grey) and are presented in Chapter 5 including interim

results of each processing step. The final chapter (Section 6) summarises and discusses

the obtained results, draws conclusions, links them to the context of current research,

and proposes ideas for future work.

MEG Resting Raw Data (300 s)

Bandpass Filter (0.5-120 Hz)Mean Spectral

Power

COH / iCOHy Mean COH /

iCOHy

Legend:

MEG: Magnetoencephalography

COH: Coherence

iCOHy: Imaginary Part of Coherency

LFP: Local Field Potential

SSP: Signal Space Projection

LFP R-Peak

Average

tCCA

tCCA Mean

Spectral PowerMean COH /

iCOHy after

tCCA Removal

ICA ICA Mean

Spectral Power

fCCA

COH / iCOHy

COH / iCOHy

COH / iCOHy

Mean COH /

iCOHy after

ICA

Component

Removal

Mean COH /

iCOHy after

fCCA Removal

PCA SSP

Component

Rejection

SSP

PCA: Principal Component Analysis

ICA: Independent Component Analysis

CCA: Cardiac Cycle Artefact

t: time

f: frequency

Figure 1: Flowchart representing the steps of data processing.

2

2 Background

2.1 The Motor Functions of the Basal Ganglia

The basal ganglia (BG) are a group of subcortical brain structures located deep within

the cerebral hemispheres, in the telencephalon region of the brain. In the mammalian

brain, there are two sets of basal ganglia, mirrored in the left and right hemispheres.

The four principal and individual nuclei that make up the primate basal ganglia are the

striatum, the globus pallidus (or pallidum), the substantia nigra, and the subthalamic

nucleus (STN) (Figure 2). The striatum is divided into three important subdivisions:

the caudate nucleus, the putamen, and the ventral striatum, which includes the nucleus

accumbens (NAcc). The internal capsule, a major collection of fibres between the

neocortex and the thalamus, separates the caudate nucleus from the putamen. The

substantia nigra can be divided into the substantia nigra pars compacta (SNc) and the

substantia nigra pars reticulata (SNr) with the substantia nigra pars compacta being

the one that produces dopamine. The globus pallidus consists of an internal (GPi) and

an external (GPe) segment. The Albin-DeLong model [Albin et al., 1989] [DeLong,

1990] is the classical model of the basal ganglia and it is based on the discharge rates

of the nuclei. The major input to the basal ganglia arrives through the striatum. The

main input sources are the cerebral cortex, the thalamus and the brain stem. The

GPi and SNr give rise to the major output projections. They tonically inhibit their

target nuclei in the thalamus and brain stem. Aside from the dopaminergic neurons of

the SNr, the glutaminergic cells of the STN are the only excitatory projections of the

basal ganglia, all others are GABA-ergic and inhibitory. There are two predominant

parallel pathways from input to output nuclei through the basal ganglia (left hand

plot of Figure 3): The direct pathway travels from the striatum directly to the GPi or

the SNr. Its activation disinhibits the thalamus and therefore facilitates appropriate

cortically initiated movements. The indirect pathway detours away from the striatum,

first to the GPe and then to the STN, before finally reaching the GPi or the SNr.

Activation of the indirect pathway, in turn, further inhibits the thalamus and inhibits

movement [Kandel et al., 2000]. Hyperactivity of this pathway leads to conflicting and

unwanted motor patterns. Additionally, there is a so-called hyperdirect pathway, which

bypasses the striatum by travelling from the cortex through the STN to the GPi. It

3

also results in the inhibition of large areas of the thalamus [Nambu et al., 2002]. In

summary, temporal coordination of the three pathways leads to execution of wanted

motor actions and suppression of competing motor patterns.

Figure 2: Overview of the components of the basal ganglia in coronal view [Kandel et al., 2000].

Also of importance within this scope of work is the caudal zona incerta (cZI), which

is a small region of grey matter cells being located medially to the internal capsule

and dorsal/dorsomedial to the STN. Its function is still not clear, but recent results of

attentional and motor involvement and its relation to Parkinson’s disease are reviewed

by Mitrofanis [Mitrofanis, 2005].

2.2 Parkinson’s Disease

2.2.1 The Basal Ganglia-Thalamocortical Circuitry in Parkinson’s Disease

Parkinson’s disease (PD) was first described by James Parkinson in 1817 and is one of

the most common movement disorders, affecting up to one million people in the United

States and about 300000 people in Germany. The four cardinal and primary symptoms

of the disease are muscular rigidity, bradykinesia (slowing of physical movement) or

even akinesia (loss of physical movement), postural instability, and a characteristic

tremor at rest [Kandel et al., 2000]. The pathological hallmark of Parkinson’s disease is

4

the degeneration of dopamine-containing neurons in the substantia nigra pars compacta

(SNc), which in turn leads to dopamine (DA) loss in its terminal field in the striatum.

The loss of DA will result in net inhibition of the thalamus, through both the direct

and the indirect pathway, because of the different actions of dopamine on the two

pathways via two types of dopamine receptors (D1 and D2), which are located on

different sets of output neurons in the striatum. Activation of D1 receptors reinforces

the effect of cortical input to striatal neurons [Hernandez-Lopez et al., 1997], whereas

activation of D2 receptors more consistently decreases this effect [Nicola et al., 2000].

Both of these changes may account for the hypokinetic features seen in PD. It is worth

noting that the DA receptors comprise an additional long-term effect on corticostriatal

synapses modulating their plasticity [Shen et al., 2008]. The excessive activity in the

indirect pathway at the subthalamic nucleus appears to be an important factor in

the production of parkinsonian signs, since both lesioning [Bergman et al., 1990] and

high frequency stimulation [Hamani et al., 2004] of the subthalamic nucleus markedly

ameliorate these signs in animal models and humans. The pathophysiological role of

the hyperdirect pathway is not fully discovered yet, but in Parkinsonian rats, cortical

output to the STN is increased along the hyperdirect pathway compared to the control

group [Moran et al., 2011]. Figure 3 illustrates the basal-thalamocortical pathways in

Parkinson’s disease compared to normal condition.

2.2.2 Effects of L-DOPA Therapy

An effective medical treatment option for PD is the dopamine precursor 3,4-dihydroxy-

L-phenylalanine (L-DOPA or Levodopa), which is an amino acid made from L-Tyrosine.

Unlike dopamine, L-DOPA is able to cross the blood-brain barrier. Once entering the

central nervous system, it metabolises to dopamine. However, many patients experi-

ence wearing-off effects (deterioration of effect) and so-called “on-off” fluctuations after

three to ten years of L-DOPA and several other DA agonists intake [Marsden and

Parkes, 1976]. The essential feature of these “on-off” periods is a sudden change from

mobility (“on”) to disability in movement (“off”), which can occur many times a day.

“On” periods involve involuntary movements (dyskinesias) and are suspected to corre-

late with high plasma-levodopa levels while “off” periods are characterised by intense

akinesia with or without rigidity or tremor and are associated with a low plasma-

5

Figure 3: The basal ganglia-thalamocortical circuitry under normal conditions and in Parkinson’sdisease (adapted from Kandel et al. [Kandel et al., 2000]). Inhibitory connections are shown as greyand green arrows; excitatory connections in pink. Darker arrows indicate increased neuronal activityand lighter arrows decreased activity.

levodopa level. “Freezing” is a classical symptom of PD, which refers to a sudden

immobility of the patient when confronted with a difficult task. It might last seconds,

minutes, or sometimes hours and is relieved for a period of time by levodopa ther-

apy. Nevertheless, it has been shown that the underlying pathology of PD continues

to increase despite retention of partial responsiveness to levodopa [Hunter et al., 1973]

and therefore “freezing” episodes reappear as well. Another common feature are peak-

dose dyskinesias, which are due to levodopa over-dosage [Marsden and Parkes, 1976].

In summary, the beneficial effects of L-DOPA therapy in PD are not permanent and

are accompanied by severe motor fluctuations during the course of disease and several

unwanted side effects.

2.3 Dystonia

2.3.1 Classification of Dystonia

Generally, dystonia is characterised by sustained muscle contractions, which can cause

twisting and repetitive movements and abnormal postures. As there exist many various

6

forms of dystonia, a major classification according to the underlying causes and the

bodily distribution of the symptoms is meaningful:

Primary or idiopathic forms of dystonia occur without an apparent (structural) cause,

recognised pathological condition, or associated disease. They often have a hereditary

component. Usually, the symptoms may include dystonia, tremor, and myoclonus and

once the abnormal movements appear they do not remit. Secondary dystonias, in

contrast, result either from other disease states (e.g., Wilsons’s disease, Huntington’s

disease, or Parkinson’s disease) or brain injuries (e.g., trauma, stroke, or metabolic

alterations). If induced by certain drugs, the form of dystonia is called tardive dystonia

[Geyer and Bressman, 2006]. Therefore, their manifestations and causes vary widely.

In focal dystonia, only one single region of the body is affected. Some common

focal dystonias are summarised in Table 1. Segmental dystonia affects two or more

contiguous body parts and multifocal dystonia includes two or more non-contiguous

parts. The combination of blepharospasm and oromandibular dystonia is called Meige’s

syndrome. If legs (or one leg and the trunk) plus at least one other area of the body are

affected it is termed generalised dystonia [Geyer and Bressman, 2006]. DYT1-Dystonia

(also known as Oppenheim dystonia) is an early onset primary generalised dystonia,

which typically presents in childhood and which is inherited in an autosomal dominant

manner.

Focal Dystonia Involved Muscles Symptoms

Cervical dystonia Neck muscles Head turns, tilts, extends,flexes, or jerks repetitively

Blepharospasm Muscles around the eyes Rapid eye blinking

Focal hand dystonia Single muscle or small Task-specific involuntary muscular(musician’s dystonia group of muscles contractionsor writer’s cramp) in the hand

Oromandibular dystonia Jaw and tongue muscles Distortions of tongue and mouth

Spasmodic dysphonia Muscles of larynx Voice sounds broken/whispering

Table 1: Examples of common focal dystonias.

Far less frequent are dystonia-plus syndromes. They refer to dystonia accompanied

by other neurological features and are usually caused by genetic mutations. To this

date, at least 19 different forms can be genetically differentiated [Schmidt and Klein,

2010]. One example is the DYT11-Dystonia (Myoclonus Dystonia Syndrome), which

is dystonia plus sudden, brief, shock-like (myoclonic) movements.

7

2.3.2 The Basal Ganglia-Thalamocortical Circuitry in Dystonia

In contrast to the increased mean discharge rates in the GPi of hypokinetic disorders

such as PD, a model with a decreased pallidal output has been proposed for hyper-

kinetic disorders, in particular dystonia [DeLong, 1990]. Hyperactivity of the direct

and hypoactivity of the indirect striato-pallidal pathway and resulting lower inhibitory

output from the GPi to the thalamus would then lead to excessive involuntary move-

ments [Guehl et al., 2009] [Vitek, 2002]. In addition to the discharge rates, changes

in the pattern and degree of synchronisation of neurons in the GPi seem to also play

an important role for the flow of thalamo-cortical information and resulting movement

disturbances [Vitek, 2002]. Figure 4 illustrates the basal-thalamocortical pathways for

dystonia compared to the normal condition.

Figure 4: The basal ganglia-thalamocortical circuitry under normal conditions and in dystonia(adapted from Kandel et al. [Kandel et al., 2000]). Inhibitory connections are shown as grey andgreen arrows; excitatory connections in pink. Darker arrows indicate increased neuronal activity andlighter arrows decreased activity.

2.4 Deep Brain Stimulation

The introduction of deep brain stimulation (DBS) has led to a major breakthrough in

the treatment of PD and dystonia. In 1987, it was discovered that high-frequency DBS

8

acts as an alternative to ablative brain surgery in a reversible and adjustable manner

in PD patients [Benabid et al., 1994]. DBS entails the permanent implantation of

an electrode into the target area and its connection to an internal subcutaneous pulse

generator (pacemaker) (Figure 5). The stimulator settings such as voltage, pulse width,

and frequency can be adjusted postoperatively to improve efficacy, reduce side effects,

and adapt the DBS to the course of disease for each patient individually. To this day,

more than 90000 patients have received DBS worldwide.

Deep BrainStimulator Lead

Electrodes

Subthalamic Nucleus

Substantia Nigra

Connective Wires

Pacemaker

Figure 5: Schematics of a deep brain stimulation system with subthalamic nucleus target.

The effectiveness of DBS is suspected to be a result of changing the pathophysiolog-

ical neuronal firing pattern in the target area of stimulation, which is commonly the

STN in PD patients [Liu et al., 2008] [Brown and Eusebio, 2008] [Kühn et al., 2005]

and the GPi in dystonic patients [Coubes et al., 2004] [Tisch et al., 2007] [Lee et al.,

2007]. In PD patients, DBS of the STN helps to improve approximately 55% of the

motor symptoms [Krack et al., 2003] and increases the quality of life in those patients

significantly [Deuschl et al., 2006]. It has been reported that stimulation of the caudal

zona incerta (cZI) might even lead to greater motor improvement than stimulation

of the STN [Plaha et al., 2006]. Further results from multi-central clinical studies

have not been published yet. In patients with primary generalised dystonia, motor

scores improved by 51% after the first year of stimulation [Vidailhet et al., 2005]. The

combined analysis of another group of primary generalised or segmental dystonia pa-

9

tients revealed substantial improvement in all movement symptoms (except speech and

swallowing), the level of disability, and quality of life, after six months of neurostim-

ulation [Kupsch et al., 2006]. Continuous GPi DBS in tardive dystonia also shows

long-term improvement on motor function, quality of life, and mood [Gruber et al.,

2009]. Application of DBS has been extended to other movement and also affective

disorders as for instance essential tremor and major depression. MEG with ongoing

DBS has not been reported in group studies due to the artefacts generated by the

stimulator.

2.5 Magnetoencephalography

2.5.1 Neural Basis for the Generation of Magnetic Fields

Neuromagnetic fields are produced by synaptic currents of thousands of cortical neu-

rons, in particular pyramidal and stellate cells. Neurons consists of the soma, which

includes the nucleus and metabolic structures, the dendrites, which receive stimuli from

other cells, and the axon, a long nerve fibre transferring nerve impulses away from the

soma to other cells. Across the electrically insulating lipid bilayer cell membrane, which

surrounds the neuron, an electrical potential difference is detectable. It arises from the

action of ion channels, ion pumps, and ion transporters embedded in the membrane,

which maintain different ion concentrations of mainly sodium (Na+), potassium (K+),

and chloride (Cl−) inside and outside the cell. The electrical behaviour of the membrane

can be represented by the network shown in Figure 6 with CM being the membrane

capacitance and E being the so-called membrane potential. The total ionic membrane

current (I) is divided into sodium and potassium currents (INa and IK) and a small

leakage current Il containing chloride and other ions.

ENa, EK, and El are the equilibrium potentials and gNa, gK, and gl the corresponding

conductances (RNa = 1gNa

, RK = 1gK

, Rl = 1gl

), so that INa = gNa(E-ENa), IK =

gK(E-EK), and Il = gl(E-El). The equilibrium potential is determined by Boltzmann’s

principle [Boltzmann, 1896] in such a way that the concentration C of each ion tends

toward thermal equilibrium between the intra- and extracellular compartments:

C ≈ exp

(−|e|V

kBT

)(1)

10

Figure 6: Electrical circuit representing the membrane (C = capacitance, E = equilibrium potential,I = current, K = potassium, l = leakage, M = membrane, Na = sodium, R = resistance) [Hodgkinand Huxley, 1952].

Here, V is the potential difference between the outside and the inside of the cell, kB is

the Boltzmann’s constant (kB = 1.3806504 · 10−23 J), T is the absolute temperature,

and e is the absolute value of the electronic charge (e = 1.602 · 10−19 As). From this,

the Nernst equation can be obtained:

V = Vin − Vext =kBT

|e|ln

(Cext

Cin

)(2)

The equilibrium membrane voltages for each ion type can be calculated with the given

intra- and extracellular concentrations at body temperature (Table 2). The equilibrium

potential is the membrane potential at which there is no net flux of the ion species across

the cell membrane.

Species Intracellular Extracellular Equilibriumof Concentration Concentration PotentialIon [mmol/l] [mmol/l] [mV]

K+ 400 20 -75Na+ 50 440 +55Cl− 52 560 -60

Table 2: Intra- and extracellular ion concentrations during resting state of the cell (Na = sodium,K = potassium, Cl = chloride) [Kandel et al., 2000].

When taking into account the concentrations and permeabilities (P ) of all those ion

types simultaneously, the Goldman’s equation [Goldman, 1943] can be constructed and

11

the resting potential can be obtained:

V =kBT

|e|ln

(P (K+)Cext,K+ + P (Na+)Cext,Na

+ + P (Cl−)Cin,Cl−

P (K+)Cin,K+ + P (Na+)Cin,Na+ + P (Cl−)Cext,Cl

−

)= −70 mV (3)

Signal transfer occurs via voltage pulses traveling along the axons. Once those action

potentials arrive at the synapse at the end of the axon and reach the firing threshold

of about −40 mV (depolarisation), special presynaptic channels open and release neu-

rotransmitters into the synaptic cleft. These, in turn, activate receptors at the post-

synaptic side and allow Na+, K+, and Cl− ions to pass through the cell membrane of

the second cell and change its membrane potential. This event is called the postsy-

naptic potential (PSP). Because of the longer temporal duration of the PSP (10 ms)

compared to the duration of an action potential (1 ms), the temporal summation of

those currents is much more effective and therefore mainly contributes to the signals

detected by the MEG. In addition to this intracellular primary current source, passive

ohmic currents (volume currents or secondary currents) are set up in the surrounding

medium, which complete the loop of ionic flow. MEG measurements result from both

the primary and secondary currents, but in fact the primary currents are the dominant

contributer. The PSP produces a dipolar field oriented along the dendrite. The current

I through the synapse can be calculated from the change of voltage ∆V as:

I =∆V

λRs

(4)

Rs = 4/(πd2σin) is the resistance of the intracellular fluid, where d is the diameter of

the dendrite and σin is the intracellular conductivity. λ = (gmRs)−

1

2 , where gm is the

conductance of the membrane.

The currents from thousands of neurons sum up as the pyramidal cells are arranged

in parallel layers. They give rise to an electrical field and a magnetic field B that is

oriented perpendicular to the current I:

dB =µ0

4π

Idl × r̂

r2(5)

µ0 is the magnetic permeability of free space, dl the length of the current element, r̂

is the unit vector pointing from the location in space at which the magnetic field is

12

evaluated to the location of the current element, and r is the distance between those

two locations. Figure 7 illustrates a current dipole, the returning volume currents, and

the magnetic field lines around the dipole.

Figure 7: Current dipole (thick arrow) in a homogeneous conducting medium. Examples of volumecurrents (dashed curves) and magnetic field lines (B) [Hämäläinen et al., 1993].

In finite perfectly spherical conductors, the volume current causes an equal but oppo-

site field to that generated by the primary current. Hence, the electrical as well as the

magnetic external net field is then zero and there is no magnetic field over the scalp for

a dipole oriented radially to the skull (Figure 8). An example of a resulting magnetic

topographic dipolar field map can be seen in Figure 9. The magnetic field B is directly

obtained by the primary current#»

I p in an infinite homogeneous conductor [Hämäläinen

et al., 1993]:

#»

B( #»r ) =µ0

4π

∫(#»

I p + σ#»

E) ×

#»

R

R3dv′ (6)

E is the electric field and r is the point where the field is computed and#»

R = #»r − #»r ′.

For a current dipole#»

I p( #»r ) =#»

Qδ( #»r − #»r ′) in a spherically symmetric conductor, B(r)

becomes

#»

B( #»r ) =µ0

4π

F ( #»r , #»r Q)(#»

Q × #»r Q) − (#»

Q × #»r Q#»r )∇F ( #»r , #»r Q)

F ( #»r , #»r Q)2(7)

with#»

Q being the charge dipole (a line element of current I pumped from a sink at #»r 1 to

13

a source at #»r 2, so that#»

Q =#»

I ( #»r 2−#»r 1)) [Sarvas, 1987]. F ( #»r , #»r Q) = a(ra+r2−

#»r Q#»r ),

∇F ( #»r , #»r Q) = r−1a2 +a−1 #»a #»r +2a+2r) #»r − (a+2r +a−1 #»a #»r ) #»r Q, and #»a = ( #»r − #»r Q)

and a = | #»a | and r = | #»r |. Consequently, if the primary current#»

I p is radial (and#»

Q and#»

R are parallel and their cross product is zero)#»

B becomes zero, so MEG is sensitive to

the tangential components of the primary current.

Figure 8: (a) Coronal section of the human brain. The cortex is indicated by dark colour. (b) Theconvoluted nature gives rise to currents flowing tangentially or radially relative to the surface of thehead. (c) Tangential currents produce magnetic fields that are observable outside the head. (d) Radialcurrents do not produce magnetic fields outside the head. (e) Magnetic fields due to cortical sourcesexit and reenter the scalp (q = electric charge). [Vrba and Robinson, 2001].

Figure 9: Example of a dipolar topographic field map calculated from an MEG signal as a top viewof a head with nose and ears indicated.

Therefore, MEG measures activity from the fissures (sulci) and not of the gyri of the

cortex. Fortunately, about two-thirds of the cortex is in sulci [Hari and Kaukoranta,

1985] and all primary sensory areas of the brain are situated in the fissures [Hämäläinen

et al., 1993]. For fields to be detectable, it is necessary to have simultaneous activation

of 104 to 105 cells [Hämäläinen et al., 1993]. These neuromagnetic signals are typically

in the range of 50 to 1000 fT, whereas the magnetic field of the earth has a magnitude

of about 50 µT, and the urban magnetic noise of about 500 nT. To overcome those

14

large differences between signal and noise, a combination of sufficient suppression of

the magnetic background (shielding) (cf Chapter 4.2) and a magnetic detector of high

sensitivity (cf. Chapter 2.5.2) are indispensable.

2.5.2 Detection of Neuromagnetic Fields

In 1968, the first measurement of magnetic fields of the human brain was accomplished

by David Cohen [Cohen, 1968]. He used an induction coil to detect fluctuating magnetic

fields in the range of 1-2 pT produced by alpha-rhythm currents. However, these early

biomagnetic measurements resulted in quite noisy data. In addition to even stronger

and more elaborated shielding rooms, James Zimmermann developed an extremely

sensitive detector of magnetic flux called the SQUID (superconducting quantum in-

terference device) in the late 1960s [Zimmermann et al., 1970], which is standard in

neuromagnetic research to this day. The so-called dc SQUID is customarily made of a

superconducting niobium (Nb) ring, which is interrupted by two extremely thin insu-

lating barriers (weak links) called Josephson junctions. These junctions are for instance

made up of thin slices of silicium (Si) nitride (5 to 10 µm). Josephson described for

the first time in a theoretical paper how pairs of electrons can tunnel through these

gaps [Josephson, 1962]. In a superconducting metal, the resistance drops abruptly to

zero when the material is cooled below its critical temperature, which is 9.25 K for Nb.

The SQUIDs are cooled in a liquid helium (He) cryostat as the boiling point for He is

at 4.2 K. Electrons are then bound together to Cooper pairs due to electron-phonon

interaction. These composite bosons are characterised by opposite spins, the same di-

rection of movement, the same velocity, and the same phase correlation [Cooper, 1956].

Furthermore, there is no interior magnetic field within the metal when superconductive

(Meissner effect) and any exterior magnetic field is excluded [Meissner and Ochsenfeld,

1933]. A superconductive current can cross the Josephson junction:

I = Ic sin θ (8)

θ is the quantum-mechanical phase difference across the junction, and Ic is the max-

imum critical current that can be sustained without loss of superconductivity. If the

applied current exceeds the critical current, the kinetic energy exceeds the bond energy

15

of the Cooper electron pairs, so that they separate. As a result, a voltage drop V across

the tunnel barrier can be measured. The time-dependent phase difference is then

δθ

δt=

2π

Φ0

V (9)

where Φ0 is the the magnetic flux quantum (Φ0 = h/2e = 2.067833636 · 10−15 Tm2)

[Josephson, 1962]. The transition between the zero-voltage regime and the running

state is hysteretic, if the damping to the resistance R across the junction is small.

For this reason, a shunt resistance and a capacitor are connected in parallel with the

Josephson junction [Hämäläinen et al., 1993]. This causes the junctions to work in a

nonhysteretic mode. Equation 8 then becomes

I =V

R+ Ic sin θ + C

dV

dt(10)

If the external magnetic field is changing, a bias current IB will be generated within

the ring. The currents through the two junctions sum up to the bias current IB

IB = I1 + I2 (11)

that can increase or decrease the magnetic flux Φ through the loop towards the next

or previous integral multiple of Φ0:

Φ =Φ0

2π(θ1 − θ2) = Φa +

L

2(I1 − I2) (12)

L is the inductance of the ring and Φa is the externally applied magnetic flux. This

quantisation relation shows that the response of the SQUID to magnetic flux is periodic

in the applied flux Φa with the period Φ0. Once the bias current exceeds the critical

current for the junction, the superconducting ring becomes resistive and a voltage

appears across the junction, which thus is a function of the applied magnetic field and

the period equal to Φ0 [Hämäläinen et al., 1993]:

V = RIc(dν

dτ) (13)

16

where ν = (θ1 + θ2)/2 and τ = 2πRIct/Φ0. The total magnetic flux is given by

Φ = Φ0(θ1 − θ2)

2π(14)

The SQUID flux-to-voltage transfer function is a multivalued periodic sinusoidal func-

tion and magnetic field changes that are smaller than the magnetic flux quantum Φ0 can

be measured when choosing an adequate operating point for the SQUID, which usually

comprises the steep part where the magnitude of the transfer coefficient VΦ = δV/δΦa

is maximum (see Figure 10) [Vrba and Robinson, 2001]:

a

a

Figure 10: Flux (or field)-to-voltage transfer function of a dc SQUID (Φa = applied magnetic flux,Φ0 = magnetic flux quantum, V = voltage) [Vrba and Robinson, 2001].

Figure 11 gives a schematic overview of the dc SQUID magnetometer: The super-

conducting flux transformer couples the SQUID sensors to the measured signals and

increases the overall magnetic field sensitivity. It consists of a total of three coils: two

pickup coils (shown as only one coil in Figure 11) with the overall inductance Lp, which

are closest to the subject’s head and therefore exposed to the measured magnetic fields,

and a signal coil with the inductance Ls, which inductively couples the flux transformer

to the SQUID ring.

The two pickup coils form an axial first-order gradiometer (Figure 12) and are con-

nected in series but wound in opposite directions. Thus, the coil system becomes

insensitive to spatially uniform changes in the background field, but responds to inho-

mogeneous changes, which arise near the first pickup coil [Hämäläinen et al., 1993] [Vrba

and Robinson, 2001].

17

Figure 11: Schematic diagram of a dc SQUID magnetometer (Bext = external magnetic field, C =capacitance, Φa = applied magnetic flux, IB = bias current, Ish = shielding current, Lp = pickupcoil inductance, Ls = signal coil inductance, M = mutual inductance, R = resistance, V = voltage)[Hämäläinen et al., 1993].

Figure 12: Axial first-order gradiometer consisting of two coils that are connected in series andwound in opposite direction [Hämäläinen et al., 1993].

A shielding current Ish is induced between the two coils with ap being the effective area

of the pickup coils:

Ish = Bext

ap

(Lp + Ls)(15)

The applied magnetic flux is then coupled to the SQUID ring through the mutual

inductance M [Hämäläinen et al., 1993].:

Φa = MIsh (16)

2.6 Local Field Potentials

LFPs are extracellular voltage fluctuations reflecting the sum of synaptic activity in

the dendrites of a local neuronal population flowing across the resistance of the local

18

extracellular space. The influx of cations at the synapse results in a local extracellular

sink, which, in turn, needs to be balanced by an outwards flux to the extracellular

space along the neuron, the so-called passive current [Buzsáki et al., 2012].

2.7 Electromyography

Surface electromyography (EMG) measures the electric potential of muscle cells by

placing electrodes onto the skin. The detected potential is generated by action poten-

tials (APs) that appear when the muscle is contracted. One motor neuron and all the

muscle fibres it innervates is called motor unit. When the APs reach the neuromuscular

junction (the point where the nerve contacts the muscle) after being carried down the

motor neuron, action potentials are elicited in all the muscle fibres of that particular

motor unit. The sum of electrical activity of multiple motor units is the recordable

signal found in the EMG. More and more muscle fibres produce APs when the strength

of muscle contraction is increased. Electrode placement needs to be consistent across

subjects and a stable EMG signal needs to be obtained. For this, the sensor location is

chosen to be two bipolar sites on one muscle in relation to a line between two anatom-

ical landmarks. From the skin surface, the electrode can be arranged longitudinally

with respect to the long axis of the muscle or transversely, perpendicular to the long

axis.

2.8 Electrooculography

In order to detect eye movements, electrodes are placed above and below or alongside

the eye. If the eye is moved, a potential difference between the two electrodes can be

measured and yields the eye position.

19

3 Current Status of Research

Deep brain stimulation (DBS) offers the opportunity to directly record from deep brain

areas that are otherwise inaccessible. Patients can be studied during the time interval

between DBS electrode implantation and following connection to a pulse generator ly-

ing underneath the abdominal skin. Within these two to five days, the DBS electrode

leads are externalised and can be used as recording electrodes to capture synchronised

extracellular voltage changes originating from the dendrites of a large number of neigh-

bouring neurons. These local field potentials (LFPs) reflect synchronised oscillatory

activity [Levy et al., 2002] [Hammond et al., 2007] and can roughly be divided into

three main frequency bands: i) the low frequency activity (5 to 12Hz) that is predomi-

nant in the GPi of patients with dystonia [Chen et al., 2006], ii) the beta (β) band (13

to 30 Hz) found exaggerated at various sites within the basal ganglia-cortical loop of

PD patients [Brown, 2006], and iii) the high gamma (γ) band (>60 Hz) inconsistently

present in PD at rest during dopaminergic medication [Brown et al., 2001]. These

frequency bands are dynamically and systemically modulated by task and affected by

movement and have been suggested to play an important role in understanding the

pathophysiology of movement disorders [Brown, 2003].

In the STN of patients suffering from PD, β band activity is prominent, antagonises

motor-related processing, and is suppressed prior to and during voluntary movement

as well as during L-DOPA treatment in parallel with clinical improvement of motor

deficits [Levy et al., 2002] [Kühn et al., 2004] [Kühn et al., 2006]. Suppression of

enhanced β activity (by movement, DBS, or L-DOPA) may allow other frequencies

more relevant for information coding (such as γ band activity) to emerge. Event-

related synchronisation (ERS), meaning a percentage band power increase in relation

to premovement baseline, occurs around onset of voluntary movement in the γ band

[Cassidy et al., 2002]. In PD patients under L-DOPA, this characteristic is more

pronounced in the STN contralateral to the moved hand [Androulidakis et al., 2007]

[Brücke et al., 2012]. Similarily, idiopathic dystonia patients show movement-related

γ band synchronisation in the GPi contralateral to movement [Brücke et al., 2008]. In

a recent study from Brücke and Bock et al., the same phenomena is observed in the

ventralis intermedius nucleus of the thalamus in patients with essential tremor [Brücke

et al., ]. These results suggest that lateralisation of movement-related γ activity may

20

represent a physiological state in the BG and the thalamus. During rest, dystonia

patients show dominant oscillations in the α (8 to 12 Hz) band in the GPi [Silberstein

et al., 2003]. Thalamic γ activity has additionally been found during arousal in tremor

patients [Brücke et al., ].

Using simultaneously recorded LFPs and electroencephalography (EEG), it was shown

that basal ganglia oscillations from the STN are coupled to oscillations in distant brain

regions such as cortical areas [Cassidy et al., 2002] [Lalo et al., 2008]. Here, coher-

ence in the β range was also diminished by movement and administration of L-DOPA.

In dystonic patients, pallidal α LFP activity and involuntary dystonic activity of the

affected muscles are coherent [Chen et al., 2006].

Magnetoencephalography (MEG) studies have provided new insights into physiolog-

ical mechanisms of motor control and pathophysiological mechanisms of tremor disor-

ders by analysing oscillatory cerebral networks [Schnitzler et al., 2006]. Particularly,

an entrainment of basal ganglia and motor cortical structures in PD patients has been

shown [Pollok et al., 2008]. The apparent importance of γ activity for movement con-

trol is also reflected at the cortical level [Crone et al., 1998a] [Pfurtscheller et al., 2003].

Therefore, synchronous activity of different groups of neurons within the motor system

might allow reliable communication between distant motor areas.

In MEG recordings of DBS patients, beamforming method has been shown to be

one way of finessing the high-amplitude artefact problem that originates from the

percutaneous DBS extension wire, which is connected to the actual stimulation elec-

trodes [Litvak et al., 2010]. However, the beamforming approach requests different

assumptions. The most important one is that the neural sources are uncorrelated. If

signals are coherent, the beamformer will partly fail and signal cancellation as well

as cross-talk effects can occur, which in turn are likely to distort subsequent coupling

analyses between different brain regions [Reddy et al., 1987]. Interaction measures that

are less prone to this effect, such as the imaginary part of the coherency instead of the

coherence, have been proposed and successfully been applied [Nolte et al., 2004]. But

beyond that, secondary-cross talk can occur, which refers to the circumstance that two

additional pairs of coherent sources can both leak to the cortical sites between inter-

action has been measured leading to mislocalisations [Hui et al., 2010]. Furthermore,

beamforming assumes that all patients’ heads are equally and perfectly close to the

21

MEG sensors [Brookes et al., 2008], which is difficult to be guaranteed. Against this

background, analysis of our MEG and LFP data sets was not carried out using the

beamforming method. Instead, it was rather aimed at developing alternative methods

to overcome the occurring artefact.

The first topographical characterisation of coherence patterns during rest between

the STN and cortex was possible because of these developments or the use of special

non-ferromagnetic DBS kits not being available for this study. This way, two spatially

and spectrally separated networks have been identified in PD patients [Litvak et al.,

2011]. One is a temporoparietal-brainstem network which showed coherence with the

STN in the α (7 to 13 Hz) band, whilst the other network was predominantly frontal

and coherent in the β (15 to 35 Hz) band, being increased by dopaminergic medication.

A similar study showed coherent activity in low (12 to 20 Hz) and high (20 to 35 Hz)

beta range between STN and sensorimotor and premotor cortex as well as α range

coherence with temporal areas [Hirschmann et al., 2011].

22

4 Methods

4.1 Patients and Surgery

Thirteen patients diagnosed with PD (two females, mean age 56.92 ± 10.24 years, mean

disease duration 8.38 ± 3.62 years) and eleven patients diagnosed with dystonia (seven

females, mean age 48.18 ± 11.19 years, mean disease duration 14.50 ± 8.82 years)

were included in this study. All patients participated with informed consent and the

permission of the ethics committees of Charité Hospital Berlin and in accordance with

the standards set by the Declaration of Helsinki. Their clinical details including age,

gender, disease duration, and deep brain stimulation targets are summarised in Ta-

ble 3. The table items are arranged in the chronological order of their recordings. All

patients underwent simultaneous bilateral implantation of DBS electrodes in the STN,

cZI, or GPi, respectively. The permanent quadripolar macroelectrode used was model

3389 (Medtronic Neurologic Division, Minneapolis, MN, USA) featuring four platinum-

iridium cylindrical surfaces (1.27 mm diameter and 1.5 mm length) and centre-to-centre

separation of 1.5 mm. The four contacts are numbered 0, 1, 2, and 3 with 0 being the

most caudal and 3 being the most cranial contact. The operative procedure has been

described previously [Kühn et al., 2005] [Kupsch et al., 2006]. The intended coordinates

at the tip of the electrode (contact 0) to target the STN were 12 mm from the midline, 0

to 2 mm behind the midcommissural point and 4 to 5 mm below the anterior-posterior

commissure (AC-PC) line. The cZI coordinates are 14.1 ± 1.6 mm from midline, 5.7

± 1.5 mm behind the midcommissural point, and 2.1 ± 1 mm below the AC-PC line.

For the GPi of the dystonic patients, the coordinates were 18.8 to 22 mm lateral from

the midline, 2 to 4 mm in front of the midcommissural point, and 4 to 5 mm below

the AC-PC line. These target coordinates were based on individual stereotactic T2-

weighted magnetic resonance images (MRIs) and in most of the patients confirmed by

intra-operative macrostimulations as well as microelectrode recordings to define bor-

ders of brain regions. Postoperatively, patients underwent either T2-weighted MRI or

computer tomography (CT) scans. Two representative examples of electrode position

within the STN and the GPi are illustrated in Figure 13 showing coronal and horizon-

tal postoperative T2-weighted MRI sections. Because of the fact that all patients who

received electrodes in the cZI are subject of a randomised controlled double blinded

23

study, MRI scans are not available for those patients.

A

B

C

D

Figure 13: Localisation of DBS electrodes in coronal (A and C) and horizontal (B and D) sectionsof the postoperative MRI of case 2 (A and B) and 4 (C and D). The white arrows point at the DBSelectrode artefacts of the GPi (A and B) and STN (C and D).

24

Case Age Diagnosis Disease Deep Brain(Years)/ Duration StimulationGender (Years) Target

1 59/M Parkinson’s Disease 13 Subthalamic Nucleus

2 48/F Segmental Dystonia 20 Internal Globus Pallidus

3 58/F Parkinson’s Disease 11 Subthalamic Nucleus


5 55/M Segmental Dystonia 12 Internal Globus Pallidus

6 52/F Meige Syndrome 10-15 Internal Globus Pallidus

7 51/F Cervical Dystonia 3 Internal Globus Pallidus


9 48/F Segmental Dystonia 6 Internal Globus Pallidus

10 68/M Cervical Dystonia 23 Internal Globus Pallidus



13 66/F Parkinson’s Disease 9 Subthalamic Nucleus

14 68/M Parkinson’s Disease 11 Caudal Zona Incerta


16 38/M Myoclonus Dystonia Syndrome (DYT 11) 32 Internal Globus Pallidus




20 58/M Cervical Dystonia 20 Internal Globus Pallidus



23 24/F Segmental Dystonia (DYT 1) 16 Internal Globus Pallidus


Table 3: Clinical details of all patients involved in this study. M = male, F = female

25

4.2 Recordings

Recordings were performed during the time interval (two to five days) between DBS

electrode insertion and following connection to an internally implanted subcutaneous

pulse generator, when the DBS electrode leads were externalised. The electrode wires

leave the skull on the left hemisphere. Local field potentials and MEG signals were

recorded while patients were laying on their backs with their head inside the helmet

of a 125-channel whole-head magnetoencephalography axial gradiometer system (KIT,

Eagle Technology, Kanazawa, Japan), which consists of 125 gradiometers and three

magnetometers. The two pickup coils have a diametre of 15.5 mm each and a distance

of 50 mm between them. The pickup coil that is closest to the head is about 20 mm

away from the surface of the dewar where the patients’ head touches and placed at a

distance of 75 mm from the SQUID sensors. The array of SQUID sensors is installed

in the liquid helium dewar keeping the sensors at around 4.2 K [Kado et al., 1999].

The dewar is situated within an electrically and magnetically shielded room (AK3b,

VAC Hanau, Germany). The shields consists of one layer of copper and two layers

of mu-metal, which is a nickel-iron alloy (approximately 75% nickel, 15% iron, plus

copper and molybdenum) featuring a very high magnetic permeability (about 80.000

compared to several thousand for ordinary steel and 1 for air). Figure 14 shows the

MEG measuring facility at Physikalisch-Technische Bundesanstalt (PTB) Berlin.

26

Figure 14: MEG measuring facility at Physikalisch-Technische Bundesanstalt (PTB) Berlin.

LFP signals were acquired with a 32-channel low noise electroencephalographic (EEG)

amplifier. All signals were recorded quasi-continuously at 2000 Hz with a 500 Hz low-

pass filter. LFP and all other electric signals are recorded monopolarly and referenced

to a common reference, which is contact 0 of the left DBS electrode. Figure 15 shows

an example deep brain electrode bipolar recording of 1 s length.

Figure 15: Deep brain electrode local field potential recording of 1 s length.

Electromyograms (EMGs) of the left and right bottom side muscles of the forearm

(flexor digitorum superficialis) were recorded using the common reference (patients 1 to

6) or bipolarly (from patient 7 onwards) with reference to the muscle tendon to detect

27

finger movements. Monopolar electrocardiograms (ECGs) were recorded starting with

patient 8 with exception of patient 21. A ground electrode was positioned on the

patient’s forehead. Electroencephalographic (EEG) electrodes were placed on position

Cz and Pz according to the international 10-20 EEG placement system [Jasper, 1958].

Each patient was in addition equipped with five fiducial coils to determine the position

of the patient’s head in the MEG coordinate system. Two coils were placed at the left

and right preauricular points (LPA, RPA), one at the nasion and two somewhere at the

occiput to estimate the size of the head. To control for artefacts due to eye saccades,

electrooculographic (EOG) signals were recorded in two patients (patient 23 and 24).

For this, EEG electrodes were placed below and at the outer corner of the eye. The

potential difference between the electrode pair yields the eye position. During the rest

recording session of patient 23, a fixation cross was put on the screen, also to control

for eye movements.

PD patients were recorded twice in one session, once after overnight withdrawal from

dopaminergic medication (hereafter referred to as OFF state) and 30 to 60 minutes

after intake of 100 to 200 mg of L-DOPA (hereafter referred to as ON state). Patient

13 has only been recorded during ON state, due to severe impairment when withdrawn

from Parkinsonian medication.

Each recording session consisted of a resting block and a task block. As data from

the task block is not discussed in this study, its description is omitted. During the

300 s of rest recordings, patients were asked to keep their eyes open and move as little

as possible.

4.3 Unified Parkinson’s Disease Rating Scale

The Unified Parkinson’s Disease Rating Scale (UPDRS) [Fahn et al., 1987] is the

most widely used rating scale for disability and impairment in PD [Ramaker et al.,

2002]. It consists of six parts with the third part (UPDRS-III) dealing with motor

examination. The UPDRS-III comprises a systematic evaluation of various distal and

axial PD symptoms. It is divided into 14 items including speech, facial expression,

tremor at rest and action tremor, rigidity, hand and finger movements, leg agility,

arising from chair, posture and gait, postural stability, and body bradykinesia. A

28

score of 0 to 4 can be assigned for each item. 0 refers to normal characteristics and

4 implies most severe impairment of the respective symptom. Therefore, a sub-total

between 0 and 108 can be achieved for the motor section. Appendix A shows the

motor examination part of the UPDRS and Table 4 shows the achieved scores of the

Parkinson’s disease patients during medicational ON and OFF state (as explained

in Section 4.2). Note that patient 13 has only been recorded and therefore only rated

during ON state and case 17 did not show any L-DOPA responsiveness, which could be

due to the tremor dominant clinical PD picture. The mean OFF score of all PD patients

(n = 12) is 29.91 ± 18.88 and the mean ON score is 20.09 ± 12.90 (without patient 13).

ON and OFF scores differ significantly (paired Student’s t-test, p = 0.0007547). The

OFF mean for the STN patients (n = 9) is 28.2 ± 21.96 and the ON mean is 20.13 ±

15.24 (significantly different, paired Student’s t-test, p = 0.01468). The OFF and ON

means for the ZI group (n = 3) are 34.33 ± 7.37 and 20 ± 4.36 (significantly different,

paired Student’s t-test, p = 0.01942).

4.4 Data Preprocessing

4.4.1 Head Positioning

Due to different head’s geometries as well as different levels of uncomfortableness, there

is a large variability in head positioning (Figure 16).

Figure 16: Fiducials of all patients. The black dots are the MEG sensors. The yellow stars illustratethe fiducials placed on the RPA, the black stars the LPA positions, and the red squares represent thenasion fiducials. Axis units are [mm].

The optimal position would be, if the patient’s head were fully inside the helmet,

29

Case

3

Case

4

Case

11

Case

12

Case

13*

Case

14

Case

15

Case

17

Case

19

Case

21

Case

22

Case

24

Speech 2/2 1/1 3/2 0/0 1 2/2 1/1 0/0 2/1 1/1 1/1 1/1Face Ex-pression 4/4 0/0 1/1 0/0 2 3/3 1/1 1/2 2/1 1/1 1/1 1/1Tremorat RestFace: 0/0 0/0 0/0 0/0 0 0/0 0/0 0/0 0/0 0/0 0/0 0/0Hands:Right 0/0 4/3 0/0 0/0 0 1/0 0/0 2/2 0/0 0/0 0/0 1/1Left 1/0 3/3 0/0 2/0 0 1/0 0/0 0/0 0/0 0/0 0/0 2/0Feet:Right 0/0 3/2 0/0 0/0 0 0/0 0/0 1/1 0/0 0/0 0/0 1/0Left 2/1 2/2 0/0 1/0 0 0/0 0/0 0/0 0/0 0/0 0/0 2/0ActionTremorRight 1/0 4/3 0/0 0/0 0 1/0 1/0 1/1 0/0 0/0 0/0 0/0Left 1/1 3/1 1/0 1/0 0 1/0 0/0 0/0 0/0 1/1 0/0 0/0RigidityNeck: 0/0 0/0 0/0 0/0 0 2/0 0/0 0/0 1/1 0/0 2/1 0/0Arms:Right 3/1 1/1 0/0 1/1 2 2/0 1/1 1/1 2/2 0/1 2/2 1/1Left 3/3 2/1 1/0 2/2 1 2/1 1/1 0/0 2/2 2/0 2/1 2/1Legs:Right 2/1 1/1 0/0 0/0 2 2/0 1/1 0/0 2/2 3/2 2/2 0/0Left 4/2 1/1 1/0 0/0 1 2/1 1/1 0/0 2/2 3/2 2/1 1/1FingerTapsRight 3/1 2/2 1/0 0/0 2 2/1 0/0 1/0 1/0 2/2 0/0 1/2Left 3/1 3 /3 0/0 2/2 2 2/1 0/0 0/0 1/0 2/1 1/0 2/1HandGripsRight 2/1 2/2 0/0 0/0 2 2/1 1/1 0/0 2/0 1/0 1/1 1/0.5Left 4/1 3/3 0/0 2/2 2 2/1 1/1 0/0 2/1 2/1 1/1 1/0.5Pronate/SupinateRight 4/3 3/2 0/0 1/0 3 1/1 1/1 0/0 1/0 2/0 1/1 1/0.5Left 4/3 3/2 0/0 3/2 3 1/1 1/1 0/0 1/0 2/1 1/1 1/0.5LegAgilityRight 4/3 2/1 0/0 0/0 2 1/1 1/0 0/0 1/1 3/0 1/1 2/1Left 4/3 3/2 1/0 1/0 2 1/1 1/1 0/0 1/1 3/0 1/1 1/0ArisefromChair 4/4 1/0 0/0 0/0 0 1/1 0/0 0/0 0/0 2/2 0/0 0/0Posture 4/3 1/0 2/1 0/0 2 1/1 1/1 0/0 1/1 2/1 0/0 1/1Gait 4/3 1/1 1/1 0/0 1 2/2 1/1 0/0 2/2 3/2 0/0 1/1PosturalStability 4/4 1/0 0/0 0/0 2 1/2 0/0 0/0 2/1 2/2 0/0 0/0Brady-kinesia: 3/3 3/2 2/2 1/0 2 2/2 1/1 0/0 2/2 3/2 0/0 2/1

Subtotal 70/48 53/39 14/7 17/11 34 37/23 16/14 7/7 30/20 40/22 19/15 26/15

Table 4: Motor UPDRS subscores of the PD patients OFF and ON medication (OFF/ON). Fordetailed description of the motor examination part of the UPDRS see Appendix A.1. (*only ON)

30

so that the sensors are really close to the scalp as for instance seen in patient 3 (left

hand plot of Figure 17). However, some patients were not able to move further into

the helmet for example like patient 12 (right hand plot of Figure 17).

Figure 17: Head positioning of patient 3 (left hand plot) and 12 (right hand plot). The green meshis a sphere approximating the head. The black dots illustrate the MEG sensors and the red dots showthe five fiducials.

For the far out positions the inverse solution for source localisation as for instance

proposed by Gross and colleagues [Gross et al., 2001] might be unreliable, because geo-

metrical information can be incomplete or not trustworthy. Therefore, further analysis

in particular coherence measures will be restricted to sensor level.

4.4.2 Re-referencing, Rejection of Oversaturated Magnetic Channels, and Fil-

tering

LFP signals were re-referenced to the adjacent contact pairs 01, 12, and 23 of each

electrode (Figure 15) by subtracting those contact pairs from each other. Oversaturated

magnetic channels close to the artefact source within the left hemisphere have been

sorted out. Figure 18 shows two examples of how many and which channels have

been excluded from further data analysis. On average, a mean of 108.54± 10.69 MEG

channels were included for further analysis. LFP and magnetic channels were then

bandpass filtered between 0.5 and 120 Hz.

31

Figure 18: MEG channels of patient 9 (left plot) and 17 (right plot). Top view of the head withnose and ears indicated.

4.4.3 Mean Total Spectral Power of Magnetic Channels

The mean total spectral power of all magnetic channels have been calculated for each

case using the multitaper frequency transformation and a Hanning window between 0

and 100 Hz in steps of 1 Hz width. Results of some examples are shown in Section 5.1.1.

4.5 Coherence and Coherency

Coherency is a complex correlation function in the frequency domain [Nunez et al.,

1997] and can be computed from the cross-spectrum, which is defined as follows:

Sij(f) =⟨Mi(f)s∗j(f)

⟩(17)

Mi(f) is the Fourier transform of a time domain MEG signal Mi(t) of channel i and

sj(f) is the Fourier transform of an LFP signal sj(t) of channel j. ∗ means complex

conjugation and 〈〉 denotes the expectation value, which is an estimation from the

average over a sufficiently large number of epochs. Coherency (cohy) is now defined as

the normalised cross-spectrum [Nolte et al., 2004]:

cohy(f) =Sij(f)√

(Sii(f)Sjj(f)(18)

32

Coherence (coh) is the absolute value of coherency:

coh = |cohy(f)| (19)

The imaginary part of the coherency (icohy) has turned out to be an even more powerful

tool for brain connectivity analyses than the coherence (coh) [Nolte et al., 2004] [Sander

et al., 2010], because it only depends on interacting sources and contains information

about the time-delay between two signals. This feature can be regarded as rather physi-

ological, as there is a processing delay within the cortico-subthalamo-pallidal pathway.

In unanesthetised monkeys, the latency of the early excitation responses evoked by

motor cortical stimulation in the STN amount to 5.8 ± 4.5 ms and to the GPi to 7.8

± 2.4 ms [Nambu et al., 2000]. Thus, icohy is not prone to volume conduction [Nolte

et al., 2004], which is a rather instantaneous process.

icohy = Imag(cohy(f)) (20)

Coherence as well as the imaginary part of coherency have been calculated for each

patient between all magnetic channels and one electrode contact at a time. Frequencies

between 48 and 52 Hz were removed by a notch filter to avoid artefacts due to 50 Hz

interference from mains noise. Examples of the resulting topographic patterns of some

patients can been seen in Section 5.1.2.

4.6 Treatment and Removal of the Cardiac Cycle Artefact

4.6.1 Principal Component Analysis and Subsequent Signal Space Projection to

Remove the Cardiac Cycle Artefact in Time Domain

In MEG data, the heartbeat generates the cardiac artefact (CA). The CA reflects

the electrical current within the heart muscle [Jousmäki and Hari, 1996], which is a

significant contribution to the MEG even though MEG is recorded at a distance of

some 200 mm from the heart. In patients with externalised electrode wires, a second

artefact can be detected, which is the cardiac cycle artefact (CCA) [Litvak et al., 2010].

The CCA refers to the time variable magnetic field due to local pulsations of the blood

vessels moving the weakly magnetised (externalisation) wires relative to the MEG

33

sensors. Both artefacts can especially be identified by finding the R wave, which is the

deflection of highest amplitude within the electrocardiographic (ECG) QRS complex.

The QRS complex reflects the depolarisation of the heart’s right and left ventricles

and lasts about 100 ms. The CCA is of much higher amplitude than the CA as it

has a technical origin, for which reason the CA can be ignored within the scope of

this work. The left hand plot of Figure 19 shows an example of an ECG found in the

electromyogram (EMG) of the left flexor digitorum superficialis muscle in patient 4.

This may happen, because the electric potential from the heart is conducted to all

parts of the body and may therefore interfere with the electromyographic signals from

the muscles. The R peak has then been used as a trigger for event-related averaging in

all channels with trials of 600 ms length (300 ms before and 300 ms after trigger onset).

Hence, the average of the ECG in the EMG channel across n = 461 trials demonstrates

the QRS complex in the right hand plot of Figure 19.

0 0.5 1 1.5 2 2.5 3−6

−4

−2

0

2

4

6

Time [s]

Vol

tage

[V]

−0.2 −0.1 0 0.1 0.2−6

−4

−2

0

2

4

6

Time [s]

Vol

tage

[V]

Q

R

S

Figure 19: Electrocardiographic QRS complex in the electromyogram of the left flexor digitorum

superficialis muscle of patient 4 (left hand plot) and averaged across n = 461 trials time-locked to theR peak (right hand plot).

As it can be assumed that the magnetic and electric fields propagate instantaneously,

the averaged CCA (aCCA) can be calculated by time-locking the magnetic channels

to the ECG. Figure 20 shows the resulting topographic maps of all magnetic channels

(90 ≤ n ≤ 110, because saturated channels close to the artefact source have been

sorted out) for time epochs of 20 ms. The patient’s head is seen from above with

sketched ears and nose to denote its orientation. The little black dots indicate the

locations of the sensors. The averaged CCA is the pattern that is located within the

34

left hemisphere around the fronto-parietal area, where the electrode wires are placed in

loops underneath the skin. The intensity of the magnetic field changes are colour-coded

and given in tesla. Positive values are shown in yellow, orange and red colour shades

and represent sources, while negative blue-colored values show sinks. Arterial blood

flow velocity is about 1 m/s in thick blood vessels, therefore the temporal allocation of

the aCCA pattern differs from the one of the QRS complex and the strongest aCCA

signal appears later than the R peak at 0 ms.

35

Figure 20: R peak aligned MEG data (n = 124 channels) of patient 4 for time epochs of 20 ms(indicated in tesla) demonstrating the averaged cardiac cycle artefact.

Figure 21: Partially removed averaged cardiac cycle artefact in the R peak aligned MEG data(n = 124 channels) of patient 4 for time epochs of 20 ms (indicated in tesla). The first five principalcomponents have been removed from the data set by applying the signal space projection method.

36

Principal component analysis (PCA) is an eigenvector-based multivariate analysis to

identify the dominant patterns in a data set under the constraint of orthogonality in

space and time. Orthogonality in time is uncorrelatedness. The goal is an orthogonal

linear transformation of the data set to a new coordinate system in order to focus on

the relationship between single data points. There are basically six steps to perform a

PCA analysis on a data set using the covariance method [Smith, 2002]:

• Create a data matrix with zero empirical mean by subtracting the mean from

each of the data dimensions.

• Calculate the covariance matrix.

• Find the eigenvectors and their corresponding eigenvalues of the covariance ma-

trix.

• Sort those pairs in order of decreasing eigenvalue. The eigenvector with the

highest eigenvalue is the first principle component of the data set and shows the

most significant relationship between the data points.

• Construct a feature vector consisting of the sorted eigenvectors, in which the

eigenvectors with the lowest eigenvalues can be ignored to reduce dimensions.

• Derive the new data set by taking the transpose of the feature vector and multiply

it with the transposed mean-adjusted data matrix.

The PCA of the time domain CCA (tCCA) can now be used and calculated in order

to identify the pattern and dimensionality of the artefact in the resting data [Bock et al.,

2012]. Figure 22 shows the resulting eigenvalues in decreasing order. The eigenvalues

represent the energy of the eigenvectors. A sharp bend at the fifth eigenvalue can be

seen in all curves, indicating that the dimensionality of the tCCA is 5 ≤ dimCCA ≤ 10

and fairly similar for all data sets. Eigenvalues of all patients are shown in Figure 31

in Section 5.2.1.

37

0 5 10 15 20 25 300

0.2

0.4

0.6

0.8

1

1.2

1.4x 10

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Eig

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10−14

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Log 10

Eig

enva

lue

Figure 22: The first 30 eigenvalues of case 4 ordered by size.

Figure 23 shows the topographic distributions of the first twelve principal compo-

nents, which are the eigenvectors normalised to the length of 1. The principal compo-

nents are orthogonal.

Figure 23: The topographic distribution of the first twelve principal components of case 4.

Given that the PCA eigenvalues of the tCCA have a similar dependence it is rea-

sonable to use the signal space projection (SSP) method as proposed by Uusitalo

and Ilmoniemi [Uusitalo and Ilmoniemi, 1997] to suppress the most powerful CCA

components from the data set [Bock et al., 2012]. For this, the PCA decomposition

38

is backprojected to the channel level after removing the first five principal compo-

nents. If A1,...,n (90 ≤ n ≤ 110) is the matrix containing the sorted eigenvectors

eigenvector1, eigenvector2, ..., eigenvectork, the data set X(t) after SSP equals

X(t)SSP = {(0) (0) (0) (0) (0)A6,...,k}A−1Xraw(t) (21)

where Xraw(t) is the raw data set. Figure 21 shows the same data set as used before

after SSP has been applied.

In order to assess whether the artefact removal described before is frequency-specific

or not and in what extent an energy reduction can be observed, the mean total spectral

power of all magnetic channels has been calculated for each patient and for each brain

hemisphere again as described before in Section 4.5. Likewise, the coherence and the

imaginary part of the coherence have been calculated after the applied SSP method.

Results of some patients are shown in Section 5.2.2 and 5.2.3.

4.6.2 Removal of the Artefact Components found by Independent Component

Analysis

In contrast to PCA, independent component analysis (ICA) is not only restricted to

an orthogonal basis but also allows an oblique-angled basis. It has been successfully

applied for MEG artefact identification and removal [Rong and Contreras-Vidal, 2006]

[Hyvärinen et al., 2010]. The temporal decorrelation approach is based on the cross-

covariance function of the signal, where the source cross-variance function is a set

of diagonal additive subcomponents supposing mutual statistical independence of the

sources [James and Hesse, 2005]. The mixing model basis for the ICA is as follows:

X(t) = As(t) (22)

where X(t) contains the MEG sensor signals, A is the mixing matrix, and s(t) contains

the independent components. The second order blind identification (SOBI) algorithm

[Belouchrani et al., 1997] and subsequent exclusion of the first five and most powerful

ICA components have been applied on the MEG data sets to remove the CCA. As trying

out other ICA algorithms such as FastICA [Hyvärinen and Oja, 2000] or JADE [Cardoso

39

and Souloumiac, 1993] would have gone beyond the scope of this work, the SOBI

algorithm was chosen, because it has successfully been used in similar data sets [Sander

et al., 2007]. SOBI is defined as follows:

• Calculate the covariance matrix.

• Find the eigenvectors and their corresponding eigenvalues of the covariance ma-

trix.

• Form a whitening matrix.

• Estimation of a set of covariance matrices of the whitened signals for a preselected

fixed set of time lags.

• Obtain orthogonal unitary matrix that diagonalises the set of covariance matrices

by applying joint approximate diagonalisation.

• Derive the new data set or the mixing matrix, respectively, by multiplying the

Moore-Penrose pseudoinverse of the whitening matrix by the unitary matrix.

4.6.3 Principal Component Analyis and Subsequent Signal Space Projection to

Remove the Cardiac Cycle Artefact in Frequency Domain

Another way of verifying and removing the cardiac cycle artefact can be performed in

the frequency domain (fCCA). For this, at first the coherence and the imaginary part of

the coherency have been calculated between the MEG channels and the ECG channel,

or EMG channel (provided that the heartbeat has been detected in there) respectively,

for each patient in order to detect the fCCA. Figure 24 illustrates a representative

example. A bipolar coh and a dipolar icohy pattern, respectively, arises again on the

left hemisphere, where electrode wires leave the skull. Patterns resemble the ones seen

in R peak averaged MEG data (upper plot of Figure 21).

40

Figure 24: Coherence and imaginary part of coherency between MEG channels and ECG signal forcase 11 (ON) for frequencies between 0 and 50 Hz in steps of 2 Hz width.

41

Afterwards, the topographic patterns of the coh and icohy between the MEG channels

and the ECG between 1 and 5 Hz have been removed from the corresponding coh and

icohy MEG-LFP datasets that were calculated before as described in Chapter 4.5. For

this, signal space projection (SSP) method [Uusitalo and Ilmoniemi, 1997] as used for

tCCA removal (Chapter 4.6.1) has now been applied in the frequency domain to keep

the subspace that is orthogonal to the topographies of the fCCA.

4.6.4 Local Field Potential Analysis in Consideration of Cardiac Pulsations

The local field potentials of all electrode contact pairs were also averaged time-locked

to the R peak of the QRS complex in analogy to the MEG channels as described before

in Chapter 4.6.1. This way, it could be tested whether cardiac pulsations were also

present within the electric LFP channels and hence affecting the deep brain electrodes

or not.

4.7 Time-Shift Principal Component Analysis to Validate Coherence

Observed coherence patterns need to be validated to avoid spurious effect interpreta-

tion. Coherence itself is a statistical measure and needs special types of significance

testing and validation. One non-statistical method proposed for coherence validation

is the time-shifted principal component analysis (TSPCA). The basic idea is, that am-

bient field noise in the scope of MEG recordings can be estimated and suppressed by

the use of reference MEG channels {M_refk(t)} being located outside the MEG hel-

met [Volegov et al., 2004]. The reference sensors measure no brain activity, but the

ambient field noise, which contributes to the regular MEG sensors {Mi(t)}. The fol-

lowing algorithm multiplies the reference sensor signals by a factor (the mixing matrix)

and removes the noise by subtracting the resulting values from the MEG sensor signal.

The MEG signal after noise subtraction Mdenoised(t) is then

Mdenoised(t) = M(t) − A · q(t) (23)

with q(t) being the noise signal, in this case the reference MEG channels {M_refk(t)}.

A is computed by least square minimisation L2 of the residual in the signal after

42

subtracting the background, which, in turn, is the signal-reference sensor correlation

matrix CMq multiplied by the inverse of the reference-reference correlation matrix Cqq:

A = CMq · C−1qq (24)

CMq = [〈Mi|qj〉] (25)

Cqq = [〈qk|qj〉] (26)

i refers to the index of MEG channels, k to the index of reference channels.

Time-shift PCA (TSPCA) can now be applied in order to remove a noise signal in

case that only a filtered version of the noise signal is present in the signal of interest.

This method was proposed by Cheveigné and Simon [de Cheveigné and Simon, 2007]

and implies the use of filters that are created by time-shifting the reference signals,

forming an orthogonal basis, projecting the MEG brain channels onto this basis, and

subsequently removing the projections. The TSPCA algorithm is as follows:

MTSPCA(t) = M(t) − A′ · q̃n(t) (27)

where MTSPCA again is the cleaned signal, A′ the matrix of coefficients calculated by

orthogonalisation via PCA and projection, and q̃n(t) represents the set of (positively

and negatively) time-shifted reference channel signals. A can be understood as the

coefficient of an infinite impulse response (FIR) filter applied to the reference signal

before subtraction from the MEG signal.

A = CMqn· C−1

qnqm(28)

CMqn= [〈Mi|qj〉] (29)

43

Cqnqm= [〈qk(t − n)|qj(t − m)〉] (30)

Using now the LFP signal sl(t) as the noise signal, the LFP signal and its time-shifted

copies are regressed out of the MEG data [Bock et al., 2013]. This should remove any

inherent coherence between LFP and MEG signals. The time-shift operation represents

a periodic filter function in the frequency domain with a periodicity given by the

time-shift value [Sander et al., 2007]). Therefore, TSPCA can be viewed as multiple

least square minimisations using narrow bandwidth reference signals. For TSPCA two

parameters have to be chosen, one is the set of delays τm and the other is the length of

adjacent windows used for the calculation. TSPCA does not perform well with respect

to the coh removal if the complete 300 s data set enters the calculation [Bock et al.,

2013]. This indicates that the coupling changes its intensity and frequency on a scale of

seconds and this non-stationarity cannot be picked up using the 300 s as a continuous

block of data. A window length between 5 and 30 s gave good coh suppression. The

set of time delays was {0.001, 0.006, 0.011, ..., 0.5} s, using a shorter maximum delay

did not suppress the coh below 5 Hz [Bock et al., 2013]. A longer maximum delay

would not be appropriate for the window length of 5 s. The TSPCA calculation will

possibly introduce discontinuities at the window boundaries, but since the subsequent

spectral estimation is aligned with the window length this will not be of importance.

TSPCA has been applied here to data after tCCA and ICA artefact suppression. For

this, coh was first calculated for all three contact pairs of the right electrode and all

MEG channels of the right hemisphere. Subsequently, LFP signal with the strongest

coherence between 0 and 50 Hz to the MEG in each patient was selected manually for

further processing of the TSPCA. Figure 25 shows an exemplary icohy topography for

data after ICA component removal (c.f. Chapter 4.6.2) and after TSPCA using the

LFP signal to illustrate how dipolar patterns (here between 8 and 12 Hz and between 24

to 28 Hz) vanish. As a consistency check, the TSPCA was furthermore calculated using

a random normally distributed time series instead of the LFP signal. The resulting

coh should be unchanged with this preprocessing as the random time series should not

remove the signature of coupling between LFP and MEG signals. For the analysis on

the group level an average coh (Avgrh(arctanh(coh))) for the MEG channels of the

right hemisphere was calculated. This channel average was chosen to minimise even

44

further the influence of the CCA on the results as the right hemisphere LFP signals

couple mainly to right hemisphere cortical areas. The weighting of the coh by the

hyperbolic arctangent function is needed to obtain a normally distributed parameter

for proper averaging (e.g., [Amjad et al., 1997]).

Figure 25: Topoplots of icohy of patient 15 after ICA component removal (top plot) and subsequentTSPCA using the LFP signal (bottom plot) in 4 Hz steps from 0 to 50 Hz. Color ranges from -0.2 to0.2 (blue to orange, green is zero).

4.8 Thresholded Coherence to Test for Significance

In order to test if coh and icohy values are significantly different from zero, permutation

testing has been applied, which is a way of providing a threshold. The standard method

in testing coherence consists of randomising the order of time slices of one recorded

signal against the other. This destroys any inherent coherence effects and observed

patterns are due to chance and the chance distribution can be estimated by repeating

the procedure. Before calculating coh and icohy giving mean values of an ensemble as

introduced in Section 4.5, filtered raw data can be sliced into time sequences of N slices

45

each of 1 s length. The original order of slices is |1|2|3|...|N − 2|N − 1|N |. To estimate

the “no effect” distribution of coh and icohy the order of the slices can be randomised

in one variable only, so that |1|N − 2|N − 20|...|2|15|N |. If this procedure is repeated

multiple times, σ of a random permutation distribution is obtained. Then, original coh

and icohy values are thresholded at level σ and lower values are set to zero. This step

of analysis has also been restricted to the right electrode only.

4.9 Group Averages

Group averages of all patients are calculated in order to quantitatively compare the

proposed methods of artefact removal and to find disease-specific neurophysiological

differences. As in most of the cases oversaturated MEG channels of the left hemisphere

have been taken out for further analysis (see Chapter 4.4.2), only ipsilateral right hemi-

sphere coh and icohy respectively, will be considered in the group averages. The LFP

signals obtained from the three contact pairs of the right electrode yield three coh and

three icohy functions and the LFP signal with the strongest coherence between 0 and

50 Hz to the MEG in each patient is selected manually for further processing. Then,

the mean coh and icohy averages are compared before and after artefact removal using

the tCCA, fCCA, and ICA method in each of the five patient groups, namely dysto-

nia, PD with target point STN during medicational ON and OFF state, and PD with

target point ZI ON and OFF. For this, a paired Student’s t-test has been applied in

each frequency bin between the data set before artefact removal and after each artefact

removal method, because most important is the comparison towards the original data

set and not towards a different method of artefact removal. Furthermore, the patient

groups can be compared in order to look for disease-specific coupling differences be-

tween BG and cortical areas regarding coupling strength and frequency bands again

validated by Student’s t-tests. As patient 13 has only been recored during medicational

ON state as reported before (Chapter 4.2), this case will be left out from this part of

group analysis. The Lilliefors test was applied to confirm normality of the compared

data sets.

Note that patient 16 has been discarded from the group average analysis due to the

different clinical picture of a Myoclonus Dystonia Syndrome compared to those of the

remaining dystonic patients included in this study. The myoclonic movements might

46

be the origin of strong coh and icohy patterns between the right GPi and the motor

cortex between 44 and 48 Hz in this patient (Figure 26), as none of the other patients

showed coupling within this frequency band.

Figure 26: Topographic patterns of icohy between the MEG channels and contact 2 of the right GPielectrode of patient 16.

47

5 Results

5.1 Results of Data Before Artefact Removal

5.1.1 Mean Total Spectral Power of Magnetic Channels

Examples of mean total spectral power of the magnetic channels of four different cases

are presented in Figure 27. Maximum power strength at frequencies around 1 to 2 Hz

differs between patients and reaches from 10−26 to 10−23 T2. The mean total spectral

power curves decrease monotonically with increasing frequency apart from peaks at

50 Hz, which are due to mains noise. In some patients, local maximum points of lower

frequencies below 50 Hz can be observed for frequency bands of up to 5 Hz width, for

instance in case 7 around 20 Hz (Figure 27, upper left plot).

0 20 40 60 80 10010

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[T2 ] Case 7

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[T2 ] Case 9

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[T2 ] Case 15, OFF

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[T2 ] Case 19, ON

Figure 27: Mean total spectral power of the magnetic channels of case 7, 9, 15 and 19 for frequenciesbetween 0 and 100 Hz.

48

5.1.2 Coherence and Imaginary Part of Coherency

Representative examples of topoplots of coherence and imaginary part of coherency

between one electrode contact and all MEG channels. Topoplots are shown for case 1

(OFF) (Figure 28), 4 (ON) (Figure 29), and 6 (Figure 30). Scaling reaches from -0.4

to 0.4 both for values of coh and icohy, although coherence only comprises positive

values. However, map colors of the topoplots are clearer represented this way. Bluish

colors represent negative values, greenish colors values close to zero, and reddish tones

depict positive values up to 0.4. Typical bipolar coherence patterns occur for lower

frequencies smaller than 30 Hz as for instance seen in plots between 10 and 18 Hz in

the right hemisphere in the upper half of Figure 28, or between 24 and 28 Hz in the

top plot of Figure 30. In most of the cases, patterns are conserved or extenuated when

looking at the icohy compared to the coherence. Under 10 Hz, stronger suspicious

artefactual coherence patterns can be observed in most of the cases, which are not

bipolar, but rather spread out within large areas of both hemispheres. Examples for

these patterns can be seen in the coherence plot of Figure 28 for frequencies between

2 and 4 Hz and in Figure 29 for coherence between 4 and 8 Hz. The corresponding

patterns of the icohy are not as prominent as those for cohs as seen in case 1 and 4

(Figures 28 and 29). In higher frequencies above 50 Hz, rarely any coh or icohy can be

observed in the majority of the patients (data not shown), apart from, e.g., in case 17

(Figure 40) and 24 (ON) (Figure 36) presented in the following sections (Chapter 5.2.3

and 5.3.2).

49

Figure 28: Topoplots of coherence (top plots) and imaginary part of coherency (bottom plots)between MEG channels and contact 3 of the right electrode (r3) for case 1 (OFF) for frequenciesbetween 0 and 50 Hz in steps of 2 Hz width.

50

Figure 29: Topoplots of coherence (top plots) and imaginary part of coherency (bottom plots)between MEG channels and contact 1 of the left electrode (l1) for case 4 (ON) for frequencies between0 and 50 Hz in steps of 2 Hz width.

51

Figure 30: Topoplots of coherence (top plots) and imaginary part of coherency (bottom plots)between MEG channels and contact 3 of the right electrode (r3) for case 6 for frequencies between 0and 50 Hz in steps of 2 Hz width.

52

5.2 Results of Data after Cardiac Cycle Artefact Removal in Time Domain

5.2.1 Principal Component Analysis of the Cardiac Cycle Artefact in Time Do-

main

The resulting eigenvalues in decreasing order of each patient (n = 26, 11 dystonia

patients, 13 PD patients ON, 12 PD patients OFF) are shown in Figure 31.

0 5 10 15 20 25 300

1

2

3

4

5x 10

−12

Index

Eig

enva

lue

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0 5 10 15 20 25 3010

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0 5 10 15 20 25 300

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0 5 10 15 20 25 3010

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10−1

100

Index

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Eig

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Standardized Values

Figure 31: The first 30 eigenvalues ordered by size of all patients and conditions (n = 36) in absolutevalues (top plots) and standardised values (bottom plots).

5.2.2 Mean Total Spectral Power of Magnetic Channels after Cardiac Cycle

Artefact Removal by Signal Space Projection in Time Domain

Examples of mean total spectral power of the magnetic channels after removing the

tCCA by SSP of four different cases are presented in Figure 32. Maximum power

strength at frequencies around 1 to 2 Hz differs between patients and reaches from

53

10−26 to 10−23 T2. The mean total spectral power curves decrease monotonically with

increasing frequency apart from peaks at 50 Hz, which are due to mains noise. In some

patients, local maximum points of lower frequencies below 50 Hz can be observed for

frequency bands of up to 5 Hz width, for instance in case 8 around 15 Hz (Figure 32,

upper right plot). Compared to the original mean total spectral power of the same

patients, power is decreased in all frequencies. It is worth noting that this reduction is

highest below 30 Hz, in some patients up to 40 Hz.

0 20 40 60 80 10010

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before tCCA SSPafter tCCA SSP

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[T2 ] Case 8


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[T2 ] Case 14, OFF


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[T2 ] Case 24, ON


Figure 32: Mean total spectral power of the magnetic channels of case 1, 8, 16, and 24 after tCCAremoval by SSP for frequencies between 0 and 100 Hz. The grey curves represent the original databefore artefact removal.

5.2.3 Coherence and Imaginary Part of Coherency after Cardiac Cycle Artefact

Removal by Signal Space Projection in Time Domain

Representative examples of topoplots of coherence between one electrode contact and

all MEG channels after tCCA removal by SSP are presented from four patients. For

54

clarity reasons, icohy topoplots are not shown here. Instead, coh data before artefact

removal are presented in order to compare and focus on the artefact influencing coupling

measures. Topoplots are shown for case 1 (OFF) (Figure 33), 8 (Figure 34), 13 (ON)

(Figure 35), and 24 (ON) (Figure 36). Scaling reaches from -0.4 to 0.4 for values

of coh, again for a clearer representation. Bluish colors represent negative values,

greenish colors values close to zero, and reddish tones depict positive values up to 0.4.

The typical bipolar coherence patterns between 10 and 18 Hz of case 1 in the right

hemisphere in the bottom plots of Figure 33 are preserved after tCCA removal while

spread out artefactual patterns up to 6 Hz are removed in the same patient (Figure 33).

However, some cases still show those suspicious patterns even after tCCA removal, for

example seen in Figure 35 for case 13 between 0 to 4 Hz. Nevertheless, the strength as

well as the extent of those patterns are highly reduced after artefact removal. If no clear

coh patterns are present, the tCCA method seems to not change the existing state,

e.g., adding spurious coherence patterns or suppressing spatial energy (Figure 34). In

higher frequencies above 50 Hz, rarely any coh or icohy can be observed in the majority

of the patients (data not shown). One exception can however be seen in Figure 36. In

this case, clearly artefactual patterns in the left parietal or temporal lobe that arise

throughout the whole spectrum (50 to 100 Hz) are likewise reduced after applying the

tCCA removal method.

55

Figure 33: Topoplots of coherence between MEG channels and contact 3 of the right electrode (r3)for case 1 (OFF) for frequencies between 0 and 50 Hz in steps of 2 Hz width before (top plots) andafter (bottom plots) tCCA removal by SSP.

56

Figure 34: Topoplots of coherence between MEG channels and contact 1 of the right electrode (r1)for case 8 for frequencies between 0 and 50 Hz in steps of 2 Hz width before (top plots) and after(bottom plots) tCCA removal by SSP.

57

Figure 35: Topoplots of coherence between MEG channels and contact 3 of the right electrode (r3)for case 13 (ON) for frequencies between 0 and 50 Hz in steps of 2 Hz width before (top plots) andafter (bottom plots) tCCA removal by SSP.

58

Figure 36: Topoplots of coherence between MEG channels and contact 3 of the right electrode (r3)for case 24 (ON) for frequencies between 50 and 100 Hz in steps of 2 Hz width before (top plots) andafter (bottom plots) tCCA removal by SSP.

59

5.3 Results of Data after Removing the Artefact Components found by

Independent Component Analysis

5.3.1 Mean Total Spectral Power of Magnetic Channels after Independent Com-

ponent Removal

Examples of mean total spectral power of the magnetic channels after removing the

artefact by applying ICA of four different cases are presented in Figure 37. Maximum

power strength at frequencies around 1 to 2 Hz differs between patients and reaches

from 10−26 to even 10−22 T2. Apart from the mains noise peaks at 50 Hz local maximum

points of lower frequencies below 50 Hz can be observed for frequency bands of up to

5 Hz width in 19 out of 36 cases, for instance in case 15 and case 21 between 5 and 10 Hz

(Figure 37, bottom plots). Spectral power between 2 and 10 Hz is reduced compared

to the original data. For frequencies higher than 10 Hz, spectral power is higher than

the original one. Still, total overall power is reduced, because on the logarithmic scale,

power is highest for low frequencies.

60

0 20 40 60 80 10010

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before artefact removalafter ICA component removal

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[T2 ] Case 21, ON


Figure 37: Mean total spectral power of the magnetic channels of case 1, 3, 15, and 21 after removalof artefact components found by ICA for frequencies between 0 and 100 Hz. The grey curves representthe original data before artefact removal.

5.3.2 Coherence and Imaginary Part of Coherency after Independent Compo-

nent Removal

Some representative examples of topoplots of coherence between one electrode con-

tact and all MEG channels after removal of the artefact components found by ICA.

Topoplots are demonstrated for case 5 (Figure 38), case 15 (ON) (Figure 39), and

case 17 (ON) (Figure 40). Again, scaling reaches from -0.4 to 0.4 for values of coh and

bluish colors represent negative values, while greenish colors represent values close to

zero, and reddish tones positive values up to 0.4. Coherence patterns seem to be atten-

uated in all frequency bands by the ICA SSP method. In Figure 38 for example, bipolar

coh patterns between 16 and 20 Hz and artefactual patterns below 6 Hz are suppressed

similarly strong. Likewise, coherence in the low frequency range below 10 Hz as well

61

as bipolar coherence patterns in the β band between 20 and 28 Hz are diminished in

equal measure in Figure 39. Albeit, suppression in this case is very weak. Figure 40

is another example for stronger suppression, this time in the higher frequency range

between 50 and 100 Hz. Here, again, patterns in all frequency ranges (52 to 58 Hz,

66 to 74 Hz, 80 to 84 Hz, and 92 to 100 Hz) are removed out of the coh topography.

Therefore, ICA component removal achieved by SOBI algorithm does not seem to be

suitable within the scope of this work, as it does not seem to distinguish clearly between

artefactual coherence patterns in the low frequency range and physiological frequency

patterns above 10 Hz.

62

Figure 38: Topoplots of coherence between MEG channels and contact 2 of the left electrode (l2)for case 5 for frequencies between 0 and 50 Hz in steps of 2 Hz width before (top plots) and after(bottom plots) removal of artefact components found by ICA.

63

Figure 39: Topoplots of coherence between MEG channels and contact 3 of the right electrode (r3)for case 15 (ON) for frequencies between 0 and 50 Hz in steps of 2 Hz width before (top plots) andafter (bottom plots) removal of artefact components found by ICA.

64

Figure 40: Topoplots of coherence between MEG channels and contact 1 of the right electrode (r1)for case 17 (ON) for frequencies between 50 and 100 Hz in steps of 2 Hz width before (top plots) andafter (bottom plots) removal of artefact components found by ICA.

65

5.4 Results of Data after Cardiac Cycle Artefact Removal in Frequency

Domain

5.4.1 Coherence and Imaginary Part of Coherency after Cardiac Cycle Artefact

Removal by Signal Space Projection in Frequency Domain

In the following, coh and icohy patterns after fCCA removal compared to data before

artefact removal are shown for case 6 (Figure 41) and case 4 (ON) (Figure 42). Scaling

of the topoplots reaches as mentioned before from -0.4 to 0.4, while bluish colors

represent negative and reddish colors positive values. Greenish colors represent values

close to zero. The top plot of Figure 41 shows the coherence. Topographic bipolar

patterns in the β range between 24 and 28 Hz are preserved after artefact removal,

while low frequency artefactual patterns around 2 Hz are attenuated (bottom plot

of Figure 41). However, it is particularly noticeable, that the positivity of the coh

is not preserved after applying the fCCA method for artefact removal in frequency

domain described in Section 4.6.3 (bottom plot of Figure 41). Therefore, according to

the definition of coh, we can no longer regard and term the result as coherence. For

this reason, unlike in the previous sections of tCCA and ICA removal methods results

(Chapter 5.2.3 and 5.3.2) presenting exclusively coh results, icohy patterns are shown

here for case 4 (Figure 42). Non focal patterns between 2 and 6 Hz are suppressed, while

bipolar icohy patterns between 6 and 10 Hz are less suppressed and still recognisable.

Hence, artefact removal method in the frequency domain might as well be working

within this scope, but should only be applied and assessed for icohy.

66

Figure 41: Topoplots of coherence between MEG channels and contact 1 of the right electrode (r1)for case 6 for frequencies between 0 and 50 Hz in steps of 2 Hz width before (top plots) and after(bottom plots) fCCA removal by SSP.

67

Figure 42: Topoplots of icohy between MEG channels and contact 1 of the right electrode (r1) forcase 4 (ON) for frequencies between 0 and 50 Hz in steps of 2 Hz width before (top plots) and after(bottom plots) fCCA removal by SSP.

68

5.5 Results of Electrooculographic Data of Two Patients

Electrooculographic (EOG) data of patient 23 and 24 revealed that patients did not

perform eye movements other than blinking neither without (patient 23) nor with

using a fixation cross (patient 24) during rest recordings (data not shown). Figure 43

demonstrates an EOG episode of patient 24. No saccadic eye movements have been

found. Besides, there is no coherence between EOG and MEG channels (data not

shown). Consequently, the cardiac cycle artefact is not likely to originate from eye

movements.

9.5 10 10.5 11 11.5 12 12.5−0.2

−0.15

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Vol

tage

[µV

]

Figure 43: Electrooculography of Case 24

5.6 Results of Local Field Potential Analysis Averaged Time-Locked to the

R Peak

The following figure (Figure 44) shows examples of R peak time-locked data of the LFP

channels of all deep brain electrode contact pairs of patients 4 (OFF), 7, 14 (OFF),

and 22 (ON). There is no recognisable homogeneous pattern found, which could be

comparable to the QRS complex. Intra- as well as inter-patient time courses are rather

disparate.

69

−0.2 −0.1 0 0.1 0.2−0.015

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Case 22 (ON)

l1l2l3r1r2r3

Figure 44: LFP data averaged to the R peak of patient 4 (top left plot), 7 (top right plot), 14(bottom left plot), and 22 (bottom right plot) for left (l) and right (r) electrode contact pairs (1 refersto contact pair 0 and 1; 2 refers to contact pair 1 and 2; and 3 refers to contact pair 2 and 3).

5.7 Group Results of Time-Shift Principal Component Analysis

The group means of the right ipsilateral arctangent hyperbole-function of the average

coh for the dystonia (top row plots), PD STN OFF (middle row plots) and PD STN ON

(bottom row plots) group are shown in Figure 45 for different types of preprocessing.

In all plots, the red curve represents the mean before any processing has been applied.

The blue curve, labelled ICA AR or tCCA, is obtained after ICA artefact rejection

(right hand plots) or tCCA removal (left hand plots), respectively. In all groups, the

average coh reduces strongly below 5 Hz after preprocessing compared to the red curve

representing the untreated data sets. The black curve, labelled TSPCA(5)LFPrx, is

the mean curve for the TSPCA calculation with a window length of 5 s. The result

after regressing out the LFP signal from the MEG signal by TSPCA shows, that peaks

70

below the 30 Hz range are reduced in most of the cases. Exceptions are found in the

dystonia group after ICA component removal (right top plot) and in the PD OFF also

after applying the ICA method (right middle plot). Here, coh is not suppressed below

10 Hz. In the PD STN ON group after tCCA removal (left bottom plot), reduction

between 10 and 30 Hz is not pronounced either. Above 30 Hz, the TSPCA adds

persistent spurious coh in all cases. Hence, concerning the effectiveness and in addition

the validity of coherence, it can only be noted that in the α and β range (10 to 30 Hz)

within the PD STN OFF group (middle plots), within the dystonia group after tCCA

removal preprocessing (left top plot), and within the PD STN ON group after ICA

artefact removal (right bottom plot), coh is suppressed and therefore depicts a real

ispilateral coupling between right hemisphere and right STN or GPi, respectively. The

green curve, labelled TSPCA(5)rnd, is the TSPCA result with a window length of 5 s

using a random normally distributed time series as a control calculation. It is identical

to the blue curve. This shows that a random time series used in the TSPCA does not

suppress the cohs. Still this control calculation has to be performed with care as a

window length of 1 s destroyed any coh indicating that overfitting can occur.

71

0 10 20 30 40 50 60 700

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Dystonia

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tCCA + TSPCA(5) rnd

no preprocessing tCCA

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Dystonia

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ICA AR no preprocessing

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ICA AR + TSPCA(5) rnd

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tCCA no preprocessing

tCCA + TSPCA(5) LFPrX

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PD, STN, ON

ICA AR no preprocessing

ICA AR + TSPCA(5) LFPrX ICA AR + TSPCA(5) rnd

Figure 45: Group mean of the right ipsilateral arctangent hyperbole-function of the average coh fordystonia (n = 10, upper row), PD OFF (n = 9, middle row), and PD ON (n = 10, lower row) groupfor different types of preprocessing. The red curve shows untreated data before preprocessing and theblue curve corresponds to artefact removal by tCCA (left hand plots) or ICA method respectively(right hand plots). The black curve is the result after TSPCA calculations using the LFP signal anda 5 s window. The green curve is the TSPCA result with a window length of 5 s using a randomnormally distributed time series.

72

5.8 Results of Thresholded Coherence and Imaginary Part of Coherency

Below, representative examples for the thresholded coherence and the imaginary part of

coherency are shown for several cases and artefact removal methods. In analogy to all

previous topographic plots, scaling reaches from -0.4 to 0.4 for values of coh and icohy.

Bluish colors represent negative values, greenish colors values close to zero, and reddish

tones depict positive values up to 0.4. The first two figures (Figure 46 and 47) show

data before artefact removal with (bottom plot) and without (top plot) application

of thresholded icohy (case 9, Figure 46) and coh (case 24 (OFF), Figure 47). It can

be seen that dipolar icohy patterns between 2 and 6 Hz and between 26 and 28 Hz

are preserved, while more fuzzy icohy contour lines, e.g., especially between 10 and

20 Hz and between 42 to 50 Hz disappear after thresholding the data set (Figure 46).

Artefactual coh patterns below 5 Hz are preserved and also spread-out patterns in

the higher frequency range (24 to 30 Hz) stay almost unchanged after thresholding,

whereas areas with contour lines of coh values close to zero diminish greatly (Figure 47).

Figure 48 represents an example of thresholded icohy for data after tCCA artefact

removal by SSP. Typical strong dipolar icohy patterns in the beta band between 14

and 30 Hz and artefactual low frequency patterns below 5 Hz are well preserved after

thresholding, whereas smaller unspecific patterns do not exceed the level of significance

(e.g., between 4 and 10 Hz, and between 32 and 50 Hz). Similar results can be observed

for data sets after ICA component removal and subsequent thresholding. One example

presented here is case 10 (Figure 49). Diffuse and weak non-bipolar coh patterns are

not significant and therefore set to zero, as for example seen for frequencies from 10

to 22 Hz. Not much of an effect brings the thresholding for coh and icohy of data, in

which the artefact is removed in frequency space. Figure 50 illustrates a representative

example of icohy topographies of case 17 (OFF). As there seems to be a quite high

icohy level in all frequency bands, none of the patterns are set to zero. As there is

not much coh or icohy observed in most of the cases above 50 Hz, those weak and

diffuse patterns are consequently removed after thresholding has been applied (data

not shown).

73

Figure 46: Topoplots of icohy (top plot) and thresholded (σ = 1.82, p < 0.01) icohy (bottom plot)between MEG channels and contact 2 of the right electrode (r2) for case 9 for frequencies between 0and 50 Hz in steps of 2 Hz width.

74

Figure 47: Topoplots of coh (top plot) and thresholded (σ = 1.82, p < 0.01) coh (bottom plot)between MEG channels and contact 2 of the right electrode (r2) for case 24 (OFF) for frequenciesbetween 0 and 50 Hz in steps of 2 Hz width.

75

Figure 48: Topoplots of icohy (top plot) and thresholded (σ = 1.82, p < 0.01) icohy (bottom plot)between MEG channels and contact 3 of the right electrode (r3) for case 13 (ON) for frequenciesbetween 0 and 50 Hz in steps of 2 Hz width after tCCA removal by SSP.

76

Figure 49: Topoplots of coh (top plot) and thresholded (σ = 1.82, p < 0.01) coh (bottom plot)between MEG channels and contact 1 of the right electrode (r1) for case 10 for frequencies between 0and 50 Hz in steps of 2 Hz width after artefact removal by ICA method.

77

Figure 50: Topoplots of icohy (top plot) and thresholded (σ = 1.82, p < 0.01) icohy (bottom plot)between MEG channels and contact 1 of the right electrode (r1) for case 17 (OFF) for frequenciesbetween 0 and 50 Hz in steps of 2 Hz width after fCCA removal by SSP.

78

5.9 Results of Group Averages

5.9.1 Results of Mean Coherence and Imaginary Part of Coherency Comparisons

between before and after Artefact Removal

In the following, group averages of coh and icohy are shown for dystonia patients

(Figure 51 and 52), PD patients targeted at the STN OFF (Figure 53 and 54) and ON

(Figure 55 and 56) dopaminergic medication. For each patient group, the mean right

ispilateral coh and the mean absolute value of the right ipsilateral icohy between MEG

and LFP signal is calculated from each patient’s most reactive electrode contact pair

(see Section 4.9). In the left hand plot of each figure, each patient’s mean is shown in

a different colour while the mean over the patient group is presented by a black thick

curve. The right hand plot of each figure shows the mean curves before artefact removal

(black), after tCCA removal (dark blue), after ICA component removal (turquoise), and

only for the absolute icohy mean after fCCA removal (pale blue). Frequency ranges

that show significant differences (p < 0.05) between untreated data and the applied

artefact removal method are drawn in the same colour but with a thicker line.

Figure 51 shows that the mean coh is much smaller below 10 Hz after artefact removal.

The mean across all patients decreases below 5 Hz from 0.1 to 0.065 after tCCA removal

and to 0.045 after ICA component removal. Only the ICA component removal method

shows significant differences around 4 and 5 Hz compared to untreated data. Above

10 Hz, the coherence curves of untreated and treated data look quite similar. It is

noticeable that all curves follow exactly four frequency peaks located at 10, 18, 22, and

30 Hz. The mean absolute value of the icohy across patients below 5 Hz decreases from

0.04 to 0.03 and 0.025 for data after tCCA and ICA component removal, respectively,

and down to even 0.015 after fCCA removal (Figure 52). Changes are not significantly

different. Compared to the curve representing untreated data, icohy after ICA removal

method runs similarly above 10 Hz. tCCA and fCCA removal both suppress icohy up

to 40 Hz.

In the group of STN PD patients OFF medication (Figure 53), tCCA and ICA

component removal method both differ significantly from untreated coh means for

frequency ranges around 3 Hz (both methods), 8 Hz (tCCA), and 14 Hz (ICA). Above

from 18 Hz, all curves run similarly. The significant episode of the tCCA curve represent

79

the longest episode of all groups and conditions. The tCCA method might therefore

be regarded as the most valuable tool within this scope for CCA removal, as it highly

suppresses the artefact below 10 Hz and at the same time preserves physiological or

pathophysiological coherence patterns above 10 Hz. icohy values (Figure 54) seem to

be suppressed by artefact removal, but not consistently throughout all patients. The

mean of treated data therefore does not differ significantly in any frequency range.

After ICA component removal, the mean is even higher than the one of untreated data

in particular between 20 and 70 Hz.

For PD ON data (Figure 55), only coh values after tCCA removal differ significantly

from untreated data between 1 and 7 Hz by being reduced by about 0.1. Again, tCCA

method seems to be an optimal tool to remove the cardiac cycle artefact without sup-

pressing coherence in higher frequency ranges. Above 10 Hz, in this patient group

coherence is preserved after artefact removal. Significant icohy (Figure 56) differences

when compared to untreated data can be seen around 7 to 8 Hz after ICA component

removal. Curves of all artefact removal methods are below the curve of data before arte-

fact removal underneath from 20 Hz. Between 20 and 70 Hz, the curve that represents

absolute icohy values after ICA component removal runs above the one representing

untreated data.

Data sets from the three PD patients with electrodes in the cZI do not show sig-

nificantly suppressed mean coh values after artefact removal as well as OFF and ON

dopminergic medication and likewise, mean absolute icohy curves do not differ signifi-

cantly (data not shown).

80

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Dystonia, before artefact removal

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Dystonia

before artefact removalafter tCCA removalafter ICA component removal

Figure 51: Right ipsilateral group coh averages for dystonia patients (n = 10) before artefact removal(left plot). Coloured lines represent each patient’s mean coh and the thick black is the mean over thepatient’s curves, which is compared to those means that represent the different methods of removal(right plot). The black line shows data before artefact removal, the dark blue line represents dataafter tCCA removal and the turquoise line represents data after ICA component removal. Significantlydifferent (paired Student’s t-test, p < 0.05) frequency ranges in relation to untreated data are markedby thick episodes.

0 10 20 30 40 50 60 700

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|Imag

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Dystonia, before artefact removal

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Frequency [Hz]

|Imag

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Dystonia

before artefact removalafter tCCA removalafter ICA component removalafter fCCA removal

Figure 52: Right ipsilateral group icohy averages for dystonia patients (n = 10) before artefactremoval (left plot). Coloured lines represent each patient’s mean of the absolute value of icohy andthe thick black is the mean over the patient’s curves, which is compared to those means that representthe different methods of removal (right plot). The black line shows data before artefact removal, thedark blue line represents data after tCCA removal, the pale blue line represents data after fCCAremoval and the turquoise line represents data after ICA component removal. Significantly different(paired Student’s t-test, p < 0.05) frequency ranges in relation to untreated data are marked by thickepisodes.

81

0 10 20 30 40 50 60 700

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PD, STN, OFF, before artefact removal

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PD, STN, OFF


Figure 53: Right ipsilateral group coh averages for PD patients (STN, OFF, n = 9) before artefactremoval (left plot). Coloured lines represent each patient’s mean coh and the thick black is the meanover the patient’s curves, which is compared to those means that represent the different methods ofremoval (right plot). The black line shows data before artefact removal, the dark blue line representsdata after tCCA removal and the turquoise line represents data after ICA component removal. Sig-nificantly different (paired Student’s t-test, p < 0.05) frequency ranges in relation to untreated dataare marked by thick episodes.

0 10 20 30 40 50 60 700

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PD, STN, OFF, before artefact removal

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|Imag

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PD, STN, OFF


Figure 54: Right ipsilateral group icohy averages for PD patients (STN, OFF, n = 9) before artefactremoval (left plot). Coloured lines represent each patient’s mean of the absolute value of icohy andthe thick black is the mean over the patient’s curves, which is compared to those means that representthe different methods of removal (right plot). The black line shows data before artefact removal, thedark blue line represents data after tCCA removal, the pale blue line represents data after fCCAremoval and the turquoise line represents data after ICA component removal. Significantly different(paired Student’s t-test, p < 0.05) frequency ranges in relation to untreated data are marked by thickepisodes.

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0 10 20 30 40 50 60 700

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PD, STN, ON


Figure 55: Right ipsilateral group coh averages for PD patients (STN, ON, n = 10) before artefactremoval (left plot). Coloured lines represent each patient’s mean coh and the thick black is the meanover the patient’s curves, which is compared to those means that represent the different methods ofremoval (right plot). The black line shows data before artefact removal, the dark blue line representsdata after tCCA removal and the turquoise line represents data after ICA component removal. Sig-nificantly different (paired Student’s t-test, p < 0.05) frequency ranges in relation to untreated dataare marked by thick episodes.

0 10 20 30 40 50 60 700

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PD, STN, ON, before artefact removal

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PD, STN, ON


Figure 56: Right ipsilateral group icohy averages for PD patients (STN, ON, n = 10) before artefactremoval (left plot). Coloured lines represent each patient’s mean of the absolute value of icohy andthe thick black is the mean over the patient’s curves, which is compared to those means that representthe different methods of removal (right plot). The black line shows data before artefact removal, thedark blue line represents data after tCCA removal, the pale blue line represents data after fCCAremoval and the turquoise line represents data after ICA component removal. Significantly different(paired Student’s t-test, p < 0.05) frequency ranges in relation to untreated data are marked by thickepisodes.

83

5.9.2 Results of Disease-specific Mean Coherence and Imaginary Part of Co-

herency Comparisons

No significant differences have been found between ON and OFF state for PD patients

with electrodes targeting the STN for ispilateral coh and icohy before and after artefact

removal (paired Student’s t-test, p < 0.05, data not shown).

Comparing data of PD patients with electrode target STN versus ZI leads to signifi-

cant differences (Student’s t-test, p < 0.05) for icohy data after tCCA and after fCCA

removal for both medicational ON and OFF state (Figure 57).

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After tCCA removal

PD STN OFFPD ZI OFF

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After fCCA removal

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After tCCA removal

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After fCCA removal

PD STN ONPD ZI ON

Figure 57: Right ipsilateral group mean absolute icohy averages for PD STN (n = 9) vs. ZI (n = 3)after tCCA removal OFF (left top plot) and ON medication (left bottom plot), and after fCCA removalOFF (right top plot) and ON medication (right bottom plot). Solid lines represent data from STNpatients and dashed lines data from ZI patients. Bold black lines and dots on the x-axis representsignificantly different (Student’s t-test, p < 0.05) frequency bands and bins.

As ON and OFF data of PD STN patients do not differ significantly, comparing

the three groups PD STN ON, OFF, and dystonia by applying Kruskal-Wallis one-

84

way analysis of variance [Kruskal and Wallis, 1952], the nonparametric equivalent to

one-way analysis of variance (ANOVA), does not seem to be accurate at this point.

Firstly, if PD ON and OFF groups do not differ, differences between all three groups are

not supposable and secondly, groups are not independent. Hence, Student’s t-test was

applied between PD STN ON and dystonia and between PD STN OFF and dystonia. In

the following, only curve pairs are presented that show significant differences (Student’s

t-test, p < 0.05), such as coh before artefact removal and after ICA component removal

between the PD ON and the dystonia group (bottom plots), as well as between the PD

OFF and the dystonia group (Figure 58, top plots). Differences are mainly pronounced

in the α band around 10 Hz and in the β band between 17 and 28 Hz. Significant

icohy differences have been found between the PD OFF and the dystonia group before

artefact removal and after tCCA and fCCA removal, and between the PD ON and the

dystonia group after fCCA removal (Figure 59). Here, apart from one small bin around

10 Hz for untreated data, differences can also be found in the gamma band in many

small bins of 1 to at most 3 Hz width.

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Before artefact removal

PD STN OFFDystonia

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After ICA component removal

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PD STN ONDystonia

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After ICA component removal

PD STN ONDystonia

Figure 58: Disease-specific mean coh differences between PD STN OFF (n = 9) and dystonia group(n = 11) before (left top plot) and after ICA component removal (right top plot) and between PD STNON and dystonia group before (left bottom plot) and after ICA component removal (right bottomplot). Orange lines represent STN OFF patients, magenta lines PD STN ON patients and red linesdystonia patients. Bold black lines and dots on the x-axis represent significantly different (Student’st-test, p < 0.05) frequency bands and bins.

86

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PD STN OFFDystonia

0 10 20 30 40 50 60 700

0.02

0.04

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PD STN OFFDystonia

0 10 20 30 40 50 60 700

0.02

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After fCCA removal

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0 10 20 30 40 50 60 700

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After fCCA removal

PD STN ONDystonia

Figure 59: Disease-specific mean absolute icohy differences between PD STN OFF (n = 9) anddystonia group (n = 11) before (left top plot) and after tCCA (right top plot) and fCCA removal(left bottom plot) and between PD STN ON and dystonia group after fCCA removal (left bottomplot). Orange lines represent STN OFF patients, magenta lines PD STN ON patients and red linesdystonia patients. Bold black lines and dots on the x-axis represent significantly different (Student’st-test, p < 0.05) frequency bands and bins.

87

6 Discussion and Conclusion

6.1 Evaluation of Artefact Removal

After demonstrating the technical feasibility of the highly challenging set-up of simul-

taneous MEG and LFP recordings, coupling analysis with focus on the identification

and removal of the CCA can be assessed. For this, the three different artefact removal

methods based on tCCA removal, fCCA removal, and ICA component removal are dis-

cussed in detail. Starting by looking at the resting MEG data before artefact removal,

it can be seen that spectral power is greatest at lower frequencies in all patients (cf.

Chapter 5.1.1). This could mean that slower neuronal cortical oscillations are based

on increased firing of larger neuronal assemblies [Buzsáki, 2011]. Possibly, the cardiac

cycle artefact might be one of the causes. But from the present point of view, deeper

investigation of the characteristic of this artefact is necessary, such as looking at co-

herence and imaginary part of coherency between MEG channels and contact pairs of

the deep brain electrodes capturing local field potentials from the basal ganglia. Next

to prominent dipolar and bipolar topographic patterns in many patients both in the α

band and the β band in the majority of cases located above the cortical motor area,

spurious artefactual coh and icohy can be observed between 1 and 10 Hz covering large

cortical areas (Chapter 5.1.2). The level of magnetisation is strongly variable among

patients (cf. Chapter 5.2.2) and could inter alia be due to the time point of MRI

recordings after electrode implantation. As explained in Chapter 3, α and β activity

in dystonia, or PD patients respectively, are in line with findings of LFP recordings

from the electrode target points of those patient groups and will be discussed in the

following section (Chapter 6.2). However, patterns below 10 Hz, even if overlapping

with substantiated α activity, seem to be not focal and not dipolar enough to have their

origin in synchronised BG and cortical oscillations reflected by coh or icohy. In hardly

any of the patients, any coupling can be found above 50 Hz, which is again in line with

common literature (cf. Section 3), although some PD patients ON dopaminergic med-

ication might be showing γ activity during rest, whereupon γ activity in the STN and

the GPi is highly associated with movement. It is a well-known problem, that some

PD patients ON levodopa suffer from dyskinesias and therefore unwanted movements,

which could induce those γ oscillations, although dyskinesias could also been related to

88

a 4 to 11 Hz peak [Rodriguez-Oroz et al., 2011]. Due to the susceptibility of the MEG

recordings to movement artefacts, this study tried to exclude patients who showed

pronounced exaggerated tremor or dyskinesias during medicational ON state. Further

inspection of coupling and the CCA will hence be focused on frequency bands below

50 Hz. It becomes apparent that in the midst of physiological and pathophysiological

neuronal oscillations, the CCA somehow overlaps and disturbs -in various magnitudes-

the possibility of detailed coupling analyses arising from this particular recording situ-

ation. For this reason, the core part of this discussion is the precise identification and

removal assessment of the artefact.

The CCA does clearly not originate from eye movements and is not coupled to them

either (see Section 5.5). Averaging the MEG data time-locked to the R peak of the

cardiac QRS complex proves the association of the CCA with the heart beat (Figure 20

and 21). The fact that the CCA is mainly found in the left hemisphere around the

burr holes where the electrode wires leave the skull leads to the conclusion that cardiac

pulsations move the wires and induce distorting magnetic fields. It remains to be

shown that the CCA is responsible especially for those low frequency patterns described

before and to compare the three different CCA removal methods. As the curves of the

ordered eigenvalues found by PCA show the same bend at index 5 for all patients

(Figure 22), the amount of energy distribution of the CCA in relation to the rest of

the data seems to be quite similar across patients. Therefore, taking them out and

backprojecting the data on the channel level by applying SSP is a consequent step of

artefact removal. The mean total spectral power after tCCA removal decreases most

below 10 Hz compared to the original data sets (Figure 32) indicating that the tCCA

has a predominant impact on low frequency activity. This argument can be reinforced

when looking at coh data after tCCA removal: While bipolar patterns between 10

and about 40 Hz are well preserved, the spurious spread out patterns below 10 Hz

are removed or at least diminished regarding their intensity in values of coherency.

In some cases, tCCA removal even uncovers coh, which were not visible beforehand.

The tCCA removal method is an appropriate tool for the detection and removal of

CCA with concurrent preservation of coupling patterns that are not affected by the

CCA. The two other remaining methods lead to less successful results concerning the

desirable characteristics of distinction between artefactual and non-artefactual coh and

89

icohy patterns, respectively. Removing the first five components found by ICA leads

to a completely different spectral energy distribution (5.3.1) than the one seen after

tCCA removal. After ICA component removal, spectral energy was reduced only below

10 Hz. For higher frequencies the spectral energy did not change or even increased

compared to untreated data. However, total overall energy was still decreased. Results

for coh patterns are not that consistent. In some patients, patterns below 10 Hz

were diminished and coh in higher frequency bands were preserved, whereas other

cases showed either diminishment also in dipolar coh patterns across all frequencies or

hardly any removal of patterns at all. This method can therefore not be considered as

reliable and helpful as the tCCA removal method. Removal of the fCCA limited to 5 Hz

cannot be evaluated by looking at the coh patterns, as the positivity of the coh is not

sustained after performing those vector projections. For this reason, icohy analysis has

been applied and showed that patterns resembling the shape of the fCCA were rejected

from the MEG data sets. Nonetheless, operating in frequency space does not lead to

optimal attributes either. Apparently, the CCA manifests more complex properties

than just the shape of the MEG-ECG coupling patterns. And even if this would be

the case, it is not said that projection out the fCCA does not remove spectral power

of non-artefactual patterns. This is indeed possible when looking at the absolute mean

of the icohy within each group (Chapter 5.9.1) compared to those of the other two

methods. Note that the mean total spectral power of magnetic channels after fCCA

removal analogous to those of the other methods could not be shown as it was operated

in frequency domain. Concluding, it can therefore be observed that tCCA removal

method seems to be the most favourable tool to exclude the artefact without restraining

pathophysiological and physiological coupling patterns when looking at individual data

sets. In order to generalise the assessment of CCA removal, comparisons of the methods

have been translated to the group level.

Group averages of dystonia, PD STN ON, and PD STN OFF groups demonstrate,

that for coh, tCCA and ICA component removal methods both provide appropriate

results (Chapter 5.9.1). Applied on the dystonia group, the ICA method yields slightly

better results, as reduction between 3 and 8 Hz is more severe and partly significantly

different from coh of untreated data (Figure 51). In PD ON and OFF groups, results are

opposed (Figure 55 and Figure 53). Here, tCCA removal suppresses lower frequencies

90

significantly and reduces the artefact more strongly. When looking at the absolute

values of the icohy, the fCCA removal method has to be considered as well. However,

fCCA not only suppresses the artefactual episode below 10 Hz, but also takes out

energy across all frequency bands in all three groups (Figure 52, 56, and 54), most

distinctive in the PD STN ON group (Figure 56). It might be the case, that the fCCA

pattern that was projected out of the icohy data sets, resembles patterns of potential

icohy patterns in the α and β ranges. Backprojecting the fCCA and using less than

5 Hz might suppress less. Showing only one significantly different episode of suppressed

artefactual icohy, ICA component removal method advantages over tCCA removal. In

addition, tCCA also suppresses potential icohy above 10 Hz, whereas icohy after ICA

component removal even shows higher β peaks than untreated data in the PD patients

both ON and OFF medication. For the ON group, icohy after applied ICA method

presents some γ peaks, but it remains however speculative if those peaks are typical

resting γ activity that was mentioned before. Due to the small quantity, results of the

PD patients with ZI target points are not meaningful and are not discussed at this

point.

It still remains unclear, why the artefact which is generated or at least partly gener-

ated by cardiac pulsations is reflected to this extent within the coh and icohy values.

For this, the pulsations overlapping the magnetic fields of the MEG channels and the

electric signals of the deep brain electrodes must hold a fixed relative phase-relationship.

However, the artefact does not seem to be present within the electric signal recorded

from the deep brain electrodes, as averaging of the local field potentials time-locked to

the R peak does not show any heartbeat related pattern in any patient (Chapter 5.6).

Consequently, coh needs to undergo further inspection concerning its validity, as it

seems that although solely existing in one of the two signals, the artefact shows in the

coupling measures coh and icohy. One method to validate coherence was introduced

by Sander et al. [Sander et al., 2010] by off-line re-referencing the LFP signal using

a surface EEG signal. This mimics coupling with zero phase lag and demonstrates,

that volume conduction is not the origin of the originally observed coh patterns. The

re-referencing method is limited in scope regarding its implementation as it relies on

a specific hardware configuration. The TSPCA method presented here (Section 4.7)

does not have this limitation and it identifies coherence patterns and frequency bands,

91

in which significant coupling occurs between subcortical and cortical areas [Bock et al.,

2013]. The method does not imply inverse modelling or assumptions about the spatial

coh distribution. The effectiveness of TSPCA in reducing peaks below 30 Hz shows

that there is indeed methodically valid coupling between the LFP and the MEG sig-

nal, although results for all groups and removal methods are not that straightforward.

Below 10 Hz, it is not quite clear what results TSPCA yields, because tCCA as well

as ICA artefact removal apply strong changes to the existing coh (Figure 45). Never-

theless, in a band range between 10 and 30 Hz, which covers the interesting coupling

phenomena in the α and β band, coh can be considered as valid in most instances.

Unfortunately, TSPCA is not able to validate the artefactual coh below 10 Hz. Thus,

the question why the CCA reflects in coh and icohy values is still unsolved and needs

further investigation.

6.2 Pathophysiological Coupling

Contrary to previous findings [Eusebio et al., 2009] [Schnitzler and Gross, 2005] [Williams

et al., 2002] -though in methodically different studies-, there were no significant BG-

cortical coupling differences in the PD STN group between medicational ON and OFF

state for frequencies between 0 and 70 Hz. It has especially been shown, that oscilla-

tory activity in the β band during rest picked up by local field potentials of the STN

in PD patients, decrease after levodopa intake [Kühn et al., 2006] [Ray et al., 2008].

Although those oscillations are locally generated, they might reflect oscillations of the

BG-cortical network [Hammond et al., 2007] [Brown and Williams, 2005], because of

the fact that activity within this frequency band is coherent between the GPi, the

STN and cortical regions [Cassidy et al., 2002] [Fogelson et al., 2007] [Marsden et al.,

2001] [Schnitzler and Gross, 2005]. In order to avoid movement artefacts, PD patients

had been chosen with respect to little dyskinetic movements especially when put on

dopaminergic medication. For this reason, it might be the case, that the PD ON group

does not represent groups similar to those being part of the studies mentioned before.

Some patients might not have been in a real ON state when recorded. However, both

groups (STN and ZI) each differ significantly between ON and OFF assessed by the

UPDRS scores (cf. Section 4.3). Although the neurologists assessing the scores were

not identical for all patients, ON and OFF scores of one patient were assessed by the

92

same person in all cases. At least two other studies are however in accordance with

the outcome found here. Apart from an increase in β band coupling between STN

and a prefrontal cortical region, no significant effect has been found in STN-cortex

coherence of PD patients following levodopa treatment [Litvak et al., 2011] [Lalo et al.,

2008]. The authors conclude that this coherence might be a pathological increase of

rather physiological activity [Litvak et al., 2011] [Hammond et al., 2007] [Brown and

Williams, 2005], and that dopaminergic medication might allow for better reactivity

to tasks and stimuli than modifying the connectivity [Litvak et al., 2011].

Until today, only very little is known about the function and exact integrity within the

BG-cortical network of the DBS target caudal ZI, but there surely are connections to

BG, thalamus and cortex [Mitrofanis, 2005]. Within the scope of this work, coupling

has been shown between the ZI and cortical areas mainly in the β band suggesting

involvement in the BG-cortical network. Due to the very small number of only three

ZI PD patients, discussing the icohy differences between STN and ZI does not seem to

be reasonable. Moreover, significantly different bands are not broader than 2 Hz. It

should only be noted at this point, that during ON and OFF state, ZI patients seem

to feature a peak around 20 Hz being superior to that of the STN group.

Disease-specific coupling differences between PD and dystonia patients are mainly

noticeable in the α and β range. Surprisingly, PD patients show stronger coh and

icohy between STN and cortex than dystonic patients between GPi and cortex within

both, even the α band. LFP recordings of the GPi of dystonic patients stood out by

strong spectral power peaks between 3 and 12 Hz [Chen et al., 2006] and 4 and 10 Hz,

respectively [Silberstein et al., 2003], exceeding the spectral power of PD patients. A

recent electrocorticography study compared local field potentials of the primary motor

and sensory cortices of PD (OFF) and dystonic patients and revealed alpha-beta peaks

for both groups with the mean of the PD group being (not significantly) higher in terms

of spectral power and frequency [Crowell et al., 2012] similar to those reported for

healthy subjects [Crone et al., 1998b]. Coupling might favour certain frequency bands

and as mentioned before, in Parkinson’s disease β activity might be prominent within

the whole BG-cortical network, whereas in dystonia the exaggerated α activity from

the GPi might not be coherent to cortical areas. Consequently, it seems that disease-

specific coupling is much more complicated and subtle than just being segregated into

93

certain frequency bands.

6.3 Limitations

After discussing the major findings in greater detail, limitations of the experimental

approach should be addressed at this point. As explained in Section 2.5.1, neuromag-

netic signals are very weak and therefore prone to be contaminated by interference

of electric artefacts from the environment even if recordings are performed within a

shielded set-up. Moreover, MEG recordings can be easily distorted by movements of

the patient, which are almost impossible to avoid. Besides, it does not seem to be pos-

sible that all patients put their head equally close inside the MEG helmet. Therefore

and maybe also because of different levels of magnetisation, the strength of magnetic

signals and the CCA differs strongly which complicates comparisons across patients

especially in terms of absolute components and eigenvalues. Recorded signals should

thus be treated carefully concerning interpretations and conclusions. The use of an

eye-tracking system in all patients could have been a guarantee for the CCA not be-

ing partly generated or superimposed by eye saccades. Another technical problem is

to obtain the exact spatiotemporal localisation of the neural current sources (inverse

problem) and that MEG does not provide any anatomical or structural information of

the brain if not combined with MR images. However, the primary aim of this work

was the detection and removal of the CCA. Localisation is of minor interest except

for coupling analysis regarding pathophysiological differences. It is also important to

note that when comparing different patient groups characterised by different movement

disorders, there is no healthy control group for DBS patients. Not even administration

of L-DOPA fully normalises the BG physiology in PD patients. Therefore, one still has

to bear in mind that any activity in the BG could be related to the pathophysiology

of Parkinson’s disease rather than reflecting a physiological state. In fact, levodopa

acts as a destabilising stimulus by exposing the BG to large, extrasynaptic levels of

DA and reducing the firing rate of GPi neurons. This leads to dysregulation of intra-

cellular signals and plastic changes [Obeso et al., 2008] [Koller and Melamed, 2007].

Another important limitation is the fact that the placement of the electrodes in the

STN, GPI, or cZI can only be considered presumptive in spite of direct visualisation

of the STN in the individual stereotactic T2-weighted MRI, and confirmation of the

94

target coordinates by intra-operative direct macrostimulation, microrecordings, and

additional post-operative MRI or stereotactic CT scans (Chapter 4.1). Especially in

some PD patients with target point STN, micro-lesion effects of the target area may

occur, which is defined as clinical improvement without stimulation [Granziera et al.,

2007] and could lead to a lower number of neurons contributing to the recorded LFP

signal and a weaker pronounced difference between medicational ON and OFF state in

those patients.

6.4 Future Work

The most important topic of future work should be the meticulous investigation of

coh and icohy properties and their reaction to artefacts such as the CCA. To find out

whether the spurious coherences are due to the strength of the periodic artefact in

the MEG signal, it is reasonable to examine synthetical data sets containing filtered

ECG signals. For the sake of completeness, other ICA algorithms, such as FastICA or

JADE, could be tried out and compared to the initial artefact removal methods.

In order to specify those cortical areas showing coherence with the DBS target point

and thusly identifying resting BG-cortical networks, for each patient MEG channels

should be assigned to one of the cortical lobes. Using a different approach based on

source instead of sensor level, data could furthermore be analysed by depicting the cor-

tical locations using beamforming methods [Litvak et al., 2010]. More PD ZI patients

should be included in this study to obtain more precise information about spectral

properties and ZI-cortical coupling during rest. This way, comparisons especially to

previous LFP studies of STN oscillations might yield new insights about the integration

of the ZI and its role in motor function and Parkinson’s disease. Beyond that, future

work could include simultaneous MEG and LFP recordings during DBS stimulation to

ascertain how stimulation of the target areas affects coupling within the cortical-BG

loops.

Beyond, future work could especially include the application of the parallel factor

analysis (PARAFAC) algorithm to the multichannel spectrogram of the MEG/LFP

[Miwakeichi et al., 2004], which is another way of extracting components and thereby

excluding the artefact and subsequently reconstructing the data sets. The main advan-

95

tage of the PARAFAC is that it provides a unique decomposition of multidimensional

data sets without imposing orthogonality and independence to the components, leading

to a simultaneous estimation of the spatial, temporal, and spectral characteristics or sig-

natures. To make use of another way of looking into connection between MEG and LFP

signals other than coherence, the multiway partial least-squares (N-PLS) model [Bro,

1996] [Martínez-Montes et al., 2004] could be applied, which is another multilinear

decomposition model related to the PARAFAC analysis. The N-PLS model consists

of decomposing two sets of multidimensional data (dependent and independent) using

PARAFAC models, such that the correlation between common characteristics (e.g.,

temporal signatures) is maximised. Therefore in this case, in contrast to coherence,

the relevant temporal correlated activity is not determined for one specific frequency

in pairs of electrodes, but it is fully characterised by its spectral and spatial signatures

both from MEG and LFP data.

96

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A Appendix

A.1 Unified Parkinson’s Disease Rating Scale (UPDRS)

III. Motor Examination

18. Speech0 = Normal.1 = Slight loss of expression, diction and/or volume.2 = Monotone, slurred but understandable; moderately impaired.3 = Marked impairment, difficult to understand.4 = Unintelligible.

19. Facial Expression0 = Normal.1 = Minimal hypomimia, could be normal “Poker Face”.2 = Slight but definitely abnormal diminution of facial expression.3 = Moderate hypomimia; lips parted some of the time.4 = Masked or fixed faces with severe or complete loss of facial

expression; lips parted 1/4 inch or more.

20. Tremor at Rest0 = Absent.1 = Slight and infrequently present.2 = Mild in amplitude and persistent. Or moderate in amplitude, but only

intermittently present.3 = Moderate in amplitude and present most of the time.4 = Marked in amplitude and present most of the time.

21. Action or Postural Tremor of Hands0 = Absent.1 = Slight; present with action.2 = Moderate in amplitude, present with action.3 = Moderate in amplitude with posture holdings as well as action.4 = Marked in amplitude, interferes with feeding.

22. Rigidity [Judged on passive movement of major joints with patientrelaxed in sitting position; ignore cogwheeling.]0 = Absent.1 = Slight or detectable only when activated by mirror or other movements.2 = Mild to moderate.3 = Marked, but full range of motion easily achieved.4 = Severe, range of motion achieved with difficulty.

23. Finger Taps [Patient taps thumb with index finger in rapid successionwith widest amplitude possible, each hand separately.]0 = Normal.1 = Mild slowing and/or reduction in amplitude.2 = Moderately impaired. Definite and early fatiguing. May have

occasional arrests in movement.3 = Severely impaired. Frequent hesitation in initiating movements or

arrests in ongoing movement.4 = Can barely perform the task.

24. Hand Movements [Patient opens and closes hands in rapidsuccession with widest amplitude possible, each hand separately.]0 = Normal.

110

1 = Mild slowing and/or reduction in amplitude.2 = Moderately impaired. Definite and early fatiguing. May have



25. Rapid Alternating Movements of Hands [Pronation-supinationmovements of hands, vertically or horizontally, with as large anamplitude as possible, each hand separately.]0 = Normal.1 = Mild slowing and/or reduction in amplitude.2 = Moderately impaired. Definite and early fatiguing. May have



26. Leg Agility [Patient taps heel on ground in rapid succession, picking upentire leg. Amplitude should be about 3 inches.]0 = Normal.1 = Mild slowing and/or reduction in amplitude.2 = Moderately impaired. Definite and early fatiguing. May have



27. Arising from Chair [Patient attempts to arise from a straight-backwood or metal chair with arms folded across chest.]0 = Normal.1 = Slow; or may need more than one attempt.2 = Pushes self up from arms of seat.3 = Tends to fall back and may have to try more than one time, but

can get up without help.4 = Unable to arise without help.

28. Posture0 = Normal erect.1 = Not quite erect, slightly stooped posture; could be normal for

older person.2 = Moderately stooped posture, definetely abnormal; can be slightly

leaning to one side.3 = Severely stooped posture with kyphosis; can be moderately

leaning to one side.4 = Marked flexion with extreme abnormality of posture.

29. Gait0 = Normal.1 = Walks slowly, may shuffle with short steps, but no festination

(hastening steps) or propulsion.2 = Walks with difficulty, but requires little or no assistance; may

have some festination, short steps, or propulsion.3 = Severe disturbance of gait, requiring assistance.4 = Cannot walk at all, even with assistance.

30. Postural Stability [Response to sudden, strong posteriordisplacement produced by pull on shoulders while patienterect with eyes open and feet slightly apart. Patient is prepared,and can have had some practice runs.]

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0 = Normal.1 = Retropulsion, but recovers unaided.2 = Absence of postural response; would fall if not caught by examiner.3 = Very unstable, tends to lose balance spontaneously.4 = Unable to stand without assistance.

31. Body Bradykinesia and Hypokinesia [Combining slowness,hesitancy, decreased armswing, small amplitude, and poverty ofmovement in general.]0 = None.1 = Minimal slowness, giving movement a deliberate character;

could be normal for some persons. Possibly reduced amplitude.2 = Mild degree of slowness and poverty of movement which is

definitely abnormal. Alternatively, some reduced amplitude.3 = Moderate slowness, poverty or small amplitude of movement.4 = Marked slowness, poverty or small amplitude of movement.

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